DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 1-27 were previously pending and subject to a non-final rejection dated June 1, 2026. In Response, submitted July 29, 2026, claims 1, 10, and 19 were amended. Therefore, claims 1-27 are currently pending and subject to the following final rejection.
Response to Arguments
Applicant’s remarks on Page 13 of the Response regarding the previous objection to the claims have been fully considered and are the objections are withdrawn in light of the amended claims.
Applicant’s remarks on Pages 13-15 of the Response, regarding the previous rejection of the claims under 35 U.S.C. 101, have been fully considered and are not found persuasive.
On Pages 13-14 of the Response, Applicant argues “Representative Claim 1 recites a specific technical process for optimizing battery charging operations in an electric vehicle charging context. Specifically, the claim requires predicting electricity demand based on a current state of charge (SoC) of batteries; computing costs associated with drawing electricity across partitioned time intervals; generating an optimized schedule using a Hamiltonian expression that includes a battery chemistry dependent penalty function using the current SoC as an input parameter; and causing the resulting set of operations to be performed on the physical batteries. This is not a commercial interaction or a method of human behavior. It is a computational and physical process operating on measurable physical quantities (SoC, battery chemistry, grid electricity demand) to produce a concrete result (an optimized charging schedule that is actually executed).”
Examiner notes, as discussed further in the detailed rejection below, “predicting electricity demand based on a current state of charge (SoC) of batteries; computing costs associated with drawing electricity across partitioned time intervals; [and] generating an optimized schedule using a Hamiltonian expression that includes a battery chemistry dependent penalty function using the current SoC as an input parameter” are recitations of the abstract idea, and unhelpful in bringing the claims to eligibility. These are processes that are used to process data inputs, such as current SoC, a battery chemistry dependent penalty function, and grid electricity demand, in order to determine “an optimized charging schedule” which is used as part of a commercial interaction. When viewed as a whole, the claimed limitations recite an abstract idea properly categorized as “a certain method of organizing human activity”, specifically a commercial interaction. While additional elements, such as “causing the resulting set of operations to be performed”, exist these additional elements do not preclude the claims from reciting an abstract idea at Step 2A, Prong One.
On Page 14 of the Response, Applicant argues “In the instant application, the distinguishing technical improvement is the use of a Hamiltonian expression incorporating a battery chemistry dependent penalty function that takes the current SoC as an input parameter. This is not a generic optimization technique. The Hamiltonian formulation structures the optimization problem in a physically meaningful way, and the battery chemistry dependent penalty function constrains the optimization specifically based on the electrochemical characteristics of the actual battery being charged. This directly improves the technical operation of EV charging by: reducing battery degradation by avoiding charging profiles that are harmful for the specific battery chemistry in use; improving charging efficiency by accounting for SoC-dependent charging behavior; and producing actionable, physically grounded schedules rather than merely abstract cost recommendations. These are not features of a generic commercial optimization method, but rather are features of a specific, technically grounded computational approach to a real engineering problem.”
Examiner notes, computational elements and grounding in real engineering problems do not inherently make the claimed invention technical or non-abstract. As discussed further in the detailed rejection below, “use of a Hamiltonian expression incorporating a battery chemistry dependent penalty function that takes the current SoC as an input parameter” is abstract data processing. While it is alleged that this is not a “generic optimization technique”, this limitation is not categorized as abstract because it is or isn’t “generic”, it is categorized as such because this simply describes the abstract data processing that is used to calculate data outputs (such as the optimized schedule) form data inputs (such as battery chemistry information, and current SoC data). Simply because the Hamiltonian expression utilizes data about physical objects as inputs, and data about the battery chemistry as an optimization constraint does not inherently make the mathematical concepts captured in the Hamiltonian expression and the penalty functions any less abstract or any more physical themselves. The alleged improvement of basing the optimization on “the electrochemical characteristics of the actual battery” is an improvement to the abstract optimization process as it improves the data being fed into the abstract processes rather than providing a technical improvement to the battery itself, the battery charger, or any other technology. While this improved data may create downstream benefits to additional elements (such as reducing battery degradation and improving charging efficiency) these improvements are found in the alleged abstract improvement to “avoiding charging profiles that are harmful for the specific battery chemistry” of a battery, and “accounting for SoC-dependent charging behaviors”. The “charging profiles” and “accounting for SoC-dependent charging behaviors” are merely data outputs and inputs that drive these benefits, rather than any actual improvement to the technology itself. Further, “producing actionable, physically grounded schedules rather than merely abstract cost recommendations” is entirely abstract, and therefore unhelpful in bringing the claims to eligibility, as the “actionable, physically grounded schedules” represents mere data outputs.
On Pages 14-15 of the Response, Applicant argues “this limitation requires that the optimized schedule be physically executed - that actual charging operations be performed on actual batteries in accordance with the computed schedule. This is not a field-of-use limitation. It is a transformation of a physical system (the battery's state of charge) based on the output of the claimed method.”
Examiner notes, the specification and the claims are devoid of detail regarding how the claimed invention performs the “causing the set of operations specified in the optimized schedule to be performed to charge the electric vehicle” and what this limitation entails. Specification para. 100 provides a good overview of what some of these operations may be, however the specification only notes at face value that at least a subset of these operations are “used to charge the electric vehicle to satisfy the predicted electricity demand of the electric vehicle” (See at least specification, Paras. 19, 131). There is absolutely no detail on how the claimed system actually carries this process out. It could be carried out myriad of ways such as merely sending instructions in the form of the optimized schedule to an entity for them to follow beyond the scope of the claimed invention. Therefore, under BRI, this high level of disclosure supports the Examiner’s findings that this limitation merely generally links the performing of the set of operations to the field of EV charging technology, but fails to integrate the abstract idea into a practical application.
On Page 15 of the Response, Applicant argues “The combination of: a Hamiltonian expression as the optimization framework, a battery chemistry dependent penalty function within that Hamiltonian, the use of the current SoC as an input parameter to that penalty function, and partitioned non-overlapping time intervals for cost computation, is not a well-understood, routine, or conventional activity in the field of EV charging optimization. In view of the above, independent Claim 1, and all claims depending therefrom, are directed to patent eligible subject matter. Likewise, independent Claims 10 and 19 and all claims depending respectively therefrom, are directed to patent eligible subject matter. Therefore, reconsideration and withdrawal of the rejections of Claims 1-27 is respectfully requested.”
Examiner notes, “well-understood, routine, and conventional” is a single test available to Examiners when analyzing claims at Step 2B, however this test does not form the basis of any rejection in the detailed rejection below nor the prior Office Action. Therefore, the argument is moot. Similar to the discussion above, regarding Step 2A, Prong Two, these claims are found to not amount to significantly more because the amount to merely “apply it” or only generally linking the abstract idea to a field of use.
Applicant’s remarks on Page 15-18 of the Response, regarding the previous rejection of the claims under 35 U.S.C. 103, have been fully considered but are not found persuasive or are found moot in light of the amended claims.
On Pages 16-18 of the Response, Applicant argues “The cited art individually and collectively fails to disclose one or more of these features. For example, none of the cited art at least fails to disclose or in any way suggest the claim features of ‘…’ in Claim 1. For at least the reason set forth above, Claim 1 is patentable over the cited art. Reconsideration and removal of the rejection is respectfully requested. … Each of Claims 10 and 19 recites features similar to those of Claim 1 as discussed above so the above arguments equally apply. Thus, each of Claims 10 and 19 should be allowed for reasons similar to those set forth for Claim 1. Reconsideration and removal of the rejection is respectfully requested. … Each of Claims 2-9, 11, 12, 20 and 21 depends on one of the independent claims with similar features as discussed above and should be allowed at least on that basis, as well as for the additional features each of Claims 2-9, 11, 12, 20 and 21 individually recites. Reconsideration and removal of the rejection is respectfully requested.
Examiner notes, it is unclear which specific aspects of these features are allegedly not taught by the previously cited combination of Baba in view of Ito, and further in view of Malisani. The argument that the previously cited combination does not teach the limitation “a Hamiltonian expression that includes a battery chemistry dependent penalty function using the current SoC as an input parameter” is moot in light of the amended claims. However, as discussed in the detailed rejection below, “predicting an electricity demand of the electric vehicle based on a current state of charge (SoC) of one or more batteries of the electric vehicle;” is taught by Baba in Paras. 50, 73, and 98. “computing one or more costs associated with drawing electricity from the grid during one or more time intervals, wherein the specific time window is partitioned into a plurality of consecutive non-overlapping time intervals that include the one or more time intervals;” is taught by Baba in Paras. 98 and 133. “performing a set of operations with the one or more batteries of the electric vehicle, wherein the set of operations include at least a subset of operations used to charge the electric vehicle to satisfy the predicted electricity demand of the electric vehicle; causing the set of operations to be performed to charge the electric vehicle” is taught by Baba in Paras. 70-72 and 99. Baba in view of Ito teaches “generating, based at least in part on the one or more costs (as taught by Baba), an optimized schedule for performing a set of operations with the one or more batteries of the electric vehicle (as taught by Baba)” in Ito Paras. 12-14. Baba in view of Ito goes on to modify the set of operations caused to be performed by Baba to be “the set of operations specified in the optimized schedule” in Ito Para. 37-38 and 46. Therefore the 103 is maintained.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-27 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1-9 are directed to a method (i.e., a process); claims 10-18 are directed to a non-transitory computer readable media (i.e., a machine); claims 19-27 are directed to a system (i.e., a machine). Therefore, claims 1-27 all fall within the one of the four statutory categories of invention.
Step 2A, Prong One
Independent claim 1, 10, and 19 substantially recites determining a specific time window during which an electric vehicle is connecting with a charging station, wherein the charging station is configured to draw electricity from a grid to charge the electric vehicle;
predicting an electricity demand of the electric vehicle based on a current state of charge (SoC) of one or more batteries of the electric vehicle;
computing one or more costs associated with drawing electricity from the grid during one or more time intervals, wherein the specific time window is partitioned into a plurality of consecutive non-overlapping time intervals that include the one or more time intervals;
generating, based at least in part on the one or more costs and a Hamiltonian expression that includes a battery chemistry dependent penalty function using the current SoC as an input parameter, an optimized schedule for performing a set of operations with the one or more batteries of the electric vehicle, wherein the set of operations include at least a subset of operations used to charge the electric vehicle to satisfy the predicted electricity demand of the electric vehicle.
The limitations stated above are processes/functions that under broadest reasonable interpretation covers “certain methods of organizing human activity” (commercial interactions) of charging optimization. Therefore, the claim recites an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. Claims 1, 10, and 19 as a whole amount to: (i) merely invoking generic components as a tool to perform the abstract idea or “apply it” (or an equivalent), and (ii) generally links the use of a judicial exception to a particular technological environment or field of use. The claims recite additional elements of (i) one or more non-transitory computer readable media storing a program of instructions that is executable (claims 10, 19), (ii) one or more computing processors (claims 10, 19), and (iii) causing the set of operations specified in the optimized schedule to be performed to charge the electric vehicle (claims 1, 10, and 19).
The additional elements of (i) one or more non-transitory computer readable media storing a program of instructions that is executable, and (ii) one or more computing processors are recited at a high level of generality (see [0146] of the Applicants Specification discussing the one or more non-transitory computer readable media storing a program of instructions that is executable and [0145] discussing one or more computing processors) such that, when viewed as whole/ordered combination, it amounts to no more than mere instruction to apply the judicial exception using generic computer components or “apply it” (See MPEP 2106.05(f)).
The additional element of (iii) causing the set of operations specified in the optimized schedule to be performed to charge the electric vehicle are recited at a high level of generality (See [0100] and [0131] of the Applicant's Specification discussing the causing the set of operations specified in the optimized schedule to be performed to charge the electric vehicle) such that when viewed as whole/ordered combination, do no more than generally link the use of the judicial exception to a particular technological environment or field of use (i.e. EV charging technology) (See MPEP 2106.05(h)).
Accordingly, these additional elements, when viewed as a whole/ordered combination [See Figures 1 and 5 showing all the additional elements (i) one or more non-transitory computer readable media storing a program of instructions that is executable, (ii) one or more computing processors, and (iii) causing the set of operations specified in the optimized schedule to be performed to charge the electric vehicle in combination], do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, the claim is directed to an abstract idea.
Step 2B
As discussed above with respect to Step 2A Prong Two, the additional elements amount to no more than: (i) “apply it” (or an equivalent), and (ii) generally link the use of a judicial exception to a particular technological environment or field of use, and are not a practical application of the abstract idea. The same analysis applies here in Step 2B, i.e., (i) merely invoking the generic components as a tool to perform the abstract idea or “apply it” (See MPEP 2106.05(f)); and (ii) generally linking the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)), does not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B. Thus, even when viewed as a whole/ordered combination, nothing in the claims adds significantly more (i.e., an inventive concept) to the abstract idea. Thus, the claims 1, 10, and 19 are ineligible.
Dependent Claims 2-9, 11-18, and 20-27 merely narrow the previously recited abstract idea limitations. For reasons described above with respect to claims 1, 10, and 19 these judicial exceptions are not meaningfully integrated into a practical application or significantly more than the abstract idea. Thus, claims 2-9, 11-18, and 20-27 are also ineligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-27 are rejected under 35 U.S.C. 103 as being unpatentable over Baba (US 20140312841) (hereafter Baba) in view of Ito (US 20120323386) (hereafter Ito) and further in view of Malisani (“Optimal charging scheduling of electric vehicles: the co-charging case”; May 3, 2022) (hereafter Malisani) and even further in view of Malisani (US 20230373336) (hereafter Malisani II).
In regards to claim 1, Baba discloses a method comprising: determining a specific time window during which an electric vehicle is connecting with a charging station, wherein the charging station is configured to draw electricity from a grid to charge the electric vehicle; (Para. 36, 71; Claim 11) (“electricity distribution system, a house 10 connected to an electrical grid 20 and the electric vehicle EV can be connected with a power line (i.e. wherein the charging station is configured to draw electricity from a grid to charge the electric vehicle).” “This operation starts from step S1 in response to detection of connection of the electric vehicle EV to the house 10. In the step S1, the electricity management device 14 determines through the entire operation controller 108 whether the electric vehicle EV is scheduled not to travel the next day (i.e. specific time window during which an electric vehicle is connecting with a charging station). When the electric vehicle EV is scheduled to travel the next day, the process goes to step S3, and otherwise, the process goes to step S2. In this process, the entire operation controller 108 reads the date and time of the next-scheduled travel (i.e. specific time window during which an electric vehicle is connecting with a charging station) which are stored in the next-scheduled travel time storage unit 109 through an input at the schedule input unit 14b. The date and time of the next-scheduled travel is set as shown in FIG. 7, for example.” “An electricity management method”)
Baba discloses predicting an electricity demand of the electric vehicle based on a current state of charge (SoC) of one or more batteries of the electric vehicle; (Para. 50, 73, 98) (“In response to the entry of the electric vehicle EV, the EV charger/discharger 13A supplies to the electricity management device 14, connection information representing that the connection with the electric vehicle EV is "on state" (operation (1): entry to the parking space). The electric vehicle EV sends rechargeable battery information to the electricity management device 14 through an EV-side controller 31. The rechargeable battery information includes remaining battery power of the rechargeable battery 32 (i.e. a current state of charge (SoC) of one or more batteries of the electric vehicle)” “the rechargeable battery 32 of the electric vehicle EV is charged to a predetermined target value by the leaving time of the electric vehicle EV. In this process, the charging/discharging controller 110 supplies to the EV charger/discharger 13A, the charging/discharging control signal to charge the electric vehicle EV. The EV charger/discharger 13A extracts grid power from the distribution board 11 in response to the charging/discharging control signal and supplies the same to the electric vehicle EV. Moreover, the charging/discharging controller 110 calculates the amount of surplus electricity by subtracting the amount of in-house power consumption from the amount of PV-generated electricity. When the amount of surplus electricity is short of the amount of electricity needed (i.e. predicting an electricity demand of the electric vehicle) to charge the electric vehicle EV, the grid power is supplied to the EV charger/discharger 13A from the distribution board 11.” “FIG. 9 shows changes in the amount of consumption of PV-generated electricity, the amount of electricity sold, the amount of discharging electricity from the EV, the amount of electric load as house power consumption, the amount of grid power as purchased electricity, the amount of charging electricity for the EV, and the amount of PV-generated electricity as in-house generated electricity. FIG. 10 shows changes in the amount of charge in the rechargeable battery 32 (i.e. current state of charge (SoC) of one or more batteries of the electric vehicle) of the electric vehicle EV.” That is, the Figures 9 and 10 depict the system tracking the state of charge of rechargeable battery 32 (i.e. current state of charge (SoC) of one or more batteries of the electric vehicle) with known travel periods on days 1, 2, and 8, as well as requiring the battery 32 to reach a target charge level for the travel period, therefore the system must determine how much electricity is required to charge the battery 32 from its current SOC to the target charge level (i.e. predicting an electricity demand of the electric vehicle based on a current state of charge (SoC) of one or more batteries of the electric vehicle).)
Baba discloses computing one or more costs associated with drawing electricity from the grid during one or more time intervals, wherein the specific time window is partitioned into a plurality of consecutive non-overlapping time intervals that include the one or more time intervals; (Para. 98, 133) (“FIG. 11 shows changes in the CO.sub.2 emission counter value.” “the CO.sub.2 emission counter value is increased or reduced based on the amount of CO.sub.2 emissions obtained by multiplying the charging/discharging electricity of the electric vehicle EV by the CO.sub.2 emission coefficient but may be increased or reduced based on an index other than the amount of CO.sub.2 emissions. For example, instead of the CO.sub.2 emission coefficient, the index to change the counter value may be unit price of electricity. In this case, when the electric vehicle EV is charged from the grid power, the electricity management device 14 multiplies the amount of charging electricity by the unit price of electricity to calculate the price of the electricity and increases the counter value (i.e. computing one or more costs associated with drawing electricity from the grid during one or more time intervals).” That is, while Fig. 11 shows the emissions counter value over several time intervals (i.e. one or more time intervals), it is clear that this counter is contemplated to also show the price paid for electricity from the grid (i.e. computing one or more costs associated with drawing electricity from the grid during one or more time intervals).)
Baba discloses performing a set of operations with the one or more batteries of the electric vehicle, wherein the set of operations include at least a subset of operations used to charge the electric vehicle to satisfy the predicted electricity demand of the electric vehicle; (Para. 70-72, 99) (“a description is given of the procedure of the aforementioned operation of the electricity management device 14 to control charging/discharging … This operation starts from step S1 in response to detection of connection of the electric vehicle EV to the house 10. In the step S1, the electricity management device 14 determines through the entire operation controller 108 whether the electric vehicle EV is scheduled not to travel the next day. … In the next step S2, the electric vehicle EV is not discharged, and the rechargeable battery 32 of the electric vehicle EV is charged to a predetermined target value by the leaving time of the electric vehicle EV (i.e. performing a set of operations with the one or more batteries of the electric vehicle, wherein the set of operations include at least a subset of operations used to charge the electric vehicle to satisfy the predicted electricity demand of the electric vehicle).” “in a time period T1 after the travel hours, the rechargeable battery 32 of the electric vehicle EV is charged from the grid power from the house 10 (operation (1) of charging the EV) because other travel hours are scheduled after the time period T1. … When the electric vehicle EV is connected to the house 10 after the travel hours following the charging of the electric vehicle EV, the electricity distribution system does not have a travel schedule of the next day. Accordingly, as shown in FIG. 10, operation (2) of charging the electric vehicle EV is performed during a time period T2 (i.e. performing a set of operations with the one or more batteries of the electric vehicle, wherein the set of operations include at least a subset of operations used to charge the electric vehicle to satisfy the predicted electricity demand of the electric vehicle) when there is a surplus of PV-generated electricity on the next day. By the operation (2) of charging the electric vehicle EV (i.e. performing a set of operations with the one or more batteries of the electric vehicle, wherein the set of operations include at least a subset of operations used to charge the electric vehicle to satisfy the predicted electricity demand of the electric vehicle), the amount of charge in the electric vehicle EV is increased in FIG. 10. … operation (3) of charging the electric vehicle EV is performed although the amount of surplus of PV-generated electricity is small, and the amount of charge in the electric vehicle EV increases slightly in FIG. 10. … there is a large amount of surplus of PV-generated electricity, and operation (3) charging the electric vehicle EV (i.e. performing a set of operations with the one or more batteries of the electric vehicle, wherein the set of operations include at least a subset of operations used to charge the electric vehicle to satisfy the predicted electricity demand of the electric vehicle) is performed using the large surplus of PV-generated electricity.”)
Baba discloses causing the set of operations to be performed to charge the electric vehicle. (Para. 70-72, 99) (“a description is given of the procedure of the aforementioned operation of the electricity management device 14 to control charging/discharging … This operation starts from step S1 in response to detection of connection of the electric vehicle EV to the house 10. In the step S1, the electricity management device 14 determines through the entire operation controller 108 whether the electric vehicle EV is scheduled not to travel the next day. … In the next step S2, the electric vehicle EV is not discharged, and the rechargeable battery 32 of the electric vehicle EV is charged to a predetermined target value by the leaving time of the electric vehicle EV (i.e. causing the set of operations to be performed to charge the electric vehicle).” “in a time period T1 after the travel hours, the rechargeable battery 32 of the electric vehicle EV is charged from the grid power from the house 10 (operation (1) of charging the EV) because other travel hours are scheduled after the time period T1. … When the electric vehicle EV is connected to the house 10 after the travel hours following the charging of the electric vehicle EV, the electricity distribution system does not have a travel schedule of the next day. Accordingly, as shown in FIG. 10, operation (2) of charging the electric vehicle EV is performed during a time period T2 (i.e. causing the set of operations to be performed to charge the electric vehicle) when there is a surplus of PV-generated electricity on the next day. By the operation (2) of charging the electric vehicle EV (i.e. causing the set of operations to be performed to charge the electric vehicle), the amount of charge in the electric vehicle EV is increased in FIG. 10. … operation (3) of charging the electric vehicle EV is performed although the amount of surplus of PV-generated electricity is small, and the amount of charge in the electric vehicle EV increases slightly in FIG. 10. … there is a large amount of surplus of PV-generated electricity, and operation (3) charging the electric vehicle EV (i.e. causing the set of operations to be performed to charge the electric vehicle) is performed using the large surplus of PV-generated electricity.”)
Baba does not explicitly disclose, however Ito, in the same field of endeavor, discloses generating, based at least in part on the one or more costs of Baba and, an optimized schedule for performing a set of operations of Baba (Para. 12-14) (“The consumption control unit determines the charge-discharge schedule (i.e. generating, based at least in part on the one or more costs of Baba, an optimized schedule for performing a set of operations of Baba) as having an optimal evaluation index … The evaluation index may be, for example, a cost of the electric power or an emission amount of carbon dioxide (i.e. one or more costs of Baba).”)
Baba does not explicitly disclose, however Ito, in the same field of endeavor, discloses causing the set of operations to be performed to charge the electric vehicle of Baba is specified in the optimized schedule. (Para. 37-38, 46) (“The charge power line 27 is wired to inside of the charge station 15, and is coupled to a charge-discharge cable 28 that extends from a body of the charge station 15 to outside of the charge station 15. … the charge station 15 includes a control pilot (CPLT) board (not illustrated), a power line communication (PLC) unit (not illustrated), and the control ECU 26. … A CPLT line and a ground (GND) line are disposed together with a power line in the charge-discharge cable 28, to allow communication of a CPLT signal. The CPLT board performs a charge control of the in-vehicle battery 14 as its main function.” “The controller 18 controls the electric power charged to each of the batteries 24a, 24b and to the in-vehicle battery 14, and controls the electric power discharged from each of the batteries 24a, 24b and from the in-vehicle battery 14 to the electric wiring 11 according to a charge-discharge schedule stored in the memory unit.”)
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the electricity management and distribution system of Baba with the power supply systems and methods of Ito in order to improve the efficiency of the system. (Ito – Para. 14)
Baba in view of Ito does not explicitly disclose, however Malisani, in the same field of endeavor, discloses generating, based at least in part on a Hamiltonian expression that includes a penalty function using the current SoC as an input parameter, an optimized schedule of Ito (Pg. 1, 2, 7) (“the problem of the scheduling of several chargers distributed over a an electrical grid is addressed and the scheduling is handled … the authors address the optimal assignment of tours to be processed of a mixed fleet of combustion and electric vehicles together with the optimal charging scheduling (i.e. generating an optimized schedule of Ito)” “θx ∈ R+: weight of state of charge penalization (i.e. a penalty function using the current SoC as an input parameter)” “Pontryagin Maximum Principle approach for solving the unconstrained optimal control problem: Solving the lower bounding problem consists in solving a sequence of unconstrained optimal control problems (UOCPs) (44)-(45). To solve this sequence of UOCPs, a Pontryagin Maximum Principle (PMP) based approach is used. To do so, let us first define the Hamiltonian [12] of problem (44)-(45) (i.e. a Hamiltonian expression)” That is, equation (44) discloses “θx” as an input for the Hamiltonian used in finding an optimal solution (i.e. generating, based at least in part on a Hamiltonian expression that includes a penalty function using the current SoC as an input parameter, an optimized schedule of Ito).)
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the electricity management and distribution system of Baba in view of Ito with the optimal charging scheduling of Malisani in order to better minimize costs of charging seen from the charger users. (Malisani – Pg. 3)
Baba in view of Ito in view of Malisani does not explicitly disclose, however Malisani II, in the same field of endeavor, discloses the Hamiltonian expression of Malisani is a Hamiltonian expression that includes a battery chemistry dependent penalty function (Para. 96, 105, 154) (“xλ: state of charge of the vehicle with charge rate λ; pc +: maximum charging power; fc: dynamics of the state of charge of the battery; ρ: battery efficiency; uocv: open-circuit voltage of the battery; rc: internal resistance of the battery (i.e. a battery chemistry dependent)” “a model can be represented by open-circuit voltage uocv, internal resistance rc and limit power pc + parameters. Preferably, the set of parameters of the electric vehicle comprises at least one battery modeling so as to model charging of the battery, in order to implement an optimal battery charge (i.e. an optimized schedule of Ito), according to the battery type (i.e. battery chemistry) and to these characteristics (electric vehicle batteries can be of different types and have different characteristics)” “ battery charge models may be used. These models describe mathematically the power limits, the efficiency according to the battery type, the state of charge and the temperature of the battery.” “an approach based on Pontryagin's maximum principle can be used to solve the optimal control problem … To solve this sequence, an approach based on Pontryagin's maximum principle (PMP) can be used. Therefore first the Hamiltonian (i.e. the Hamiltonian expression of Malisani) H of the problem is defined as follows: [ EQUATION ] (i.e. the Hamiltonian expression of Malisani is a Hamiltonian expression that includes a battery chemistry dependent penalty function)”)
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the electricity management and distribution system of Baba in view of Ito and further in view of Malasani with the optimal charging sequencing of Malisani II in order to allow the system to better account for a variety of batteries. (Malisani II – Para. 10)
In regards to claim 2, Baba in view of Ito and further in view of Malisani and even further in view of Malisani II discloses the limitations of claim 1. Baba discloses wherein each of the one or more costs is generated dependent on one or more of: a greenhouse gas emission cost associated with producing electricity during at least one of the one or more time intervals in the specific time window; a utility cost for electricity charged by an operator in connection with electricity consumption in at least one of the one or more time intervals; (Para. 13-14) (“the carbon dioxide emission coefficient indicating an amount of carbon dioxide emissions per unit of the grid power (i.e. a greenhouse gas emission cost associated with producing electricity during at least one of the one or more time intervals in the specific time window) … the electricity buying price coefficient indicating electricity buying price per unit of the grid power (i.e. a utility cost for electricity charged by an operator in connection with electricity consumption in at least one of the one or more time intervals) ”)
As discussed above, Baba discloses wherein each of the one or more costs is generated dependent on one or more of: a greenhouse gas emission cost associated with producing electricity during at least one of the one or more time intervals in the specific time window; a utility cost for electricity charged by an operator in connection with electricity consumption in at least one of the one or more time intervals. Baba in view of Ito does not explicitly disclose, however Malisani, in the same field of endeavor, discloses wherein each of the one or more costs is generated using the Hamiltonian expression that is dependent on one or more of: a utility cost for electricity charged by an operator in connection with electricity consumption in at least one of the one or more time intervals; or a penalty that depends at least in part on one or more penalty cost factors relating to one of: the batteries of the electric vehicle (Pg. 1, 2, 3, 7) (“the problem of the scheduling of several chargers distributed over a an electrical grid is addressed and the scheduling is handled … the authors address the optimal assignment of tours to be processed of a mixed fleet of combustion and electric vehicles together with the optimal charging scheduling (i.e. generating an optimized schedule of Ito)” “θx ∈R+: weight of state of charge penalization … copt(nx) = ∫ price ∑nxk=1 λkp+ c,k(xλk)dt: total charging cost with optimal charging rates λ. … cref(nx) = ∫price ∑nxk=1 pc,k dt: total charging cost with reference charging powers pc.” “a time-of-use charging rate (i.e. a utility cost for electricity charged by an operator in connection with electricity consumption in at least one of the one or more time intervals) to compute the charging cost (i.e. and the charging speed is represented using a quadratic cost on difference between the current and the target state of charge” “Pontryagin Maximum Principle approach for solving the unconstrained optimal control problem: Solving the lower bounding problem consists in solving a sequence of unconstrained optimal control problems (UOCPs) (44)-(45). To solve this sequence of UOCPs, a Pontryagin Maximum Principle (PMP) based approach is used. To do so, let us first define the Hamiltonian [12] of problem (44)-(45) (i.e. a Hamiltonian expression)” That is, equation (44) discloses “cost” and θx as an input for the Hamiltonian used in finding an optimal solution (i.e. generating, based at least in part on a Hamiltonian expression that includes a penalty function using the current SoC as an input parameter, an optimized schedule of Ito).)
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the electricity management and distribution system of Baba in view of Ito with the optimal charging scheduling of Malisani in order to better minimize costs of charging seen from the charger users. (Malisani – Pg. 3)
In regards to claim 3, Baba in view of Ito and further in view of Malisani and even further in view of Malisani II disclose the limitations of claim 1. Baba does not explicitly disclose, however Ito, in the same field of endeavor, discloses wherein each of the one or more costs of Baba is dependent a penalty that depends at least in part on one or more penalty cost factors relating to one of: the batteries of the electric vehicle; (Para. 83, 91) (“The optimization parameters and constraints depend on the embodiment and can include: … customer battery levels needs, starting battery levels, power of charging, among other parameters (i.e. a penalty that depends at least in part on one or more penalty cost factors relating to one of: the batteries of the electric vehicle or user preferences).” “the load manager application 903 can then generate estimated system state from vehicle data, historical AMI data, and external data (such as temperature or projected temperature) at step 1309.”)
Baba does not explicitly disclose, however Ito, in the same field of endeavor, discloses wherein the one or more penalty cost factors include one or more of: the SoC of the one or more batteries of the electric vehicle; one or more operational temperatures of the one or more batteries of the electric vehicle. (Para. 83, 91) (“The optimization parameters and constraints depend on the embodiment and can include: … customer battery levels needs, starting battery levels, power of charging, among other parameters.” “the load manager application 903 can then generate estimated system state from vehicle data, historical AMI data, and external data (such as temperature or projected temperature) at step 1309.”)
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the electricity management and distribution system of Baba with the power supply systems and methods of Ito in order to improve the efficiency of the system. (Ito – Para. 14)
As discussed above, Baba in view of Ito discloses wherein each of the one or more costs of Baba is dependent on a penalty that depends at least in part on one or more penalty cost factors relating to one of: the batteries of the electric vehicle. Baba in view of Ito does not explicitly disclose, however Malisani, in the same field of endeavor, discloses wherein each of the one or more costs of Baba is generated using a Hamiltonian expression that is dependent on a penalty that depends at least in part on one or more penalty cost factors relating to one of: the batteries of the electric vehicle of Ito; wherein the one or more penalty cost factors include one or more of: the SoC of the one or more batteries of the electric vehicle; (Pg. 1, 2, 7) (“the problem of the scheduling of several chargers distributed over a an electrical grid is addressed and the scheduling is handled … the authors address the optimal assignment of tours to be processed of a mixed fleet of combustion and electric vehicles together with the optimal charging scheduling” “θx ∈ R+: weight of state of charge penalization (i.e. wherein the one or more penalty cost factors include one or more of: the SoC of the one or more batteries of the electric vehicle)” “Pontryagin Maximum Principle approach for solving the unconstrained optimal control problem: Solving the lower bounding problem consists in solving a sequence of unconstrained optimal control problems (UOCPs) (44)-(45). To solve this sequence of UOCPs, a Pontryagin Maximum Principle (PMP) based approach is used. To do so, let us first define the Hamiltonian [12] of problem (44)-(45) (i.e. a Hamiltonian expression) … The exit condition from Algorithm 2 allows to stop the algorithm when the perturbation on the optimal cost provided by the penalty function εpint(x,ϕ(ν),Mk) is negligible with respect to the original cost.” That is, equation (44) discloses “θx” as an input for the Hamiltonian used in finding an optimal solution (i.e. each of the one or more costs of Baba is generated using a Hamiltonian expression that is dependent on a penalty that depends at least in part on one or more penalty cost factors relating to one of: the batteries of the electric vehicle of Ito; wherein the one or more penalty cost factors include one or more of: the SoC of the one or more batteries of the electric vehicle;).)
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the electricity management and distribution system of Baba in view of Ito with the optimal charging scheduling of Malisani in order to better minimize costs of charging seen from the charger users. (Malisani – Pg. 3)
In regards to claim 4, Baba in view of Ito and further in view of Malisani and even further in view of Malisani II disclose the limitations of claim 1. Baba discloses wherein the charging station is located at a home; (Para. 36) (“In this electricity distribution system, a house 10 connected to an electrical grid 20 and the electric vehicle EV can be connected with a power line. In the house 10, a distribution board 11, plural load appliances 12 (1 to n), a charging/discharging converter 13, an electricity management device 14, a communication unit 15, and a electricity generation device 16 are provided.”)
Baba discloses wherein a second electricity demand of the home during the specific time window is predicted; (Para. 11, 76) (“the controller estimates an amount of electricity generated by the electricity generation device on the next day and an electricity demand of the house (i.e. a second electricity demand of the home during the specific time window is predicted) on the next day” “the entire operation controller 108 therefore includes another function as a power generation/in-house electric load estimation unit 14a which estimates the amount of PV-generated electricity and the amount of in-house power consumption (i.e. a second electricity demand of the home during the specific time window is predicted).”)
Baba discloses wherein the set of operations specified in the schedule includes operations to charge the batteries of the electric vehicle for satisfying the predicted electricity demand of the electric vehicle in one or more first time intervals in the specific time window, to recharge the batteries of the electric vehicle, in one or more second time intervals in the specific time window, for storing energy to be transferred to the home, and to transfer the energy stored in the batteries of the electric vehicle to the home for satisfying at least a portion of the predicted second electricity demand of the home. (Para. 102-103, 106) (“In a time period T4 … operation (3) charging the electric vehicle EV is performed using the large surplus of PV-generated electricity (i.e. to recharge the batteries of the electric vehicle, in one or more second time intervals in the specific time window, for storing energy to be transferred to the home). … In a time period T5 … operation (1) of discharging electricity from the electric vehicle EV to the house 10 is performed (i.e. to transfer the energy stored in the batteries of the electric vehicle to the home for satisfying at least a portion of the predicted second electricity demand of the home).” “In a time period T8 shown in FIG. 9, there is a large surplus of PV-generated electricity, and operation (6) of charging the electric vehicle EV is performed using the large surplus of PV-generated electricity. As a result, the amount of charge of the electric vehicle EV is nearly fully charged (i.e. wherein the set of operations specified in the schedule includes operations to charge the batteries of the electric vehicle for satisfying the predicted electricity demand of the electric vehicle in one or more first time intervals in the specific time window) ”)
Baba does not explicitly disclose, however Ito, in the same field of endeavor, discloses the schedule of Baba is the optimized schedule (Para. 11) (“The consumption control unit evaluates the series of charge-discharge schedules (i.e. the schedule of Baba) by way of an evaluation index. The evaluation index is calculated for each of the charge-discharge schedule, and the consumption control unit controls the charge-discharge of electric power of each of the storage units according to the charge-discharge schedule having an optimal evaluation index (i.e. the optimized schedule).”)
Baba does not explicitly disclose, however Ito, in the same field of endeavor, discloses wherein the optimized schedule is generated further based on the second electricity demand of the home of Baba; (Para. 10-12, 59) (“the consumption control unit determines a series of charge-discharge schedules based on (i) a predicted power consumption schedule providing transitional electric power consumption during the prediction period by the electric load based on a use history of the electric load, … the consumption control unit controls the charge-discharge of electric power of each of the storage units according to the charge-discharge schedule having an optimal evaluation index. … The consumption control unit determines the charge-discharge schedule as having an optimal evaluation index, based on the predicted power consumption schedule, the predicted power generation schedule, a connection period during which the in-vehicle storage unit is electrically coupled to the wiring” “The relationship between consumed electric power by the general electric load 12 and time is described in the following with reference to FIGS. 3-8. … an in-house consumption of electric power (kW) over time”)
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the electricity management and distribution system of Baba with the power supply systems and methods of Ito in order to improve the efficiency of the system. (Ito – Para. 14)
In regards to claim 5, Baba in view of Ito and further in view of Malisani and even further in view of Malisani II disclose the limitations of claim 1. Baba does not explicitly disclose, however Ito, in the same field of endeavor, discloses wherein a plurality of costs is generated for the plurality of consecutive non-overlapping time intervals; wherein each cost in the plurality of costs is generated for a respective time interval in the plurality of consecutive non-overlapping time intervals. (Para. 56-62) (“an electric power price balance (i.e. cost of electric power), which is calculated by subtracting (i) a sell price of electric power according to the amount of the reverse-flow to the electric wiring 11 from (ii) a purchase price of electric power according to the amount of the supplied electric power from the electric wiring 11. … Based on the predicted power consumption schedule, the predicted power generation schedule, and the charge-discharge characteristics of each of the batteries 14, 24a, 24b, a series of charge-discharge schedule may be determined for the optimal evaluation index. When the evaluation index is provided as the electric power price balance, the charge-discharge schedule with the lowest electric power price balance (i.e. lowest cost) is provided as the charge-discharge schedule that is used to control the charge and discharge … The relationship between consumed electric power by the general electric load 12 and time is described in the following with reference to FIGS. 3-8. In FIGS. 3-8, the horizontal axis (i.e. X-axis) is a measurement of time, and the vertical axis (Y-axis) is a measurement of power (kW). Specifically, the diagrams provide the photovoltaic power generation amount (kW), the charge-discharge electricity amount of each of the storage units 24a, 24b (kW), the state of charge (SOC) of each of the storage units 24a, 24b (kWh), a net consumption of electric power (kW), and an in-house consumption of electric power (kW) over time. In the following, the price of electric power may be provided, for example, as 21.2 Yen/kWh for time zones of 7:00 to 9:00 and 17:00 to 24:00, 31.4 Yen/kWh for time zones of 9:00 to 17:00, and 9.3 Yen/kWh for time zones of 24:00 to 7:00 (mid-night time zone). In addition, the sell price may be a fixed rate, such as 48 Yen/kWh for all time. … When the battery is used, the electric power stored in during mid-night time zone is used as the daytime electric power consumption. The cost of the electric power for the example of FIG. 4 is 4.6 Yen. Therefore, the system 10 of the present disclosure that uses two batteries 24a, 24b has a lower electric power cost than the system of FIG. 4.” That is, in order to optimize the schedule over price, the system generates costs for every time zones (i.e. the plurality of consecutive non-overlapping time intervals of Baba).)
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the electricity management and distribution system of Baba with the power supply systems and methods of Ito in order to improve the efficiency of the system. (Ito – Para. 14)
In regards to claim 6, Baba in view of Ito and further in view of Malisani and even further in view of Malisani II disclose the limitations of claim 5. Baba does not explicitly disclose, however Ito, in the same field of endeavor, discloses wherein the subset of operations used to charge the electric vehicle to satisfy the predicted electricity demand of the electric vehicle is scheduled to be performed in a subset of time intervals, corresponding to the lowest costs among the plurality of costs, in the plurality of consecutive non-overlapping time intervals. (Para. 26, 49) (“The power supply contract may provide for a lower price rate for electric power during a certain time zone (i.e. time period), such as a mid-night time zone, which may be from 24:00 to 7:00. In the following, the mid-night time zone is provided as the time zone in which the price rate for electric power is the lowest. However, the time zone in which the price rate for electric power is the lowest is not limited to the mid-night times zone, and may vary.” “the controller 18 operates the electrical storage unit 13 to charge each of the batteries 24a, 24b during the mid-night time zone in order to receive the lower price rate for the electric power.”)
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the electricity management and distribution system of Baba with the power supply systems and methods of Ito in order to improve the efficiency of the system. (Ito – Para. 14)
In regards to claim 7, Baba in view of Ito and further in view of Malisani and even further in view of Malisani II disclose the limitations of claim 1. Baba does not explicitly disclose, however Ito, in the same field of endeavor, discloses wherein the schedule is generated for a specific optimization level among a plurality of optimization levels in addition to a non-optimized level; wherein the plurality of optimization levels includes one or more of: a first optimization level in which only the electricity demand of the electric vehicle is satisfied, or one or more third optimization levels in which multiple recharging time intervals, among the plurality of consecutive non- overlapping time intervals, are used to store energy in the batteries of the electric vehicle for transferring to the home. (Para. 53, 76, 102-103) (“When the electric vehicle EV is scheduled to travel the next day, for example, the electricity management device 14 extracts charging electricity a from the grid power supply 20a and charges the rechargeable battery 32 from the charging electricity a (operation (3): charging) (i.e. a non-optimized level). When there is a surplus electricity c by electricity generation of the electricity generation device 16, the electricity management device 14 charges the rechargeable battery 32 of the electric vehicle EV from the surplus electricity through a power conditioner 16A (operation (4): charging from surplus electricity). (i.e. wherein the schedule is generated for a specific optimization level among a plurality of optimization levels; a first optimization level in which only the electricity demand of the electric vehicle is satisfied)” “the electricity management device 14 may be configured to estimate the surplus by in-house electricity generation in a period until the leaving time of the electric vehicle EV and make a charging schedule so that the electric vehicle EV is charged from the PV-generated electricity as much as possible.” “operation (3) charging the electric vehicle EV is performed … In a time period T5 … operation (1) of discharging electricity from the electric vehicle EV to the house 10 is performed. (i.e. wherein the schedule is generated for a specific optimization level among a plurality of optimization levels; one or more third optimization levels in which multiple recharging time intervals, among the plurality of consecutive non- overlapping time intervals, are used to store energy in the batteries of the electric vehicle for transferring to the home.)”)
Baba does not explicitly disclose, however Ito, in the same field of endeavor, discloses the schedule of Baba is the optimized schedule (Para. 11) (“The consumption control unit evaluates the series of charge-discharge schedules (i.e. the schedule of Baba) by way of an evaluation index. The evaluation index is calculated for each of the charge-discharge schedule, and the consumption control unit controls the charge-discharge of electric power of each of the storage units according to the charge-discharge schedule having an optimal evaluation index (i.e. the optimized schedule).”)
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the electricity management and distribution system of Baba with the power supply systems and methods of Ito in order to improve the efficiency of the system. (Ito – Para. 14)
In regards to claim 8, Baba in view of Ito and further in view of Malisani and even further in view of Malisani II disclose the limitations of claim 1. Baba discloses wherein the schedule includes only a combination of one or more charging time intervals during which the batteries of the electric vehicle are charged to satisfy the predicted electricity demand of the electric vehicle; one or more recharging time intervals during which the batteries of the electric vehicle are charged to store energy in the batteries of the electric vehicle to be transferred to the home; and one or more home bound energy transfer time intervals during which the stored energy in the batteries of the electric vehicle is transferred to the home to satisfy at least a portion of a predicted second electricity demand of the home. (Para. 76, 100) (“the electricity management device 14 may be configured to estimate the surplus by in-house electricity generation in a period until the leaving time of the electric vehicle EV and make a charging schedule so that the electric vehicle EV is charged from the PV-generated electricity as much as possible.” “operation (2) of charging the electric vehicle EV is performed during a time period T2 (i.e. charging time intervals during which the batteries of the electric vehicle are charged to satisfy the predicted electricity demand of the electric vehicle) … In a time period T3 shown in FIG. 9, operation (3) of charging the electric vehicle EV is performed (i.e. charging time intervals during which the batteries of the electric vehicle are charged to satisfy the predicted electricity demand of the electric vehicle) … In a time period T4 … there is a large amount of surplus of PV-generated electricity, and operation (3) charging the electric vehicle EV is performed using the large surplus of PV-generated electricity (i.e. one or more recharging time intervals during which the batteries of the electric vehicle are charged to store energy in the batteries of the electric vehicle to be transferred to the home). … In a time period T5 shown in FIG. 9, there is a small capacity remaining in the rechargeable battery 32 of the electric vehicle EV because of the charging in the time period T4, and the amount of surplus of PV-generated electricity is estimated to be large on the next day. Accordingly, operation (1) of discharging electricity from the electric vehicle EV to the house 10 is performed. (i.e. one or more home bound energy transfer time intervals during which the stored energy in the batteries of the electric vehicle is transferred to the home to satisfy at least a portion of a predicted second electricity demand of the home.)”)
Baba does not explicitly disclose, however Ito, in the same field of endeavor, discloses the schedule of Baba is the optimized schedule (Para. 11) (“The consumption control unit evaluates the series of charge-discharge schedules (i.e. the schedule of Baba) by way of an evaluation index. The evaluation index is calculated for each of the charge-discharge schedule, and the consumption control unit controls the charge-discharge of electric power of each of the storage units according to the charge-discharge schedule having an optimal evaluation index (i.e. the optimized schedule).”)
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the electricity management and distribution system of Baba with the power supply systems and methods of Ito in order to improve the efficiency of the system. (Ito – Para. 14)
In regards to claim 9, Baba in view of Ito and further in view of Malisani and even further in view of Malisani II disclose the limitations of claim 1. Baba discloses wherein the specific time window represents one of: a single contiguous time duration, or two or more discontinuous time durations separated by intermediate time durations excluded from the specific time window. (Para. 97) (“FIG. 10, the results of simulation are the results of calculation for the situation where the rechargeable battery 32 of the electric vehicle EV is nearly fully charged and the electric vehicle EV is not scheduled to be used after the electric vehicle EV travel hours, the normal charging hours, and the electric vehicle EV travel hours. To be specific, the results of simulation show charging and discharging in eight days, in which the electric vehicle EV travels on the first, second, and eighth days.” That is, the specific time can be interpreted as the time from plug in to the travel hours on day 2 (i.e. a continuous time duration), the time from plug in after the travel hours on day 2 to the travel hours on day 8 (i.e. a continuous time duration), or as the time from plug in to the travel hours on day 2 and the time from plug in after the travel hours on day 2 to the travel hours on day 8 (i.e. two or more discontinuous time durations separated by intermediate time durations excluded from the specific time window).)
In regards to claim 10, Baba discloses one or more non-transitory computer readable media storing a program of instructions that is executable by one or more computing processors to perform: determining a specific time window during which an electric vehicle is connecting with a charging station, wherein the charging station is configured to draw electricity from a grid to charge the electric vehicle; (Para. 36, 55, 71) (“electricity distribution system, a house 10 connected to an electrical grid 20 and the electric vehicle EV can be connected with a power line (i.e. wherein the charging station is configured to draw electricity from a grid to charge the electric vehicle).” “The electricity management device 14 is a computer including a storage unit … a CPU, and a program, and the CPU executes the program” “This operation starts from step S1 in response to detection of connection of the electric vehicle EV to the house 10. In the step S1, the electricity management device 14 determines through the entire operation controller 108 whether the electric vehicle EV is scheduled not to travel the next day (i.e. specific time window during which an electric vehicle is connecting with a charging station). When the electric vehicle EV is scheduled to travel the next day, the process goes to step S3, and otherwise, the process goes to step S2. In this process, the entire operation controller 108 reads the date and time of the next-scheduled travel (i.e. specific time window during which an electric vehicle is connecting with a charging station) which are stored in the next-scheduled travel time storage unit 109 through an input at the schedule input unit 14b. The date and time of the next-scheduled travel is set as shown in FIG. 7, for example.”)
Baba discloses predicting an electricity demand of the electric vehicle based on a current state of charge (SoC) of one or more batteries of the electric vehicle; (Para. 73, 98) (“In response to the entry of the electric vehicle EV, the EV charger/discharger 13A supplies to the electricity management device 14, connection information representing that the connection with the electric vehicle EV is "on state" (operation (1): entry to the parking space). The electric vehicle EV sends rechargeable battery information to the electricity management device 14 through an EV-side controller 31. The rechargeable battery information includes remaining battery power of the rechargeable battery 32 (i.e. a current state of charge (SoC) of one or more batteries of the electric vehicle)” “the rechargeable battery 32 of the electric vehicle EV is charged to a predetermined target value by the leaving time of the electric vehicle EV. In this process, the charging/discharging controller 110 supplies to the EV charger/discharger 13A, the charging/discharging control signal to charge the electric vehicle EV. The EV charger/discharger 13A extracts grid power from the distribution board 11 in response to the charging/discharging control signal and supplies the same to the electric vehicle EV. Moreover, the charging/discharging controller 110 calculates the amount of surplus electricity by subtracting the amount of in-house power consumption from the amount of PV-generated electricity. When the amount of surplus electricity is short of the amount of electricity needed (i.e. predicting an electricity demand of the electric vehicle) to charge the electric vehicle EV, the grid power is supplied to the EV charger/discharger 13A from the distribution board 11.” “FIG. 9 shows changes in the amount of consumption of PV-generated electricity, the amount of electricity sold, the amount of discharging electricity from the EV, the amount of electric load as house power consumption, the amount of grid power as purchased electricity, the amount of charging electricity for the EV, and the amount of PV-generated electricity as in-house generated electricity. FIG. 10 shows changes in the amount of charge in the rechargeable battery 32 (i.e. current state of charge (SoC) of one or more batteries of the electric vehicle) of the electric vehicle EV.” That is, the Figures 9 and 10 depict the system tracking the state of charge of rechargeable battery 32 (i.e. current state of charge (SoC) of one or more batteries of the electric vehicle) with known travel periods on days 1, 2, and 8, as well as requiring the battery 32 to reach a target charge level for the travel period, therefore the system must determine how much electricity is required to charge the battery 32 from its current SOC to the target charge level (i.e. predicting an electricity demand of the electric vehicle based on a current state of charge (SoC) of one or more batteries of the electric vehicle).)
Baba discloses computing one or more costs associated with drawing electricity from the grid during one or more time intervals, wherein the specific time window is partitioned into a plurality of consecutive non-overlapping time intervals that include the one or more time intervals; (Para. 98, 133) (“FIG. 11 shows changes in the CO.sub.2 emission counter value.” “the CO.sub.2 emission counter value is increased or reduced based on the amount of CO.sub.2 emissions obtained by multiplying the charging/discharging electricity of the electric vehicle EV by the CO.sub.2 emission coefficient but may be increased or reduced based on an index other than the amount of CO.sub.2 emissions. For example, instead of the CO.sub.2 emission coefficient, the index to change the counter value may be unit price of electricity. In this case, when the electric vehicle EV is charged from the grid power, the electricity management device 14 multiplies the amount of charging electricity by the unit price of electricity to calculate the price of the electricity and increases the counter value (i.e. computing one or more costs associated with drawing electricity from the grid during one or more time intervals).” That is, while Fig. 11 shows the emissions counter value over several time intervals (i.e. one or more time intervals), it is clear that this counter is contemplated to also show the price paid for electricity from the grid (i.e. computing one or more costs associated with drawing electricity from the grid during one or more time intervals).)
Baba discloses performing a set of operations with the one or more batteries of the electric vehicle, wherein the set of operations include at least a subset of operations used to charge the electric vehicle to satisfy the predicted electricity demand of the electric vehicle. (Para. 70-72, 99) (“a description is given of the procedure of the aforementioned operation of the electricity management device 14 to control charging/discharging … This operation starts from step S1 in response to detection of connection of the electric vehicle EV to the house 10. In the step S1, the electricity management device 14 determines through the entire operation controller 108 whether the electric vehicle EV is scheduled not to travel the next day. … In the next step S2, the electric vehicle EV is not discharged, and the rechargeable battery 32 of the electric vehicle EV is charged to a predetermined target value by the leaving time of the electric vehicle EV (i.e. performing a set of operations with the one or more batteries of the electric vehicle, wherein the set of operations include at least a subset of operations used to charge the electric vehicle to satisfy the predicted electricity demand of the electric vehicle).” “in a time period T1 after the travel hours, the rechargeable battery 32 of the electric vehicle EV is charged from the grid power from the house 10 (operation (1) of charging the EV) because other travel hours are scheduled after the time period T1. … When the electric vehicle EV is connected to the house 10 after the travel hours following the charging of the electric vehicle EV, the electricity distribution system does not have a travel schedule of the next day. Accordingly, as shown in FIG. 10, operation (2) of charging the electric vehicle EV is performed during a time period T2 (i.e. performing a set of operations with the one or more batteries of the electric vehicle, wherein the set of operations include at least a subset of operations used to charge the electric vehicle to satisfy the predicted electricity demand of the electric vehicle) when there is a surplus of PV-generated electricity on the next day. By the operation (2) of charging the electric vehicle EV (i.e. performing a set of operations with the one or more batteries of the electric vehicle, wherein the set of operations include at least a subset of operations used to charge the electric vehicle to satisfy the predicted electricity demand of the electric vehicle), the amount of charge in the electric vehicle EV is increased in FIG. 10. … operation (3) of charging the electric vehicle EV is performed although the amount of surplus of PV-generated electricity is small, and the amount of charge in the electric vehicle EV increases slightly in FIG. 10. … there is a large amount of surplus of PV-generated electricity, and operation (3) charging the electric vehicle EV (i.e. performing a set of operations with the one or more batteries of the electric vehicle, wherein the set of operations include at least a subset of operations used to charge the electric vehicle to satisfy the predicted electricity demand of the electric vehicle) is performed using the large surplus of PV-generated electricity.”)
Baba does not explicitly disclose, however Ito, in the same field of endeavor, discloses generating, based at least in part on the one or more costs of Baba, an optimized schedule for performing a set of operations of Baba (Para. 12-14) (“The consumption control unit determines the charge-discharge schedule (i.e. generating, based at least in part on the one or more costs of Baba, an optimized schedule for performing a set of operations of Baba) as having an optimal evaluation index … The evaluation index may be, for example, a cost of the electric power or an emission amount of carbon dioxide (i.e. one or more costs of Baba).”)
Baba does not explicitly disclose, however Ito, in the same field of endeavor, discloses causing the set of operations of Baba specified in the optimized schedule to be performed to charge the electric vehicle of Baba. (Para. 37-38, 46) (“The charge power line 27 is wired to inside of the charge station 15, and is coupled to a charge-discharge cable 28 that extends from a body of the charge station 15 to outside of the charge station 15. … the charge station 15 includes a control pilot (CPLT) board (not illustrated), a power line communication (PLC) unit (not illustrated), and the control ECU 26. … A CPLT line and a ground (GND) line are disposed together with a power line in the charge-discharge cable 28, to allow communication of a CPLT signal. The CPLT board performs a charge control of the in-vehicle battery 14 as its main function.” “The controller 18 controls the electric power charged to each of the batteries 24a, 24b and to the in-vehicle battery 14, and controls the electric power discharged from each of the batteries 24a, 24b and from the in-vehicle battery 14 to the electric wiring 11 according to a charge-discharge schedule stored in the memory unit.”)
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the electricity management and distribution system of Baba with the power supply systems and methods of Ito in order to improve the efficiency of the system. (Ito – Para. 14)
Baba in view of Ito does not explicitly disclose, however Malisani, in the same field of endeavor, discloses generating, based at least in part on a Hamiltonian expression that includes a penalty function using the current SoC as an input parameter, an optimized schedule of Ito (Pg. 1, 2, 7) (“the problem of the scheduling of several chargers distributed over a an electrical grid is addressed and the scheduling is handled … the authors address the optimal assignment of tours to be processed of a mixed fleet of combustion and electric vehicles together with the optimal charging scheduling (i.e. generating an optimized schedule of Ito)” “θx ∈ R+: weight of state of charge penalization (i.e. a penalty function using the current SoC as an input parameter)” “Pontryagin Maximum Principle approach for solving the unconstrained optimal control problem: Solving the lower bounding problem consists in solving a sequence of unconstrained optimal control problems (UOCPs) (44)-(45). To solve this sequence of UOCPs, a Pontryagin Maximum Principle (PMP) based approach is used. To do so, let us first define the Hamiltonian [12] of problem (44)-(45) (i.e. a Hamiltonian expression)” That is, equation (44) discloses “θx” as an input for the Hamiltonian used in finding an optimal solution (i.e. generating, based at least in part on a Hamiltonian expression that includes a penalty function using the current SoC as an input parameter, an optimized schedule of Ito).)
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the electricity management and distribution system of Baba in view of Ito with the optimal charging scheduling of Malisani in order to better minimize costs of charging seen from the charger users. (Malisani – Pg. 3)
Baba in view of Ito in view of Malisani does not explicitly disclose, however Malisani II, in the same field of endeavor, discloses the Hamiltonian expression of Malisani is a Hamiltonian expression that includes a battery chemistry dependent penalty function (Para. 96, 105, 154) (“xλ: state of charge of the vehicle with charge rate λ; pc +: maximum charging power; fc: dynamics of the state of charge of the battery; ρ: battery efficiency; uocv: open-circuit voltage of the battery; rc: internal resistance of the battery (i.e. a battery chemistry dependent)” “a model can be represented by open-circuit voltage uocv, internal resistance rc and limit power pc + parameters. Preferably, the set of parameters of the electric vehicle comprises at least one battery modeling so as to model charging of the battery, in order to implement an optimal battery charge (i.e. an optimized schedule of Ito), according to the battery type (i.e. battery chemistry) and to these characteristics (electric vehicle batteries can be of different types and have different characteristics)” “ battery charge models may be used. These models describe mathematically the power limits, the efficiency according to the battery type, the state of charge and the temperature of the battery.” “an approach based on Pontryagin's maximum principle can be used to solve the optimal control problem … To solve this sequence, an approach based on Pontryagin's maximum principle (PMP) can be used. Therefore first the Hamiltonian (i.e. the Hamiltonian expression of Malisani) H of the problem is defined as follows: [ EQUATION ] (i.e. the Hamiltonian expression of Malisani is a Hamiltonian expression that includes a battery chemistry dependent penalty function)”)
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the electricity management and distribution system of Baba in view of Ito and further in view of Malasani with the optimal charging sequencing of Malisani II in order to allow the system to better account for a variety of batteries. (Malisani II – Para. 10)
In regard to claim 11, Baba in view of Ito and further in view of Malisani and even further in view of Malisani II disclose the limitations of claim 10. The remainder of the limitations of this claim are rejected using the same rationale as claim 2.
In regard to claim 12, Baba in view of Ito and further in view of Malisani and even further in view of Malisani II disclose the limitations of claim 10. The remainder of the limitations of this claim are rejected using the same rationale as claim 3.
In regard to claim 13, Baba in view of Ito and further in view of Malisani and even further in view of Malisani II disclose the limitations of claim 10. The remainder of the limitations of this claim are rejected using the same rationale as claim 4.
In regard to claim 14, Baba in view of Ito and further in view of Malisani and even further in view of Malisani II disclose the limitations of claim 10. The remainder of the limitations of this claim are rejected using the same rationale as claim 5.
In regard to claim 15, Baba in view of Ito and further in view of Malisani and even further in view of Malisani II disclose the limitations of claim 10. The remainder of the limitations of this claim are rejected using the same rationale as claim 6.
In regard to claim 16, Baba in view of Ito and further in view of Malisani and even further in view of Malisani II disclose the limitations of claim 10. The remainder of the limitations of this claim are rejected using the same rationale as claim 7.
In regard to claim 17, Baba in view of Ito and further in view of Malisani and even further in view of Malisani II disclose the limitations of claim 10. The remainder of the limitations of this claim are rejected using the same rationale as claim 8.
In regard to claim 18, Baba in view of Ito and further in view of Malisani and even further in view of Malisani II disclose the limitations of claim 10. The remainder of the limitations of this claim are rejected using the same rationale as claim 9.
In regards to claim 19, Baba discloses a determining a specific time window during which an electric vehicle is connecting with a charging station, wherein the charging station is configured to draw electricity from a grid to charge the electric vehicle; (Para. 36, 55, 71) (“electricity distribution system, a house 10 connected to an electrical grid 20 and the electric vehicle EV can be connected with a power line (i.e. wherein the charging station is configured to draw electricity from a grid to charge the electric vehicle).” “The electricity management device 14 is a computer including a storage unit … a CPU, and a program, and the CPU executes the program” “This operation starts from step S1 in response to detection of connection of the electric vehicle EV to the house 10. In the step S1, the electricity management device 14 determines through the entire operation controller 108 whether the electric vehicle EV is scheduled not to travel the next day (i.e. specific time window during which an electric vehicle is connecting with a charging station). When the electric vehicle EV is scheduled to travel the next day, the process goes to step S3, and otherwise, the process goes to step S2. In this process, the entire operation controller 108 reads the date and time of the next-scheduled travel (i.e. specific time window during which an electric vehicle is connecting with a charging station) which are stored in the next-scheduled travel time storage unit 109 through an input at the schedule input unit 14b. The date and time of the next-scheduled travel is set as shown in FIG. 7, for example.”)
Baba discloses predicting an electricity demand of the electric vehicle based on a current state of charge (SoC) of one or more batteries of the electric vehicle; (Para. 73, 98) (“In response to the entry of the electric vehicle EV, the EV charger/discharger 13A supplies to the electricity management device 14, connection information representing that the connection with the electric vehicle EV is "on state" (operation (1): entry to the parking space). The electric vehicle EV sends rechargeable battery information to the electricity management device 14 through an EV-side controller 31. The rechargeable battery information includes remaining battery power of the rechargeable battery 32 (i.e. a current state of charge (SoC) of one or more batteries of the electric vehicle)” “the rechargeable battery 32 of the electric vehicle EV is charged to a predetermined target value by the leaving time of the electric vehicle EV. In this process, the charging/discharging controller 110 supplies to the EV charger/discharger 13A, the charging/discharging control signal to charge the electric vehicle EV. The EV charger/discharger 13A extracts grid power from the distribution board 11 in response to the charging/discharging control signal and supplies the same to the electric vehicle EV. Moreover, the charging/discharging controller 110 calculates the amount of surplus electricity by subtracting the amount of in-house power consumption from the amount of PV-generated electricity. When the amount of surplus electricity is short of the amount of electricity needed (i.e. predicting an electricity demand of the electric vehicle) to charge the electric vehicle EV, the grid power is supplied to the EV charger/discharger 13A from the distribution board 11.” “FIG. 9 shows changes in the amount of consumption of PV-generated electricity, the amount of electricity sold, the amount of discharging electricity from the EV, the amount of electric load as house power consumption, the amount of grid power as purchased electricity, the amount of charging electricity for the EV, and the amount of PV-generated electricity as in-house generated electricity. FIG. 10 shows changes in the amount of charge in the rechargeable battery 32 (i.e. current state of charge (SoC) of one or more batteries of the electric vehicle) of the electric vehicle EV.” That is, the Figures 9 and 10 depict the system tracking the state of charge of rechargeable battery 32 (i.e. current state of charge (SoC) of one or more batteries of the electric vehicle) with known travel periods on days 1, 2, and 8, as well as requiring the battery 32 to reach a target charge level for the travel period, therefore the system must determine how much electricity is required to charge the battery 32 from its current SOC to the target charge level (i.e. predicting an electricity demand of the electric vehicle based on a current state of charge (SoC) of one or more batteries of the electric vehicle).)
Baba discloses computing one or more costs associated with drawing electricity from the grid during one or more time intervals, wherein the specific time window is partitioned into a plurality of consecutive non-overlapping time intervals that include the one or more time intervals; (Para. 98, 133) (“FIG. 11 shows changes in the CO.sub.2 emission counter value.” “the CO.sub.2 emission counter value is increased or reduced based on the amount of CO.sub.2 emissions obtained by multiplying the charging/discharging electricity of the electric vehicle EV by the CO.sub.2 emission coefficient but may be increased or reduced based on an index other than the amount of CO.sub.2 emissions. For example, instead of the CO.sub.2 emission coefficient, the index to change the counter value may be unit price of electricity. In this case, when the electric vehicle EV is charged from the grid power, the electricity management device 14 multiplies the amount of charging electricity by the unit price of electricity to calculate the price of the electricity and increases the counter value (i.e. computing one or more costs associated with drawing electricity from the grid during one or more time intervals).” That is, while Fig. 11 shows the emissions counter value over several time intervals (i.e. one or more time intervals), it is clear that this counter is contemplated to also show the price paid for electricity from the grid (i.e. computing one or more costs associated with drawing electricity from the grid during one or more time intervals).)
Baba discloses performing a set of operations with the one or more batteries of the electric vehicle, wherein the set of operations include at least a subset of operations used to charge the electric vehicle to satisfy the predicted electricity demand of the electric vehicle. (Para. 70-72, 99) (“a description is given of the procedure of the aforementioned operation of the electricity management device 14 to control charging/discharging … This operation starts from step S1 in response to detection of connection of the electric vehicle EV to the house 10. In the step S1, the electricity management device 14 determines through the entire operation controller 108 whether the electric vehicle EV is scheduled not to travel the next day. … In the next step S2, the electric vehicle EV is not discharged, and the rechargeable battery 32 of the electric vehicle EV is charged to a predetermined target value by the leaving time of the electric vehicle EV (i.e. performing a set of operations with the one or more batteries of the electric vehicle, wherein the set of operations include at least a subset of operations used to charge the electric vehicle to satisfy the predicted electricity demand of the electric vehicle).” “in a time period T1 after the travel hours, the rechargeable battery 32 of the electric vehicle EV is charged from the grid power from the house 10 (operation (1) of charging the EV) because other travel hours are scheduled after the time period T1. … When the electric vehicle EV is connected to the house 10 after the travel hours following the charging of the electric vehicle EV, the electricity distribution system does not have a travel schedule of the next day. Accordingly, as shown in FIG. 10, operation (2) of charging the electric vehicle EV is performed during a time period T2 (i.e. performing a set of operations with the one or more batteries of the electric vehicle, wherein the set of operations include at least a subset of operations used to charge the electric vehicle to satisfy the predicted electricity demand of the electric vehicle) when there is a surplus of PV-generated electricity on the next day. By the operation (2) of charging the electric vehicle EV (i.e. performing a set of operations with the one or more batteries of the electric vehicle, wherein the set of operations include at least a subset of operations used to charge the electric vehicle to satisfy the predicted electricity demand of the electric vehicle), the amount of charge in the electric vehicle EV is increased in FIG. 10. … operation (3) of charging the electric vehicle EV is performed although the amount of surplus of PV-generated electricity is small, and the amount of charge in the electric vehicle EV increases slightly in FIG. 10. … there is a large amount of surplus of PV-generated electricity, and operation (3) charging the electric vehicle EV (i.e. performing a set of operations with the one or more batteries of the electric vehicle, wherein the set of operations include at least a subset of operations used to charge the electric vehicle to satisfy the predicted electricity demand of the electric vehicle) is performed using the large surplus of PV-generated electricity.”)
Baba does not explicitly disclose, however Ito, in the same field of endeavor, discloses generating, based at least in part on the one or more costs of Baba, an optimized schedule for performing a set of operations of Baba (Para. 12-14) (“The consumption control unit determines the charge-discharge schedule (i.e. generating, based at least in part on the one or more costs of Baba, an optimized schedule for performing a set of operations of Baba) as having an optimal evaluation index … The evaluation index may be, for example, a cost of the electric power or an emission amount of carbon dioxide (i.e. one or more costs of Baba).”)
Baba does not explicitly disclose, however Ito, in the same field of endeavor, discloses causing the set of operations of Baba specified in the optimized schedule to be performed to charge the electric vehicle of Baba. (Para. 37-38, 46) (“The charge power line 27 is wired to inside of the charge station 15, and is coupled to a charge-discharge cable 28 that extends from a body of the charge station 15 to outside of the charge station 15. … the charge station 15 includes a control pilot (CPLT) board (not illustrated), a power line communication (PLC) unit (not illustrated), and the control ECU 26. … A CPLT line and a ground (GND) line are disposed together with a power line in the charge-discharge cable 28, to allow communication of a CPLT signal. The CPLT board performs a charge control of the in-vehicle battery 14 as its main function.” “The controller 18 controls the electric power charged to each of the batteries 24a, 24b and to the in-vehicle battery 14, and controls the electric power discharged from each of the batteries 24a, 24b and from the in-vehicle battery 14 to the electric wiring 11 according to a charge-discharge schedule stored in the memory unit.”)
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the electricity management and distribution system of Baba with the power supply systems and methods of Ito in order to improve the efficiency of the system. (Ito – Para. 14)
Baba in view of Ito does not explicitly disclose, however Malisani, in the same field of endeavor, discloses generating, based at least in part on a Hamiltonian expression that includes a penalty function using the current SoC as an input parameter, an optimized schedule of Ito (Pg. 1, 2, 7) (“the problem of the scheduling of several chargers distributed over a an electrical grid is addressed and the scheduling is handled … the authors address the optimal assignment of tours to be processed of a mixed fleet of combustion and electric vehicles together with the optimal charging scheduling (i.e. generating an optimized schedule of Ito)” “θx ∈ R+: weight of state of charge penalization (i.e. a penalty function using the current SoC as an input parameter)” “Pontryagin Maximum Principle approach for solving the unconstrained optimal control problem: Solving the lower bounding problem consists in solving a sequence of unconstrained optimal control problems (UOCPs) (44)-(45). To solve this sequence of UOCPs, a Pontryagin Maximum Principle (PMP) based approach is used. To do so, let us first define the Hamiltonian [12] of problem (44)-(45) (i.e. a Hamiltonian expression)” That is, equation (44) discloses “θx” as an input for the Hamiltonian used in finding an optimal solution (i.e. generating, based at least in part on a Hamiltonian expression that includes a penalty function using the current SoC as an input parameter, an optimized schedule of Ito).)
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the electricity management and distribution system of Baba in view of Ito with the optimal charging scheduling of Malisani in order to better minimize costs of charging seen from the charger users. (Malisani – Pg. 3)
Baba in view of Ito in view of Malisani does not explicitly disclose, however Malisani II, in the same field of endeavor, discloses the Hamiltonian expression of Malisani is a Hamiltonian expression that includes a battery chemistry dependent penalty function (Para. 96, 105, 154) (“xλ: state of charge of the vehicle with charge rate λ; pc +: maximum charging power; fc: dynamics of the state of charge of the battery; ρ: battery efficiency; uocv: open-circuit voltage of the battery; rc: internal resistance of the battery (i.e. a battery chemistry dependent)” “a model can be represented by open-circuit voltage uocv, internal resistance rc and limit power pc + parameters. Preferably, the set of parameters of the electric vehicle comprises at least one battery modeling so as to model charging of the battery, in order to implement an optimal battery charge (i.e. an optimized schedule of Ito), according to the battery type (i.e. battery chemistry) and to these characteristics (electric vehicle batteries can be of different types and have different characteristics)” “ battery charge models may be used. These models describe mathematically the power limits, the efficiency according to the battery type, the state of charge and the temperature of the battery.” “an approach based on Pontryagin's maximum principle can be used to solve the optimal control problem … To solve this sequence, an approach based on Pontryagin's maximum principle (PMP) can be used. Therefore first the Hamiltonian (i.e. the Hamiltonian expression of Malisani) H of the problem is defined as follows: [ EQUATION ] (i.e. the Hamiltonian expression of Malisani is a Hamiltonian expression that includes a battery chemistry dependent penalty function)”)
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the electricity management and distribution system of Baba in view of Ito and further in view of Malasani with the optimal charging sequencing of Malisani II in order to allow the system to better account for a variety of batteries. (Malisani II – Para. 10)
In regard to claim 20, Baba in view of Ito and further in view of Malisani and even further in view of Malisani II disclose the limitations of claim 19. The remainder of the limitations of this claim are rejected using the same rationale as claim 11.
In regard to claim 21, Baba in view of Ito and further in view of Malisani and even further in view of Malisani II disclose the limitations of claim 19. The remainder of the limitations of this claim are rejected using the same rationale as claim 12.
In regard to claim 22, Baba in view of Ito disclose the limitations of claim 19. The remainder of the limitations of this claim are rejected using the same rationale as claim 4.
In regard to claim 23, Baba in view of Ito and further in view of Malisani and even further in view of Malisani II disclose the limitations of claim 19. The remainder of the limitations of this claim are rejected using the same rationale as claim 5.
In regard to claim 24, Baba in view of Ito and further in view of Malisani and even further in view of Malisani II disclose the limitations of claim 23. The remainder of the limitations of this claim are rejected using the same rationale as claim 6.
In regard to claim 25, Baba in view of Ito and further in view of Malisani and even further in view of Malisani II disclose the limitations of claim 19. The remainder of the limitations of this claim are rejected using the same rationale as claim 7.
In regard to claim 26, Baba in view of Ito and further in view of Malisani and even further in view of Malisani II disclose the limitations of claim 19. The remainder of the limitations of this claim are rejected using the same rationale as claim 8.
In regard to claim 27, Baba in view of Ito and further in view of Malisani and even further in view of Malisani II disclose the limitations of claim 19. The remainder of the limitations of this claim are rejected using the same rationale as claim 9.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Auberger – US 20220234459 – discloses optimization of charging scheduling of an EV.
Bhargava – US 20220009372 – discloses optimization of charging scheduling of an EV.
Grace – US 11027624 – discloses optimizing charging scheduling based on battery chemistry.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID G GODBOLD whose telephone number is (571)272-5036. The examiner can normally be reached M-F 8-5.
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/DAVID G. GODBOLD/Examiner, Art Unit 3628
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