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 . Claims 1-11 and 13-15 have been reviewed and are under consideration by this office action.
Notice to Applicant
The following is a Non-Final Office action. Applicant, on 12/09/2025, amended claims and cancelled claim 12. Claims 1-11 and 13-15 are pending in this application and have been rejected below.
Response to Amendment
Applicant’s amendments are received and acknowledged.
The amended claims overcome the 112 rejections and are therefore withdrawn.
The arguments with regards to the 103 Rejections are moot in view of the new line of 103 Rejections seen below.
Response to Arguments - 35 USC § 101
Applicant’s arguments with respect to the 35 USC 101 rejections have been fully considered, but they are not persuasive.
Applicant contends that the claims recite an improvement to methods and systems for performing battery pack replacements.
Examiner respectfully disagrees. The asserted improvement merely improves upon the abstract idea itself and does not constitute an improvement to the overall technology or technological field.
Applicant contends that the claimed steps recite an improvements.
Examiner respectfully disagrees. Determining a required number of vehicles for a distribution, computing a forecast SOH or aging prediction, computing a replacement strategy, and providing the strategy are all concepts capable of being performed in the human mind (via pen and paper) and are further certain methods of organizing human activity as the claims are directed towards improving lifetimes of energy storages of a fleet of vehicles missions (See Specification, [pg. 2, para. 1]).
Applicant contends that the claims optimize electrical storage systems and are directed to an improvement in the technology.
Examiner respectfully disagrees. The asserted improvement merely improves upon the abstract idea itself and does not constitute an improvement to the overall technology or technological field.
The 101 rejection is updated and maintained below.
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 1-11 and 13-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, claim(s) 1-11 and 13-15 is/are directed to statutory categories.
Step 2A, Prong One – The claims are found to recite limitations that set forth the abstract idea(s), namely in independent claims recite a series of steps for the abstract idea recited below.
Regarding the claims, (additional elements bolded)
Regarding Claim(s) 1, 14, and 15, A computer-implemented method for improving lifetimes of electrical energy storages of a fleet of vehicles each associated with one of several missions, the method comprising:
acquiring, by a processor device of a computer system, data indicating hard vehicle fleet performance constraints including: a total energy and power constraints in the fleet electrical energy storages for fulfilling the fleet missions, and energy and power requirements for fulfilling vehicle missions for individual vehicles,
determining, by the processor device, present state of health (SOH) of each of the electrical energy storages of the fleet,
identifying, by the processor device, electrical energy storages of vehicles in the fleet with SOH such that the energy and power requirement for its mission is not fulfilled,
acquiring, by the processor device, data of warehouse inventory of available electrical energy storages and their SOH,
acquiring, by the processor device, a schedule of planned workshop visits of vehicles of the fleet including the vehicles having electrical energy storages with insufficient SOH and at least one other vehicle of the fleet,
for a given distribution of vehicle missions on a fleet level, determining, by the processor device, a required minimum number of vehicles to fulfil the vehicle missions on the fleet level,
computing, by the processor device, a forecast SOH or an aging prediction for each of the electrical energy storages of the fleet for each of the missions of the fleet,
for the required minimum number of vehicles, computing, by the processor device, an electrical energy storage replacement/reshuffle strategy including a required electrical energy storage distribution among the required minimum number of vehicles that meet the hard constraints with their forecast SOH with minimum margin, and a replacement time slot schedule that minimizes vehicle downtime for reshuffling of electrical energy storage between vehicles and/or replacement from the warehouse by utilizing the already planned workshop visits, and
providing, by the processor device, an instruction including the electrical energy storages replacement/reshuffle strategy.
Regarding Claim(s) 10, A system for improving lifetimes of electrical energy storages of a fleet of vehicles each associated with one of several missions comprising:
a data collector acquiring data indicating hard vehicle fleet performance constraints including: a total energy and power constraints in the fleet of electrical energy storages for fulfilling the fleet missions, and energy and power requirements for fulfilling vehicle missions for individual vehicles, wherein the data collector is computer instructions stored in memory and executed by a processor,
a data acquisitor retrieving present state of health (SOH) data of each of the electrical energy storages of the fleet, wherein the data acquisitor is computer instructions stored in memory and executed by the processor
a comparator identifying electrical energy storages of vehicles in the fleet with SOH such that the energy requirement for its mission is not fulfilled, wherein the comparator is computer instructions stored in memory and executed by the processor,
a memory storage having stored data of warehouse inventory of available electrical energy storages and their SOH, and a schedule of planned workshop visits of vehicles of the fleet including the vehicles having electrical energy storages with insufficient SOH and at least one other vehicle of the fleet,
a control unit configured to: for a given distribution of vehicle missions on a fleet level, determine a required minimum number of vehicles to fulfil the vehicle missions on the fleet level,
compute a forecast SOH for each of the electrical energy storages of the fleet for each of the missions of the fleet,
for the required minimum number of vehicles, compute an electrical energy storage replacement/reshuffle strategy including an required electrical energy storage distribution among the required minimum number of vehicles that meet the hard constraints with their forecast SOH with minimum margin, and a replacement time slot schedule that minimizes vehicle downtime for reshuffling of electrical energy storage between vehicles and/or replacement from the warehouse by utilizing the already planned workshop visits, and
provide an instruction including the electrical energy storages replacement/reshuffle strategy, wherein the control unit is computer instructions stored in memory and executed by the processor.
Regarding Claim(s) 11, The system according to claim 10, wherein the control unit is configured to provide the instruction to a user interface and/or to store the electrical energy storage replacement/reshuffle strategy on a memory storage.
Regarding Claim(s) 13, A non-transitory computer-readable storage medium comprising instructions, which when executed by the processor device, cause the processor device to perform the method of claim 1.
Further regarding Claim(s) 14, A control system comprising one or more control units wherein the control system is computer instructions stored in memory and executed by the processor, configured to perform:
Further regarding Claim(s) 15, A server comprising- a control system comprising one or more control units wherein the control system is computer instructions stored in memory and executed by the processor, configured to perform:
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea groupings of “Mental processes—concepts performed in the human mind” (observation, evaluation, judgment, opinion) as the claims are directed towards acquiring data, determining a state of health, identifying energy storages, determining number of vehicles to fulfill missions, computing ( i.e. calculating) an energy storage reshuffle, and providing an instruction all of which are concepts capable of being performed in the human mind (i.e. via pen and paper).
Further the claims are directed towards the abstract idea grouping of “Certain methods of organizing human activity” — commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations) and/or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) as the claims are directed towards improving lifetimes of energy storages of a fleet of vehicles (See Specification, [pg. 2, para. 1]).
Step 2A, Prong Two - This judicial exception is not integrated into a practical application. The independent claims utilize the additional elements bolded above. The additional elements are performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h).
Step 2B - The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are just “apply it” on a computer. (See MPEP 2106.05(f) – Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h).
Regarding Claim(s) 2-9, the claim further narrows the abstract idea or recite additional elements previously rejected in the independent claims.
Accordingly, the claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claim(s) 1, 7, and 9-11 and 13-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 20210284043 A1) in view of Pettersson et al. (US 20130046457 A1), and Abari et al. (US 20190197798 A1).
Regarding Claim(s) 1, Wang teaches: A computer-implemented method for improving lifetimes of electrical energy storages of a fleet of vehicles each associated with one of several missions, the method comprising: (Wang, [158]; a scheduler may use an algorithm and/or a charge distribution map to determine where to allocate charge, either by peer-to-peer charging or charging via MoCS. The scheduler may operate according to a set of optimization goals, which guide the scheduler in determining an optimal distribution of charge throughout an EV fleet and Wang, [162]; maximize battery life by considering the depth of discharge of each EV and Wang, [81]; decisions regarding how to use the different battery sets during operation can be determined directly at a battery powered entity, a computing device, or a cloud-based artificial intelligence (AI) application can optimally make the decision for a network of battery-operated entities to optimize the overall network efficiency).
acquiring, by a processor device of a computer system, data indicating hard vehicle fleet performance constraints including: a total energy and power constraints in the fleet electrical energy storages for fulfilling the fleet missions, and energy and power requirements for fulfilling vehicle missions for individual vehicles, (Wang, [17]; a method can be carried out that comprises: determining a first set of battery characteristics related to battery architecture; optimizing the first set of battery characteristics based at least upon at least one of: budget requirements, chemical compatibility requirements, and electrical requirements; determining, based at least upon the optimized first set of battery characteristics, a second set of battery characteristics; optimizing the second set of battery characteristics based at least upon at least one of: use requirements, space requirements, and structural requirements; and providing a battery architecture recommendation comprising the optimized first set of battery characteristics and the optimized second set of battery characteristics and Wang, [148]; As illustrated in FIG. 10, entity c, will likely require an additional 110 units of charge to complete the desired trip, however a surplus of charge is expected for each of entity c1, entity c2, entity c3, and entity c4 at the end of the corresponding desired trip, as currently planned and Wang, [176]; the first and third electric vehicles 202a, 202b can have a total electric charge level that is above an upper threshold and/or a charge level in excess of the required charge for to reach their respective destinations. In some embodiments, the second electric vehicle 206a and fifth electric vehicle 208a, can have a total electric charge level that is above a lower threshold and/or a charge level insufficient for the vehicles to reach their respective destinations).
determining, by the processor device, present state of health (SOH) of each of the electrical energy storages of the fleet, (Wang, [121]; In some embodiments, a battery powered entity (e.g., a vehicle) may communicate a current location, a current speed, a destination, a planned route, a battery state, and the like information to the computing device 10 and Wang, [148]; As illustrated in FIG. 10, entity c, will likely require an additional 110 units of charge to complete the desired trip, however a surplus of charge is expected for each of entity c1, entity c2, entity c3, and entity c4 at the end of the corresponding desired trip, as currently planned). Examiner notes that Wang teaches a state of health under the broadest reasonable interpretation but Pettersson also explicitly teaches a state of health.
identifying, by the processor device, electrical energy storages of vehicles in the fleet with SOH such that the energy and power requirement for its mission is not fulfilled, (Wang, [148]; As illustrated in FIG. 10, entity c, will likely require an additional 110 units of charge to complete the desired trip, however a surplus of charge is expected for each of entity c1, entity c2, entity c3, and entity c4 at the end of the corresponding desired trip, as currently planned).
acquiring, by the processor device, a schedule of planned workshop visits of vehicles of the fleet including the vehicles having electrical energy storages with insufficient SOH and at least one other vehicle of the fleet, (Wang, [105]; the computing device, such as a server or a cloud computing environment, can be configured to maintain the charge distribution map based at least upon available sources of charging, entities may be in need of charging, and other relevant aspects and information related to the preparation and enactment of a charge transaction schedule and Wang, [149]; In another example, as illustrated in FIG. 12, it is noted that entity c will likely require an additional 110 units of charge to complete the desired trip, however a surplus of charge is expected for each of entity c1, entity c2, entity c3, and entity c4 at the end of each corresponding desired trip, as currently planned. As illustrated in FIG. 13, the computing device, with the assistance of specialized computer programs such as the route planning algorithm, the charge transaction scheduling algorithm, and/or the artificial intelligence program, suggests re-routing entity c3 to align the new route of entity c3 with the existing route of entity c and scheduling a charge transaction between entity c and entity c3 for during the period of time when the trip routes of entity c3 and entity c align. By re-routing c3 and scheduling a charge transaction between entity c3 and entity c, entity c is able to complete the desired trip, without compromising the ability of entity c3 to complete its desired trip, and without disturbing or re-routing the other entities in the locality. In addition, the computing device is configured to optimize the overall charge usage and travel time for all involved entities, meaning that c3 was not chosen at random as the entity to re-route, but rather that all possible or many of the possible re-routing options were considered in real-time or near real-time by the computing device and the optimal re-routing scenario in terms of overall charge use and travel time was chosen. In this particular example, the computing device was able to re-route only one entity (entity c3) and, while there was an increase in the trip duration for entity c3 and a small loss of electrical charge based at least on the re-routing of entity c3, it was the optimal schedule in that the least number of entities experienced a loss of charge and the least number of entities experienced the smallest increase in trip duration and Wang, [246]; In some embodiments, with a periodicity of RC I, the information inside the “Entity Information Database 5004” can be used in a Compute or Recompute block 5008 to compute, e.g., entity battery state switch decisions, re-routing requirements, the charge transaction schedule, and/or the like).
computing, by the processor device, a forecast SOH or an aging prediction for each of the electrical energy storages of the fleet for each of the missions of the fleet, (Wang, [129]; In some embodiments, the plurality of vehicles can be subdivided into any type or number of classes or groups of vehicles based at least on the charge level of the battery for each vehicle. For instance, the charge level of batteries for vehicles might be subdivided into four groups as follows: i) Vehicle A: 76%-100% of capacity, ii) Group B: 50%-75% of capacity, iii) Group C: 26%-50% of capacity, and iv) Group D: 0%-25% of capacity. As illustrated in FIG. 4, Group A vehicles are identified as 102, Group B vehicles are identified as 104, Group C vehicles are identified as 106, and Group D vehicles are identified as 108. In some embodiments, such as when the plurality of vehicles are subdivided into four groups as described, Group D vehicles may be prioritized in terms of routing and scheduling a charge transaction, with descending levels of prioritization for, respectively, Group C vehicles, Group B vehicles, and Group A vehicles. In some embodiments, Group A vehicles, and perhaps even Group B vehicles, may be removed from the schedule completely for a pre-determined time based at least upon an estimation of when the battery of said vehicles will likely be depleted of electric charge sufficiently to re-classify said vehicles as Group C or Group D).
providing, by the processor device, an instruction including the electrical energy storages… strategy. (Wang, [246]; In some embodiments, with a periodicity of RC I, the information inside the “Entity Information Database 5004” can be used in a Compute or Recompute block 5008 to compute, e.g., entity battery state switch decisions, re-routing requirements, the charge transaction schedule, and/or the like). Examiner notes that Wang teaches a strategy with regards to an electric storage plan, while Pettersson below explicitly teaches the replacement portion of the limitation.
While Wang teaches managing charging stations, Wang does not appear to explicitly teach: acquiring, by the processor device, data of warehouse inventory of available electrical energy storages and their SOH, However, Wang in view of the analogous art of Pettersson (i.e. EV charging strategies) does teach the entirety of the limitation: (Pettersson, [68]; By characteristics of the reconditioning locations, information on things like charging capacity, capability, number of charging slots, stock and condition of batteries in stock, availability of the battery-stock and the charging-slots, scheduled reservations or fixed bookings of the batteries on stock or changing-slots and others, can be obtained and Pettersson, [45]; The most important factor therein is the remaining amount of consumable electrical energy stored in the battery, e.g. measured in kWh. Other factors such as full capacity, elapsed regeneration cycles, operating hours, maintenance schedule, temperature, age, manufacturer, type, model, maximum and optimal supply and charge currents, optimal charge profile, expected charging time to full capacity, a weighted age or an overall health of the battery which is dependent on its usage history, can be comprised in such condition information of the storage as they directly or indirectly influence the amount of energy available. The content of electrical energy or charge in such a storage can be evaluated e.g. by the known methods of measurement of the cell-voltage or cell-current, the impedance of the cell or other physical values). While Wang does teach a state of health under the broadest reasonable interpretation, Pettersson more explicitly teaches the state of health as well.
While Wang teaches constraints for forecast SOH and energy storage strategies, Wang does not appear to teach utilizing planned visit. However, Wang/Pettersson does teach: for the… vehicles, computing, by the processor device, an electrical energy storage replacement/reshuffle strategy including a required electrical energy storage distribution among the… vehicles that meet the hard constraints with their forecast SOH with minimum margin, and a replacement time slot schedule that minimizes vehicle downtime for reshuffling of electrical energy storage between vehicles and/or replacement from the warehouse by utilizing the already planned workshop visits, and (Pettersson, [12]; method of changing the target of travel to a charging location when the battery level drops below a certain threshold might be acceptable if the recharging of the energy storage can be done within short time or the vehicle is of a kind such as an autonomous electrical lawn mower, but it is not desirable for personal transportation and time consuming regeneration or charging processes and (Pettersson, [56]; information about planned or expected stopovers and durations of stopovers as well as further planned destinations and destinations to continue on afterwards are valuable information, since those can help to forecast the consumption of battery power with less uncertainty. For example such information about stopover durations and further targets allows to determine whether a recharge during a stopover is sufficient for storing enough energy to reach the further planned target, or if for example an exchange of the battery, even before reach of the intermediate stopover location would be the overall better option, whereby since a charging at the intermediate stopover is possible, also a replacement by a not fully charged battery would do, since the rest of the charge to full capacity can be done during the stopover).
While Wang/Pettersson optimizing missions for fleet vehicles, neither appears to explicitly teach a minimum required number of vehicles. However, Wang/Pettersson in view of the analogous art of Abari (i.e. fleet maintenance) does teach: for a given distribution of vehicle missions on a fleet level, determining, by the processor device, a required minimum number of vehicles to fulfil the vehicle missions on the fleet level, (Abari, [33]; an example method for generating a prediction of demand for autonomous vehicles and scheduling an autonomous vehicle for service based on the prediction including the particular steps of the method of FIG. 2, this disclosure contemplates any suitable method for generating a prediction of demand for autonomous vehicles and scheduling an autonomous vehicle for service based on the prediction including any suitable steps, which may include all, some, or none of the steps of the method of FIG. 2, where appropriate. Furthermore, although this disclosure describes and illustrates particular components, devices, or systems carrying out particular steps of the method of FIG. 2, this disclosure contemplates any suitable combination of any suitable components, devices, or systems carrying out any suitable steps of the method of FIG. 2.and Abari, [39]; In particular embodiments, the actual supply may be affected by the number of autonomous vehicles that are currently receiving services at various service centers (and are therefore not included in the pool of autonomous vehicles available to fulfill demand) and the number of autonomous vehicles that need to be serviced but have not yet been scheduled for service (such as any vehicles that do not meet minimum requirements for being included in the pool of autonomous vehicles available to fulfill demand).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Wang including managing charging stations with the teachings of Pettersson including statuses of warehouses in order to optimize the costs and delays relative to each vehicle. (Pettersson, [68, 70]; the price for regeneration can be of importance and included in the decision e.g. whether to choose a close but pricey or a remoter but also cheaper location…. a load balancing over the plurality of reconditioning locations, a load balancing on the roads, an optimisation of vehicle delays due to charging times, avoidance of traffic jams at rush-hours, or any combination of such.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Wang including constraints for forecast SOH and energy storage strategies with the teachings of Pettersson including utilizing planned visits in order to determine the most optimal plan minimizing stops and delays (Pettersson, [56]; For example such information about stopover durations and further targets allows to determine whether a recharge during a stopover is sufficient for storing enough energy to reach the further planned target, or if for example an exchange of the battery, even before reach of the intermediate stopover location would be the overall better option, whereby since a charging at the intermediate stopover is possible, also a replacement by a not fully charged battery would do, since the rest of the charge to full capacity can be done during the stopover).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Wang/Pettersson optimizing missions for fleet vehicles of Abari including a minimum number of vehicles required in order to ensure there are enough vehicle to meet demand and prevent servicing vehicles needed that are needed to meet demand (Abari, [39]; In particular embodiments, the actual supply may be affected by the number of autonomous vehicles that are currently receiving services at various service centers (and are therefore not included in the pool of autonomous vehicles available to fulfill demand) and the number of autonomous vehicles that need to be serviced but have not yet been scheduled for service (such as any vehicles that do not meet minimum requirements for being included in the pool of autonomous vehicles available to fulfill demand).
Regarding Claim(s) 7, While Wang does teach fleet metrics, Wang does not appear to teach the specific metric. Wang/Pettersson/Abari teaches: The method according to claim 1, wherein determining, by the processor device, present state of health of each of the electrical energy storages of the fleet includes measuring voltage, electrical current, and temperature data of the electrical energy storages. (Pettersson, [45]; The most important factor therein is the remaining amount of consumable electrical energy stored in the battery, e.g. measured in kWh. Other factors such as full capacity, elapsed regeneration cycles, operating hours, maintenance schedule, temperature, age, manufacturer, type, model, maximum and optimal supply and charge currents, optimal charge profile, expected charging time to full capacity, a weighted age or an overall health of the battery which is dependent on its usage history, can be comprised in such condition information of the storage as they directly or indirectly influence the amount of energy available. The content of electrical energy or charge in such a storage can be evaluated e.g. by the known methods of measurement of the cell-voltage or cell-current, the impedance of the cell or other physical values).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Wang including fleet metrics, with the teachings of Pettersson including specific metrics in order to know further metrics that relate to amount of energy available. (Pettersson, [45]; can be comprised in such condition information of the storage as they directly or indirectly influence the amount of energy available. The content of electrical energy or charge in such a storage can be evaluated e.g. by the known methods of measurement of the cell-voltage or cell-current, the impedance of the cell or other physical values. But also a measurement of the in and outflow of energy can be used to determine the present energy content).
Regarding Claim(s) 9, Wang/Pettersson/Abari teaches: The method according to claim 1, wherein the mission of the fleet includes a set of different missions with different energy and power requirements. (Wang, [149]; entity c will likely require an additional 110 units of charge to complete the desired trip, however a surplus of charge is expected for each of entity c1, entity c2, entity c3, and entity c4 at the end of each corresponding desired trip, as currently planned. As illustrated in FIG. 13, the computing device, with the assistance of specialized computer programs such as the route planning algorithm, the charge transaction scheduling algorithm, and/or the artificial intelligence program, suggests re-routing entity c3 to align the new route of entity c3 with the existing route of entity c and scheduling a charge transaction between entity c and entity c3 for during the period of time when the trip routes of entity c3 and entity c align. By re-routing c3 and scheduling a charge transaction between entity c3 and entity c, entity c is able to complete the desired trip, without compromising the ability of entity c3 to complete its desired trip, and without disturbing or re-routing the other entities in the locality).
Regarding Claim(s) 10, Wang teaches: A system for improving lifetimes of electrical energy storages of a fleet of vehicles each associated with one of several missions comprising: (Wang, [158]; a scheduler may use an algorithm and/or a charge distribution map to determine where to allocate charge, either by peer-to-peer charging or charging via MoCS. The scheduler may operate according to a set of optimization goals, which guide the scheduler in determining an optimal distribution of charge throughout an EV fleet and Wang, [162]; maximize battery life by considering the depth of discharge of each EV and Wang, [81]; decisions regarding how to use the different battery sets during operation can be determined directly at a battery powered entity, a computing device, or a cloud-based artificial intelligence (AI) application can optimally make the decision for a network of battery-operated entities to optimize the overall network efficiency).
a data collector acquiring data indicating hard vehicle fleet performance constraints including: a total energy and power constraints in the fleet of electrical energy storages for fulfilling the fleet missions, and energy and power requirements for fulfilling vehicle missions for individual vehicles, wherein the data collector is computer instructions stored in memory and executed by a processor, (Wang, [17]; a method can be carried out that comprises: determining a first set of battery characteristics related to battery architecture; optimizing the first set of battery characteristics based at least upon at least one of: budget requirements, chemical compatibility requirements, and electrical requirements; determining, based at least upon the optimized first set of battery characteristics, a second set of battery characteristics; optimizing the second set of battery characteristics based at least upon at least one of: use requirements, space requirements, and structural requirements; and providing a battery architecture recommendation comprising the optimized first set of battery characteristics and the optimized second set of battery characteristics and Wang, [148]; As illustrated in FIG. 10, entity c, will likely require an additional 110 units of charge to complete the desired trip, however a surplus of charge is expected for each of entity c1, entity c2, entity c3, and entity c4 at the end of the corresponding desired trip, as currently planned and Wang, [176]; the first and third electric vehicles 202a, 202b can have a total electric charge level that is above an upper threshold and/or a charge level in excess of the required charge for to reach their respective destinations. In some embodiments, the second electric vehicle 206a and fifth electric vehicle 208a, can have a total electric charge level that is above a lower threshold and/or a charge level insufficient for the vehicles to reach their respective destinations and Wang, [252]; computer program products may include one or more software components including, for example, software objects, methods, data structures, or the like. A software component may be coded in any of a variety of programming languages. An illustrative programming language may be a lower-level programming language such as an assembly language associated with a particular hardware architecture and/or operating system platform. A software component comprising assembly language instructions may require conversion into executable machine code by an assembler prior to execution by the hardware architecture and/or platform.).
a data acquisitor retrieving present state of health (SOH) data of each of the electrical energy storages of the fleet, wherein the data acquisitor is computer instructions stored in memory and executed by the processor, (Wang, [121]; In some embodiments, a battery powered entity (e.g., a vehicle) may communicate a current location, a current speed, a destination, a planned route, a battery state, and the like information to the computing device 10 and Wang, [148]; As illustrated in FIG. 10, entity c, will likely require an additional 110 units of charge to complete the desired trip, however a surplus of charge is expected for each of entity c1, entity c2, entity c3, and entity c4 at the end of the corresponding desired trip, as currently planned and Wang, [252] recitation above). Examiner notes that Wang teaches a state of health under the broadest reasonable interpretation but Pettersson also explicitly teaches a state of health.
a comparator identifying electrical energy storages of vehicles in the fleet with SOH such that the energy requirement for its mission is not fulfilled, wherein the comparator is computer instructions stored in memory and executed by the processor, (Wang, [148]; As illustrated in FIG. 10, entity c, will likely require an additional 110 units of charge to complete the desired trip, however a surplus of charge is expected for each of entity c1, entity c2, entity c3, and entity c4 at the end of the corresponding desired trip, as currently planned and Wang, [252] recitation above).
a memory storage having stored data of…. and a schedule of planned workshop visits of vehicles of the fleet including the vehicles having electrical energy storages with insufficient SOH and at least one other vehicle of the fleet ((Wang, [105]; the computing device, such as a server or a cloud computing environment, can be configured to maintain the charge distribution map based at least upon available sources of charging, entities may be in need of charging, and other relevant aspects and information related to the preparation and enactment of a charge transaction schedule and Wang, [149]; In another example, as illustrated in FIG. 12, it is noted that entity c will likely require an additional 110 units of charge to complete the desired trip, however a surplus of charge is expected for each of entity c1, entity c2, entity c3, and entity c4 at the end of each corresponding desired trip, as currently planned. As illustrated in FIG. 13, the computing device, with the assistance of specialized computer programs such as the route planning algorithm, the charge transaction scheduling algorithm, and/or the artificial intelligence program, suggests re-routing entity c3 to align the new route of entity c3 with the existing route of entity c and scheduling a charge transaction between entity c and entity c3 for during the period of time when the trip routes of entity c3 and entity c align. By re-routing c3 and scheduling a charge transaction between entity c3 and entity c, entity c is able to complete the desired trip, without compromising the ability of entity c3 to complete its desired trip, and without disturbing or re-routing the other entities in the locality. In addition, the computing device is configured to optimize the overall charge usage and travel time for all involved entities, meaning that c3 was not chosen at random as the entity to re-route, but rather that all possible or many of the possible re-routing options were considered in real-time or near real-time by the computing device and the optimal re-routing scenario in terms of overall charge use and travel time was chosen. In this particular example, the computing device was able to re-route only one entity (entity c3) and, while there was an increase in the trip duration for entity c3 and a small loss of electrical charge based at least on the re-routing of entity c3, it was the optimal schedule in that the least number of entities experienced a loss of charge and the least number of entities experienced the smallest increase in trip duration and Wang, [246]; In some embodiments, with a periodicity of RC I, the information inside the “Entity Information Database 5004” can be used in a Compute or Recompute block 5008 to compute, e.g., entity battery state switch decisions, re-routing requirements, the charge transaction schedule, and/or the like).
a control unit configured to: compute a forecast SOH for each of the electrical energy storages of the fleet for each of the missions of the fleet, (Wang, [129]; In some embodiments, the plurality of vehicles can be subdivided into any type or number of classes or groups of vehicles based at least on the charge level of the battery for each vehicle. For instance, the charge level of batteries for vehicles might be subdivided into four groups as follows: i) Vehicle A: 76%-100% of capacity, ii) Group B: 50%-75% of capacity, iii) Group C: 26%-50% of capacity, and iv) Group D: 0%-25% of capacity. As illustrated in FIG. 4, Group A vehicles are identified as 102, Group B vehicles are identified as 104, Group C vehicles are identified as 106, and Group D vehicles are identified as 108. In some embodiments, such as when the plurality of vehicles are subdivided into four groups as described, Group D vehicles may be prioritized in terms of routing and scheduling a charge transaction, with descending levels of prioritization for, respectively, Group C vehicles, Group B vehicles, and Group A vehicles. In some embodiments, Group A vehicles, and perhaps even Group B vehicles, may be removed from the schedule completely for a pre-determined time based at least upon an estimation of when the battery of said vehicles will likely be depleted of electric charge sufficiently to re-classify said vehicles as Group C or Group D and Wang, [252] recitation above).
provide an instruction including the electrical energy storages… strategy, wherein the control unit is computer instructions stored in memory and executed by the processor.. (Wang, [246]; In some embodiments, with a periodicity of RC I, the information inside the “Entity Information Database 5004” can be used in a Compute or Recompute block 5008 to compute, e.g., entity battery state switch decisions, re-routing requirements, the charge transaction schedule, and/or the like and Wang, [252] recitation above).). Examiner notes that Wang teaches a strategy with regards to an electric storage plan, while Pettersson below explicitly teaches the replacement portion of the limitation.
While Wang teaches managing charging stations, Wang does not appear to explicitly teach:…warehouse inventory of available electrical energy storages and their SOH, However, Wang in view of the analogous art of Pettersson (i.e. EV charging strategies) does teach the entirety of the limitation: (Pettersson, [68]; By characteristics of the reconditioning locations, information on things like charging capacity, capability, number of charging slots, stock and condition of batteries in stock, availability of the battery-stock and the charging-slots, scheduled reservations or fixed bookings of the batteries on stock or changing-slots and others, can be obtained and Pettersson, [45]; The most important factor therein is the remaining amount of consumable electrical energy stored in the battery, e.g. measured in kWh. Other factors such as full capacity, elapsed regeneration cycles, operating hours, maintenance schedule, temperature, age, manufacturer, type, model, maximum and optimal supply and charge currents, optimal charge profile, expected charging time to full capacity, a weighted age or an overall health of the battery which is dependent on its usage history, can be comprised in such condition information of the storage as they directly or indirectly influence the amount of energy available. The content of electrical energy or charge in such a storage can be evaluated e.g. by the known methods of measurement of the cell-voltage or cell-current, the impedance of the cell or other physical values). While Wang does teach a state of health under the broadest reasonable interpretation, Pettersson more explicitly teaches the state of health as well.
While Wang teaches constraints for forecast SOH and energy storage strategies, Wang does not appear to teach utilizing planned visit. However, Wang/Pettersson does teach: for the… vehicles, computing, by the processor device, an electrical energy storage replacement/reshuffle strategy including a required electrical energy storage distribution among the… vehicles that meet the hard constraints with their forecast SOH with minimum margin, and a replacement time slot schedule that minimizes vehicle downtime for reshuffling of electrical energy storage between vehicles and/or replacement from the warehouse by utilizing the already planned workshop visits, and (Pettersson, [12]; method of changing the target of travel to a charging location when the battery level drops below a certain threshold might be acceptable if the recharging of the energy storage can be done within short time or the vehicle is of a kind such as an autonomous electrical lawn mower, but it is not desirable for personal transportation and time consuming regeneration or charging processes and (Pettersson, [56]; information about planned or expected stopovers and durations of stopovers as well as further planned destinations and destinations to continue on afterwards are valuable information, since those can help to forecast the consumption of battery power with less uncertainty. For example such information about stopover durations and further targets allows to determine whether a recharge during a stopover is sufficient for storing enough energy to reach the further planned target, or if for example an exchange of the battery, even before reach of the intermediate stopover location would be the overall better option, whereby since a charging at the intermediate stopover is possible, also a replacement by a not fully charged battery would do, since the rest of the charge to full capacity can be done during the stopover).
While Wang/Pettersson optimizing missions for fleet vehicles, neither appears to explicitly teach a minimum required number of vehicles. However, Wang/Pettersson in view of the analogous art of Abari (i.e. fleet maintenance) does teach: for a given distribution of vehicle missions on a fleet level, determining, by the processor device, a required minimum number of vehicles to fulfil the vehicle missions on the fleet level, (Abari, [33]; an example method for generating a prediction of demand for autonomous vehicles and scheduling an autonomous vehicle for service based on the prediction including the particular steps of the method of FIG. 2, this disclosure contemplates any suitable method for generating a prediction of demand for autonomous vehicles and scheduling an autonomous vehicle for service based on the prediction including any suitable steps, which may include all, some, or none of the steps of the method of FIG. 2, where appropriate. Furthermore, although this disclosure describes and illustrates particular components, devices, or systems carrying out particular steps of the method of FIG. 2, this disclosure contemplates any suitable combination of any suitable components, devices, or systems carrying out any suitable steps of the method of FIG. 2.and Abari, [39]; In particular embodiments, the actual supply may be affected by the number of autonomous vehicles that are currently receiving services at various service centers (and are therefore not included in the pool of autonomous vehicles available to fulfill demand) and the number of autonomous vehicles that need to be serviced but have not yet been scheduled for service (such as any vehicles that do not meet minimum requirements for being included in the pool of autonomous vehicles available to fulfill demand).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Wang including managing charging stations with the teachings of Pettersson including statuses of warehouses in order to optimize the costs and delays relative to each vehicle. (Pettersson, [68, 70]; the price for regeneration can be of importance and included in the decision e.g. whether to choose a close but pricey or a remoter but also cheaper location…. a load balancing over the plurality of reconditioning locations, a load balancing on the roads, an optimisation of vehicle delays due to charging times, avoidance of traffic jams at rush-hours, or any combination of such.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Wang including constraints for forecast SOH and energy storage strategies with the teachings of Pettersson including utilizing planned visits in order to determine the most optimal plan minimizing stops and delays (Pettersson, [56]; For example such information about stopover durations and further targets allows to determine whether a recharge during a stopover is sufficient for storing enough energy to reach the further planned target, or if for example an exchange of the battery, even before reach of the intermediate stopover location would be the overall better option, whereby since a charging at the intermediate stopover is possible, also a replacement by a not fully charged battery would do, since the rest of the charge to full capacity can be done during the stopover).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Wang/Pettersson optimizing missions for fleet vehicles of Abari including a minimum number of vehicles required in order to ensure there are enough vehicle to meet demand and prevent servicing vehicles needed that are needed to meet demand (Abari, [39]; In particular embodiments, the actual supply may be affected by the number of autonomous vehicles that are currently receiving services at various service centers (and are therefore not included in the pool of autonomous vehicles available to fulfill demand) and the number of autonomous vehicles that need to be serviced but have not yet been scheduled for service (such as any vehicles that do not meet minimum requirements for being included in the pool of autonomous vehicles available to fulfill demand).
Regarding Claim(s) 11, Wang/Pettersson/Abari teaches: The system according to claim 10, wherein the control unit is configured to provide the instruction to a user interface and/or to store the electrical energy storage replacement/reshuffle strategy on a memory storage. (Wang, [270]; The external computing entity 800 may also comprise a user interface (that can comprise a display 816 coupled to a processing element 808) and/or a user input interface (coupled to a processing element 808). For example, the user interface may be a user application, browser, user interface, and/or similar words used herein interchangeably executing on and/or accessible via the external computing entity 800 to interact with and/or cause display of information/data from the computing device 700, as described herein and Wang, [246]; In some embodiments, with a periodicity of RC I, the information inside the “Entity Information Database 5004” can be used in a Compute or Recompute block 5008 to compute, e.g., entity battery state switch decisions, re-routing requirements, the charge transaction schedule, and/or the like). Examiner notes that Wang teaches a strategy with regards to an electric storage plan, while Pettersson below explicitly teaches the replacement portion of the limitation).
Regarding Claim(s) 13, Wang/Pettersson/Abari teaches: A non-transitory computer-readable storage medium comprising instructions, which when executed by the processor device, cause the processor device to perform the method of claim 1. (Wang, [07]; According to a fourth embodiment, an apparatus can be provided that comprises: at least one processor and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the processor, cause the apparatus to at least).
Regarding Claim(s) 14, Wang teaches: A control system comprising one or more control units wherein the control system is computer instructions stored in memory and executed by the processor, configured to perform: (Wang, [105]; According to an eleventh embodiment, a computer program product, such as a computer program product comprising a non-transitory computer readable medium storing computer readable instructions, the computer readable instructions operable to cause an apparatus and Wang, [252]; Embodiments of the present invention may be implemented in various ways, including as computer program products that comprise articles of manufacture. Such computer program products may include one or more software components including, for example, software objects, methods, data structures, or the like. A software component may be coded in any of a variety of programming languages. An illustrative programming language may be a lower-level programming language such as an assembly language associated with a particular hardware architecture and/or operating system platform. A software component comprising assembly language instructions may require conversion into executable machine code by an assembler prior to execution by the hardware architecture and/or platform).
Examiner notes that the remainder of the limitations are substantially similar to claim 1 using the control system described above. The claims are rejected similarly to those of claim 1.
Regarding Claim(s) 15, A server comprising- a control system comprising one or more control units wherein the control system is computer instructions stored in memory and executed by the processor, configured to perform: (Wang, [105]; In some embodiments, the computing device, such as a server or a cloud computing environment, can be configured to maintain the charge distribution map based at least upon available sources of charging, entities may be in need of charging, and other relevant aspects and information related to the preparation and enactment of a charge transaction schedule. In other words, in some embodiments, the cloud computing environment or the like can use algorithms or other means for scheduling charge transactions between of heterogeneous or homogeneous mobile entities within a charging network and Wang, [252]; Embodiments of the present invention may be implemented in various ways, including as computer program products that comprise articles of manufacture. Such computer program products may include one or more software components including, for example, software objects, methods, data structures, or the like. A software component may be coded in any of a variety of programming languages. An illustrative programming language may be a lower-level programming language such as an assembly language associated with a particular hardware architecture and/or operating system platform. A software component comprising assembly language instructions may require conversion into executable machine code by an assembler prior to execution by the hardware architecture and/or platform).
Examiner notes that the remainder of the limitations are substantially similar to claim 1 using the control units described above. The claims are rejected similarly to those of claim 1.
Claim(s) 2-4 and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 20210284043 A1) in view of Pettersson et al. (US 20130046457 A1), and Abari et al. (US 20190197798 A1), and Zhou et al. (US 20160052505 A1).
Regarding Claim(s) 2, Wang teaches: computing the electrical energy storage replacement/reshuffle strategy from the computed electrical energy storage distribution. (Wang, [246]; In some embodiments, with a periodicity of RC I, the information inside the “Entity Information Database 5004” can be used in a Compute or Recompute block 5008 to compute, e.g., entity battery state switch decisions, re-routing requirements, the charge transaction schedule, and/or the like). Examiner notes that Wang teaches a strategy with regards to an electric storage plan, while Pettersson below explicitly teaches the replacement portion of the limitation). Examiner notes that Pettersson teaches swapping/replacement while Zhou below is relied upon to teach the energy storage distribution.
While Wang does teach cost as a parameter, Pettersson more explicitly teaches cost as a function: The method of claim 1, computing the strategy comprises, for each combination of electrical energy storage: determining a cost function for the vehicle missions and different electrical energy storage combinations including corresponding state of health levels in each of the electrical energy storage packs, (Pettersson, [68]; By characteristics of the reconditioning locations, information on things like charging capacity, capability, number of charging slots, stock and condition of batteries in stock, availability of the battery-stock and the charging-slots, scheduled reservations or fixed bookings of the batteries on stock or changing-slots and others, can be obtained and Pettersson, [71]; The relevant optimisation criteria can be weighted and combined by application of a cost function which helps to optimize against multiple demands. For example the optimisation could be directed to consider minimizing the individual travelling times of the entities of transportation means to 50% but also to reducing the overall energy consumption of the whole set of transportation means to 25% by still fulfilling the stopover demands of the drivers to a certain range which seems acceptable at 25% and Pettersson, [claim 1]; the optimization being done on a cost function combining at least: a spatial distribution of the set of batteries over the reconditioning stations, and a forecast of desired stocks; a travelling time of transportation means; an overall energy consumption of one or multiple entities of the set of transportation means; and a priority of one or multiple entities of the set of transportation means
While Wang cost functions, state of health/charge and energy storage, Wang does not appear to explicitly teach: determining a power split between a set of electrical energy storages of each electrical energy storage combination; However, Wang/Pettersson in view of the analogous art of Zhou (i.e. vehicle management) does teach the entirety of the limitation: (Zhou, [62]; At step 120 the impact of operating energy storage units 14, 16 according to the power split is determined. Specifically, the time-variable desired performance data, initial power split, and real-time SOC and real-time SOH data is input to degradation models for the energy storage units 14, 16 at step 120. The degradation models 120 are used to determine a change in the state of health, ΔSOH, for each energy storage unit 14, 16 as well as a change in the state of charge, ΔSOC, for each energy storage unit 14, 16 based on the real-time SOC and SOH values 116 and the initial power split 114. At step 120, the degradation models are also used to determine the maximum power available from the first and second energy storage units 14, 16, which decreases over the life of the first and second energy storage units 14, 16.
determining an ageing rate or ageing dynamics of the present electrical energy storage configuration based on the determined power split for the classified vehicle usage type and the determined present state of health of the electrical energy storage packs; (Zhou, [55]; An optimization algorithm 108 is applied to the simulation module 98 to determine an optimized configuration for energy storage system 12, taking into account the physics-based models 104 and degradation models 106 of the various options for energy storage units, the operation use data 100, and economic scenario data 102 for the propulsion system).
computing the electrical energy storage distribution across the fleet by optimizing the cost function without violating the hard vehicle fleet performance constraints, and (Zhou, [53]; a selection of predetermined or standard duty or drive cycles, such as a city drive cycle and a highway drive cycle, which include details on how the power demand over the exemplary drive cycle varies. Simulation module 98 also receives economic scenario data 102 that includes parameters that account for variations in the initial capital costs of various types of energy storage units and vehicle operations costs including, for example, operating costs for vehicles incorporating different types of energy storage units and/or costs to recharge different types of energy storage units and Zhou, [58]; In addition to providing an optimized design configuration for energy storage system 12 through the offline use of simulation module 98, embodiments of the invention also provide online optimization of energy storage system 12 through the operation of a dynamic power-split control technique 112 illustrated in FIG. 5, during which controller 64 selectively draws power from energy storage units 14, 16 according to a control strategy designed to optimize the power split among the energy storage units 14, 16 while maximizing the overall operating efficiency of propulsion system 10. The power-split control technique 112 is operated in real-time and effects a split of the total power demand for the propulsion system 10 between the energy storage units 14, 16 while considering a broad range of possible usage patterns for the energy storage units, degradation models for the energy storage units, awareness of possible future demands, and a power dispatch algorithm). Examiner notes that Zhou teaches energy distribution while meeting demand needs.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Wang including cost as a parameter with the teachings of Pettersson including a cost function in order to optimize costs for the system (Pettersson, [38]; The parameters to be optimized, which are used to characterize the optimized solution, are in general a plurality of such parameters which will be combined in a desired way e.g. by an appropriate cost function).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Wang/Pettersson including minimizing cost functions, state of health/charge and energy storage with the teachings of Zhou including power splits and ageing dynamics in order to optimize overall energy efficiency and lifespan (Zhou, [10]; provide an electric and/or hybrid electric propulsion system that improves overall system efficiency and optimizes the operation and lifespan of the energy storage units and operating efficiency, while permitting the propulsion system to be manufactured at a reduced cost).
Regarding Claim(s) 3, Wang/Pettersson/Abari/Zhou teaches: The method of claim 2, wherein optimizing the cost function minimizes the monetary cost of reshuffling or replacing electrical energy storages while still fulfilling the fleet missions. (Wang, [38]; The parameters to be optimized, which are used to characterize the optimized solution, are in general a plurality of such parameters which will be combined in a desired way e.g. by an appropriate cost function and Wang, [71]; The relevant optimisation criteria can be weighted and combined by application of a cost function which helps to optimize against multiple demands. For example the optimisation could be directed to consider minimizing the individual travelling times of the entities of transportation means to 50% but also to reducing the overall energy consumption of the whole set of transportation means).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Wang/Pettersson including minimizing cost functions, state of health/charge and energy storage with the teachings of Zhou including power splits and ageing dynamics in order to optimize overall energy efficiency and lifespan (Zhou, [10]; provide an electric and/or hybrid electric propulsion system that improves overall system efficiency and optimizes the operation and lifespan of the energy storage units and operating efficiency, while permitting the propulsion system to be manufactured at a reduced cost).
Regarding Claim(s) 4, Wang/Pettersson/Abari/Zhou teaches: The method of claim 2, wherein optimizing the cost function minimizes the SOH degradation of the electrical energy storages on a fleet level while still fulfilling the fleet missions. (Zhou, [10]; access degradation models for the plurality of energy storage units, and optimize usage of the plurality of energy storage units during real-time operation of the vehicle propulsion system based on the degradation models and Zhou, [67]; the multi-objective optimization algorithm at step 130, power-split control technique 112 begins an operating loop that tests and validates the power split output by the multi-objective optimization algorithm. As illustrated in FIG. 5, the power-split control technique 112 returns to step 120 and determines the impact of the new power split from the degradation models, in a similar manner as described above with respect to the initial power split. Power-split control technique 112 then proceeds to step 122 and determines whether operating according to the new power split violates any system constraint functions.
Regarding Claim(s) 8, Wang/Pettersson/Abari teaches: The method according to claim 1, wherein identifying, by the processor device, electrical energy storages of vehicles in the fleet with SOH such that the energy requirement for its mission is not fulfilled, comprises: computing the post-mission SOH of the electrical energy storage of each vehicle from the present SOH and the computed… applied to the respective mission, and comparing the post-mission SOH to the respective the energy and power requirement. (Wang, [146]; As illustrated in FIG. 10, a plurality of mobile entities (c, c1, c2, c3 and c4) are in motion within a particular locality, each mobile entity having a particular destination. As illustrated, E represents the amount of charge the entities will be left with after completing their respective trips. A negative E value indicates that the corresponding entity will likely require additional charge to finish the desired trip, while a positive E value indicates that the corresponding entity will have surplus charge after making the desired trip and Wang, [Fig. 10-15]; visual representation of comparison a completion of trips).
While Wang/Pettersson teaches minimizing cost functions, state of health/charge and energy storage, Wang/Pettersson does not appear to explicitly teach: computing, by the processor device, a power split between the electrical energy storages of each vehicle from measurement voltage and electrical current data input to a model; However, Wang in view of the analogous art of Zhou (i.e. vehicle management) does teach the entirety of the limitation: (Zhou, [56]; The SOH of the energy storage units 14, 16 refers to the ability of the energy storage units 14, 16 to meet rated performance during discharge (e.g., supplying a load) or during charge. The SOH may be determined from a variety of parameters. For example, where the energy storage units 14, 16 include one or more batteries, the SOH may be based on a battery terminal voltage as a function of current, an estimate of internal battery resistance, a battery temperature, a battery voltage at a given value of the SOC, and/or trends of battery resistance over the life or calendar age of a battery and Zhou, [76]; a dynamic voltage control technique 136 for regulating the DC bus voltage of propulsion system 10 is set forth. In addition to controlling the power split between the energy storage units 14, 16 of energy storage system 12, controller 64 also dynamically controls the DC link voltage of the DC bus 36 accordingly so that propulsion system 10 can approach its optimal efficiency during operation. As described in detail below, controller 64 monitors a DC voltage of the DC bus 36 and computes an optimal voltage command for each time step of operation and continually transmits the voltage commands to the first and second bi-directional DC-DC converters 26, 28 via control lines 66).
computing, by the processor device, ageing dynamics of the electrical energy storage of each vehicle based on the determined power splits; (Zhou, [55]; An optimization algorithm 108 is applied to the simulation module 98 to determine an optimized configuration for energy storage system 12, taking into account the physics-based models 104 and degradation models 106 of the various options for energy storage units, the operation use data 100, and economic scenario data 102 for the propulsion system).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Wang including minimizing cost functions, state of health/charge and energy storage with the teachings of Zhou including power splits and ageing dynamics in order to optimize overall energy efficiency and lifespan (Zhou, [10]; provide an electric and/or hybrid electric propulsion system that improves overall system efficiency and optimizes the operation and lifespan of the energy storage units and operating efficiency, while permitting the propulsion system to be manufactured at a reduced cost).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 20210284043 A1) in view of Pettersson et al. (US 20130046457 A1), Abari et al. (US 20190197798 A1), Zhou et al. (US 20160052505 A1), and Hooshyar et al. (US 20220171450 A1).
Regarding Claim(s) 5, While Wang/Pettersson/Abari/Zhou teach minimizing cost and energy storage distribution, none of the cited art appears to teach second use applications. However, Wang/Pettersson/Abari/Zhou in view of the analogous art of Hooshyar (i.e. vehicle management) does teach: The method of claim 2, comprising retrieving information of possible reuse second applications, and including second applications outside the vehicle fleet when computing the electrical energy storage distribution from minimization of the cost function determining, to thereby determine if any electrical energy storages should be reused in a second application. (Hooshyar, [03]; A battery has however a limited service life during which it is able to meet demands on e.g. available energy and power in the vehicle application. When the battery is no longer able to meet the demands, it may be used in other, less demanding, second applications. The residual value in terms of state of health (SoH) of the battery when it reaches the end of its first service life, such as in the vehicle application, has been studied before. This residual value may be used to determine a suitable second application for the battery unit).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Wang/Pettersson/Abari/Zhou including minimizing cost and energy storage distribution with the teachings of Hooshyar including second use applications in order to find a usage for an energy source once it is no longer capable of meeting the current demands (Hooshyar, [03]; A battery has however a limited service life during which it is able to meet demands on e.g. available energy and power in the vehicle application. When the battery is no longer able to meet the demands, it may be used in other, less demanding, second applications. The residual value in terms of state of health (SoH) of the battery when it reaches the end of its first service life, such as in the vehicle application, has been studied before. This residual value may be used to determine a suitable second application for the battery unit).
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 20210284043 A1) in view of Pettersson et al. (US 20130046457 A1), Abari et al. (US 20190197798 A1), and Zhou et al. (US 20160052505 A1), and Galbraith et al. (US 20240157836 A1).
Regarding Claim(s) 6, While Wang/Pettersson/Abari/Zhou teaches optimizing a cost function, they do not appear to teach mixed integer optimizer. However, Wang/Pettersson/Abari in view of the analogous art of Galbraith (i.e. vehicle management) does teach: The method according to claim 2, optimizing the cost function is performed by solving a mixed integer non-linear optimization problem applied to the cost function. (Galbraith, [77]; The difference between optimization algorithms lies in how they obtain their initial set(s) of decision variable values, how they decide on subsequent decision variable values, and so on. An optimizer may be of any suitable type or types, for example: a linear programming optimizer, a mixed-integer nonlinear programming optimizer, a data driven optimizer, a stochastic programming optimizer, etc. Once an optimal value(s) of a decision variable is determined, assets may be controlled based on the optimal value, for example by providing the optimal value and/or indicating actions to assets. Further, optimal value(s) of the decision variable may be transmitted in a signal to one or more other computing devices, such as devices of EV users).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to have combined the teachings of Wang/Pettersson/Abari/Zhou including optimizing a cost function with the teachings of Galbraith including a mixed integer non-linear optimization in order to allow for a plurality of different optimization algorithms to suit more users needs (Galbraith, [77]; The difference between optimization algorithms lies in how they obtain their initial set(s) of decision variable values, how they decide on subsequent decision variable values, and so on. An optimizer may be of any suitable type or types, for example: a linear programming optimizer, a mixed-integer nonlinear programming optimizer, a data driven optimizer, a stochastic programming optimizer, etc. Once an optimal value(s) of a decision variable is determined, assets may be controlled based on the optimal value, for example by providing the optimal value and/or indicating actions to assets. Further, optimal value(s) of the decision variable may be transmitted in a signal to one or more other computing devices, such as devices of EV users).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JEREMY L GUNN whose telephone number is (571)270-1728. The examiner can normally be reached Monday - Friday 6:30-4:30.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jerry O'Connor can be reached on (571) 272-6787. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JEREMY L GUNN/ Examiner, Art Unit 3624