DETAILED ACTION
Status of the Application
Claims 1-20 have been examined in this application. This communication is the first action on the merits. The information disclosure statement (IDS) submitted on 01/09/2026, 03/25/2026; was filed with this application. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner
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 .
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
This action is a Non-Final Action on the merits in response to the application filed on 05/08/2025.
Claims 1-20 remain pending in this application.
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 1-9 are directed towards an apparatus and claims 10-20 are directed towards a method, all of which are among the statutory categories of invention.
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claim recites at least one step or act. Thus, the claim is to a process, which is one of the statutory categories of invention. (Step 1: YES).
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
With respect to claims 1-20, the independent claims (claims 1 and 10) are directed to managing of electric vehicles, In independent claim 1, the bolded limitations emphasized below correspond to the abstract ideas of the claimed invention:
1. clustering, via a first processing unit, a plurality of registered electric vehicles;
determining, via a second processing unit, whether an electric vehicle of the plurality of registered electric vehicles has a demand response (DR) participation plan;
determining, via a third processing unit, whether a charging and discharging condition of the electric vehicle having the demand response participation plan satisfies a preset condition.
these steps fall within and recite an abstract ideas because they are directed to mathematical concepts (including mathematical relationship); a method of organizing human activity which includes commercial interactions (including agreements in the form of contracts; marketing or sales activities or behaviors; business relations) (See MPEP 2106.04(a)(2), subsection II).
If a claim limitation, under its broadest reasonable interpretation math or commercial interaction, then it falls within the ” mathematical concepts”; “method of organizing human activity” grouping of abstract ideas. Therefore, If the identified limitation(s) falls within any of the groupings of abstract ideas enumerated in the MPEP 2106, the analysis should proceed to Prong Two. (Step 2A, Prong One: YES).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). The claim recites the additional elements of memory, processor, processing unit, electric vehicles, (Claim 10 recites computing device, memory, processor, electric vehicles). The claims recite the steps are performed by the memory, processor, processing unit, electric vehicles.
The limitations of
An electrical vehicle control apparatus for charging and discharging scheduling of an electric vehicle, comprising:
a memory storing computer-executable instructions; and
at least one processor configured to access the memory and execute the instructions, wherein the instructions comprise:
charging or discharging the electric vehicle, via a fourth processing unit, using a charging and discharging scheduling when the charging and discharging condition of the electric vehicle fails to satisfy the preset condition.
are mere data processing and organizing recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05.
Further, the limitations are recited as being performed by memory, processor, processing unit, electric vehicles. The memory, processor, processing unit, electric vehicles are recited at a high level of generality. In limitation (a), memory, processor, processing unit, electric vehicles are used as a tool to perform the generic computer function of receiving data. See MPEP 2106.05(f). The memory, processor, processing unit, electric vehicles are used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f).
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05. As explained with respect to Step 2A, Prong Two, the additional elements are the memory, processor, processing unit, electric vehicles. The additional elements were found to be insignificant extra-solution activity in Step 2A, Prong Two, because they were determined to be insignificant limitations as necessary data processing and organizing.
However, a conclusion that an additional element is insignificant extra solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g). As discussed in Step 2A, Prong Two above, the recitations of
An electrical vehicle control apparatus for charging and discharging scheduling of an electric vehicle, comprising:
a memory storing computer-executable instructions; and
at least one processor configured to access the memory and execute the instructions, wherein the instructions comprise:
charging or discharging the electric vehicle, via a fourth processing unit, using a charging and discharging scheduling when the charging and discharging condition of the electric vehicle fails to satisfy the preset condition.
are recited at a high level of generality. These elements amount to electronic recordkeeping and storing and retrieving information are well understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. 10 As discussed in Step 2A, Prong Two above, the recitation of a memory, processor, processing unit, electric vehicles to perform limitations amounts to no more than mere instructions to apply the exception using a generic computer component. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. (Step 2B: NO).
Dependent claims 2-9 and 11-20 do not contain any new additional elements. Rather, these claims offer further descriptive limitations of elements found in the independent claims. In this case, the claims are rejected for the same reasons at step 2a, prong one; step 2a, prong 2; and step 2b. Thus, the claim is not patent eligible.
Regarding the dependent claims, dependent claims 2-9 recite processing units. The dependent claims 2-9 and 11-20 recite limitations that are not technological in nature and merely limits the abstract idea to a particular environment. Claims 2-9 and 11-20 recites memory, processor, processing unit, electric vehicles which are considered an insignificant extra-solution activities of data processing and organizing; see MPEP 2106.05(g). Claims 2-9 and 11-20 recites memory, processor, processing unit, electric vehicles, which merely recites an instruction to apply the abstract idea using a generic computer component; MPEP 2106.05(f). Additionally, claims 2-9 and 11-20 recite steps that further narrow the abstract idea. No additional elements are disclosed in the dependent claims that were not considered in independent claims 1, 9, and 17. Therefore claims 2-9 and 11-20 do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself.
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 of this title, 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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Publication US 20240116388, Kiessling, et al. to hereinafter Kiessling in view of United States Patent Publication US 20240069568, Vemuri, et al.
Referring to Claim 1, Kiessling teaches an electrical vehicle control apparatus for charging and discharging scheduling of an electric vehicle, comprising: a memory storing computer-executable instructions; at least one processor configured to access the memory and execute the instructions, wherein the instructions comprise (
Kiessling teaches “methods, systems, and devices for scheduling electric vehicle charging across multiple customers and multiple fleets of electric vehicles.” (Kiessling: Sec. 0003). Kiessling further discloses “an electric vehicle charging control system can comprise an electric vehicle charging apparatus positioned at an electrical utility customer site… and a control system in electronic communication with the electricity consumption sensor. The control system can comprise at least one processor and memory component coupled to the at least one processor, wherein the memory component comprises machine executable code that, upon execution by the at least one computer processor, implements any of the methods indicated above or elsewhere herein.” (Kiessling: Sec. 0010)):
determining, via a third processing unit, whether a charging and discharging condition of the electric vehicle having the demand response participation plan satisfies a preset condition (
Kiessling teaches assessing charging conditions relative to a target and updating schedules accordingly. Specifically, method 200 in FIG. 2 includes steps to “Assess current state of charge relative to a target state of charge” and “Take corrective action.” (Kiessling: Fig. 2, Sec. 0044). The method also includes “Receive real time event updates from vehicles, charging stations, or automated grid signals” and “Update charging schedule based on optimization rules and real time events.” (Kiessling: Fig. 2, Sec. 0042, 0043). “The power cost minimization logic 450 may be configured to adjust a charging method plan to decrease the deviation of an actual state of charge trajectory from the state of charge nominal trajectory. The state of charge optimization logic may be configured to adjust a charging method plan based on a target state of charge, a state of charge nominal trajectory, an initial state of charge, a predicted state of charge, or any combination thereof. The real-time optimization logic 470 may be configured to update one or more charging schedules in real time,” (Kiessling: Sec. 0056)
Kiessling describes dynamic schedule updates based on system state. Kiessling discloses determining whether the current charging condition (e.g., actual state of charge relative to target) satisfies or deviates from desired trajectories, and taking action when it does not.);
charging or discharging the electric vehicle, via a fourth processing unit, using a charging and discharging scheduling when the charging and discharging condition of the electric vehicle fails to satisfy the preset condition (
Kiessling teaches that “a charging method plan may comprise… periods of charging, periods of discharging, power levels or rates of charging, and power levels or rates of discharging.” (Kiessling: Sec. 0028). Kiessling further discloses that “the optimizer system may provide instructions to the electric vehicle to charge while system load is low and energy costs are low” and “provide instructions to the electric vehicle to discharge when system load is high and energy costs are high.” (Kiessling: Sec. 0038). Kiessling also explains that charging schedules are updated “in real time in response to an event or a change in a system state,” for example “when an electric vehicle is plugged into a charging station” or when system load changes. (Kiessling: Sec. 0027, 0028).
Kiessling teaches that, when conditions (e.g., SoC trajectories, grid load, cost, (Kiessling: Sec. 0049, 0055)) are not aligned with desired targets (i.e., fail a preset condition), the system uses a charging/discharging schedule to command charging or discharging of the vehicle.).
Kiessling does not explicitly teach clustering, via a first processing unit, a plurality of registered electric vehicles; determining, via a second processing unit, whether an electric vehicle of the plurality of registered electric vehicles has a demand response (DR) participation plan
However, Vemuri teaches these limitations.
clustering, via a first processing unit, a plurality of registered electric vehicles (
Vemuri teaches clustering a plurality of electric vehicles into groups (“virtual boxes”) based on their physical locations. Vemuri states that “the electric vehicles 14 in the fleet 12 may be divided into a plurality of virtual boxes 100… encapsulated within a global box 102. The command unit 18 may respectively group the electric vehicles 14 in the virtual boxes 100 based in part on their physical locations.” (Vemuri: Sec. 0024). Vemuri further notes that “the modules 50 of FIG. 1 may be executed for the electric vehicles 14 individually or for a group of electric vehicles 14 within the same virtual box.” (Vemuri: Sec. 00225).);
determining, via a second processing unit, whether an electric vehicle of the plurality of registered electric vehicles has a demand response (DR) participation plan (
Kiessling teaches that “a charging method plan may comprise… periods of charging, periods of discharging, power levels or rates of charging, and power levels or rates of discharging.” (Kiessling: Sec. 0028). Kiessling further discloses that “the optimizer system may provide instructions to the electric vehicle to charge while system load is low and energy costs are low” and “provide instructions to the electric vehicle to discharge when system load is high and energy costs are high.” (Kiessling: Sec. 0038). Kiessling also explains that charging schedules are updated “in real time in response to an event or a change in a system state,” for example “when an electric vehicle is plugged into a charging station” or when system load changes. (Kiessling: Sec. 0027, 0028).
.
Vemuri teaches maintaining EV-specific data and external demand factors used to preemptively schedule charging. Vemuri discloses that an “Electric Vehicle Database 154 covers respective battery charge levels of the electric vehicles 14, battery age, charging cycles, the fleet charge type, the fleet capacity, the level of autonomy, the mobility status and route status of the electric vehicles 14.” (Vemuri: Sec. 0030). Vemuri also teaches that an “External Factors Database 156 provides input related to the time of day, day of month, weather, probability of fleet demand for each of the virtual boxes 100, and detour time to suitable charging stations 20.” (Vemuri: Sec. 0030). “Referring to FIG. 3 , an Output Module 160 receives the results of the Preemptive Decision Module 158. The Preemptive Decision Module 158 may be updated at regular intervals or triggered by specific events. The triggers may include a change in status of at least one of the electric vehicles 14 (e.g., change in availability and battery status), a change in status of at least one driver 16 (e.g., on duty, assigned/not assigned) and a change in status of a task (e.g., modified task location or changed deadline)” (Vemuri: Sec. 0031)
The Examiner is interpreting Vemuri teaches that, in DR-oriented fleet environments, the “probability of fleet demand” combined with EV-specific status data is the kind of information used to determine whether a given EV is enrolled or available for DR participation, as disclosing “DR participation plan” );
Kiessling and Vemuri are both directed to the analysis of electric vehicles (See Kiessling at 0005, 0035, 0068; Vemuri at 0007, 0009, 0018). Kiessling discloses that additional elements such as the real-time updates can be considered (See Kiessling at 0027). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to include the vehicle clustering and per-vehicle status management taught by Vemuri within the real-time fleet charging and discharging scheduling apparatus of Kiessling, with the motivation of improving scalability and location-aware scheduling of charging and discharging across large fleets, as well as enabling pre-emptive decisions based on local demand and vehicle status. Vemuri expressly teaches grouping vehicles into virtual boxes and using external demand information to inform scheduling (“the system 10 distributes the intra-fleet charge power to even out power between the virtual boxes 100 that have a higher power demand relative to others that have a lower power demand,” Vemuri: Sec. 0030; “the External Factors Database 156 provides input related to… probability of fleet demand for each of the virtual boxes 100,” see paragraph ).
Referring to Claim 2, Kiessling teaches the electrical vehicle control apparatus of claim 1, wherein the instructions further comprise determining, via the third processing unit, the charging and discharging condition fails to satisfy the preset condition (
Kiessling teaches assessing a current state of charge relative to a target state of charge and determining whether corrective action is needed. Kiessling discloses a method including steps to “Assess current state of charge relative to a target state of charge” and “Take corrective action.” (Kiessling: Fig. 2). Kiessling further describes that “the optimizer system may assess a current state of charge of one or more electric vehicles. The optimizer system may compare the current state of charge to a target state of charge or a predicted state of charge. The optimizer system may determine if action should be taken to correct for a discrepancy between a current state of charge, a target state of charge, or a predicted state of charge.” (Kiessling: Sec. 0044).)
Kiessling does not explicitly teach when an actual SoC value of the electric vehicle differs from an expected SoC by a preset first range or more.
However, Vemuri teaches when an actual SoC value of the electric vehicle differs from an expected SoC by a preset first range or more (
Vemuri teaches per-vehicle battery charge levels and threshold-based determinations for charge conditions. Vemuri discloses that “the EV Database 154 covers respective battery charge levels of the electric vehicles 14, battery age, charging cycles, the fleet charge type, the fleet capacity, the level of autonomy, the mobility status and route status of the electric vehicles 14.” (Vemuri: Sec. 0030). Vemuri further teaches that in the Idle Flow Module 400, the command unit “is adapted to determine whether the charge level of the electric vehicles 14 having an idle status is greater than a predetermined or specific percentage (e.g., 65% charge). If not (block 406–NO), the Module 400 proceeds to block 408… If the charge level of the electric vehicles 14 having an idle status is greater than the specific percentage (block 406–YES)… the command unit 18 proceeds to block 410…” (Vemuri: Sec. 0045).
The Examiner is interpreting this as teaching state of charge, Vemuri uses a stored battery-level value for each EV and compares it against a “predetermined or specific percentage (e.g., 65% charge)” threshold to decide whether a condition is satisfied.).
Kiessling and Vemuri are both directed to the analysis of electric vehicles (See Kiessling at 0005, 0035, 0068; Vemuri at 0007, 0009, 0018). Kiessling discloses that additional elements such as the real-time updates can be considered (See Kiessling at 0027). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to include the vehicle clustering and per-vehicle status management taught by Vemuri within the real-time fleet charging and discharging scheduling apparatus of Kiessling, with the motivation of improving scalability and location-aware scheduling of charging and discharging across large fleets, as well as enabling pre-emptive decisions based on local demand and vehicle status. Vemuri expressly teaches grouping vehicles into virtual boxes and using external demand information to inform scheduling (“the system 10 distributes the intra-fleet charge power to even out power between the virtual boxes 100 that have a higher power demand relative to others that have a lower power demand,” Vemuri: Sec. 0030; “the External Factors Database 156 provides input related to… probability of fleet demand for each of the virtual boxes 100,” see paragraph ).
Referring to Claim 3, Kiessling teaches the electrical vehicle control apparatus of claim 1, Kiessling does not explicitly teach wherein the instructions further comprise determining, via the third processing unit, the charging and discharging condition fails to satisfy the preset condition when an actual plug-in time differs from an expected plug-in time by a preset second range or more.
However, Vemuri teaches wherein the instructions further comprise determining, via the third processing unit, the charging and discharging condition fails to satisfy the preset condition when an actual plug-in time differs from an expected plug-in time by a preset second range or more (
Vemuri teaches threshold-based decisions using time-related deviations in the context of routing to charging stations, which is directly analogous to comparing actual plug-in times against expected times. In the Station Mapping Module 500, Vemuri discloses that “per block 502 of FIG. 7, the command unit 18 is adapted to determine whether the electric vehicle 14 is currently on a planned route. If so (block 502–YES), the Module 500 proceeds to block 504 to determine whether a route deviation for the electric vehicle 14 to reach a target charging station 20 exceeds a threshold time. If so (block 504–YES), Module 500 proceeds to block 506 to continue searching for better matches of charging stations 20 and Module 500 is ended. If not (block 504–NO), Module 500 proceeds to block 508 where the information pertaining to the target charging stations 20 is sent to the electric vehicle 14 and Module 500 is ended.” (Vemuri: Sec. 0048).
Vemuri teaches evaluates deviations in route timing (“route deviation… exceeds a threshold time”) against a predetermined time threshold and changes behavior when that threshold is exceeded.).
Kiessling and Vemuri are both directed to the analysis of electric vehicles (See Kiessling at 0005, 0035, 0068; Vemuri at 0007, 0009, 0018). Kiessling discloses that additional elements such as the real-time updates can be considered (See Kiessling at 0027). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to include the vehicle clustering and per-vehicle status management taught by Vemuri within the real-time fleet charging and discharging scheduling apparatus of Kiessling, with the motivation of improving scalability and location-aware scheduling of charging and discharging across large fleets, as well as enabling pre-emptive decisions based on local demand and vehicle status. Vemuri expressly teaches grouping vehicles into virtual boxes and using external demand information to inform scheduling (“the system 10 distributes the intra-fleet charge power to even out power between the virtual boxes 100 that have a higher power demand relative to others that have a lower power demand,” Vemuri: Sec. 0030; “the External Factors Database 156 provides input related to… probability of fleet demand for each of the virtual boxes 100,” see paragraph ).
Referring to Claim 4, Kiessling teaches the electrical vehicle control apparatus of claim 1, Kiessling does not explicitly teach wherein the instructions further comprise determining, via the third processing unit, the charging and discharging condition fails to satisfy the preset condition when an actual plug-out time differs from an expected plug-out time by a preset third range or more.
However, Vemuri teaches wherein the instructions further comprise determining, via the third processing unit, the charging and discharging condition fails to satisfy the preset condition when an actual plug-out time differs from an expected plug-out time by a preset third range or more. (
Vemuri teaches threshold-based decisions involving time-related deviations in the context of routing to vehicles to charging stations, which is directly analogous to comparing actual plug-in times against expected times. In the Station Mapping Module 500, Vemuri discloses that “per block 502 of FIG. 7, the command unit 18 is adapted to determine whether the electric vehicle 14 is currently on a planned route. If so (block 502–YES), the Module 500 proceeds to block 504 to determine whether a route deviation for the electric vehicle 14 to reach a target charging station 20 exceeds a threshold time. If so (block 504–YES), Module 500 proceeds to block 506 to continue searching for better matches of charging stations 20 and Module 500 is ended. If not (block 504–NO), Module 500 proceeds to block 508 where the information pertaining to the target charging stations 20 is sent to the electric vehicle 14 and Module 500 is ended.” (Vemuri: Sec. 0048).
Vemuri teaches an actual route timing deviation (“route deviation… to reach a target charging station 20”) versus an expected route timing and compares it to “a threshold time,” changing system behavior when the deviation exceeds that threshold.
Kiessling and Vemuri are both directed to the analysis of electric vehicles (See Kiessling at 0005, 0035, 0068; Vemuri at 0007, 0009, 0018). Kiessling discloses that additional elements such as the real-time updates can be considered (See Kiessling at 0027). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to include the vehicle clustering and per-vehicle status management taught by Vemuri within the real-time fleet charging and discharging scheduling apparatus of Kiessling, with the motivation of improving scalability and location-aware scheduling of charging and discharging across large fleets, as well as enabling pre-emptive decisions based on local demand and vehicle status. Vemuri expressly teaches grouping vehicles into virtual boxes and using external demand information to inform scheduling (“the system 10 distributes the intra-fleet charge power to even out power between the virtual boxes 100 that have a higher power demand relative to others that have a lower power demand,” Vemuri: Sec. 0030; “the External Factors Database 156 provides input related to… probability of fleet demand for each of the virtual boxes 100,” see paragraph ).
Referring to Claim 5, Kiessling teaches the electrical vehicle control apparatus of claim 1, wherein the instructions further comprise scheduling, via the fourth processing unit, a first charging and discharging of the electric vehicle in a first cluster (see Vemuri), wherein the charging and discharging condition of the electric vehicle fails to satisfy the preset condition (
Kiessling teaches generating and executing charging and discharging schedules for individual vehicles when their conditions require adjustment. In one aspect, it states that “the present disclosure provides a computer implemented method for managing an electric vehicle fleet, the method comprising: receiving notification of an arrival of a vehicle at a charging station… generating a charging method plan for the vehicle based on the charging metrics of the customer vehicle fleet and data relating to power ancillary services, wherein the charging method plan comprises a charging and discharging schedule for the vehicle, updating the charging schedule for the vehicle at any point during the duty cycle.” (Kiessling: Sec. 0005).
Kiessling further discloses that a charging method plan “may comprise one or more charging schedules for one or more electric vehicles. The one or more charging schedules may comprise charging schedules for vehicles as fast-response vehicles… and slow-response vehicles… A charging schedule may be updated in response to a change in a charging schedule of an electric vehicle, for example, when an electric vehicle is plugged into a charging station, or a charging electric vehicle is requested to enter service.” (Kiessling: Sec. 0028). It also states that “a charging schedule may be updated in real time in response to an event or a change in a system state. For example, a charging schedule may be updated as an electric vehicle is plugged into a charging station.” (Kiessling: Sec. 0027).
In FIG. 2, method 200 illustrates creating a charging schedule and then, upon assessing state of charge and taking corrective action, “Update charging schedule to increase revenue starting at current time”. (Kiessling: Fig. 2, Sec. 0003).
Kiessling teaches passages show that the system schedules charging (and discharging) for a vehicle when its charging condition is not on target and then executes that schedule.).
Kiessling does not explicitly teach a first charging and discharging of the electric vehicle in a first cluster.
However, Vemuri teaches a first charging and discharging of the electric vehicle in a first cluster (
For clustering, Vemuri discloses that “the electric vehicles 14 in the fleet 12 may be divided into a plurality of virtual boxes 100… encapsulated within a global box 102. The command unit 18 may respectively group the electric vehicles 14 in the virtual boxes 100 based in part on their physical locations.” (Vemuri: Sec. 0024). It further notes that “the modules 50 of FIG. 1 may be executed for the electric vehicles 14 individually or for a group of electric vehicles 14 within the same virtual box.” (Vemuri: Sec. 0025).
For scheduling the first charging in a cluster when a condition is not satisfied, the Proportion Monitoring Module 200 explicitly teaches triggering charging for a particular vehicle when its discharge category exceeds a percent allocation. Vemuri states that “per block 202 of FIG. 4, the command unit 18 is programmed to assess the energy requirements of the electric vehicles 14…” and “Advancing to block 204, the command unit 18 is adapted to proportion the battery power based on the assessment of block 202, in other words, optimize the battery proportions based on dynamic discharge needs.” (Vemuri: Sec. 0033). It then discloses that “advancing to block 208, the command unit 18 is programmed to determine whether the discharge categories are within their percent allocations in each of the electric vehicles 14. If not (block 208–NO), for example, if transportation discharge has exceeded its percent allocation, the Module 200 proceeds to block 212 where the electric vehicle 14 in question is signaled to charge. For example, the electric vehicle 14 may be directed or mapped to the best available charging station.” (Vemuri: Sec. 0038).
Vemuri teaches that for a vehicle within a particular virtual box (cluster), when its energy-use condition is outside the allowed allocation (fails a preset condition), the system schedules and initiates a charging operation for that vehicle in the context of that cluster.),
Kiessling and Vemuri are both directed to the analysis of electric vehicles (See Kiessling at 0005, 0035, 0068; Vemuri at 0007, 0009, 0018). Kiessling discloses that additional elements such as the real-time updates can be considered (See Kiessling at 0027). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to include the vehicle clustering and per-vehicle status management taught by Vemuri within the real-time fleet charging and discharging scheduling apparatus of Kiessling, with the motivation of improving scalability and location-aware scheduling of charging and discharging across large fleets, as well as enabling pre-emptive decisions based on local demand and vehicle status. Vemuri expressly teaches grouping vehicles into virtual boxes and using external demand information to inform scheduling (“the system 10 distributes the intra-fleet charge power to even out power between the virtual boxes 100 that have a higher power demand relative to others that have a lower power demand,” Vemuri: Sec. 0030; “the External Factors Database 156 provides input related to… probability of fleet demand for each of the virtual boxes 100,” see paragraph ).
Referring to Claim 6, Kiessling teaches the electrical vehicle control apparatus of claim 5, wherein the instructions further comprise determining, via a fifth processing unit, whether a DR response amount condition corresponding to the demand response (see Vemuri) and the charging and discharging condition of the electric vehicle is satisfied according to the first charging and discharging scheduling (
Kiessling teaches determining whether revenue/ancillary-services response conditions associated with vehicle-to-grid discharging are satisfied by a given charging/discharging schedule. Kiessling explains that “such parameters may include schedule and charge requirements of each vehicle in a fleet of electric vehicles, cost of energy and charging depot infrastructure, utility power meter control, and revenue from vehicle to grid discharging to power ancillary services.” (Kiessling: Sec. 0003). It further states that the charging method plan may be generated based on factors including “predicted revenue from vehicle to grid (V2G) discharging.” (Kiessling: Sec. 0034).
Kiessling also describes an optimizer that adjusts schedules to increase gross contribution, balancing state-of-charge trajectories and cost/revenue objectives: “Machine learning may be used to coordinate charging strategies or charging schedules between one or more electric vehicles… to increase revenue from vehicle to grid powering of ancillary services (AS). The machine learning system may be trained using any of the parameters disclosed herein, for example a vehicle state of charge, a nominal charging trajectory… a revenue from ancillary services, a power cost, a charging or discharging rate…” (Kiessling: Sec. 0029). FIG. 3 specifically shows decreasing a “deviation from state of charge nominal trajectories,” decreasing an “accuracy” deviation factor, and decreasing a “cost matrix across depot charging cycle,” with an objective to “Decrease sum of a, b, and c.” (Kiessling: Fig. 3).
Kiessling teaches that Kiessling’s system evaluates whether a candidate charging/discharging scheduling satisfies both SoC-trajectory conditions and revenue/ancillary-service response objectives, and adjusts schedules when those “conditions” are not met.).
Kiessling does not explicitly teach a DR response amount condition corresponding to the demand response.
However, Vemuri teaches a DR response amount condition corresponding to the demand response (
Vemuri teaches using fleet-level demand metrics and thresholds to decide whether power and demand conditions are satisfied, which is directly analogous to determining a “response amount condition.” Vemuri discloses that the Circle Aggregation Module 300 “represents aggregated proportion management for the virtual boxes 100”( Vemuri: Sec. 0039) and that “per block 302… the command unit 18 is adapted to dynamically monitor the total battery charge level for each of the virtual boxes 100, by summing up the charge levels of the electric vehicles 14 within the virtual boxes 100.” It then “maps the total battery charge data relative to the total power demand for the virtual boxes 100” (block 304) and “determines whether there is a respective power gap, i.e., whether the total battery charge level exceeds the total power demand respectively for the virtual boxes 100.” (Vemuri: Sec. 0040
Vemuri teaches when there is no sufficient battery level relative to demand, Vemuri changes scheduling and energy transfer decisions (blocks 308–314), indicating that the system is checking whether a response-capacity condition is satisfied.)
Kiessling and Vemuri are both directed to the analysis of electric vehicles (See Kiessling at 0005, 0035, 0068; Vemuri at 0007, 0009, 0018). Kiessling discloses that additional elements such as the real-time updates can be considered (See Kiessling at 0027). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to include the vehicle clustering and per-vehicle status management taught by Vemuri within the real-time fleet charging and discharging scheduling apparatus of Kiessling, with the motivation of improving scalability and location-aware scheduling of charging and discharging across large fleets, as well as enabling pre-emptive decisions based on local demand and vehicle status. Vemuri expressly teaches grouping vehicles into virtual boxes and using external demand information to inform scheduling (“the system 10 distributes the intra-fleet charge power to even out power between the virtual boxes 100 that have a higher power demand relative to others that have a lower power demand,” Vemuri: Sec. 0030; “the External Factors Database 156 provides input related to… probability of fleet demand for each of the virtual boxes 100,” see paragraph ).
Referring to Claim 7, Kiessling teaches the electrical vehicle control apparatus of claim 6, wherein the instructions further comprise scheduling, via the fourth processing unit, a second charging and discharging on the registered electric vehicle when the first charging and discharging scheduling fails to satisfy at least one of the DR response amount condition corresponding to the demand response and the charging and discharging condition of the electric vehicle (
Kiessling teaches updating or regenerating charging schedules when earlier schedules or conditions do not meet performance objectives. In FIG. 2, method 200 begins by creating a charging schedule, executing it, assessing SoC and other states, and then updating the schedule. Kiessling describes steps: “Create charging schedule to increase revenue starting at current time” (step 210), “Execute charging schedule during time interval N” (step 220), “Receive real time event updates from vehicles, charging stations, or automated grid signals” (step 230), “Update charging schedule based on optimization rules and real time events” (step 240), “Assess current state of charge relative to a target state of charge” (step 250), “Take corrective action” (step 260), and then “Update charging schedule to increase revenue starting at current time” (step 270) and “Update in real time” (step 280). (Kiessling: Fig. 2, Sec. 0040-0044).
The detailed description elaborates that “an initial charging schedule for one or more electric vehicles may be prepared based on the charging method plan and subsequently updated in real time in response to changes in parameters and system states.” (Kiessling: Sec. 0028). Kiessling further explains that machine learning-based optimization can update schedules in order to “increase gross contribution, increase revenue, decrease cost, increase grid stability and flexibility, and coordinate electric vehicle schedules.” (Kiessling: Sec. 0029).
Kiessling teaches that when an existing or “first” charging schedule fails to meet SoC trajectories, cost, or response objectives, the system generates and executes an updated or “second” schedule for the same registered vehicle.).
Kiessling does not explicitly teach at least one of the DR response amount condition corresponding to the demand response and the charging and discharging condition of the electric vehicle .
However, Vemuri teaches at least one of the DR response amount condition corresponding to the demand response and the charging and discharging condition of the electric vehicle (
Vemuri provides concrete examples of re-scheduling and re-proportioning actions when initial schedules/allocations fail to satisfy demand conditions. In Circle Aggregation Module 300, after determining whether there is a power gap (block 306), Vemuri describes further action if the gap persists: if a sufficient number of high-performing vehicles are present in the virtual box, “the command unit 18 reschedules or reproportions the electric vehicles 14 per block 312 within the same virtual box based on the amount of power needed, e.g., facilitating V2V energy transfer from higher-performing ones of the electric vehicles 14 to the lower performing ones.” If not, it reproportions vehicles in adjacent boxes (Vemuri: Sec. 0042).
Similarly, the Idle Flow Module 400 and Station Mapping Module 500 demonstrate repeated decision-making loops wherein the system, upon determining that certain thresholds or demand conditions are not met (e.g., idle EV counts vs. thresholds, predicted high usage, or power demand projections), modifies the routes and charging assignments of vehicles (Vemuri: Sec. 0024, 0045, 0046, 0053).
Vemuri teaches that when an initial configuration (the first schedule/assignment) does not satisfy demand or charge-allocation conditions, the system performs a second round of scheduling or mapping for the same vehicles.).
Kiessling and Vemuri are both directed to the analysis of electric vehicles (See Kiessling at 0005, 0035, 0068; Vemuri at 0007, 0009, 0018). Kiessling discloses that additional elements such as the real-time updates can be considered (See Kiessling at 0027). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to include the vehicle clustering and per-vehicle status management taught by Vemuri within the real-time fleet charging and discharging scheduling apparatus of Kiessling, with the motivation of improving scalability and location-aware scheduling of charging and discharging across large fleets, as well as enabling pre-emptive decisions based on local demand and vehicle status. Vemuri expressly teaches grouping vehicles into virtual boxes and using external demand information to inform scheduling (“the system 10 distributes the intra-fleet charge power to even out power between the virtual boxes 100 that have a higher power demand relative to others that have a lower power demand,” Vemuri: Sec. 0030; “the External Factors Database 156 provides input related to… probability of fleet demand for each of the virtual boxes 100,” see paragraph ).
Referring to Claim 8, Kiessling teaches the electrical vehicle control apparatus of claim 7, wherein the instructions further comprise sequentially scheduling, via the fourth processing unit, cluster- specific charging and discharging (see Vemuri) and charging and discharging on the electric vehicle belonging to the cluster (
Kiessling teaches that charging strategies and schedules may be determined for each fleet and depot, and that schedules for individual vehicles are executed and updated in sequence over time. For example, it states that “distinct charging strategies may be implemented by one or more charging depots comprising one or more charging stations.”. Kiessling describes determining charging strategies “for one or more electric vehicle fleets comprising a plurality of electric vehicles” and that “the charging method plan may be implemented by one or more fleets comprising one or more electric vehicles.” (Kiessling: Sec. 0027).
Method 200 in FIG. 2 shows a process for “providing, provisioning, operating and continuously updating charging strategies and charging schedules for one or more customers, one or more electric vehicle fleets, at one or more charging depots” (Kiessling: Sec. 0039) and includes steps of creating a charging schedule, executing it for a time interval, receiving real-time event updates, and updating the schedule (“Create charging schedule… Execute charging schedule during time interval N… Update charging schedule based on optimization rules and real time events… Update charging schedule to increase revenue starting at current time… Update in real time.”) (Kiessling: Fig. 2, Sec. 0040, 0056, 0096).
Kiessling teaches performs time-sequential charging and discharging scheduling for vehicles and for depots/fleets, but it does not call these groups “clusters” or explicitly state “cluster-specific charging and discharging.”).
Kiessling does not explicitly teach cluster- specific charging and discharging.
However, Vemuri teaches cluster- specific charging and discharging (
Vemuri explicitly teaches clusters of vehicles (virtual boxes) and scheduling charging operations for vehicles in and across those clusters. It discloses that “the electric vehicles 14 in the fleet 12 may be divided into a plurality of virtual boxes 100… encapsulated within a global box 102. The command unit 18 may respectively group the electric vehicles 14 in the virtual boxes 100 based in part on their physical locations.” (Vemuri: Sec. 0024). It further states that “the modules 50 of FIG. 1 may be executed for the electric vehicles 14 individually or for a group of electric vehicles 14 within the same virtual box.” (Vemuri: Sec. 0025).
Vemuri also teaches sequential or staged scheduling actions at both cluster and vehicle level. In Circle Aggregation Module 300, the command unit first monitors “the total battery charge level for each of the virtual boxes 100” (Vemuri: Sec. 0040) and maps it relative to total power demand (blocks 302–304), then determines whether there is a power gap (block 306), and, depending on that result, compiles a list of idle vehicles, reschedules or reproportions vehicles within the same virtual box, or reproportions idle vehicles in adjacent virtual boxes (blocks 308–314). (Vemuri: Sec. 0040-0042).
In Proportion Monitoring Module 200, after cluster-level proportioning, the system determines whether discharge categories are within their allocations (block 208) and, if not, “the Module 200 proceeds to block 212 where the electric vehicle 14 in question is signaled to charge. For example, the electric vehicle 14 may be directed or mapped to the best available charging station.” (Vemuri: Sec. 0038).
Vemuri shows (i) cluster-specific management of charge/discharge allocations for virtual boxes and (ii) subsequent per-vehicle scheduling of charging for vehicles belonging to those clusters.)
Kiessling and Vemuri are both directed to the analysis of electric vehicles (See Kiessling at 0005, 0035, 0068; Vemuri at 0007, 0009, 0018). Kiessling discloses that additional elements such as the real-time updates can be considered (See Kiessling at 0027). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to include the vehicle clustering and per-vehicle status management taught by Vemuri within the real-time fleet charging and discharging scheduling apparatus of Kiessling, with the motivation of improving scalability and location-aware scheduling of charging and discharging across large fleets, as well as enabling pre-emptive decisions based on local demand and vehicle status. Vemuri expressly teaches grouping vehicles into virtual boxes and using external demand information to inform scheduling (“the system 10 distributes the intra-fleet charge power to even out power between the virtual boxes 100 that have a higher power demand relative to others that have a lower power demand,” Vemuri: Sec. 0030; “the External Factors Database 156 provides input related to… probability of fleet demand for each of the virtual boxes 100,” see paragraph ).
Referring to Claim 9, Kiessling teaches the electrical vehicle control apparatus of claim 1, wherein the instructions further comprise scheduling, via the fourth processing unit, the charging and discharging scheduling of the electric vehicle that fails to have the demand response participation (see Vemuri) plan when the electric vehicle is plugged in (
Kiessling teaches scheduling charging and discharging for vehicles as they are plugged in, regardless of whether they are participating in grid-level ancillary services, and updating schedules in response to plug-in events. Kiessling discloses that “a charging schedule is updated in real time in response to an event or a change in a system state. For example, a charging schedule may be updated as an electric vehicle is plugged into a charging station. A charging schedule may be updated based on an automatic generation control signal (AGC) indicating a state of an electrical grid …” (Kiessling: Sec. 0027).
Kiessling also states that the optimizer system receives parameters from the vehicle when it is plugged in and then updates its schedule: “The electric vehicle plugged into the charging station may provide parameters to the optimizer system. These parameters may include, state of charge, rate of charge, target state of charge, and predicted duration at charging station. The optimizer system may update the charging schedule based on the parameters.” (Kiessling: Sec. 0035).
Kiessling teaches that when a vehicle plugs in, the system schedules (or updates) a charging/discharging schedule for that vehicle.).
Kiessling does not explicitly teach fails to have the demand response participation.
However, Vemuri teaches fails to have the demand response participation (
Vemuri teaches that the system makes scheduling decisions for vehicles based on their status (idle, in motion, driverless/manual, etc.) and expected demand, independent of any explicit DR participation. For example, Idle Flow Module 400 determines whether a number of vehicles having an idle status is above a threshold and then searches for and assigns charging stations to those vehicles, sending scheduling information to them. Specifically, “per block 402, the command unit 18 is adapted to determine whether the number of electric vehicles 14 having an idle status in the virtual boxes 100 is greater than a respective threshold value…(Vemuri: Sec. 0044) If the charge level of the electric vehicles 14 having an idle status is greater than the specific percentage (block 406–YES)… the command unit 18 proceeds to block 410 to determine if high usage or increased power demand… is expected… (Vemuri: Sec. 0045) If high usage is expected (block 410–YES), the Module 400 proceeds to block 412 to select a number of steps. Per block 412, if the electric vehicles 14 that have an idle status are in motion, the command unit 18 searches for target charging stations 20 in their path and directs them there… If the electric vehicles 14 that have an idle status are not in motion, the command unit 18 matches them with target charging stations 20 within their virtual box 100 and sends this information… to the electric vehicle 14 in question.” (Vemuri: Sec. 0046).
Station Mapping Module 500 likewise makes scheduling decisions based on projected power demand and idle-fleet conditions, independent of any DR participation concept, sending “information pertaining to the target charging stations 20… to the electric vehicle 14” (Vemuri: Sec. 0048).
Vemuri teache schedules charging for vehicles based on contextual conditions (idle status, predicted usage, demand), not on whether a vehicle participates in a DR/ancillary-services program.)
Kiessling and Vemuri are both directed to the analysis of electric vehicles (See Kiessling at 0005, 0035, 0068; Vemuri at 0007, 0009, 0018). Kiessling discloses that additional elements such as the real-time updates can be considered (See Kiessling at 0027). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to include the vehicle clustering and per-vehicle status management taught by Vemuri within the real-time fleet charging and discharging scheduling apparatus of Kiessling, with the motivation of improving scalability and location-aware scheduling of charging and discharging across large fleets, as well as enabling pre-emptive decisions based on local demand and vehicle status. Vemuri expressly teaches grouping vehicles into virtual boxes and using external demand information to inform scheduling (“the system 10 distributes the intra-fleet charge power to even out power between the virtual boxes 100 that have a higher power demand relative to others that have a lower power demand,” Vemuri: Sec. 0030; “the External Factors Database 156 provides input related to… probability of fleet demand for each of the virtual boxes 100,” see paragraph ).
Claims 10-15 recite limitations that stand rejected via the art citations and rationale applied to claims 1-6. Regarding a method of charging and discharging scheduling of an electric vehicle that is performed by a computing device, a memory storing computer-executable instructions, and at least one processor configured to access the memory and execute the instructions(
Kiessling provides “a computer implemented method for managing an electric vehicle fleet” (Kiessling: Sec. 0005)and “a system comprising one or more processors and memory components coupled thereto. The memory components may include machine executable code that, upon execution by the one or more processors, implements any of the methods above or elsewhere herein.” (Kiessling: Sec. 0010 ),
Referring to Claim 16, Kiessling teaches the method of claim 15, further comprising scheduling a second charging and discharging on the registered electric vehicle when the first charging and discharging scheduling fails to satisfy the DR response amount condition corresponding to the demand response (see Vemuri) (
Kiessling teaches updating the schedule when vehicle-specific charging conditions (e.g., SoC trajectories) are not satisfied. As noted, method 200 includes “Assess current state of charge relative to a target state of charge” and “Take corrective action.” (Kiessling: Sec. 0040, 0043, 0044). Kiessling teaches that the optimizer “may compare the current state of charge to a target state of charge or a predicted state of charge. The optimizer system may determine if action should be taken to correct for a discrepancy between a current state of charge, a target state of charge, or a predicted state of charge.” (Kiessling: Sec. 0044). It may adjust the charging schedule accordingly (Kiessling: Sec. 0056).
Kiessling teaches when the first schedule does not satisfy the vehicle’s SoC-related charging/discharging condition, Kiessling’s system generates an updated schedule (second charging/discharging).).
Kiessling does not explicitly teach DR response amount condition corresponding to the demand response.
However, Vemuri teaches DR response amount condition corresponding to the demand response (
Vemuri teaches that the system makes scheduling decisions for vehicles based on their status (idle, in motion, driverless/manual, etc.) and expected demand, independent of any explicit DR participation. For example, Idle Flow Module 400 determines whether a number of vehicles having an idle status is above a threshold and then searches for and assigns charging stations to those vehicles, sending scheduling information to them. Specifically, “per block 402, the command unit 18 is adapted to determine whether the number of electric vehicles 14 having an idle status in the virtual boxes 100 is greater than a respective threshold value…(Vemuri: Sec. 0044) If the charge level of the electric vehicles 14 having an idle status is greater than the specific percentage (block 406–YES)… the command unit 18 proceeds to block 410 to determine if high usage or increased power demand… is expected… (Vemuri: Sec. 0045) If high usage is expected (block 410–YES), the Module 400 proceeds to block 412 to select a number of steps. Per block 412, if the electric vehicles 14 that have an idle status are in motion, the command unit 18 searches for target charging stations 20 in their path and directs them there… If the electric vehicles 14 that have an idle status are not in motion, the command unit 18 matches them with target charging stations 20 within their virtual box 100 and sends this information… to the electric vehicle 14 in question.” (Vemuri: Sec. 0046).
Station Mapping Module 500 likewise makes scheduling decisions based on projected power demand and idle-fleet conditions, independent of any DR participation concept, sending “information pertaining to the target charging stations 20… to the electric vehicle 14” (Vemuri: Sec. 0048).
Vemuri teache schedules charging for vehicles based on contextual conditions (idle status, predicted usage, demand), not on whether a vehicle participates in a DR/ancillary-services program.
Vemuri teaches re-proportioning/rescheduling vehicles when power-demand conditions are not satisfied, which corresponds to scheduling a second operation after the first arrangement fails to meet a response-amount requirement. In Circle Aggregation Module 300, once the command unit “determines whether there is a respective power gap, i.e., whether the total battery charge level exceeds the total power demand respectively for the virtual boxes 100” (Vemuri: Sec. 0040) (block 306), it may compile a list of idle vehicles and “reschedules or reproportion the electric vehicles 14 per block 312 within the same virtual box based on the amount of power needed” or reproportion idle vehicles in adjacent boxes (block 314). (Vemuri: Sec. 0042).
Vemuri teaches rescheduling occurs because the initial allocation (first configuration) fails to satisfy the required power demand, i.e., a response-amount condition.)
Kiessling and Vemuri are both directed to the analysis of electric vehicles (See Kiessling at 0005, 0035, 0068; Vemuri at 0007, 0009, 0018). Kiessling discloses that additional elements such as the real-time updates can be considered (See Kiessling at 0027). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to include the vehicle clustering and per-vehicle status management taught by Vemuri within the real-time fleet charging and discharging scheduling apparatus of Kiessling, with the motivation of improving scalability and location-aware scheduling of charging and discharging across large fleets, as well as enabling pre-emptive decisions based on local demand and vehicle status. Vemuri expressly teaches grouping vehicles into virtual boxes and using external demand information to inform scheduling (“the system 10 distributes the intra-fleet charge power to even out power between the virtual boxes 100 that have a higher power demand relative to others that have a lower power demand,” Vemuri: Sec. 0030; “the External Factors Database 156 provides input related to… probability of fleet demand for each of the virtual boxes 100,” see paragraph ).
Referring to Claim 17, Kiessling teaches the method of claim 16, further comprising scheduling the second charging and discharging on the registered electric vehicle when the first charging and discharging scheduling fails to satisfy the charging and discharging condition of the electric vehicle (
Kiessling teaches updating the schedule when vehicle-specific charging conditions (e.g., SoC trajectories) are not satisfied. As noted, method 200 includes “Assess current state of charge relative to a target state of charge” and “Take corrective action.” (Kiessling: Sec. 0040, 0043, 0044). Kiessling teaches that the optimizer “may compare the current state of charge to a target state of charge or a predicted state of charge. The optimizer system may determine if action should be taken to correct for a discrepancy between a current state of charge, a target state of charge, or a predicted state of charge.” (Kiessling: Sec. 0044). It may adjust the charging schedule accordingly (Kiessling: Sec. 0056).
Kiessling teaches when the first schedule does not satisfy the vehicle’s SoC-related charging/discharging condition, Kiessling’s system generates an updated schedule (second charging/discharging).).
Referring to Claim 18, Kiessling teaches the method of claim 17, wherein the scheduling the second charging and discharging includes scheduling cluster-specific (see Vemuri) charging and discharging (
Kiessling teaches scheduling for fleets and depots but does not explicitly define clusters. It teaches that charging strategies may be distinct “for a plurality of charging depots” (Kiessling: Sec. 0093) and that “a charging method plan may be fleet-based” coordinating schedules for “one or more electric vehicle fleets, one or more charging depots, or one or more customers.” (Kiessling: Sec. 0028).).
Kiessling does not explicitly teach cluster-specific.
However, Vemuri teaches cluster-specific (
Vemuri explicitly teaches cluster-specific management, using virtual boxes as clusters. As previously cited, “the electric vehicles 14 in the fleet 12 may be divided into a plurality of virtual boxes 100… The command unit 18 may respectively group the electric vehicles 14 in the virtual boxes 100 based in part on their physical locations.” (Vemuri: Sec. 0024). Modules “may be executed for the electric vehicles 14 individually or for a group of electric vehicles 14 within the same virtual box.” (Vemuri: Sec. 0025). Circle Aggregation Module 300 monitors and manages charge and demand “for each of the virtual boxes 100,” summing charge and comparing against demand (Vemuri: Sec. 0039, 0040).
Vemuri teaches when a power gap exists, the module reschedules vehicles within the same virtual box (block 312) or adjacent boxes (block 314), which is cluster-specific scheduling of energy transfers.
Kiessling and Vemuri are both directed to the analysis of electric vehicles (See Kiessling at 0005, 0035, 0068; Vemuri at 0007, 0009, 0018). Kiessling discloses that additional elements such as the real-time updates can be considered (See Kiessling at 0027). Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to include the vehicle clustering and per-vehicle status management taught by Vemuri within the real-time fleet charging and discharging scheduling apparatus of Kiessling, with the motivation of improving scalability and location-aware scheduling of charging and discharging across large fleets, as well as enabling pre-emptive decisions based on local demand and vehicle status. Vemuri expressly teaches grouping vehicles into virtual boxes and using external demand information to inform scheduling (“the system 10 distributes the intra-fleet charge power to even out power between the virtual boxes 100 that have a higher power demand relative to others that have a lower power demand,” Vemuri: Sec. 0030; “the External Factors Database 156 provides input related to… probability of fleet demand for each of the virtual boxes 100,” see paragraph ).
Claims 19 and 20 recite limitations that stand rejected via the art citations and rationale applied to claims 8 and 9.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Matthiesenet al., U.S. Pub. 20180308191, (discussing the operation and managing of an autonomous vehicle).
Kiessling et al., W.O. Pub. WO2021055843, (discussing the managing of the fleet of electric vehicle).
Goebel et al., Aggregator-controlled EV charging in pay-as-bid reserve markets with strict delivery constraints, https://ieeexplore.ieee.org/abstract/document/7394201/, IEEE Transactions on Power Systems, 2016 (discussing the managing of the charging of electric vehicle).
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