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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/25/2026 has been entered.
Status of Claims
This office action is in response to Applicant Amendments and Remarks filed on 01/25/2026, for application number 18/501,686 filed on 11/03/2023, in which claims 1-20 were previously presented for examination.
Claims 1-6, 10, 12-15, and 17-20 are amended.
Claims 1-20 are currently pending in this application
Response to Arguments
Applicant Amendments and Remarks filed on 01/25/2026 in response to the Final office action mailed on 10/24/2025 have been fully considered and are addressed as follows:
Regarding the Claim Rejections under 35 USC § 103: With respect to the previous claim rejections under 35 U.S.C. § 103, Applicant has amended the independent claims and these amendments have changed the scope of the original application. Therefore, the Office has supplied new grounds of rejection attached below in the NON-FINAL office action and therefore the prior arguments are considered moot.
Regarding Applicant’s allegation associated with the limitation “the predicted future battery current profile,” Wiese et al. (US 2023/0099486 A1) is cited as a teaching reference for the limitation as discussed below in the Non-Final office action.
Regarding the limitation associated with “the cost function minimization,” Applicant alleges that “Neither Guerin nor Asmus teaches or suggests partitioning a prediction horizon into segments based on a predicted future current profile and applying different health-cost models in the cost function for those segments as a function of predicted current direction and magnitude, as recited in claims 2-5, 9, 12-16, and 18-20. Because the prior art does not disclose the predicate feature of a predicted future battery current profile over the prediction horizon, it necessarily cannot disclose the dependent-claim features that classify portions of the horizon and apply different battery deterioration models based on that predicted profile. Accordingly, Applicant submits that claims 2-5, 9, 12-16, and 18-20 are not unpatentable over the cited prior art and therefore reconsideration and withdrawal of their rejetions [sic] is respectfully requested” (emphasis added) (Applicant Amendments and Remarks filed on 01/25/2026 at pg. 13). Examiner disagrees.
It is noted that claims 2-5, 9, 12-16, and 18-20 do not recite the alleged limitation “partitioning a prediction horizon into segments based on a predicted future current profile and applying different health-cost models in the cost function for those segments as a function of predicted current direction and magnitude” or limitations require the alleged features to “classify portions of the horizon and apply different battery deterioration models based on that predicted profile.” Merely reciting to “based on the predicted future battery current profile in the time horizon” or “applicable to portions of the time horizon in which the predicted future battery current profile” does not require segmentation or classification of the horizon. Further, nothing in the claims requires applying different models to segmented or classified horizons.
For at least the foregoing reasons, and the rejections outlined below, the prior art rejections are maintained.
NON-FINAL OFFICE ACTION
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 10, and 17 recite the limitation “the time horizon” in line 15, 17, and 17, respectively. There is insufficient antecedent basis for this limitation in the claims.
Claims 2-9, 11-16, and 18-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being dependent on rejected claim(s) and for failing to cure the deficiencies listed above.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 9, 10, 16, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Zhu et al. (US 2024/0391341 A1, hereinafter “Zhu”) in view of Wiese et al. (US 2023/0099486 A1, hereinafter “Wiese”) further in view of Asmus et al. (US 2015/0316903 A1, hereinafter “Asmus”).
Regarding claim 1, Zhu discloses a vehicle comprising:
a powertrain including an electric motor and a rechargeable battery coupled to the electric motor to provide drive power for the vehicle (Zhu at para. [0002]: “electric and hybrid electric vehicles, feature battery storage for purposes such as powering electric motors, electronics and other vehicle subsystems”), the rechargeable battery including a first temperature sensor configured to sense a temperature of the rechargeable battery (Zhu at para. [0038]: “the monitored thermal characteristics can include a battery system temperature and a rate of change of the battery system temperature”);
a thermal management system for the rechargeable battery using a circulating fluid to control temperature of the rechargeable battery, the thermal management system including a chiller for cooling the circulating fluid (Zhu at para. [0034]: “the battery system 22 includes a thermal system 26 that is configured to heat, cool or heat and cool the batteries within the battery system 22. The practical implementation of the thermal system 26 can include any known heating or cooling system capable of heating and cooling the battery system 22”; para. [0043]: “The battery system 122 includes conventional sensors and estimators configured to measure and/or estimate real time properties of the battery system and the thermal system including …, coolant temperature”); and
a battery thermal management system (BTMS) controller configured to control operation of the thermal management system by (Zhu at para. [0037]: “the controller 24 within the battery system 22 uses a physics-based model to predict future system states (e.g., the expected battery system temperature in 30 seconds) and proactively control heating and cooling within the battery system 22”):
obtaining a set of temperatures from the thermal management system, including at least one temperature of the circulating fluid, and a battery temperature (Zhu at para. [0038]: “the monitored thermal characteristics can include a battery system temperature and a rate of change of the battery system temperature”);
estimating a state of charge (SOC) of the battery (Zhu at para. [0043]: “The battery system 122 includes conventional sensors and estimators configured to measure and/or estimate real time properties of the battery system and the thermal system including output voltage, output current, state of charge, battery temperature, coolant temperature, ambient temperature, battery state of health”);
applying a cost function minimization to determine control parameters to issue to the thermal management system by minimizing a sum of (Zhu at para. [0005]: “the cost function is further configured to optimize for minimal lithium plating and over-drain”; para. [0006]: “wherein t is the future time of the predicted battery system temperature, Cs(t) is a lithium surface density of the battery system at time t, P2Chrg(t) is a charging power at time t, P2h(t) is a power required to heat the battery system at time t, and P2c(t) is a power required to cool the battery system at time t, and y1 and y2 are constants”; para. [0040]: “the proactive control uses a cost function to optimize a minimum combined use of charging power, heating power, and cooling power, thereby minimizing the total amount of power used in the charging operation” “The cost function associates a numerical value, or score, with each possible solution in order to compare them and select the most favorable one, the optimal solution is typically identified as the lowest cost value of the possible solutions”); and
issuing the control parameters to the thermal management system (Zhu at para. [0042]: “The control system 100 includes a control 126 having a battery/thermal model 130 and an optimizer 140 configured to operate in conjunction with each other to output control signals to a battery/thermal system 122, thereby controlling the operations of the battery/thermal system 122”).
However, Zhu does not explicitly state:
using a battery thermal model to estimate heat transfer between the circulating fluid and the battery, and a chiller model to estimate heat transfer in the chiller;
predicting, over a prediction horizon having a duration of the time horizon, a future battery current profile as a disturbance input to the battery thermal model based at least in part on a predicted future power demand derived from at least one of a speed profile and an acceleration profile for a route ahead of the vehicle, and
economic costs.
Nevertheless, Zhu at least suggests the idea of using a battery thermal model to predict future states of the temperature of the battery/thermal system (Zhu at para. [0043]-[0044]).
In the same field of endeavor, Wiese teaches:
using a battery thermal model to estimate heat transfer between the circulating fluid and the battery (Wiese at FIGS. 4-5 and para. [0014]: “An engine operating sequence graphically illustrating intrusively increasing cooling by increasing a coolant flow above a current cooling demand in response to a predicted battery temperature”; para. [0049]: “Battery cell temperature prediction module 314 may use an onboard battery thermal model, which may calculate a battery cell temperature prediction using the battery power predictions from battery power prediction module 312 along with a plurality of measurements provided by onboard sensors 304, including but not limited to a current cooling state, coolant flow, coolant temperature, cooling demands, ambient temperature, and related component temperatures”),
predicting, over a prediction horizon having a duration of the time horizon, a future battery current profile as a disturbance input to the battery thermal model based at least in part on a predicted future power demand derived from at least one of a speed profile and an acceleration profile for a route ahead of the vehicle (Wiese at para. [0048]: “A portion of a battery temperature prediction may be estimated via the battery temperature prediction module 310 of controller 12 using inputs from the cloud network 306, onboard sensors 304, and the model monitoring module 318, wherein the inputs may include one or more of driver behavior, predicted vehicle speed, predicted road grade, location, traffic congestion, and weather as a result of data from connected vehicles and/or connected infrastructure communicating with cloud network 306”; para. [0049]: “The onboard battery thermal model may include a plurality of models including a resistor-capacitor (RC) circuit model and a lumped thermal mass model. The RC circuit model may predict battery heat generation from a predicted battery current, provided by a battery power to battery current calculation facilitated by battery power prediction module 312”; para. [0053]: “Monitoring and maintaining predictions may improve accuracy of future predictions when a plurality of variable conditions may be presented during vehicle operation”; para. [0060]: “if a vehicle operator behavior includes more aggressive driving (e.g., harder accelerations), then model parameters may be updated to adjust the power prediction 312 based on the increased power demand due to aggressive driving behaviors”; para. [0080]: “a predicted battery temperature for a location 1 mile away may include a higher confidence level than a predicted battery temperature for a location 20 miles away”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the vehicle of Zhu by adding using the battery thermal model and predicting the future battery current profile as taught by Weise with a reasonable expectation of success. The motivation to modify the vehicle of Zhu in view of Weise is to improve future predictions for battery thermal management.
However, Zhu in view of Weise does not explicitly state a chiller model to estimate heat transfer in the chiller and economic costs.
In the same field of endeavor, Asmus teaches a chiller model to estimate heat transfer in the chiller (Asmus at para. [0039]: “The low level optimization may use thermal models of the subplant equipment and/or the subplant network to determine the minimum energy consumption for a given thermal energy load”; para. [0041]: “Central plant 10 is shown to include a plurality of subplants including a heater subplant 12, a heat recovery chiller subplant 14, a chiller subplant 16, a cooling tower subplant 18, a hot thermal energy storage (TES) subplant 20, and a cold thermal energy storage (TES) subplant 22”) and economic costs (Asmus at para. [0069]: “The high level cost function JHL may be the sum of the economic costs of each utility consumed by each of subplants 12-22 for the duration of the prediction period”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the vehicle of Zhu in view of Weise by adding the chiller model and the economic costs as taught by Asmus with a reasonable expectation of success. The motivation to modify the vehicle of Zhu in view of Weise further in view of Asmus is to provide energy efficient thermal management (see Asmus at para. [0002]-[0005]).
Office Note: The Office has interpreted the limitation “chiller” as “one that chills” (see “chiller,” Merriam-Webster.com Dictionary, https://www.merriam-webster.com/dictionary/chiller. Accessed 5/29/2025.).
Regarding claim 9, Zhu in view of Weise further in view of Asmus teaches the vehicle of claim 1.
Asmus further teaches wherein the thermal management system includes a heater and first and second valves for directing flow of the circulating fluid through either the heater or the chiller, and the economic costs in the cost function minimization include a term for the heater and a term for the chiller (Asmus at para. [0036]: “each subplant receives a setpoint thermal energy load to be served by the subplant ( e.g., a thermal energy per unit time) and generates equipment dispatch states and/or operational setpoints for various devices of the subplant to serve the setpoint thermal energy load. For example, each subplant may include a plurality of individual devices ( e.g., heaters, chillers, pumps, valves, etc.) configured to facilitate the functions of the subplant”; para. [0075]: “Binary optimization may minimize a cost function representing the power consumption of devices in the applicable subplant”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the vehicle of Zhu in view of Weise further in view of Asmus by adding the cost function minimization including the terms for heater and the chiller as taught by Asmus with a reasonable expectation of success. The motivation to modify the vehicle of Zhu in view of Weise further in view of Asmus is to provide energy efficient thermal management (see Asmus at para. [0002]-[0005]).
Regarding claim 10, Zhu discloses a vehicle comprising:
a powertrain including an electric motor and a rechargeable battery coupled to the electric motor to provide drive power for the vehicle (Zhu at para. [0002]: “electric and hybrid electric vehicles, feature battery storage for purposes such as powering electric motors, electronics and other vehicle subsystems”), the rechargeable battery including a first temperature sensor configured to sense a temperature of the rechargeable battery (Zhu at para. [0038]: “the monitored thermal characteristics can include a battery system temperature and a rate of change of the battery system temperature”);
a thermal management system for the rechargeable battery using a circulating fluid to control temperature of the rechargeable battery, the thermal management system including a chiller for cooling the circulating fluid (Zhu at para. [0034]: “the battery system 22 includes a thermal system 26 that is configured to heat, cool or heat and cool the batteries within the battery system 22. The practical implementation of the thermal system 26 can include any known heating or cooling system capable of heating and cooling the battery system 22”; para. [0043]: “The battery system 122 includes conventional sensors and estimators configured to measure and/or estimate real time properties of the battery system and the thermal system including …, coolant temperature”); and
a reference tracking controller coupled to the thermal management system and configured to use a set of references to control operation of the thermal management system (Zhu at para. [0043]: “The measured outputs 130 are provided to the feedback input 106 and provide the control feedback utilized within the control 126”);
a non-linear optimizing controller configured to calculate and communicate the set of references to the reference tracking controller by (Zhu at para. [0037]: “the controller 24 within the battery system 22 uses a physics-based model to predict future system states (e.g., the expected battery system temperature in 30 seconds) and proactively control heating and cooling within the battery system 22”):
obtaining a set of temperatures from the thermal management system, including at least one temperature of the circulating fluid, and a battery temperature (Zhu at para. [0038]: “the monitored thermal characteristics can include a battery system temperature and a rate of change of the battery system temperature”);
estimating a state of charge (SOC) of the battery (Zhu at para. [0043]: “The battery system 122 includes conventional sensors and estimators configured to measure and/or estimate real time properties of the battery system and the thermal system including output voltage, output current, state of charge, battery temperature, coolant temperature, ambient temperature, battery state of health”);
applying a cost function minimization to determine the set of references for controlling the thermal management system by minimizing a sum of (Zhu at para. [0005]: “the cost function is further configured to optimize for minimal lithium plating and over-drain”; para. [0006]: “wherein t is the future time of the predicted battery system temperature, Cs(t) is a lithium surface density of the battery system at time t, P2Chrg(t) is a charging power at time t, P2h(t) is a power required to heat the battery system at time t, and P2c(t) is a power required to cool the battery system at time t, and y1 and y2 are constants”; para. [0040]: “the proactive control uses a cost function to optimize a minimum combined use of charging power, heating power, and cooling power, thereby minimizing the total amount of power used in the charging operation” “The cost function associates a numerical value, or score, with each possible solution in order to compare them and select the most favorable one, the optimal solution is typically identified as the lowest cost value of the possible solutions”); and
issuing the set of references to the reference tracking controller (Zhu at para. [0042]: “The control system 100 includes a control 126 having a battery/thermal model 130 and an optimizer 140 configured to operate in conjunction with each other to output control signals to a battery/thermal system 122, thereby controlling the operations of the battery/thermal system 122”).
However, Zhu does not explicitly state:
using a battery thermal model to estimate heat transfer between the circulating fluid and the battery, and a chiller model to estimate heat transfer in the chiller;
predicting, over a prediction horizon having a duration of the time horizon, a future battery current profile as a disturbance input to the battery thermal model; and
economic costs.
Nevertheless, Zhu at least suggests the idea of using a battery thermal model to predict future states of the temperature of the battery/thermal system (Zhu at para. [0043]-[0044]).
In the same field of endeavor, Wiese teaches:
using a battery thermal model to estimate heat transfer between the circulating fluid and the battery (Wiese at FIGS. 4-5 and para. [0014]: “An engine operating sequence graphically illustrating intrusively increasing cooling by increasing a coolant flow above a current cooling demand in response to a predicted battery temperature”; para. [0049]: “Battery cell temperature prediction module 314 may use an onboard battery thermal model, which may calculate a battery cell temperature prediction using the battery power predictions from battery power prediction module 312 along with a plurality of measurements provided by onboard sensors 304, including but not limited to a current cooling state, coolant flow, coolant temperature, cooling demands, ambient temperature, and related component temperatures”);
predicting, over a prediction horizon having a duration of the time horizon, a future battery current profile as a disturbance input to the battery thermal model (Wiese at para. [0048]: “A portion of a battery temperature prediction may be estimated via the battery temperature prediction module 310 of controller 12 using inputs from the cloud network 306, onboard sensors 304, and the model monitoring module 318, wherein the inputs may include one or more of driver behavior, predicted vehicle speed, predicted road grade, location, traffic congestion, and weather as a result of data from connected vehicles and/or connected infrastructure communicating with cloud network 306”; para. [0049]: “The onboard battery thermal model may include a plurality of models including a resistor-capacitor (RC) circuit model and a lumped thermal mass model. The RC circuit model may predict battery heat generation from a predicted battery current, provided by a battery power to battery current calculation facilitated by battery power prediction module 312”; para. [0053]: “Monitoring and maintaining predictions may improve accuracy of future predictions when a plurality of variable conditions may be presented during vehicle operation”; para. [0060]: “if a vehicle operator behavior includes more aggressive driving (e.g., harder accelerations), then model parameters may be updated to adjust the power prediction 312 based on the increased power demand due to aggressive driving behaviors”; para. [0080]: “a predicted battery temperature for a location 1 mile away may include a higher confidence level than a predicted battery temperature for a location 20 miles away”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the vehicle of Zhu by adding using the battery thermal model and predicting the future battery current profile as taught by Weise with a reasonable expectation of success. The motivation to modify the vehicle of Zhu in view of Weise is to improve future predictions for battery thermal management.
However, Zhu in view of Weise does not explicitly state a chiller model to estimate heat transfer in the chiller and economic costs.
In the same field of endeavor, Asmus teaches a chiller model to estimate heat transfer in the chiller (Asmus at para. [0039]: “The low level optimization may use thermal models of the subplant equipment and/or the subplant network to determine the minimum energy consumption for a given thermal energy load”; para. [0041]: “Central plant 10 is shown to include a plurality of subplants including a heater subplant 12, a heat recovery chiller subplant 14, a chiller subplant 16, a cooling tower subplant 18, a hot thermal energy storage (TES) subplant 20, and a cold thermal energy storage (TES) subplant 22”) and economic costs (Asmus at para. [0069]: “The high level cost function JHL may be the sum of the economic costs of each utility consumed by each of subplants 12-22 for the duration of the prediction period”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the vehicle of Zhu in view of Weise by adding the chiller model and the economic costs as taught by Asmus with a reasonable expectation of success. The motivation to modify the vehicle of Zhu in view of Weise further in view of Asmus is to provide energy efficient thermal management (see Asmus at para. [0002]-[0005]).
Office Note: The Office has interpreted the limitation “chiller” as “one that chills” (see “chiller,” Merriam-Webster.com Dictionary, https://www.merriam-webster.com/dictionary/chiller. Accessed 5/29/2025.).
Regarding claim 16, Zhu in view of Weise further in view of Asmus teaches the vehicle of claim 10.
Asmus further teaches wherein the thermal management system includes a heater and first and second valves for directing flow of the circulating fluid through either the heater or the chiller, and the economic costs in the cost function minimization include a term for the heater and a term for the chiller (Asmus at para. [0036]: “each subplant receives a setpoint thermal energy load to be served by the subplant ( e.g., a thermal energy per unit time) and generates equipment dispatch states and/or operational setpoints for various devices of the subplant to serve the setpoint thermal energy load. For example, each subplant may include a plurality of individual devices ( e.g., heaters, chillers, pumps, valves, etc.) configured to facilitate the functions of the subplant”; para. [0075]: “Binary optimization may minimize a cost function representing the power consumption of devices in the applicable subplant”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the vehicle of Zhu in view of Weise further in view of Asmus by adding the cost function minimization including the terms for heater and the chiller as taught by Asmus with a reasonable expectation of success. The motivation to modify the vehicle Zhu in view of Weise further in view of Asmus is to provide energy efficient thermal management (see Asmus at para. [0002]-[0005]).
Regarding claim 17, Zhu discloses a method of controlling battery temperature in a vehicle, the vehicle including a powertrain including an electric motor and a battery coupled to the electric motor to provide drive power for the vehicle (Zhu at para. [0002]: “electric and hybrid electric vehicles, feature battery storage for purposes such as powering electric motors, electronics and other vehicle subsystems”), the rechargeable battery including a first temperature sensor configured to sense a temperature of the battery (Zhu at para. [0038]: “the monitored thermal characteristics can include a battery system temperature and a rate of change of the battery system temperature”), and a thermal management system for the battery using a circulating fluid to control temperature of the battery, the thermal management system including a chiller for cooling the circulating fluid (Zhu at para. [0034]: “the battery system 22 includes a thermal system 26 that is configured to heat, cool or heat and cool the batteries within the battery system 22. The practical implementation of the thermal system 26 can include any known heating or cooling system capable of heating and cooling the battery system 22”; para. [0043]: “The battery system 122 includes conventional sensors and estimators configured to measure and/or estimate real time properties of the battery system and the thermal system including …, coolant temperature”); the method comprising:
issuing control signals from a reference tracking controller to control operation of the thermal management system using a set of references (Zhu at para. [0042]: “The control system 100 includes a control 126 having a battery/thermal model 130 and an optimizer 140 configured to operate in conjunction with each other to output control signals to a battery/thermal system 122, thereby controlling the operations of the battery/thermal system 122”);
issuing the set of references to the reference tracking controller from a non-linear optimizing controller (Zhu at para. [0043]: “The measured outputs 130 are provided to the feedback input 106 and provide the control feedback utilized within the control 126”);
calculating the set of references at the reference tracking controller by:
obtaining a set of temperatures from the thermal management system, including at least one temperature of the circulating fluid, and a battery temperature (Zhu at para. [0038]: “the monitored thermal characteristics can include a battery system temperature and a rate of change of the battery system temperature”);
estimating a state of charge (SOC) of the battery (Zhu at para. [0043]: “The battery system 122 includes conventional sensors and estimators configured to measure and/or estimate real time properties of the battery system and the thermal system including output voltage, output current, state of charge, battery temperature, coolant temperature, ambient temperature, battery state of health”);
applying a cost function minimization to determine the set of references for controlling the thermal management system by minimizing a sum of (Zhu at para. [0005]: “the cost function is further configured to optimize for minimal lithium plating and over-drain”; para. [0006]: “wherein t is the future time of the predicted battery system temperature, Cs(t) is a lithium surface density of the battery system at time t, P2Chrg(t) is a charging power at time t, P2h(t) is a power required to heat the battery system at time t, and P2c(t) is a power required to cool the battery system at time t, and y1 and y2 are constants”; para. [0040]: “the proactive control uses a cost function to optimize a minimum combined use of charging power, heating power, and cooling power, thereby minimizing the total amount of power used in the charging operation” “The cost function associates a numerical value, or score, with each possible solution in order to compare them and select the most favorable one, the optimal solution is typically identified as the lowest cost value of the possible solutions”).
However, Zhu does not explicitly state:
using a battery thermal model to estimate heat transfer between the circulating fluid and the battery, and a chiller model to estimate heat transfer in the chiller;
predicting, over a prediction horizon having a duration of the time horizon, a battery current profile as a disturbance input to the battery thermal model; and
economic costs.
Nevertheless, Zhu at least suggests the idea of using a battery thermal model to predict future states of the temperature of the battery/thermal system (Zhu at para. [0043]-[0044]).
In the same field of endeavor, Wiese teaches:
using a battery thermal model to estimate heat transfer between the circulating fluid and the battery (Guerin at para. [0039]: “The output of the resistance rise and capacity degradation model 212 may be provided to cell resistance and heat transfer coefficient model 214. Cell resistance heat transfer coefficient model 214 may generate an estimate of the subdivision resistance and a heat transfer coefficient. The heat transfer coefficient may be derived from the geometry of a particular battery subdivision, coolant flow rate, and coolant temperature”),
predicting, over a prediction horizon having a duration of the time horizon, a battery current profile as a disturbance input to the battery thermal model (Wiese at para. [0048]: “A portion of a battery temperature prediction may be estimated via the battery temperature prediction module 310 of controller 12 using inputs from the cloud network 306, onboard sensors 304, and the model monitoring module 318, wherein the inputs may include one or more of driver behavior, predicted vehicle speed, predicted road grade, location, traffic congestion, and weather as a result of data from connected vehicles and/or connected infrastructure communicating with cloud network 306”; para. [0049]: “The onboard battery thermal model may include a plurality of models including a resistor-capacitor (RC) circuit model and a lumped thermal mass model. The RC circuit model may predict battery heat generation from a predicted battery current, provided by a battery power to battery current calculation facilitated by battery power prediction module 312”; para. [0053]: “Monitoring and maintaining predictions may improve accuracy of future predictions when a plurality of variable conditions may be presented during vehicle operation”; para. [0060]: “if a vehicle operator behavior includes more aggressive driving (e.g., harder accelerations), then model parameters may be updated to adjust the power prediction 312 based on the increased power demand due to aggressive driving behaviors”; para. [0080]: “a predicted battery temperature for a location 1 mile away may include a higher confidence level than a predicted battery temperature for a location 20 miles away”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Zhu by adding using the battery thermal model and predicting the battery current profile as taught by Wiese with a reasonable expectation of success. The motivation to modify the method of Zhu in view of Weise is to improve future predictions for battery thermal management.
However, Zhu in view of Weise does not explicitly state a chiller model to estimate heat transfer in the chiller and economic costs.
In the same field of endeavor, Asmus teaches a chiller model to estimate heat transfer in the chiller (Asmus at para. [0039]: “The low level optimization may use thermal models of the subplant equipment and/or the subplant network to determine the minimum energy consumption for a given thermal energy load”; para. [0041]: “Central plant 10 is shown to include a plurality of subplants including a heater subplant 12, a heat recovery chiller subplant 14, a chiller subplant 16, a cooling tower subplant 18, a hot thermal energy storage (TES) subplant 20, and a cold thermal energy storage (TES) subplant 22”) and economic costs (Asmus at para. [0069]: “The high level cost function JHL may be the sum of the economic costs of each utility consumed by each of subplants 12-22 for the duration of the prediction period”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the vehicle of Zhu in view of Weise by adding the chiller model and the economic costs as taught by Asmus with a reasonable expectation of success. The motivation to modify the vehicle of Zhu in view of Weise further in view of Asmus is to provide energy efficient thermal management (see Asmus at para. [0002]-[0005]).
Office Note: The Office has interpreted the limitation “chiller” as “one that chills” (see “chiller,” Merriam-Webster.com Dictionary, https://www.merriam-webster.com/dictionary/chiller. Accessed 5/29/2025.).
Claims 2-5, 12-15, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Zhu in view of Weise further in view of Asmus and Guerin et al. (US 2014/0195179 A1, hereinafter “Guerin”).
Regarding claim 2, Zhu in view of Weise further in view of Asmus teaches the vehicle of claim 1.
However, Zhu in view of Weise further in view of Asmus does not explicitly state:
wherein the BTMS controller is configured to perform the cost function minimization by selecting from at least two models of battery deterioration based on the predicted future battery current profile in the time horizon.
In the same field of endeavor, Guerin teaches wherein the BTMS controller is configured to perform the cost function minimization by selecting from at least two models of battery deterioration based on the predicted future battery current profile in the time horizon (Guerin at para. [0059]: “Specific functional models may be included that correspond to the battery model, a thermal network model, a battery life model, a cell resistance and heat transfer coefficient model, a resistance rise and capacity degradation model, and the like”; para. [0062]: “various operational steps, as well as components for carrying out operational steps, may be implemented in alternate ways depending upon the particular application or in consideration of any number of cost functions associated with the operation of the system”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the vehicle of Zhu in view of Weise further in view of Asmus regarding by adding the at least two models as taught by Guerin with a reasonable expectation of success. The motivation to modify the vehicle of Zhu in view of Weise further in view of Asmus and Guerin is to better capture and utilize information in the battery system (see Guerin at para. [0033]).
Regarding claim 3, Zhu in view of Weise further in view of Asmus teaches the vehicle of claim 1.
However, Zhu in view of Weise further in view of Asmus does not explicitly state:
wherein the BTMS controller is configured to perform the cost function minimization by using a model for battery deterioration due to aging, applicable to portions of the time horizon in which the predicted future battery current profile is predicted to be below a nominal threshold.
In the same field of endeavor, Guerin teaches wherein the BTMS controller is configured to perform the cost function minimization by using a model for battery deterioration due to aging (Guerin at para. [0059]: “Specific functional models may be included that correspond to the battery model, a thermal network model, a battery life model, a cell resistance and heat transfer coefficient model, a resistance rise and capacity degradation model, and the like”; para. [0062]: “various operational steps, as well as components for carrying out operational steps, may be implemented in alternate ways depending upon the particular application or in consideration of any number of cost functions associated with the operation of the system”), applicable to portions of the time horizon in which the predicted future battery current profile is predicted to be below a nominal threshold (Guerin at para. [0019], [0059], [0062]: The current generated by the battery system changes overtime, and the cost function minimization is applicable to any part of the operation of the system).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the vehicle of Zhu in view of Weise further in view of Asmus by adding the model for battery deterioration as taught by Guerin with a reasonable expectation of success. The motivation to modify the vehicle of Zhu in view of Weise further in view of Asmus and Guerin is to better capture and utilize information in the battery system (see Guerin at para. [0033]).
Regarding claim 4, Zhu in view of Weise further in view of Asmus teaches the vehicle of claim 1.
However, Zhu in view of Weise further in view of Asmus does not explicitly state:
wherein the BTMS controller is configured to perform the cost function minimization by using a model for battery deterioration during battery discharge due to powertrain operation, applicable to portions of the time horizon in which the predicted future battery current profile is predicted to be issued from the battery to the powertrain.
In the same field of endeavor, Guerin teaches wherein the BTMS controller is configured to perform the cost function minimization by using a model for battery deterioration during battery discharge due to powertrain operation (Guerin at para. [0059]: “Specific functional models may be included that correspond to the battery model, a thermal network model, a battery life model, a cell resistance and heat transfer coefficient model, a resistance rise and capacity degradation model, and the like”; para. [0062]: “various operational steps, as well as components for carrying out operational steps, may be implemented in alternate ways depending upon the particular application or in consideration of any number of cost functions associated with the operation of the system”), applicable to portions of the time horizon in which the predicted future battery current profile is predicted to be issued from the battery to the powertrain (Guerin at para. [0019], [0027], [0059], [0062]: The current generated by the battery system is supplied to the drivetrain components of the electric vehicle for driving the vehicle, and the cost function minimization is applicable to any part of the operation of the system).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the vehicle of Zhu in view of Weise further in view of Asmus by adding the model for battery deterioration as taught by Guerin with a reasonable expectation of success. The motivation to modify the vehicle of Zhu in view of Weise further in view of Asmus and Guerin is to better capture and utilize information in the battery system (see Guerin at para. [0033]).
Regarding claim 5, Zhu in view of Weise further in view of Asmus teaches the vehicle of claim 1.
However, Zhu in view of Weise further in view of Asmus does not explicitly state:
wherein the BTMS controller is configured to perform the cost function minimization by using a model for battery deterioration during battery charging, applicable to portions of the time horizon in which the predicted future battery current profile is predicted to be injected to the battery at a level that exceeds a nominal threshold.
In the same field of endeavor, Guerin teaches wherein the BTMS controller is configured to perform the cost function minimization by using a model for battery deterioration during battery charging (Guerin at para. [0059]: “Specific functional models may be included that correspond to the battery model, a thermal network model, a battery life model, a cell resistance and heat transfer coefficient model, a resistance rise and capacity degradation model, and the like”; para. [0062]: “various operational steps, as well as components for carrying out operational steps, may be implemented in alternate ways depending upon the particular application or in consideration of any number of cost functions associated with the operation of the system”), applicable to portions of the time horizon in which the predicted future battery current profile is predicted to be injected to the battery at a level that exceeds a nominal threshold (Guerin at para. [0019], [0020], [0059], [0062]: The battery system may be charged and discharged, and the cost function minimization is applicable to any part of the operation of the system).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the vehicle of Zhu in view of Weise further in view of Asmus by adding the model for battery deterioration as taught by Guerin with a reasonable expectation of success. The motivation to modify the vehicle of Zhu in view of Weise further in view of Asmus and Guerin is to better capture and utilize information in the battery system (see Guerin at para. [0033]).
Regarding claim 12, Zhu in view of Weise further in view of Asmus teaches the vehicle of claim 10.
However, Zhu in view of Weise further in view of Asmus does not explicitly state:
wherein the non-linear optimizing controller is configured to perform the cost function minimization by selecting from at least two models of battery deterioration based on the predicted future battery current profile in the time horizon.
In the same field of endeavor, Guerin teaches wherein the non-linear optimizing controller is configured to perform the cost function minimization by selecting from at least two models of battery deterioration based on the predicted future battery current profile in the time horizon (Guerin at para. [0059]: “Specific functional models may be included that correspond to the battery model, a thermal network model, a battery life model, a cell resistance and heat transfer coefficient model, a resistance rise and capacity degradation model, and the like”; para. [0062]: “various operational steps, as well as components for carrying out operational steps, may be implemented in alternate ways depending upon the particular application or in consideration of any number of cost functions associated with the operation of the system”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the vehicle of Zhu in view of Weise further in view of Asmus by adding the at least two models as taught by Guerin with a reasonable expectation of success. The motivation to modify the vehicle of Zhu in view of Weise further in view of Asmus and Guerin is to better capture and utilize information in the battery system (see Guerin at para. [0033]).
Regarding claim 13, Zhu in view of Weise further in view of Asmus teaches the vehicle of claim 10.
However, Zhu in view of Weise further in view of Asmus does not explicitly state:
wherein the non-linear optimizing controller is configured to perform the cost function minimization by using a model for battery deterioration due to aging, applicable to portions of the time horizon in which the predicted future battery current profile is predicted to be below a nominal threshold.
In the same field of endeavor, Guerin teaches wherein the non-linear optimizing controller is configured to perform the cost function minimization by using a model for battery deterioration due to aging (Guerin at para. [0059]: “Specific functional models may be included that correspond to the battery model, a thermal network model, a battery life model, a cell resistance and heat transfer coefficient model, a resistance rise and capacity degradation model, and the like”; para. [0062]: “various operational steps, as well as components for carrying out operational steps, may be implemented in alternate ways depending upon the particular application or in consideration of any number of cost functions associated with the operation of the system”), applicable to portions of the time horizon in which the predicted future battery current profile is predicted to be below a nominal threshold (Guerin at para. [0019], [0059], [0062]: The current generated by the battery system changes overtime, and the cost function minimization is applicable to any part of the operation of the system).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the vehicle of Zhu in view of Weise further in view of Asmus by adding the model for battery deterioration as taught by Guerin with a reasonable expectation of success. The motivation to modify the vehicle of Zhu in view of Weise further in view of Asmus and Guerin is to better capture and utilize information in the battery system (see Guerin at para. [0033]).
Regarding claim 14, Zhu in view of Weise further in view of Asmus teaches the vehicle of claim 10.
However, Zhu in view of Weise further in view of Asmus does not explicitly state:
wherein the non-linear optimizing controller is configured to perform the cost function minimization by using a model for battery deterioration during battery discharge due to powertrain operation, applicable to portions of the time horizon in which the predicted future battery current profile is predicted to be issued from the battery to the powertrain.
In the same field of endeavor, Guerin teaches wherein the non-linear optimizing controller is configured to perform the cost function minimization by using a model for battery deterioration during battery discharge due to powertrain operation (Guerin at para. [0059]: “Specific functional models may be included that correspond to the battery model, a thermal network model, a battery life model, a cell resistance and heat transfer coefficient model, a resistance rise and capacity degradation model, and the like”; para. [0062]: “various operational steps, as well as components for carrying out operational steps, may be implemented in alternate ways depending upon the particular application or in consideration of any number of cost functions associated with the operation of the system”), applicable to portions of the time horizon in which the predicted future battery current profile is predicted to be issued from the battery to the powertrain (Guerin at para. [0019], [0027], [0059], [0062]: The current generated by the battery system is supplied to the drivetrain components of the electric vehicle for driving the vehicle, and the cost function minimization is applicable to any part of the operation of the system).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the vehicle of Zhu in view of Weise further in view of Asmus by adding the model for battery deterioration as taught by Guerin with a reasonable expectation of success. The motivation to modify the vehicle of Zhu in view of Weise further in view of Asmus and Guerin is to better capture and utilize information in the battery system (see Guerin at para. [0033]).
Regarding claim 15, Zhu in view of Weise further in view of Asmus teaches the vehicle of claim 10.
However, Zhu in view of Weise further in view of Asmus does not explicitly state:
wherein the non-linear optimizing controller is configured to perform the cost function minimization by using a model for battery deterioration during battery charging, applicable to portions of the time horizon in which the predicted future battery current profile is predicted to be injected to the battery at a level that exceeds a nominal threshold.
In the same field of endeavor, Guerin teaches wherein the non-linear optimizing controller is configured to perform the cost function minimization by using a model for battery deterioration during battery charging (Guerin at para. [0059]: “Specific functional models may be included that correspond to the battery model, a thermal network model, a battery life model, a cell resistance and heat transfer coefficient model, a resistance rise and capacity degradation model, and the like”; para. [0062]: “various operational steps, as well as components for carrying out operational steps, may be implemented in alternate ways depending upon the particular application or in consideration of any number of cost functions associated with the operation of the system”), applicable to portions of the time horizon in which the predicted future battery current profile is predicted to be injected to the battery at a level that exceeds a nominal threshold (Guerin at para. [0019], [0020], [0059], [0062]: The battery system may be charged and discharged, and the cost function minimization is applicable to any part of the operation of the system).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the vehicle of Zhu in view of Weise further in view of Asmus by adding the model for battery deterioration as taught by Guerin with a reasonable expectation of success. The motivation to modify the vehicle of Zhu in view of Weise further in view of Asmus and Guerin is to better capture and utilize information in the battery system (see Guerin at para. [0033]).
Regarding claim 18, Zhu in view of Weise further in view of Asmus teaches the method of claim 17.
However, Zhu in view of Weise further in view of Asmus does not explicitly state:
wherein calculating the set of references is performed by using a model for battery deterioration due to aging, applicable to portions the time horizon in which the predicted future battery current is predicted to be below a nominal threshold.
In the same field of endeavor, Guerin teaches wherein calculating the set of references is performed by using a model for battery deterioration due to aging (Guerin at para. [0059]: “Specific functional models may be included that correspond to the battery model, a thermal network model, a battery life model, a cell resistance and heat transfer coefficient model, a resistance rise and capacity degradation model, and the like”; para. [0062]: “various operational steps, as well as components for carrying out operational steps, may be implemented in alternate ways depending upon the particular application or in consideration of any number of cost functions associated with the operation of the system”), applicable to portions the horizon in which the predicted future battery current is predicted to be below a nominal threshold (Guerin at para. [0019], [0059], [0062]: The current generated by the battery system changes overtime, and the cost function minimization is applicable to any part of the operation of the system).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Zhu in view of Weise further in view of Asmus by adding the model for battery deterioration as taught by Guerin with a reasonable expectation of success. The motivation to modify the method of Zhu in view of Weise further in view of Asmus and Guerin is to better capture and utilize information in the battery system (see Guerin at para. [0033]).
Regarding claim 19, Zhu in view of Weise further in view of Asmus teaches the method of claim 17.
However, Zhu in view of Weise further in view of Asmus does not explicitly state:
wherein calculating the set of references is performed by using a model for battery deterioration during battery discharge due to powertrain operation, applicable to portions of the time horizon in which the predicted future battery current profile is predicted to be issued from the battery to the powertrain.
In the same field of endeavor, Guerin teaches wherein calculating the set of references is performed by using a model for battery deterioration during battery discharge due to powertrain operation (Guerin at para. [0059]: “Specific functional models may be included that correspond to the battery model, a thermal network model, a battery life model, a cell resistance and heat transfer coefficient model, a resistance rise and capacity degradation model, and the like”; para. [0062]: “various operational steps, as well as components for carrying out operational steps, may be implemented in alternate ways depending upon the particular application or in consideration of any number of cost functions associated with the operation of the system”), applicable to portions of the time horizon in which the predicted future battery current profile is predicted to be issued from the battery to the powertrain (Guerin at para. [0019], [0027], [0059], [0062]: The current generated by the battery system is supplied to the drivetrain components of the electric vehicle for driving the vehicle, and the cost function minimization is applicable to any part of the operation of the system).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Zhu in view of Weise further in view of Asmus by adding the model for battery deterioration as taught by Guerin with a reasonable expectation of success. The motivation to modify the method of Zhu in view of Weise further in view of Asmus and Guerin is to better capture and utilize information in the battery system (see Guerin at para. [0033]).
Regarding claim 20, Zhu in view of Weise further in view of Asmus teaches the method of claim 17.
However, Zhu in view of Weise further in view of Asmus does not explicitly state:
wherein calculating the set of references is performed by using a model for battery deterioration during battery charging, applicable to portions of the time horizon in which the predicted future battery current is predicted to be injected to the battery at a level that exceeds a nominal threshold.
In the same field of endeavor, Guerin teaches wherein calculating the set of references is performed by using a model for battery deterioration during battery charging (Guerin at para. [0059]: “Specific functional models may be included that correspond to the battery model, a thermal network model, a battery life model, a cell resistance and heat transfer coefficient model, a resistance rise and capacity degradation model, and the like”; para. [0062]: “various operational steps, as well as components for carrying out operational steps, may be implemented in alternate ways depending upon the particular application or in consideration of any number of cost functions associated with the operation of the system”), applicable to portions of the time horizon in which the predicted future battery current is predicted to be injected to the battery at a level that exceeds a nominal threshold (Guerin at para. [0019], [0020], [0059], [0062]: The battery system may be charged and discharged, and the cost function minimization is applicable to any part of the operation of the system).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Zhu in view of Weise further in view of Asmus by adding the model for battery deterioration as taught by Guerin with a reasonable expectation of success. The motivation to modify the method of Zhu in view of Weise further in view of Asmus and Guerin is to better capture and utilize information in the battery system (see Guerin at para. [0033]).
Claims 6 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Zhu in view of Weise further in view of Asmus and Fuxman et al. (US 2017/0306871 A1, hereinafter “Fuxman”).
Regarding claim 6, Zhu in view of Weise further in view of Asmus teaches the vehicle of claim 1.
However, Zhu in view of Weise further in view of Asmus does not explicitly state wherein the BTMS controller is configured to perform the cost function minimization using a non-linear model predictive control (NMPC) analysis, a prediction horizon of the NMPC analysis defining the time horizon over which the future battery current profile is predicted.
In the same field of endeavor, Fuxman teaches wherein the BTMS controller is configured to perform the cost function minimization using a non-linear model predictive control (NMPC) analysis, a prediction horizon of the NMPC analysis defining the time horizon over which the future battery current profile is predicted (Fuxman at para. [0012]: “the economic cost function may take into consideration performance variables such as fuel consumption, energy consumption, parasitic losses, exhaust output, and so forth, when changes in operating conditions of the powertrain are measured
or future changes to the operating condition may be available”; para. [0032]: “When considering a model of the cooling system 12 and or the engine 14, such as equation (1), a non-linear cost function, for example, may take the following form”; para. [0034]: “In some cases, the controller 18 (e.g., a multivariable controller based on Model Predictive Control (MPC)) may be and/or may include a supervisory controller 40 in communication with two or more powertrain component sub-controllers 42, as shown in FIG. 3. The supervisory controller 40 may be configured to include the model (e.g., equation (1)) of the powertrain system 10 and the cost function (e.g., equation (2)) of the powertrain system 10 and determine set point trajectories for one or more condition of the cooling system 12 and the engine 14 (e.g., a set point trajectory for a temperature condition of the cooling system 12 and/or the engine 14)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the vehicle of Zhu in view of Weise further in view of Asmus by adding the NMPC analysis as taught by Fuxman with a reasonable expectation of success. The motivation to modify the vehicle of Zhu in view of Weise further in view of Asmus and Fuxman is to improve operation of cooling systems (see Fuxman at para. [0002]-[0003]).
Regarding claim 7, Zhu in view of Weise further in view of Asmus and Fuxman teaches the vehicle of claim 6.
Fuxman further teaches wherein the BTMS controller is configured to use the NMPC analysis to calculate a plurality of sets of control signals through the prediction horizon, issue a first of the sets of control signals, obtain a new set of temperatures, and repeat the cost function minimization (Fuxman at para. [0028]: “The controller 18 may be configured to set and/or propose set point trajectories for conditions of the cooling system 12 and/or the engine 14. Once set point trajectories for conditions of the cooling system 12 and/or the engine 14 are determined, the controller 18 may be configured to adjust one or more positions of the actuators 20 of the cooling system 12 and/or actuators 22 of the engine 14 to drive a value of the one or more conditions to associated condition set point trajectories. Determining the set point trajectories and/or adjusting the actuators may be performed while the controller is on-line (e.g., the cooling system 12 and/or the engine 14 are operating (e.g., during steady state and/or transient operation of the powertrain system 10) and the controller may be receiving inputs from sensors 16) and/or other inputs in real-time”; para. [0036]: “the MPC based sub-controllers 42 may determine positions of actuators 20, 22 based on the following incoming sensor measurements 32 and the following cost function”; para. [0038]: “repeat the above steps”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the vehicle of Zhu in view of Weise further in view of Asmus and Fuxman by adding calculating control signals as taught by Fuxman with a reasonable expectation of success. The motivation to modify the vehicle of Zhu in view of Weise further in view of Asmus and Fuxman is to improve operation of cooling systems (see Fuxman at para. [0002]-[0003]).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Zhu in view of Weise further in view of Asmus and Hulse et al. (US 2020/0205318 A1, hereinafter “Hulse”).
Regarding claim 8, Zhu in view of Weise further in view of Asmus teaches the vehicle of claim 1.
However, Zhu in view of Weise further in view of Asmus does not explicitly state wherein the thermal management system includes the chiller coupled to a vapor cycle cooling system, and a pump, wherein the set of control signals includes at least a control signal for operation of the pump, and a setpoint for the vapor cycle cooling system.
In the same field of endeavor, Hulse teaches wherein the thermal management system includes a chiller coupled to a vapor cycle cooling system, and a pump (Hulse at para. [0154]: “The thermal management fluid is then transported to a secondary cooling loop, such as a radiator or another refrigerated system. An example of such a system is illustrated in FIG. 2, where the thermal management fluid enters a battery pack enclosure containing a number of cells and exits the enclosure having taken up heat from the battery pack”; para. [0157]: “The thermal management fluid may be recirculated passively or actively in the device, for example by using mechanical equipment such as a pump”; para. [0203]: “Examples of refrigeration systems which can include a secondary loop refrigeration system include:”; para. [0210] “a chiller”), wherein the set of control signals includes at least a control signal for operation of the pump, and a setpoint for the vapor cycle cooling system (Hulse at para. [0147]: “This heat of operation is safely and effectively transferred to the thermal management fluid 11A by: (a) causing the liquid phase of the fluid to evaporate and form vapor 11B; or (b) raising the temperature of the liquid thermal management fluid 11A; or (c) a combination of (a) and (b)”; para. [0150]: “the thermal management system of the present invention can include sensors and control modules (not shown) which turn on the heating element when the battery temperature is below a predetermined level” “Thereafter during operation, the thermal management fluid of the present invention would serve the cooling function as described above”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the vehicle of Zhu in view of Weise further in view of Asmus by adding the chiller coupled to the vapor cycle cooling system, and the pump and controlling thereof as taught by Hulse with a reasonable expectation of success. The motivation to modify the vehicle of Zhu in view of Weise further in view of Asmus and Hulse is to provide effective thermal management of batteries (see Hulse at para. [0003]).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Zhu in view of Weise further in view of Asmus and Drees et al. (US 2016/0377306 A1, hereinafter “Drees”).
Regarding claim 11, Zhu in view of Weise further in view of Asmus teaches the vehicle of claim 10.
Zhu further discloses wherein the non-linear optimizing controller is configured to periodically calculate and communicate the set of references calculated for a first sampling time period to the reference tracking controller (Zhu at para. [0037]: “the controller 24 within the battery system 22 uses a physics-based model to predict future system states (e.g., the expected battery system temperature in 30 seconds) and proactively control heating and cooling within the battery system 22”; para. [0044]: “Within the control 126, the battery/thermal model 130 is a predictive physics-based model that uses the inputs received to predict future states ( e.g. temperature, state of charge) of the battery/thermal system 122 based on a control signal 142 output from the optimizer 140”), the reference tracking controller configured to apply the set of references to control the thermal management system using (Zhu at para. [0044]: “The predicted future temperature is provided to the optimizer as a feedback signal 132, and the optimizer 140 adjusts the controls based on the predicted future state and the received inputs”).
However, Zhu in view of Weise further in view of Asmus does not explicitly state a second sampling time period, the second sampling time period being shorter than the first sampling time period.
In the same field of endeavor, Drees teaches a second sampling time period, the second sampling time period being shorter than the first sampling time period (Drees at para. [0145]: “One advantage of the cascaded optimization process performed by demand response optimizer 630 is the optimal use of computational time. For example, the subplant level optimization performed by high level optimizer 632 may use a relatively long time horizon due to the operation of the thermal energy storage. However, the equipment level optimization performed by low level optimizer 634 may use a much shorter time horizon or no time horizon at all since the low level system dynamics are relatively fast ( compared to the dynamics of the thermal energy storage) and the low level control of the subplant equipment may be handled by BMS 606”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the vehicle of Zhu in view of Weise further in view of Asmus by adding the second sampling period as taught by Drees with a reasonable expectation of success. The motivation to modify the vehicle of Zhu in view of Weise further in view of Asmus and Drees is to provide optimal use of computational time for control optimization of thermal management (see Drees at para. [0145]).
Conclusion
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/JISUN CHOI/Examiner, Art Unit 3666
/SCOTT A BROWNE/Supervisory Patent Examiner, Art Unit 3666