Notice of Pre-AIA or AIA Status
The present application, filed on or after 2013 March 16, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 2023 November 14 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The disclosure is objected to because of the following informalities:
Reference character corrections are needed at
¶[0023] – “communication system 240” should read “communication system 236”
¶[0024] – “sensors 242” should read “sensors 238”
¶[0037] – “communication system 230” should read “communication system 236”
¶s [0040, 0042] – “BHM database 122” should read “BHM database 120”
Appropriate correction is required.
Claim Objections
Claim(s) 7, 15, 17, 20 is/are objected to because of the following informalities:
Claims 7 and 15 — "that battery health inputs" should read "the battery health inputs."
Claims 17 and 20 — missing article (“the” or “same”) before the 2nd "electrical characteristic."
Appropriate correction is required.
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 – 20 are rejected under 35 U.S.C. 101 as directed to patent-ineligible subject matter.
Step 1 — Statutory Category
Claims 1–8 are directed to a method. Claims 9–17 are directed to a system. Claims 18–20 are directed to a vehicle. Thus, the claims are directed to a process and to a machine/manufacture, each of which is one of the statutory categories of invention.
Step 2A, Prong 1 — Judicial Exception
In re independent claims 1, 9, and 18, representative claim 9 recites:
A system for controlling an electric vehicle having a battery pack, comprising: one or more processors; and one or more memory configured to store programming instructions executable by the one or more processors and configured to cause the one or more processors to, after a plurality of charging-discharging operations of the battery pack, control electric power usage of the battery pack based on a battery health measurement from a battery health model that uses a plurality of detected battery health inputs including a current life of the electric vehicle and an initial throughput characteristic; [the examiner finds that the foregoing element recites a mathematical concept and/or a mental process because it derives a battery health measurement by using a battery health model to map a plurality of numerical inputs — including a current life of the electric vehicle and an initial throughput characteristic — to a delta SOH output, a determination performable using pen and paper or in the human mind; while the claim does not itself recite a specific mathematical formula, the specification confirms that this determination is a mathematical/algorithmic operation, stating that “the battery health model is defined using a physics based model and a regression based machine learning algorithm to map the battery health inputs ... to the delta SOH as the output” (¶[0041])].
Step 2A, Prong 2 — Practical Application
This judicial exception is not integrated into a practical application because:
the following additional elements merely use a computer as a tool to perform the abstract idea: “one or more processors”; “one or more memory configured to store programming instructions executable by the one or more processors.” The claims invoke a computer merely as a tool to execute the abstract determination; the computer does not impose any meaningful limit on the practice of the abstract idea. A specific computer is not described; thus, a general purpose computer is applicable.
the following additional element does no more than generally link the use of the abstract idea to a particular technological environment or field of use: “an electric vehicle having a battery pack.” This limitation confines the abstract idea to the EV/battery context but does not alter the nature of the idea or amount to significantly more than the exception itself.
the following additional element merely adds insignificant extra-solution activity to the abstract idea: “after a plurality of charging-discharging operations of the battery pack.” This limitation amounts to necessary data-gathering activity that establishes the operational-history condition under which the battery health measurement is subsequently determined, and does not itself perform or alter that determination.
the following additional element merely adds insignificant post-solution activity to the abstract idea: “control electric power usage of the battery pack.” No specific rate, threshold, timing, or technical mechanism by which power usage is controlled is recited, such that “control” is nothing more than applying or outputting the result of the mathematical/mental determination rather than a separate technological operation; the control of electric power usage is broadly recited, and the claim does not set forth a concrete modification of the charging/discharging state of the battery pack (¶[0046]).
Step 2B — Significantly More
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, whether considered individually or as an ordered combination, are well-understood, routine, and conventional.
The “one or more processors” and “one or more memory” are described only in generic terms and are not limited to any specific, non-conventional hardware configuration; the specification confirms these components may be any “processor circuit (shared, dedicated, or group) that executes code,” “memory circuit ... that stores code executed by the processor circuit,” or “other suitable hardware components that provide the described functionality” (¶[0048]), and that the disclosed methods “may be partially or fully implemented by a special purpose computer created by configuring a general-purpose computer,” with the functional blocks translated into computer programs “by the routine work of a skilled technician or programmer” (¶[0050]) — confirming generic, off-the-shelf computing components applied in their ordinary capacity.
The “electric vehicle having a battery pack,” and the underlying practice of monitoring and controlling battery pack operation in connection with charging-discharging activity, are likewise well-understood, routine, and conventional; the specification’s own Background states that “[a]n electric vehicle (EV) includes a battery pack ... for providing power to electric motors to propel the EV” and that “[o]ne or more operational characteristics of the battery pack may be monitored to assess a battery health ... and/or control the operation of the battery pack” as pre-existing background technology (¶[0002]).
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually; the ordered combination is simply generic computing components applying the mathematical/mental determination to data gathered from, and output to, a conventional EV battery pack.
Dependent claims 2, 10, and 19 recite:
wherein the plurality of detected battery health inputs further includes a previous state of health (SOH) measurement, an average ambient temperature for a selected time period associated with the plurality of charging-discharging operations, and a delta throughput characteristic for the selected time period.
The limitation simply further defines the abstract idea — it adds additional numerical inputs (a previous SOH measurement, an average ambient temperature, and a delta throughput characteristic) to the mathematical/mental determination already recited in the parent claims — and, thus, does not make the abstract idea any less abstract.
Dependent claims 3 and 11 recite:
wherein the battery health measurement is a delta SOH that is indicative of a present electric current capacity over an initial electric current capacity.
The limitation simply further defines the abstract idea — it specifies that the battery health measurement is itself a mathematical ratio of present electric current capacity over initial electric current capacity — and, thus, does not make the abstract idea any less abstract.
Dependent claims 4 and 12 recite:
further comprising defining the battery health model employing a physics based model and regression based machine learning algorithms.
The limitation simply further defines the abstract idea — it specifies that the battery health model itself takes the form of a physics-based model and a regression-based machine learning algorithm, both of which are themselves mathematical/algorithmic techniques — and, thus, does not make the abstract idea any less abstract.
Dependent claims 5 and 13 recite:
further comprising training the battery health model employing simulation data and real-world vehicle data.
This limitation is not part of the judicial exception. Training a model is not itself a mathematical concept or mental process unless the claim recites a specific mathematical training technique (e.g., a backpropagation algorithm or a gradient descent algorithm); no such technique is recited here (cf. Eligibility Example 39, training step reciting no specific mathematical formula, with Example 47, training step reciting a specific gradient descent algorithm). This limitation is therefore an additional element, addressed at Step 2A, Prong 2 and Step 2B.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because gathering simulation and real-world data for model training is well-understood, routine, extra-solution data-gathering activity that does not alter or affect how the process steps are performed.
Dependent claims 6 and 14 recite:
further comprising selecting the battery health model from among a plurality of battery health models stored in a database based at least on vehicle identification data associated with the EV.
The portion of this limitation reciting “selecting the battery health model from among a plurality of battery health models” recites a mental process, because selecting among a finite number of pre-existing model options based on an input criterion is an evaluation/judgment that can be performed in the human mind or with pen and paper; this limitation further defines the abstract idea already recited in the parent claims and, thus, does not make the abstract idea any less abstract.
The remaining additional elements — “stored in a database” and “based at least on vehicle identification data associated with the EV” — merely use a computer as a tool to perform the abstract idea: the claims invoke generic data storage and retrieval merely as a tool to store and look up pre-existing model variants; the database and the vehicle-identification-based lookup do not impose any meaningful limit on the practice of the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because they are merely incidental or token additions to the claims that do not alter or affect how the process steps are performed.
Dependent claims 7 and 15 recite:
wherein the battery health model is further selected based on an operation state associated with that battery health inputs.
The limitation simply further defines the abstract idea — it adds an operation-state variable as an additional selection criterion within the model-selection mental process already recited in the parent claims — and, thus, does not make the abstract idea any less abstract.
Dependent claims 8 and 16 recite:
wherein the operation state includes at least one of a drive state of the EV, a charge state of the EV, and a soak state of the EV.
The limitation simply further defines the abstract idea — it enumerates the specific categories (drive, charge, or soak state) that fall within the operation-state variable already recited in the parent claims — and, thus, does not make the abstract idea any less abstract.
Dependent claims 17 and 20 recite:
further comprising a plurality of sensors arranged at the electric vehicle to detect at least one of an electrical characteristic of the battery pack and an external environment temperature about the electric vehicle, wherein the plurality of detected battery health inputs is detected based on the at least one of electrical characteristic of the battery pack and an external environment temperature about the electric vehicle.
The following additional elements merely add insignificant extra-solution activity to the abstract idea: a plurality of sensors arranged at the electric vehicle to detect an electrical characteristic of the battery pack or an external environment temperature. This limitation amounts to necessary data-gathering activity that supplies raw input data for the mathematical/mental determination and is well-understood, routine, and conventional EV/battery sensor functionality.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because generic sensor data-gathering is well-understood, routine, and conventional and does not alter or affect how the process steps are performed.
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 non-obviousness.
Claim(s) 1, 3, 9, 11, 17 – 18, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over HASHIMOTO et al. (US 2021/0339650 A1), in view of LEE (US 2015/0301122 A1).
In re independent claims 1, 9, and 18, HASHIMOTO discloses a method for controlling an EV having a battery pack (FIG. 4; ¶[0044]: motor 34, inverter 35, and battery system 40 of electric vehicle 3), comprising:
controlling electric power usage of the battery pack (CL 1.2.1 - HASHIMOTO – ¶[0064]: vehicle controller 30 sets the power limit value into inverter 35, which controls output current or output power from battery module 41).
As to claims 9 and 18, HASHIMOTO further discloses one or more processors (¶s [0036, 0056]: vehicle controller 30 and battery controller 46 composed of a microcomputer); and
one or more memory configured to store programming instructions executable by the one or more processors (¶[0056]: battery controller 46 composed of a microcomputer and a non-volatile memory).
HASHIMOTO does not expressly disclose controlling electric power after a plurality of charging-discharging operations of the battery pack, based on a battery health measurement from a battery health model that uses a plurality of detected battery health inputs including a current life of the EV and an initial throughput characteristic.
LEE teaches recording a plurality of charging-discharging operations of the battery pack (¶[0058]: battery usage information including a number of times the battery is rapidly discharged and a number of times the battery is rapidly charged), battery health measurement from a battery health model (¶[0060]: battery degradation model obtained by modeling a degradation in the battery caused by a usage and an environment of the battery) that uses a plurality of detected battery health inputs (¶[0057]: collector 120 collects either one or both of battery usage information and battery environment information) including a current life of the EV (¶[0055]: estimator 110 estimates the SOH based on a number of charge-discharge cycles) and an initial throughput characteristic (Equation 2; ¶s [0053–0054]:
C
i
n
i
t
i
a
l
, an initial value of a capacity of the battery).
It would have been obvious for a PHOSITA to combine LEE's battery health measurement to HASHIMOTO's vehicle controller in order to make battery health measurement the operative basis for control of electric power usage of the battery pack, thereby tailoring that electric power usage to the actual, corrected degradation state of the battery pack rather than to SOC and temperature alone (¶[0078]).
In re dependent claim 3 and 11, HASHIMOTO is silent to wherein the battery health measurement is a delta SOH that is indicative of a present electric current capacity over an initial electric current capacity.
LEE teaches wherein the battery health measurement is a delta SOH that is indicative of a present electric current capacity over an initial electric current capacity (Equation 2; ¶s [0053–0054]:
S
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d
(
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=
C
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×
100
).
It would have been obvious for a PHOSITA to combine LEE's battery health measurement to HASHIMOTO's vehicle controller in order to make battery health measurement the operative basis for control of electric power usage of the battery pack, thereby tailoring that electric power usage to the actual, corrected degradation state of the battery pack rather than to SOC and temperature alone (¶[0078]).
In re dependent claims 17 and 20, HASHIMOTO discloses a system further comprising a plurality of sensors arranged at the electric vehicle (FIG. 4, 5: voltage measuring unit 43 and current measuring unit 45 arranged within battery system 40 of electric vehicle 3) to detect at least one of an electrical characteristic of the battery pack and an external environment temperature about the electric vehicle (¶s [0050, 0053]: voltage measuring unit 43 measuring the voltage of each cell and current measuring unit 45 measuring the current flowing through the cells), wherein the plurality of detected battery health inputs is detected based on the at least one of electrical characteristic of the battery pack and an external environment temperature about the electric vehicle (¶[0057]: battery controller 46 estimating the SOC and SOH of the cells based on the measured voltages and currents).
Claim(s) 2, 10, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over HASHIMOTO et al. (US 2021/0339650 A1), in view of LEE (US 2025/0301122 A1), and further in view of DUAN et al. (US 2017/0242079 A1).
In re dependent claims 2, 10, and 19, HASHIMOTO is silent to wherein the plurality of detected battery health inputs further includes a previous SOH measurement, an average ambient temperature for a selected time period associated with the plurality of charging-discharging operations, and a delta throughput characteristic for the selected time period.
LEE teaches wherein the plurality of detected battery health inputs further includes a previous SOH measurement (Equations 4 – 5; ¶s [0092, 0094]:
S
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=
S
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+
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), and an average ambient temperature for a selected time period associated with the plurality of charging-discharging operations (¶[0059]: battery environment information associated with a temperature to which the battery is exposed for a period of time that is set in advance).
A PHOSITA would have been motivated to combine LEE's battery health measurement to HASHIMOTO's vehicle controller in order to make battery health measurement the operative basis for control of electric power usage of the battery pack, thereby tailoring that electric power usage to the actual, corrected degradation state of the battery pack rather than to SOC and temperature alone (¶[0078]).
LEE does not expressly teach the plurality of detected battery health inputs further includes a delta throughput characteristic for the selected time period.
DUAN teaches detected battery health inputs further includes a delta throughput characteristic for the selected time period (Equations 1 – 2; ¶[0032]: Throughput defined as
∫
T
i
T
f
i
d
t
).
It would have been obvious for a PHOSITA to combine DUAN's delta state of charge (Δ SOC) to HASHIMOTO's vehicle controller and battery health determination framework in order to quantify the incremental capacity change attributable to that selected time interval rather than relying only on cumulative, whole-life throughput values (¶[0032]).
Claim(s) 4, 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over HASHIMOTO et al. (US 2021/0339650 A1), in view of LEE (US 2025/0301122 A1), and further in view of SIMONIS (DE 10 2022 200 006 A1).
In re dependent claims 4 and 12, HASHIMOTO is silent to the method further comprising defining the battery health model employing a physics based model and regression based machine learning algorithms.
SIMONIS teaches the method further comprising defining the battery health model employing a physics based model (¶[0031]: physical aging model based on electrochemical model equations determining SOH-C/SOH-R) and regression based machine learning algorithms (¶[0032]: AI-based regression correction models, including Gaussian process and other supervised learning methods).
It would have been obvious for a PHOSITA to combine SIMONIS's physical aging model to HASHIMOTO's vehicle controller and battery health determination framework in order to define the battery health model using both a physics-based representation of the battery's electrochemical behavior and a data-driven correction that accounts for behavior the physics-based representation alone does not capture (¶s[0031–0032]).
Claim(s) 5, 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over HASHIMOTO et al. (US 2021/0339650 A1), in view of LEE (US 2025/0301122 A1), SIMONIS (DE 10 2022 200 006 A1), and further in view of BERGER et al. (US 2024/0361397 A1).
In re dependent claims 5 and 13, HASHIMOTO is silent to the method further comprising training the battery health model employing simulation data and real-world vehicle data.
BERGER teaches the method further comprising training the battery health model (¶s [0045, 0058]: basic battery model GM fine-tuned to vehicle-specific model FM using comprehensive driving-data database) employing simulation data (¶[0077]: simulated driving cycle for EC parameter identification at varying temperatures) and real-world vehicle data (¶[0088]: test-drive measured battery pack current, voltage, temperature, SOC, and cell voltages sent to server).
It would have been obvious for a PHOSITA to combine BERGER's basic battery model to HASHIMOTO's vehicle controller and battery health determination framework in order to train the battery health model with both controlled simulation data covering a range of operating temperatures and real-world vehicle data reflecting the specific vehicle's actual battery behavior (¶s [0077, 0088]).
Claim(s) 6, 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over HASHIMOTO et al. (US 2021/0339650 A1), in view of LEE (US 2025/0301122 A1), and further in view of BERGER et al. (US 2024/0361397 A1).
In re dependent claims 6 and 14, HASHIMOTO is silent to the method further comprising selecting the battery health model from among a plurality of battery health models stored in a database based at least on vehicle identification data associated with the EV.
BERGER teaches the method further comprising selecting the battery health model from among a plurality of battery health models (¶[0030]: server selecting battery model on basis of type of vehicle battery, among multiple selectable models) stored in a database (¶[0051]: state-of-health algorithms stored and executed on the server) based at least on vehicle identification data associated with the EV (¶[0030]: server selecting battery model on basis of type of vehicle battery).
It would have been obvious for a PHOSITA to combine BERGER's server-side selection of a battery model to HASHIMOTO's vehicle controller and battery health determination framework in order to select the battery health model that most accurately reflects the specific battery type installed in the vehicle rather than applying a single generic model to every vehicle. (¶[0030]).
Claim(s) 7 – 8, 15 – 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over HASHIMOTO et al. (US 2021/0339650 A1), in view of LEE (US 2025/0301122 A1), BERGER et al. (US 2024/0361397 A1), and further in view of SCHLEDER et al. (US 2022/0289070 A1).
In re dependent claims 7 and 15, HASHIMOTO is silent to wherein the battery health model is further selected based on an operation state associated with that battery health inputs.
BERGER teaches selection of a battery health model (¶[0030]: battery health model selected on basis of vehicle-battery type).
A PHOSITA would have been motivated to combine BERGER's model-selection methodology with HASHIMOTO's vehicle controller and battery health determination framework in order to select the battery health model that most accurately reflects the specific battery type installed in the vehicle rather than applying a single generic model to every vehicle. (¶[0030]).
BERGER does not expressly disclose the battery health model is further selected based on an operation state associated with that battery health inputs.
SCHLEDER teaches wherein the battery health model is further selected based on an operation state (¶[0009]: operating state of the motor vehicle determined using a control apparatus of the motor vehicle).
It would have been obvious for a PHOSITA to modify HASHIMOTO's vehicle controller to further account for the operating state of the electric vehicle, as taught by SCHLEDER, in order to condition the battery health determination on the specific operating state in which the battery health inputs were collected, since battery behavior varies with the vehicle's operating state. This inherently leads to the operation state being associated with the battery health inputs used in that determination (¶s [0016–0017]).
In re dependent claims 8 and, 16, HASHIMOTO is silent to wherein the operation state includes at least one of a drive state of the EV, a charge state of the EV, and a soak state of the EV.
SCHLEDER teaches wherein the operation state includes a soak state of the EV (¶[0016]: parked state — vehicle parked and inactive — corresponding to soak state under interpretation).
It would have been obvious for a PHOSITA to modify HASHIMOTO's vehicle controller to further account for the operating state of the electric vehicle, as taught by SCHLEDER, in order to condition the battery health determination on the specific operating state in which the battery health inputs were collected, since battery behavior varies with the vehicle's operating state (¶s [0016–0017]).
Claim(s) 1 - 3, 9- 11, 17 - 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over ZHAO et al. (US 2023/0094310 A1), and further in view of BERTRAND et al. (US 2025/0020726 Al) and DUAN et al. (US 2017/0242079 Al)
In re claims 1, 9, 18, ZHAO discloses a method and system for controlling an EV having a battery pack (FIG. I; ¶s [0028, 0030]: vehicle 12 having powertrain 10 including battery pack 24), comprising:
after a plurality of charging-discharging operations of the battery pack (¶s [0034, 0049]: battery pack 24 charging and discharging operations, state of health evaluated based on number of charge-discharge cycles among other parameters), controlling electric power usage of the battery pack (¶s [0034, 0035]: controller 30 restricting power drawn from battery pack 24 via inverter 26), that uses a plurality of detected battery health inputs (¶[0049]: plurality of parameters considered in determining state of health) including a current life of the EV (¶[0049]: age of the battery included in plurality of parameters).
ZHAO does not expressly disclose controlling electric power usage of the battery pack based on a battery health measurement from a health model that uses an initial throughput characteristic as an input; one or more processors; and one or more memory configured to store programming instructions executable by the one or more processors.
BERTRAND teaches a battery health measurement from a battery health model (¶[0048]: baseline SOH value SOHbase from table or function of empirically determined data referenced via vehicle operating conditions).
As to claim 9, BERTRAND further teaches one or more processors (¶[0023]: VCU 152 including digital processor/CPU 161); and
one or more memory configured to store programming instructions executable by the one or more processors (¶[0034]: controller with executable instructions stored in non-transitory memory to generate corrected SOH).
A person having ordinary skill in the art (PHOSITA) would have been motivated to combine BERTRAND's operating-condition-referenced baseline state-of-health model with ZHAO's battery pack controller to supply more granular and comprehensive health measurements.
BERTRAND does not expressly disclose an initial throughput characteristic.
DUAN teaches an initial throughput characteristic (¶[0031 ]: SOC estimated by amp-hour throughput integration relative to beginning-of-life battery capacity).
It would have been obvious for a PHOSITA to combine DUAN's amp-hour throughput integration technique with ZHAO's battery pack controller to derive the capacity-related input that ZHAO's state-of-health determination already considers as a parameter, yielding a more precise result.
In re claims 2, 10, 19, ZHAO is silent to wherein the plurality of detected battery health inputs further includes a previous SOH measurement, an average ambient temperature for a selected time period associated with the plurality of charging-discharging operations, and a delta throughput characteristic for the selected time period.
BERTRAND teaches wherein the plurality of detected battery health inputs further includes a previous SOH measurement (¶[0054]: SOHbase, the open-loop SOH value reported by the BMS).
A PHOSITA would have been motivated to combine BERTRAND's SOH model with ZHAO's battery pack controller to supply more granular and comprehensive health measurements.
BETRAND does not expressly disclose an average ambient temperature for a selected time period associated with the plurality of charging-discharging operations, and a delta throughput characteristic for the selected time period.
DUAN teaches an average ambient temperature for a selected time period associated with the plurality of charging-discharging operations (¶[0042]: statistic ambient temperature loaded via time and vehicle location from a prior parking period), and a delta throughput characteristic for the selected time period (¶[0032]: Throughput/Q computed over interval Ti to Tf).
It would have been obvious for a PHOSITA to combine DUAN's amp-hour throughput integration technique with ZHAO's battery pack controller to derive the capacity-related input that ZHAO's state-of-health determination already considers as a parameter, yielding a more precise result.
In re claims 3, 11, ZHAO is silent to wherein the battery health measurement is a delta SOH that is indicative of a present electric current capacity over an initial electric current capacity.
BERTRAND teaches wherein the battery health measurement is a delta SOH that is indicative of a present electric current capacity over an initial electric current capacity (¶[0002]: SOH as ratio of present maximum charge capacity to initial rated charge capacity).
It would have been obvious for a PHOSITA to combine BERTRAND's SOH model with ZHAO's battery pack controller to supply more granular and comprehensive health measurements.
In re claims 17, 20, ZHAO is silent to a system or vehicle further comprising a plurality of sensors arranged at the electric vehicle to detect at least one of an electrical characteristic of the battery pack and an external environment temperature about the electric vehicle, wherein the plurality of detected battery health inputs is detected based on the at least one of electrical characteristic of the battery pack and an external environment temperature about the electric vehicle.
BERTRAND teaches a system and vehicle further comprising a plurality of sensors (¶[0023]: sensors 154 including accelerometers, yaw rate sensors, inclinometers, temperature sensors, battery voltage/current sensors) arranged at the electric vehicle (FIG. 1; ¶[0023]: sensors 154 and related sensing components distributed at vehicle 10) to detect an electrical characteristic of the battery pack (¶[0023]: battery voltage and current sensors detecting electrical characteristic of battery pack), wherein the plurality of detected battery health inputs is detected based on electrical characteristic of the battery pack (¶[0040]: battery voltage/current values V0-V2, I0-12 used to determine DC resistance).
It would have been obvious for a PHOSITA to combine BERTRAND's SOH model with ZHAO's battery pack controller to supply more granular and comprehensive health measurements.
Claim(s) 4, 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over ZHAO et al. (US 2023/0094310 A1), BERTRAND et al. (US 2025/0020726 A1), DUAN et al. (US 2017/0242079 A1), and further in view of SIMONIS (DE 10 2022 200 006 A1).
In re claims 4, 12, ZHAO is silent to the method further comprising defining the battery health model employing a physics based model and regression based machine learning algorithms.
SIMONIS teaches the method further comprising defining the battery health model employing a physics based model (¶[0031]: physical aging model based on electrochemical model equations determining SOH-C/SOH-R) and regression based machine learning algorithms (¶[0032]: AI-based regression correction models, including Gaussian process and other supervised learning methods).
It would have been obvious for a PHOSITA to combine SIMONIS's physics-based aging model and AI-based regression correction technique with ZHAO's battery pack controller to supply the specific computational structure ZHAO requires to generate its state-of-health determination, using a known, more accurate hybrid physical/AI modeling technique.
Claim(s) 5, 13 is/are rejected under 35 U.S.C. I 03 as being unpatentable over ZHAO et al. (US 2023/0094310 Al), BERTRAND et al. (US 2025/0020726 Al), DUAN et al. (US 2017/0242079 Al), SIMONIS (DE 10 2022 200 006 Al), and further in view of BERGER et al. (US 2024/0361397 Al).
In re claims 5, 13, ZHAO is silent to the method further comprising training the battery health model employing simulation data and real-world vehicle data.
BERGER teaches the method further comprising training the battery health model (¶s [0045, 0058]: basic battery model GM fine-tuned to vehicle-specific model FM using comprehensive driving-data database) employing simulation data (¶[0077]: simulated driving cycle for EC parameter identification at varying temperatures) and real-world vehicle data (¶[0088]: test-drive measured battery pack current, voltage, temperature, SOC, and cell voltages sent to server).
It would have been obvious for a PHOSITA to combine BERGER's model-training methodology with ZHAO's battery pack controller to develop the underlying battery health model that ZHAO's determination relies on, using a known technique for producing an accurate, vehicle-specific model from combined simulated and real-world data.
Claim(s) 6, 14 is/are rejected under 35 U.S.C. I 03 as being unpatentable over ZHAO et al. (US 2023/0094310 Al), BERTRAND et al. (US 2025/0020726 Al), DUAN et al. (US 2017/0242079 Al), and further in view of BERGER et al. (US 2024/0361397 A1).
In re claims 6, 14, ZHAO is silent to the method further comprising selecting the battery health model from among a plurality of battery health models stored in a database based at least on vehicle identification data associated with the EV.
BERGER teaches the method further comprising selecting the battery health model from among a plurality of battery health models (¶[0030]: server selecting battery model on basis of type of vehicle battery, among multiple selectable models) stored in a database (¶[005 l ]: state-of-health algorithms stored and executed on the server) based at least on vehicle identification data associated with the EV (¶[0030]: server selecting battery model on basis of type of vehicle battery).
It would have been obvious for a PHOSITA to combine BERGER's model-selection methodology with ZHAO's battery pack controller to tailor the state-of-health determination to the specific vehicle battery type, improving determination accuracy.
Claim(s) 7 - 8, 15 - 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over ZHAO et al. (US 2023/0094310 Al), BERTRAND et al. (US 2025/0020726 Al), DUAN et al. (US 2017/0242079 Al), BERGER et al. (US 2024/0361397 A I), and further in view of SCHLEDER et al. (US 2022/0289070 Al).
In re claims 7, 15, ZHAO is silent to wherein the battery health model is further selected based on an operation state associated with that battery health inputs.
BERGER teaches selection of a battery health model (¶[0030]: battery health model selected on basis of vehicle-battery type).
A PHOSITA would have been motivated to combine BERGER's model-selection methodology with ZHAO's battery pack controller to tailor the state-of-health determination to the specific vehicle battery type, improving determination accuracy.
BERGER does not expressly disclose the battery health model is further selected based on an operation state associated with that battery health inputs.
SCHLEDER teaches wherein the battery health model is further selected based on an operation state (¶[0009]: operating state of the motor vehicle determined using a control apparatus of the motor vehicle).
It would have been obvious for a PHOSITA to combine SCHLEDER's vehicle operating-state determination with BERGER's model-selection methodology and ZHAO's battery pack controller to further refine model selection based on the vehicle's current operating state, in addition to vehicle-battery type.
In re claims 8, 16, ZHAO is silent to wherein the operation state includes at least one of a drive state of the EV, a charge state of the EV, and a soak state of the EV.
SCHLEDER teaches wherein the operation state includes a soak state of the EV (¶[0016): parked state - vehicle parked and inactive - corresponding to soak state under interpretation).
It would have been obvious for a PHOSITA to combine SCHLEDER's vehicle operating-state determination with BERGER's model-selection methodology and ZHAO's battery pack controller to further refine model selection based on the vehicle's current operating state, in addition to vehicle-battery type.
Prior Art Disclaimer
The prior art applied in this Office Action includes foreign patent documents that were originally published in languages other than English. Machine-generated translations of these documents were utilized to assess their relevance and content.
Conclusion
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/D. JOHANN DJANAL-MANN/ Examiner, Art Unit 2859
/JOHN T TRISCHLER/ Primary Examiner, Art Unit 2859
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