DETAILED ACTION
This office action is in response to communication filed on March 18, 2026.
Response to Amendment
Amendments filed on March 18, 2026 have been entered.
The specification has been amended.
Claims 1, 13, 16 and 19 have been amended.
Claims 1-20 have been examined
Response to Arguments
Applicant’s arguments, see Remarks (p. 8), filed on 03/18/2026, with respect to the objections to the specification have been fully considered. In view of the amendments to the specification addressing the informalities raised in the previous office action, the objections to the specification have been withdrawn.
Applicant’s arguments, see Remarks (p. 8), filed on 03/18/2026, with respect to the objections to the claims have been fully considered. In view of the amendments to the claims addressing the informalities raised in the previous office action, the objections to the claims have been withdrawn. However, upon further consideration, new objections to the claims are presented below in order to address informalities introduced by the amendments.
Applicant’s arguments, see Remarks (p. 8-12), filed on 03/18/2026, with respect to the rejection of claims 1-20 under 35 U.S.C. 103 have been fully considered but are not persuasive.
Applicant argues (p. 9) that the cited references do not disclose “obtaining multiple terminal impedance measurements for the battery in response to a plurality of periodic signals injected into the battery, wherein each of the periodic signals have a different frequency of a plurality of frequencies, wherein each of the multiple terminal impedance measurements is obtained at a respective different one of the plurality of frequencies,” as recited in claim 1.
This argument is not persuasive.
The examiner submits that, as indicated in the previous office action, Wang (US 20140372055 A1) discloses applying various driving profiles (time-varying currents) to excite the electrochemical device (see [0093]), each driving profile including a plurality of double-pulse sequences (see [0094]; see also Figs. 12 and 15), and measuring the impedance spectrum at one or more frequencies as a response of the applied driving profiles (see [0075], [0085]-[0089], [0096]), the frequencies being implied to correspond to the frequencies of the driving profile (as evidenced by Champlin - US 6137269 A, see col. 2, lines 37-55; and Champlin - US 6294897 B1, see col. 2, lines 41-59). Wang also discloses using electrochemical impedance spectroscopy – EIS (see [0168]).
Based on this, the examiner submits that the prior art of record discloses/renders obvious the claimed invention.
Applicant also argues (p. 10) that Wang discloses using a driving profile with no plurality of frequencies, as shown in the descriptions of steps (a) and (b), and then using an impulse response model to generate an impedance spectrum in step (d).
This argument is not persuasive.
The examiner submits that, as indicated in the previous office action, Wang discloses:
“The disclosed methods utilize the impedance spectrum (with a frequency range covering approximately 0.01 Hz to 10 Hz, for example) rather than a one-point impedance (e.g., DC impedance) for temperature sensing, taking into account the SOC effect on impedances” ([0075]);
“The electrochemical device may be excited with various driving profiles” ([0093]); and
“the impedance spectrum in step (d) is calculated at one or more frequencies of about 0.02, 0.05, 0.1, 0.2, 0.3, 0.4,0.5,0.6,0.7, 0.8, 0.9, 1.0, 1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, or 9.0 Hz” ([0096]), with these frequencies being implied to correspond to the frequencies in the driving profiles (as evidenced by Champlin - US 6137269 A, see col. 2, lines 37-55; and Champlin - US 6294897 B1, see col. 2, lines 41-59; Wang also discloses using electrochemical impedance spectroscopy – EIS see [0168]).
Based on this, the examiner submits that the prior art of record discloses/renders obvious the claimed invention.
Applicant further argues (p. 10) that Wang does not disclose “wherein each of the plurality of battery models comprises a multivariable polynomial regression model.” In particular, Wang does not disclose using a “regression” model for estimating the internal temperature of the battery, let alone a multivariable polynomial regression model.
This argument is not persuasive.
The examiner submits that Wang discloses the use of look-up tables (LUT) in the form of correlation equations of impedance as a function of SOC and internal temperature of the battery (see [0078]), which under the broadest reasonable interpretation teaches multivariable regression (see also [0105] and [0107]-[0108] regarding using regression during the analysis) while also describing curves (see Figs. 6 and 8-11) that can be represented by polynomial equations due to their non-linear behavior (see MPEP 2125 regarding “When the reference is a utility patent, it does not matter that the feature shown is unintended or unexplained in the specification. The drawings must be evaluated for what they reasonably disclose and suggest to one of ordinary skill in the art”).
Furthermore, the examiner submits that “Prior art is not limited just to the references being applied, but includes the understanding of one of ordinary skill in the art. The prior art reference (or references when combined) need not teach or suggest all the claim limitations, however, Office personnel must explain why the difference(s) between the prior art and the claimed invention would have been obvious to one of ordinary skill in the art. The “mere existence of differences between the prior art and an invention does not establish the invention’s nonobviousness.” Dann v. Johnston, 425 U.S. 219, 230, 189 USPQ 257, 261 (1976). The gap between the prior art and the claimed invention may not be “so great as to render the [claim] nonobvious to one reasonably skilled in the art.” Id. In determining obviousness, neither the particular motivation to make the claimed invention nor the problem the inventor is solving controls. The proper analysis is whether the claimed invention would have been obvious to one of ordinary skill in the art after consideration of all the facts. See 35 U.S.C. 103 or pre-AIA 35 U.S.C. 103(a). Factors other than the disclosures of the cited prior art may provide a basis for concluding that it would have been obvious to one of ordinary skill in the art to bridge the gap” (MPEP 2141, section III).
Based on this, the examiner submits that the prior art of record renders obvious the claimed invention.
Moreover, applicant argues (p. 10) that Wang does not disclose “inputting the multiple terminal impedance measurements into the selected one of the plurality of battery models as a plurality of inputs into the respective multivariable polynomial regression models" and "receiving an internal temperature estimate of the battery as an output of the selected one of the plurality of battery models based on inputting the multiple terminal impedance measurements into the selected one of the plurality of battery models as an output of the respective multivariable polynomial regression model.”
These arguments are not persuasive.
The examiner submits that the rejection relies on the teachings of Biletska for these features, therefore, in response to applicant’s arguments, the examiner submits that one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
The examiner also submits that applicant argues the rejection of similar features and other limitations of claims 13-20 (see p. 11-12). Regarding the similar features, the examiner maintains, as indicated above, that the prior art of record renders the claimed invention obvious. Regarding the arguments about additional features, the examiner submits that applicant’s arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/18/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 1 is objected to because of the following informalities:
Claim language “inputting the multiple terminal impedance measurements into the selected one of the plurality of battery models as a plurality of inputs into the respective multivariable polynomial regression models” should read “inputting the multiple terminal impedance measurements into the selected one of the plurality of battery models as a plurality of inputs into [[the]]a respective multivariable polynomial regression model[[s]]” in order to provide appropriate antecedence basis and clarify the recited subject matter in light of the specification details (see [0046]-[0047] describing that the selected model receives the inputs) for compliance under 35 U.S.C. 112.
Claim language “receiving an internal temperature estimate of the battery as an output of the selected one of the plurality of battery models based on inputting the multiple terminal impedance measurements into the selected one of the plurality of battery models as an output of the respective multivariable polynomial regression model” should read “receiving an internal temperature estimate of the battery as an output of the selected one of the plurality of battery models based on inputting the multiple terminal impedance measurements into the selected one of the plurality of battery models, the internal temperature estimate of the battery corresponding to [[as]] an output of the respective multivariable polynomial regression model” in order to clarify the recited subject matter for compliance under 35 U.S.C. 112.
Appropriate correction is required.
Claim 3 is objected to because of the following informalities:
Claim language should read “The method of claim [[2]]1, further comprising determining model parameters for the selected one of the plurality of battery models” in order to provide appropriate dependency for compliance under 35 U.S.C. 112 (see Claim Rejections - 35 USC § 112 section).
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 2-6 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Claim 2 recites “The method of claim 1, wherein each of the plurality of battery models comprises a multivariable polynomial regression model” which is already incorporated in claim 1. Claims 3-6 are also rejected under the same statute since they depend from base claim 2.
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Examiner’s Note
Claims 1-20 were evaluated for patent eligibility under 35 U.S.C. 101 using the SUBJECT MATTER ELIGIBILITY TEST FOR PRODUCTS AND PROCESSES described in the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence (see also 2019 Revised Patent Subject Matter Eligibility Guidance) to determine patent eligibility under 35 U.S.C. 101.
Regarding claim 1, the examiner submits that under Step 1 of the test for evaluating claims for eligibility under 35 U.S.C. 101, the claim is to a process, which is one of the statutory categories of invention.
Continuing with the analysis, under Step 2A - Prong One of the test:
the limitation “automatically selecting one of a plurality of battery models using a value of a parameter of the battery, wherein each of the plurality of battery models comprises a multivariable polynomial regression model, wherein each of the plurality of battery models has been trained and corresponds to a different range of values for the parameter, and wherein the value of the parameter of the battery falls within a range of values for the parameter corresponding to the selected one of the plurality of battery models, wherein each of the plurality of battery models is configured to generate respective output temperature estimate based on terminal impedance measurements inputs” is a process that, under its broadest reasonable interpretation in light of the specification, covers performance of the limitation using mental processes as well as mathematical concepts (e.g., selecting a multivariable polynomial regression model based on a parameter value for performing further calculations, see specification at [0027], [0031]-[0032], [0039]-[0041], [0046]-[0048]). Except for the recitation of the extra-solution activities (i.e., source/type of data being evaluated), and/or the field of use, the limitation in the context of this claim mainly refers to performing mental evaluations and/or applying mathematical concepts to select models based on information for further calculations.
Therefore, the claim recites a judicial exception under Step 2A - Prong One of the test.
Furthermore, under Step 2A - Prong Two of the test, the claim recites:
“A method for estimating an internal temperature of a battery” which generally links the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h));
“obtaining multiple terminal impedance measurements for the battery in response to a plurality of periodic signals injected into the battery, wherein each of the plurality of periodic signals have a different frequency of a plurality of frequencies, wherein each of the multiple terminal impedance measurements is obtained at a respective different one of the plurality of frequencies” which adds extra-solution activities (e.g., mere data gathering, source/type of data to be manipulated) (see MPEP 2106.05(g));
“inputting the multiple terminal impedance measurements into the selected one of the plurality of battery models as a plurality of inputs into the respective multivariable polynomial regression models” which also adds extra-solution activities (e.g., mere data inputting into models) (see MPEP 2106.05(g)); and
“receiving an internal temperature estimate of the battery as an output of the selected one of the plurality of battery models based on the inputting the multiple terminal impedance measurements into the selected one of the plurality of battery models as an output of the respective multivariable polynomial regression model” which, when considering the claim as a whole, integrates the judicial exception into a practical application by reflecting an improvement to other technology or technical field (e.g., estimating the internal temperature of a battery) (see MPEP 2106.05(a)).
Therefore, these additional elements, when considered individually and in combination, integrate the judicial exception into a practical application when viewing the claim as a whole. The claim is eligible at Prong Two of the Revised Step 2A (see 2019 Revised Patent Subject Matter Eligibility Guidance – Revised Step 2A, see also MPEP 2106.04(d)).
Similarly, independent claims 13 and 16 are directed to patent eligible subject matter as explained above with regards to claim 1.
Regarding the dependent claims 2-12, 14-15 and 17-20, they were found to be patent eligible under 35 U.S.C. 101 by incorporating the eligible subject matter of their corresponding independent claims.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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.
Claims 1-6, 8-9, 13 and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Wang (US 20140372055 A1), hereinafter ‘Wang’ in view of Biletska (US 20160202324 A1), hereinafter ‘Biletska’.
Regarding claim 1.
Wang discloses:
A method for estimating an internal temperature of a battery (Fig. 1; [0011], [0017], [0068]: a method for estimating the internal temperature of a battery is presented), the method comprising:
obtaining multiple terminal impedance measurements for the battery (Fig. 1; [0077]-[0078]: impedance spectrum is obtained by applying time-varying electrical excitation to the battery and measuring the time-varying electrical response at the battery terminals (see also [0068])) in response to a plurality of periodic signals injected into the battery ([0085]-[0089], [0093]-[0094]: the battery is excited using multiple driving profiles including double-pulse sequences (see Figs. 12 and 15, [0174]) for performing impedance measurements), wherein each of the plurality of periodic signals have a different frequency of a plurality of frequencies, wherein each of the multiple terminal impedance measurements is obtained at a respective different one of the plurality of frequencies ([0075], [0096]: the impedance spectrum is calculated at one or more frequencies (e.g., using electrochemical impedance spectroscopy – EIS, see [0168]), which are implied to correspond to the frequencies of the driving profile (as evidenced by Champlin - US 6137269 A, see col. 2, lines 37-55; and Champlin - US 6294897 B1, see col. 2, lines 41-59));
automatically selecting one of a plurality of battery models using a value of a parameter of the battery (Fig. 1; [0077]-[0078]: SOC information (value of a parameter of the battery) is also obtained from the electrical excitations and used in a look-up table (LUT) including relationships (such as graphs or equations - plurality of battery models) of impedance as a function of SOC and internal temperature of the battery, in order to estimate the internal temperature value (see also [0098]; see [0122]-[0128] regarding the method being performed by a computer, which implies that the selection of the relationship in the LUT is automatic)), wherein each of the plurality of battery models has been trained and corresponds to a different value for the parameter ([0160]-[0163], [0167]-[0172]: the LUT is built based on measuring impedances at different SOCs (see also Figs. 6 and 8-11 in which different graphs and curves are shown for corresponding battery information, which implies the use of a plurality of battery models)), and wherein the value of the parameter of the battery falls within a value for the parameter corresponding to the selected one of the plurality of battery models (Fig. 1; [0078]: internal temperature of the battery is estimated using the look-up table corresponding to the obtained SOC information (see also [0074]-[0075])), wherein each of the plurality of battery models is configured to generate respective output temperature estimate based on terminal impedance measurements inputs ([0074]: using the LUT table information, temperature information can be extracted (generated) from impedance data).
Wang does not explicitly disclose:
each of the plurality of battery models comprises a multivariable polynomial regression model;
each of the battery models corresponds to a different range of values for the parameter;
the value of the parameter of the battery falls within the range of values for the parameter corresponding to the selected one of the plurality of battery models;
inputting the multiple terminal impedance measurements into the selected one of the plurality of battery models as a plurality of inputs into the respective multivariable polynomial regression models; and
receiving an internal temperature estimate of the battery as an output of the selected one of the plurality of battery models based on inputting the multiple terminal impedance measurements into the selected one of the plurality of battery models as an output of the respective multivariable polynomial regression model.
Regarding “each of the plurality of battery models comprises a multivariable polynomial regression model”, Wang teaches:
“The look-up table may be in the form of an actual table, a graph or series of graphs, a correlation equation, or other means to capture an empirically determined correlation” ([0078]: the LUT includes correlation equations of impedance as a function of SOC and internal temperature of the battery (multivariable regression, see also [0105] and [0107]-[0108] regarding using regression during the analysis; see also Figs. 6 and 8-11 which describe curves that can be represented by polynomial equations due to their non-linear behavior)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang to incorporate each of the plurality of battery models comprising a multivariable polynomial regression model, in order to implement more accurate models representing real battery conditions (e.g., non-linear conditions).
Regarding “each of the battery models corresponds to a different range of values for the parameter; the value of the parameter of the battery falls within the range of values for the parameter corresponding to the selected one of the plurality of battery models; inputting the multiple terminal impedance measurements into the selected one of the plurality of battery models as a plurality of inputs into the respective multivariable polynomial regression models; and receiving an internal temperature estimate of the battery as an output of the selected one of the plurality of battery models based on inputting the multiple terminal impedance measurements into the selected one of the plurality of battery models as an output of the respective multivariable polynomial regression model”, Biletska teaches:
“A subject of the invention is therefore a method for estimating the state of charge of a battery comprising the following steps: a) acquiring at least one time series of measurements of voltage across the terminals of said battery, as well as at least one other time series of measurements of another physical parameter of said battery or of its environment; b) determining, as a function of said measurements, an operating regime of said battery; c) choosing, as a function of said operating regime, a non-linear regression model from among a predefined set of such models; and d) estimating the state of charge of said battery by “direct” application of said nonlinear regression model to said time series of voltage measurements and to said or to at least one said other time series of measurements ([0024]-[0028]: SOC (analogous to internal temperature estimate of a battery) is estimated by measuring voltage across the terminal (analogous to terminal impedance measurements), using measurements such as mean voltage and mean current to determine an operating regime of the battery (see [0066], analogous to value of the parameter of the battery), the operating regime being used to select a non-linear regression model (analogous to the value of the parameter of the battery falls within the range of values for the parameter corresponding to the selected one of the plurality of battery models), and estimating the SOC based on the measurements being applied to the selected model (analogous to receiving an internal temperature estimate of the battery as an output of the selected one of the plurality of battery models based on inputting the multiple terminal impedance measurements into the selected one of the plurality of battery models as an output of the respective multivariable polynomial regression model) (see also [0012] regarding estimating SOC based on parameters such as internal temperature and impedance, which implies that internal temperature can equally be obtained from impedance and SOC; [0057] regarding memory storing coefficients defining several models; and [0062]-[0070] for additional details on the analysis; see also Wang at [0076] regarding temperature information being obtained based on impedance data applied to predetermined impedance-temperature relationships at a given SOC).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang in view of Biletska to incorporate each of the battery models corresponding to a different range of values (instead of a value) for the parameter; the value of the parameter of the battery falling within the range of values (instead of a value) for the parameter corresponding to the selected one of the plurality of battery models; to input the multiple terminal impedance measurements into the selected one of the plurality of battery models as a plurality of inputs into the respective multivariable polynomial regression models; and to receive an internal temperature estimate of the battery as an output of the selected one of the plurality of battery models based on inputting the multiple terminal impedance measurements into the selected one of the plurality of battery models as an output of the respective multivariable polynomial regression model, in order to obtain much more accurate estimation models that can be applied for analysis of different types of batteries, under different conditions, and during real-time applications, as discussed by Biletska ([0023], [0068]).
Regarding claim 2.
Wang in view of Biletska discloses all the features of claim 1 as described above.
Wang does not explicitly disclose:
each of the plurality of battery models comprises a multivariable polynomial regression model
However, Wang teaches:
“The look-up table may be in the form of an actual table, a graph or series of graphs, a correlation equation, or other means to capture an empirically determined correlation” ([0078]: the LUT includes correlation equations of impedance as a function of SOC and internal temperature of the battery (multivariable regression, see also [0105] and [0107]-[0108] regarding using regression during the analysis; see also Figs. 6 and 8-11 which describe curves that can be represented by polynomial equations due to their non-linear behavior)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang in view of Biletska to incorporate each of the plurality of battery models comprising a multivariable polynomial regression model, in order to implement more accurate models representing real battery conditions (e.g., non-linear conditions).
Regarding claim 3.
Wang in view of Biletska discloses all the features of claim 2 as described above.
Wang does not explicitly disclose:
determining model parameters for the selected one of the plurality of battery models.
However, Wang further teaches:
“Battery impedance spectrum Z(f) is calibrated as the function of state-of-charge SOC and temperature T. A look-up table (LUT) of Z vs. T at different SOC is then built with the calibration ... Finally a look-up table is constructed of Z vs. T at different SOC, i.e. Z(T, SOC). The flow chart of the calibration is shown in FIG. 3” ([0160]-[00163]: the LUT is built by performing measurements of battery impedance at different temperatures and SOCs (model parameters, see also [0105] and [0107]-[0108] regarding using regression during the analysis; see also Figs. 6 and 8-11 which show different curves for different battery conditions; see also Biletska at [0057] regarding memory storing coefficients defining several models)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang in view of Biletska to determine model parameters for the selected one of the plurality of battery models, in order to easily determine the temperature once the impedance and SOC are known, as discussed by Wang ([0167]).
Regarding claim 4.
Wang in view of Biletska discloses all the features of claim 3 as described above.
Wang does not explicitly disclose:
determining the model parameters comprises:
obtaining training data from a plurality of batteries; and
applying a linear least squares fit to the training data.
However, Wang further teaches:
“Battery impedance spectrum Z(f) is calibrated as the function of state-of-charge SOC and temperature T. A look-up table (LUT) of Z vs. T at different SOC is then built with the calibration ... Finally a look-up table is constructed of Z vs. T at different SOC, i.e. Z(T, SOC). The flow chart of the calibration is shown in FIG. 3” ([0160]-[00163]: the LUT is built by performing measurements (training data) of battery impedance at different temperatures and SOCs (model parameters, see also [0105] and [0107]-[0108] regarding using regression during the analysis; see also Figs. 6 and 8-11 which show different curves for different battery conditions, with the curves indicating fitted sections between points; see also Biletska at [0068]-[0069], [0071], [0090]-[0093], [0095])).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang in view of Biletska to determine the model parameters by: obtaining training data from a plurality of batteries; and applying a linear least squares fit to the training data, in order to build robust and stable models reflecting actual measured conditions of different batteries during calibration for improving estimation accuracy.
Regarding claim 5.
Wang in view of Biletska discloses all the features of claim 4 as described above.
Wang further discloses:
the training data comprises alternating current (AC) impedance and temperature data ([0160]-[0163]: the LUT is built by performing measurements of battery impedance at different temperatures and SOCs (see also [0075], [0093] and [0168])).
Regarding claim 6.
Wang in view of Biletska discloses all the features of claim 5 as described above.
Wang further discloses:
calibrating the model parameters using at least one calibration measurement associated with the battery ([0098]-[0100]: LUT is calibrated prior to using it using normal device operating conditions).
Regarding claim 8.
Wang in view of Biletska discloses all the features of claim 1 as described above.
Wang further discloses:
the plurality frequencies are selected in order to cancel out at least one of state-of-charge (SOC) dependencies and state-of-health (SOH) dependencies ([0074]: impedance spectrum is dependent on SOC, therefore, by accounting for the effects of SOC, a more accurate temperature sensing capability is achieved).
Regarding claim 9.
Wang in view of Biletska discloses all the features of claim 1 as described above.
Wang further discloses:
the parameter comprises at least one of a state-of-health (SOH) and a state-of-charge (SOC) ([0077]-[0078]: SOC information is also obtained from the electrical excitations and used in a look-up table (LUT) to estimate the internal temperature value (see also [0098])).
Regarding claim 13.
Wang discloses:
A method for estimating an internal temperature of a battery under test (BUT) from terminal impedance measurements of the BUT (Fig. 1; [0011], [0017], [0068]: a method for estimating the internal temperature of a battery from impedance data is presented), the method comprising:
obtaining multiple terminal impedance measurements for the BUT (Fig. 1; [0077]-[0078]: impedance spectrum is obtained by applying time-varying electrical excitation to the battery and measuring the time-varying electrical response at the battery terminals (see also [0068])) in response to a plurality of periodic signals injected into the BUT ([0085]-[0089], [0093]-[0094]: the battery is excited using multiple driving profiles including double-pulse sequences (see Figs. 12 and 15) for performing impedance measurements), wherein each of the plurality of periodic signals have a different frequency of a plurality of frequencies, wherein each of the multiple terminal impedance measurements is obtained at a respective different one of the plurality of frequencies ([0075], [0096]: the impedance spectrum is calculated at one or more frequencies (e.g., using electrochemical impedance spectroscopy – EIS, see [0168]), which are implied to correspond to the frequencies of the driving profile (as evidenced by Champlin - US 6137269 A, see col. 2, lines 37-55; and Champlin - US 6294897 B1, see col. 2, lines 41-59)); and
automatically selecting one of a plurality of models using a value of a parameter of the BUT (Fig. 1; [0077]-[0078]: SOC information (parameter of the battery) is also obtained from the electrical excitations and used in a look-up table (LUT) including relationships (such as graphs or equations - plurality of battery models) of impedance as a function of SOC and internal temperature of the battery, in order to estimate the internal temperature value (see also [0098]; see [0122]-[0128] regarding the method being performed by a computer, which implies that the selection of the relationship in the LUT is automatic)), wherein each of the plurality of models corresponds to a different value for the parameter ([0160]-[0163], [0167]-[0172]: the LUT is built based on measuring impedances at different SOCs (see also Figs. 6 and 8-11 in which different graphs and curves are shown for corresponding battery information, which implies the use of a plurality of models)) and wherein the value of the parameter of the BUT falls within a value for the parameter corresponding to the selected one of the plurality of models (Fig. 1; [0078]: internal temperature of the battery is estimated using the look-up table corresponding to the obtained SOC information (see also [0074]-[0075])).
Wang does not explicitly disclose:
the plurality of models are a plurality of multivariable polynomial regression models;
each of the plurality of multivariable polynomial regression models corresponds to a different range of values for the parameter;
the value of the parameter of the BUT falls within a range of values for the parameter corresponding to the selected one of the plurality of multivariable polynomial regression models;
deriving model parameters for the selected one of the plurality of multivariable polynomial regression models, the deriving comprising:
obtaining training data from a set of training batteries; and
applying a linear least squares fit to the training data;
combining the multiple terminal impedance measurements to input the multiple terminal impedance measurement into the selected one of the plurality of multivariate polynomial regression models; and
receiving an estimate of the internal temperature of the BUT as an output of the selected one the plurality of multivariable polynomial regression models based on inputting the multiple terminal impedance measurements into the selected one of the plurality of multivariable polynomial regression models.
Regarding “the plurality of models are a plurality of multivariable polynomial regression models”, Wang teaches:
“The look-up table may be in the form of an actual table, a graph or series of graphs, a correlation equation, or other means to capture an empirically determined correlation” ([0078]: the LUT includes correlation equations of impedance as a function of SOC and internal temperature of the battery (multivariable regression, see also [0105] and [0107]-[0108] regarding using regression during the analysis; see also Figs. 6 and 8-11 which describe curves that can be represented by polynomial equations due to their non-linear behavior)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang to incorporate the plurality of models as a plurality of multivariable polynomial regression models, in order to implement more accurate models representing real battery conditions (e.g., non-linear conditions).
Regarding “each of the plurality of multivariable polynomial regression models corresponds to a different range of values for the parameter; the value of the parameter of the BUT falls within a range of values for the parameter corresponding to the selected one of the plurality of multivariable polynomial regression models; combining the multiple terminal impedance measurements to input the multiple terminal impedance measurement into the selected one of the plurality of multivariate polynomial regression models; and receiving an estimate of the internal temperature of the BUT as an output of the selected one the plurality of multivariable polynomial regression models based on inputting the multiple terminal impedance measurements into the selected one of the plurality of multivariable polynomial regression models”, Biletska teaches:
“A subject of the invention is therefore a method for estimating the state of charge of a battery comprising the following steps: a) acquiring at least one time series of measurements of voltage across the terminals of said battery, as well as at least one other time series of measurements of another physical parameter of said battery or of its environment; b) determining, as a function of said measurements, an operating regime of said battery; c) choosing, as a function of said operating regime, a non-linear regression model from among a predefined set of such models; and d) estimating the state of charge of said battery by “direct” application of said nonlinear regression model to said time series of voltage measurements and to said or to at least one said other time series of measurements ([0024]-[0028]: SOC (analogous to estimate of the internal temperature of the BTU) is estimated by measuring voltage across the terminal (analogous to multiple terminal impedance measurements), determining an operating regime of the battery (analogous to value of the parameter of the BTU) based on ranges of SOC (see also [0066]), the operating regime being used to select a model (analogous to the value of the parameter of the BUT falls within a range of values for the parameter corresponding to the selected one of the plurality of multivariable polynomial regression models), and estimating the SOC based on the measurements being applied to the selected model (analogous to combining the multiple terminal impedance measurements to input the multiple terminal impedance measurement into the selected one of the plurality of multivariate polynomial regression models, and receiving an estimate of the internal temperature of the BUT as an output of the selected one the multivariable polynomial regression models based on inputting the multiple terminal impedance measurements into the selected one of the multivariable polynomial regression model) (see also [0012] regarding estimating SOC based on parameters such as internal temperature and impedance, which implies that internal temperature can equally be obtained from impedance and SOC; [0057] regarding memory storing coefficients defining several models; and [0062]-[0070] for additional details on the analysis; see also Wang at [0076] regarding temperature information being obtained based on impedance data applied to predetermined impedance-temperature relationships at a given SOC).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang in view of Biletska to incorporate each of the plurality of multivariable polynomial regression models corresponding to a different range of values (instead of a value) for the parameter; the value of the parameter of the BTU falling within a range of values (instead of a value) for the parameter corresponding to the selected one of the plurality of multivariable polynomial regression models; to combine the multiple terminal impedance measurements to input the multiple terminal impedance measurements into the selected one of the plurality of multivariable polynomial regression models; and to receive an estimate of the internal temperature of the BTU as an output of the selected one of the plurality of multivariable polynomial regression models based on inputting the multiple terminal impedance measurements into the selected one of the plurality of multivariable polynomial regression models, in order to obtain much more accurate estimation models that can be applied for analysis of different types of batteries, under different conditions, and during real-time applications, as discussed by Biletska ([0023], [0068]).
Regarding “deriving model parameters for the selected one of the plurality of multivariable polynomial regression models, the deriving comprising: obtaining training data from a set of training batteries; and applying a linear least squares fit to the training data”, Wang further teaches:
“Battery impedance spectrum Z(f) is calibrated as the function of state-of-charge SOC and temperature T. A look-up table (LUT) of Z vs. T at different SOC is then built with the calibration ... Finally a look-up table is constructed of Z vs. T at different SOC, i.e. Z(T, SOC). The flow chart of the calibration is shown in FIG. 3” ([0160]-[00163]: the LUT is built by performing measurements (training data) of battery impedance at different temperatures and SOCs (model parameters, see also [0105] and [0107]-[0108] regarding using regression during the analysis; see also Figs. 6 and 8-11 which show different curves for different battery conditions, with the curves indicating fitted sections between points)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang in view of Biletska to derive model parameters for the selected one of the plurality of multivariable polynomial regression models, the deriving comprising: obtaining training data from a set of training batteries; and applying a linear least squares fit to the training data, in order to build robust and stable models reflecting actual measured conditions of different batteries during calibration for improving estimation accuracy.
Regarding claim 16.
Wang discloses:
A system for estimating an internal temperature of a battery from a plurality of terminal impedance measurements obtained for the battery (Fig. 1; [0026], [0039], [0074]: a method for estimating the internal temperature of a battery from impedance data is presented), wherein the plurality of terminal impedance measurements are taken (Fig. 1; [0077]-[0078]: impedance spectrum is obtained by applying time-varying electrical excitation to the battery and measuring the time-varying electrical response at the battery terminals (see also [0068])) in response to a plurality of periodic signals injected into the battery ([0085]-[0089], [0093]-[0094]: the battery is excited using multiple driving profiles including double-pulse sequences (see Figs. 12 and 15) for performing impedance measurements), wherein each of the plurality of periodic signals have a different frequency of a plurality of frequencies, wherein each of the plurality of terminal impedance measurements is obtained at a respective different one of the plurality of frequencies ([0075], [0096]: the impedance spectrum is calculated at one or more frequencies (e.g., using electrochemical impedance spectroscopy – EIS, see [0168]), which are implied to correspond to the frequencies of the driving profile (as evidenced by Champlin - US 6137269 A, see col. 2, lines 37-55; and Champlin - US 6294897 B1, see col. 2, lines 41-59)), the system comprising:
N models, wherein N is greater than one ([0078], [0098]: a look-up table (LUT) including relationships (such as graphs or equations – N models) of impedance as a function of SOC and internal temperature of the battery are used to estimate internal temperature of the battery (see also Figs. 6 and 8-11 in which different graphs and curves are shown for corresponding battery information, which implies the use of N models));
circuitry for automatically selecting one of the N models using a value of a parameter of the battery (Fig. 1; [0077]-[0078]: SOC information (parameter of the battery) is also obtained from the electrical excitations and used in the look-up table (LUT) in order to estimate the internal temperature value (see also [0098]; see [0122]-[0128] regarding the step being performed by a computer, which implies that the selection of the relationship in the LUT is automatic)), wherein each of the N models has been trained and corresponds to a different value for the parameter ([0160]-[0163], [0167]-[0172]: the LUT is built based on measuring impedances at different SOCs (see also Figs. 6 and 8-11)) and wherein the value of the parameter of the battery falls within a value for the parameter corresponding to the selected one of the N models (Fig. 1; [0078]: internal temperature of the battery is estimated using the look-up table corresponding to the obtained SOC information (see also [0074]-[0075]));
wherein the selected one of the N models is configured to combine the plurality of terminal impedance measurements (Fig. 1; [0078]: internal temperature of the battery is estimated using the look-up table (see also [0085]-[0091])).
Wang does not explicitly disclose:
the N models are N polynomial regression models;
each of the N polynomial regression models corresponds to a different range of values for the parameter and wherein the value of the parameter of the battery falls within a range of values for the parameter corresponding to the selected one of the N polynomial regression models;
wherein the selected one of the N polynomial regression models is configured to receive the plurality of terminal impedance measurements as an input and generates an estimate of the internal temperature of the battery as an output of the selected one of the N polynomial regression models based on inputting the plurality of terminal impedance measurements into the selected one of the N polynomial regression models.
Regarding “the N models are N polynomial regression models”, Wang teaches:
“The look-up table may be in the form of an actual table, a graph or series of graphs, a correlation equation, or other means to capture an empirically determined correlation” ([0078]: the LUT includes correlation equations of impedance as a function of SOC and internal temperature of the battery (multivariable regression, see also [0105] and [0107]-[0108] regarding using regression during the analysis; see also Figs. 6 and 8-11 which describe curves that can be represented by polynomial equations due to their non-linear behavior)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang to incorporate the N models as N polynomial regression models, in order to implement more accurate models representing real battery conditions (e.g., non-linear conditions).
Regarding “each of the N polynomial regression models corresponds to a different range of values for the parameter and wherein the value of the parameter of the battery falls within a range of values for the parameter corresponding to the selected one of the N polynomial regression models; wherein the selected one of the N polynomial regression models is configured to receive the plurality of terminal impedance measurements as an input and generates an estimate of the internal temperature of the battery as an output of the selected one of the N polynomial regression models based on inputting the plurality of terminal impedance measurements into the selected one of the N polynomial regression models”, Biletska teaches:
“A subject of the invention is therefore a method for estimating the state of charge of a battery comprising the following steps: a) acquiring at least one time series of measurements of voltage across the terminals of said battery, as well as at least one other time series of measurements of another physical parameter of said battery or of its environment; b) determining, as a function of said measurements, an operating regime of said battery; c) choosing, as a function of said operating regime, a non-linear regression model from among a predefined set of such models; and d) estimating the state of charge of said battery by “direct” application of said nonlinear regression model to said time series of voltage measurements and to said or to at least one said other time series of measurements ([0024]-[0028]: SOC (analogous to estimate of the internal temperature of the battery) is estimated by measuring voltage across the terminal (analogous to plurality of terminal impedance measurements), determining an operating regime of the battery (analogous to value of the parameter of the battery) based on ranges of SOC (see also [0066]), the operating regime being used to select a model (analogous to the value of the parameter of the battery falling within a range of values for the parameter corresponding to the selected one of the N polynomial regression models), and estimating the SOC based on the measurements being applied to the selected model (analogous to the selected one of the N polynomial regression models is configured to receive the plurality of terminal impedance measurements as an input and generates an estimate of the internal temperature of the battery as an output of the selected one of the N polynomial regression models based on the inputting the plurality of terminal impedance measurements into the selected one of the N polynomial regression models) (see also [0012] regarding estimating SOC based on parameters such as internal temperature and impedance, which implies that internal temperature can equally be obtained from impedance and SOC; [0057] regarding memory storing coefficients defining several models; and [0062]-[0070] for additional details on the analysis; see also Wang at [0076] regarding temperature information being obtained based on impedance data applied to predetermined impedance-temperature relationships at a given SOC).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang in view of Biletska to incorporate each of the N polynomial regression models corresponding to a different range of values (instead of a value) for the parameter, wherein the value of the parameter of the battery falls within a range of values (instead of a value) for the parameter corresponding to the selected one of the N polynomial regression models; and wherein the selected one of the N polynomial regression models is configured to receive the plurality of terminal impedance measurements as an input and generates an estimate of the internal temperature of the battery as an output of the selected one of the N polynomial regression models based on inputting the plurality of terminal impedance measurements into the selected one of the N polynomial regression models, in order to obtain much more accurate estimation models that can be applied for analysis of different types of batteries, under different conditions, and during real-time applications, as discussed by Biletska ([0023], [0068]).
Regarding claim 17.
Wang in view of Biletska discloses all the features of claim 16 as described above.
Wang does not explicitly disclose:
the circuitry comprises a demultiplexer (DEMUX) having an input connected to receive the plurality of terminal impedance measurements and N outputs connected to inputs of the N polynomial regression models.
However, Wang further teaches:
“The look-up table may be in the form of an actual table, a graph or series of graphs, a correlation equation, or other means to capture an empirically determined correlation. The correlation is of impedance as a function of temperature and SOC, so that at a given impedance and SOC, temperature can be estimated” ([0078]: LUT information on temperature is selected based on impedance and SOC data (see [0146] regarding computer system including circuitry such as a processor for performing the method); examiner interprets that a processor performs similar functions as a demultiplexer by receiving the impedance measurements and selecting the corresponding LUT table based on the SOC value).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang in view of Biletska to incorporate the circuitry comprising a demultiplexer (DEMUX) having an input connected to receive the plurality of terminal impedance measurements and N outputs connected to inputs of the N polynomial regression models, in order to alleviate the computational cost on the processor while also increasing the communication efficiency.
Regarding claim 18.
Wang in view of Biletska discloses all the features of claim 17 as described above.
Wang does not explicitly disclose:
a SELECT input of the DEMUX is connected to receive a signal corresponding to the value of the parameter.
However, Wang further teaches:
“The look-up table may be in the form of an actual table, a graph or series of graphs, a correlation equation, or other means to capture an empirically determined correlation. The correlation is of impedance as a function of temperature and SOC, so that at a given impedance and SOC, temperature can be estimated” ([0078]: LUT information on temperature is selected based on impedance and SOC data (analogous to SELECT input) (see [0146] regarding computer system including circuitry such as a processor for performing the method); examiner interprets that a processor performs similar functions as a demultiplexer by receiving the impedance measurements and selecting the corresponding LUT table based on the SOC value).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang in view of Biletska to incorporate a SELECT input of the DEMUX connected to receive a signal corresponding to the value of the parameter, in order to alleviate the computational cost on the processor while also increasing the communication efficiency.
Regarding claim 19.
Wang in view of Biletska discloses all the features of claim 16 as described above.
Wang does not explicitly disclose:
the circuitry comprises a multiplexer (MUX) having N inputs connected to receive outputs of the N polynomial regression models and the output for outputting an estimate of the internal temperature of the battery.
However, Wang further teaches:
“The look-up table may be in the form of an actual table, a graph or series of graphs, a correlation equation, or other means to capture an empirically determined correlation. The correlation is of impedance as a function of temperature and SOC, so that at a given impedance and SOC, temperature can be estimated” ([0078]: LUT information on temperature is selected based on impedance and SOC data (analogous to N inputs) (see [0146] regarding computer system including circuitry such as a processor for performing the method); examiner interprets that a processor performs similar functions as a multiplexer by receiving the impedance measurements and selecting the corresponding LUT table based on the SOC value for estimating the internal temperature of the battery).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang in view of Biletska to incorporate the circuitry comprising a multiplexer (MUX) having N inputs connected to receive outputs of the N polynomial regression models and the output for outputting an estimate of the internal temperature of the battery, in order to alleviate the computational cost on the processor while also increasing the communication efficiency.
Regarding claim 20.
Wang in view of Biletska discloses all the features of claim 19 as described above.
Wang does not explicitly disclose:
a control input of the MUX is connected to receive a signal corresponding to the value of the parameter.
However, Wang further teaches:
“The look-up table may be in the form of an actual table, a graph or series of graphs, a correlation equation, or other means to capture an empirically determined correlation. The correlation is of impedance as a function of temperature and SOC, so that at a given impedance and SOC, temperature can be estimated” ([0078]: LUT information on temperature is selected based on impedance and SOC data (analogous to control input) (see [0146] regarding computer system including circuitry such as a processor for performing the method); examiner interprets that a processor performs similar functions as a multiplexer by receiving the impedance measurements and selecting the corresponding LUT table based on the SOC value for estimating the internal temperature of the battery).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang in view of Biletska to incorporate a control input of the MUX connected to receive a signal corresponding to the value of the parameter, in order to alleviate the computational cost on the processor while also increasing the communication efficiency.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Wang, in view of Biletska, and in further view of Wuebbeler (US 20200326380 A1), hereinafter ‘Wuebbeler’.
Regarding claim 7.
Wang in view of Biletska discloses all the features of claim 1 as described above.
Wang does not disclose:
the battery comprises a rechargeable battery.
Wuebbeler teaches
“It is therefore an object of the present invention to provide a method for determining an ageing parameter KSOH, a state of charge parameter KSOC of a rechargeable battery and a temperature T featuring the steps (a) detecting an impedance Z of the rechargeable battery at different frequencies f, so that a Nyquist plot and a real part curve, which plots a real part of the impedance Z against the frequency f, are obtained, (b) determining at least one of the following SOH parameters, as specified in claim 1, (c) establishing an SOH row vector from the SOH parameters and all mixed terms and (d) multiplying the row vectors by a saved calibration vector, thereby obtaining the ageing parameter KSOH” ([0011]: impedance measurements are used to determine state of charge and temperature of a rechargeable battery).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang, in view of Biletska, and in further view of Wuebbeler, to incorporate the battery comprising a rechargeable battery, in order to determine battery conditions in applications where rechargeable batteries are commonly used (e.g., electric vehicles) for diagnostics and maintenance purposes.
Claim 10-12 and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Wang, in view of Biletska, and in further view of Kozlowski (US 20030184307 A1), hereinafter ‘Kozlowski’.
Regarding claim 10.
Wang in view of Biletska discloses all the features of claim 1 as described above.
Wang does not disclose:
the parameter comprises multiple battery parameters.
Kozlowski teaches:
“Embodiments of the present invention provide a method for using measured information to determine the condition (including the health) of batteries, other electrochemical cells, and other systems where system properties such as electrical impedance can be correlated with the condition of the system, such as system health, lifetime, remaining life, charge, and the like … The condition and health of a battery can be defined by three categories of condition parameter: State-of-Charge (SOC), State-of-Health (SOH), and State-of-Life (SOL)” ([0017]: measurement information is used to determine condition of batteries, where impedance can be correlated with conditions such as SOC, SOH and SOL (see also [0034]-[0036] regarding measurements such as voltage, current and temperature being used in determining the condition of batteries; see also Wang (at [0071]-[0073]) regarding impulse responses being used to determine battery states such as SOH, SOC, SOP, etc., which provide better characterization of the battery); examiner submits that when additional parameters such as voltage, current and temperature can be applied together with impedance for improving estimations of SOH, SOC, SOP, similarly, any of these variables can be measured to improve the estimations of the internal temperature of the battery).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang, in view of Biletska, and in further view of Kozlowski, to incorporate the parameter comprising multiple battery parameters, in order to improve the estimates of the internal temperature of the battery based on additional information about the real-time state of the battery.
Regarding claim 11.
Wang in view of Biletska discloses all the features of claim 1 as described above.
Wang does not disclose:
augmenting an equation comprising at least one of the plurality of battery models by adding a function of another measurement of the battery to the equation.
Kozlowski teaches:
“Embodiments of the present invention provide a method for using measured information to determine the condition (including the health) of batteries, other electrochemical cells, and other systems where system properties such as electrical impedance can be correlated with the condition of the system, such as system health, lifetime, remaining life, charge, and the like … The condition and health of a battery can be defined by three categories of condition parameter: State-of-Charge (SOC), State-of-Health (SOH), and State-of-Life (SOL)” ([0017]: measurement information is used to determine condition of batteries, where impedance can be correlated with conditions such as SOC, SOH and SOL (see also [0034]-[0036] regarding measurements such as voltage, current and temperature being used in determining the condition of batteries; see also Wang (at [0071]-[0073]) regarding impulse responses being used to determine battery states such as SOH, SOC, SOP, etc., which provide better characterization of the battery); examiner submits that when additional information such as voltage, current and temperature can be applied together with impedance for improving estimations of SOH, SOC, SOP, similarly, any of these variables can be added (e.g., as a function of another measurement) to improve the estimations of the internal temperature of the battery).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang, in view of Biletska, and in further view of Kozlowski, to augment an equation comprising at least one of the plurality of battery models by adding a function of another measurement of the battery to the equation, in order to better characterize the battery conditions using additional relevant information.
Regarding claim 12.
Wang in view of Biletska discloses all the features of claim 1 as described above.
Wang does not disclose:
augmenting an equation comprising at least one of the plurality of battery models to include a memory term.
Kozlowski teaches:
“Embodiments of the present invention describe new methods for assessing the condition of batteries, by determination of condition parameters correlated with the condition. A method to accurately assess the state-of-charge (SOC), state-of-health (SOH), and state-of-life (SOL) of primary and secondary batteries can provide significant benefits in operational systems. This method is based on accurate modeling of the transport mechanisms within the battery and requires careful development of electrochemical and thermal models. A novel impedance technique was previously developed to take wideband impedance data from the battery being tested. A feature extraction algorithm was implemented to identify physically meaningful information from the impedance data. These extracted virtual sensor signals (i.e. electrochemical process parameters) are saved along with the impedance data and other measured signal data into a feature vector file. The feature vector file provides input data for prediction algorithms. Three-prong Auto Regressive Moving Average (ARMA), Neural Network, and Fuzzy Logic algorithms read this file to produce predictions of the SOC, SOH, and SOL. A decision fusion algorithm combines the predictions along with historical and system information to produce a more robust prediction and confidence level” ([0019]: predictions of SOC, SOH and SOL, which are obtained by using measurements of impedance and additional information (see [0017], [0034]-[0036]; see also Wang (at [0071]-[0073]) regarding impulse responses being used to determine battery states such as SOH, SOC, SOP, etc. which provide better characterization of the battery), are combined with historical data in order to produce a more robust prediction and confidence level; examiner submits that when additional information such as voltage, current and temperature can be applied together with impedance for improving estimations of SOH, SOC, SOP, similarly, any of these variables can be measured to improve the estimations of the internal temperature of the battery).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang, in view of Biletska, and in further view of Kozlowski, to augment an equation comprising at least one of the plurality of battery models to include a memory term, in order to produce a more robust prediction and confidence level, as discussed by Kozlowski ([0019]).
Regarding claim 14.
Wang in view of Biletska discloses all the features of claim 13 as described above.
Wang further discloses:
calibrating the model parameters prior to the combining ([0098]-[0100]: LUT is calibrated prior to using it using normal device operating conditions).
Wang does not disclose:
the set of training batteries is comprised of individual batteries of a different type than the BUT.
Kozlowski teaches:
“Embodiments of the present invention describe new methods for assessing the condition of batteries, by determination of condition parameters correlated with the condition. A method to accurately assess the state-of-charge (SOC), state-of-health (SOH), and state-of-life (SOL) of primary and secondary batteries can provide significant benefits in operational systems. This method is based on accurate modeling of the transport mechanisms within the battery and requires careful development of electrochemical and thermal models. A novel impedance technique was previously developed to take wideband impedance data from the battery being tested. A feature extraction algorithm was implemented to identify physically meaningful information from the impedance data. These extracted virtual sensor signals (i.e. electrochemical process parameters) are saved along with the impedance data and other measured signal data into a feature vector file. The feature vector file provides input data for prediction algorithms. Three-prong Auto Regressive Moving Average (ARMA), Neural Network, and Fuzzy Logic algorithms read this file to produce predictions of the SOC, SOH, and SOL … The training of these algorithms can be achieved using data from lead-acid, nickel-cadmium, and lithium batteries as well as other types of various capacities, which can be run under different load, charging, and temperature conditions” ([0019]: predictions of SOC, SOH and SOL are obtained by using measurements of impedance, additional information and models (see also [0017], [0034]-[0036]; see also Wang (at [0071]-[0073]) regarding impulse responses being used to determine battery states such as SOH, SOC, SOP, etc. which provide better characterization of the battery), the models being trained with data from different types of batteries; examiner submits that when additional information such as voltage, current and temperature can be applied together with impedance for improving estimations of SOH, SOC, SOP, similarly, any of these variables can be measured to improve the estimations of the internal temperature of the battery).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang, in view of Biletska, and in further view of Kozlowski, to implement the set of training batteries comprising of individual batteries of a different type than the BUT, in order to improve diagnostic information about the battery under examination, as discussed by Kozlowski ([0019]).
Regarding claim 15.
Wang in view of Biletska discloses all the features of claim 13 as described above.
Wang does not disclose:
the set of training batteries is comprised of individual batteries that are different than the BUT, the method further comprising mapping the model parameters to a second set of model parameters corresponding to the battery under test prior to the combining.
Kozlowski teaches:
“Embodiments of the present invention describe new methods for assessing the condition of batteries, by determination of condition parameters correlated with the condition. A method to accurately assess the state-of-charge (SOC), state-of-health (SOH), and state-of-life (SOL) of primary and secondary batteries can provide significant benefits in operational systems. This method is based on accurate modeling of the transport mechanisms within the battery and requires careful development of electrochemical and thermal models. A novel impedance technique was previously developed to take wideband impedance data from the battery being tested. A feature extraction algorithm was implemented to identify physically meaningful information from the impedance data. These extracted virtual sensor signals (i.e. electrochemical process parameters) are saved along with the impedance data and other measured signal data into a feature vector file. The feature vector file provides input data for prediction algorithms. Three-prong Auto Regressive Moving Average (ARMA), Neural Network, and Fuzzy Logic algorithms read this file to produce predictions of the SOC, SOH, and SOL … The training of these algorithms can be achieved using data from lead-acid, nickel-cadmium, and lithium batteries as well as other types of various capacities, which can be run under different load, charging, and temperature conditions” ([0019]: predictions of SOC, SOH and SOL are obtained by using measurements of impedance, additional information and models (see also [0017], [0034]-[0036]; see also Wang (at [0071]-[0073]) regarding impulse responses being used to determine battery states such as SOH, SOC, SOP, etc. which provide better characterization of the battery), the models being trained with data from different types of batteries; examiner submits that when additional information such as voltage, current and temperature can be applied together with impedance for improving estimations of SOH, SOC, SOP, similarly, any of these variables can be measured and mapped to corresponding model parameters to improve the estimations of the internal temperature of the battery).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang, in view of Biletska, and in further view of Kozlowski, to incorporate the set of training batteries of individual batteries that are different than the BUT, and to map the model parameters to a second set of model parameters corresponding to the battery under test prior to the combining, in order to improve diagnostic information about the battery under examination, as discussed by Kozlowski ([0019]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
HEBIGUCHI; Hiroyuki, US 20160069963 A1, ELECTRICITY STORAGE DEVICE STATE INFERENCE METHOD
Reference discloses measuring internal impedance of a battery at a frequency at which the internal impedance does not change with temperature in order to infer SOC and SOH.
Applicant’s amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/LINA CORDERO/Primary Examiner, Art Unit 2857
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