DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Argument/Amendment
Argument and amendment filed on 09/15/2025 are considered.
Claim objection: Applicant’s discussion regarding the value of a variable K is persuasive, therefore claim 23 objection is withdrawn.
Claim interpretation: Regarding claim interpretation, applicant argues “claim limitations "data generation circuitry and estimation circuitry in claims 21, 24, 25" have been interpreted under 35 U.S.C. 112(f) because they use a generic placeholder coupled with functional language without reciting sufficient structure to achieve the functions.
Applicant respectfully submits that the use of the term "circuitry" denotes sufficient structure to avoid interpretation under 35 U.S.C. § 112(f). Specifically, a circuit has been held to not invoke 35 ULS.C. § 112) (see MPEP 2181, a portion of which is reproduced below).
Since the term "circuitry" has been found to not invoke 112), the limitations “data generation circuitry’ and “estimation circuitry” should not be interpreted under 35 U.S.C. § 112). Accordingly, reconsideration and withdrawal of these interpretations are respectfully requested.”
With careful reconsideration in view of the specification as filed, Examiner respectfully disagrees the above argument because the paragraph [0027] and Fig. 2 in the specification describes the data generation unit 5, estimation unit 6 are software units. The functions of these units are implemented by software, which is stored as program on the storage 401 and executed by the processor 400. Therefore, examiner views the claim interpretation in nonfinal office action is proper.
Rejection under 35 U.S.C 101: Applicant amendment and response regarding 35 U.S.C 101 is persuasive, therefore the rejection under 35 U.S.C 101 is withdrawn.
Rejection under 35 U.S.C 103: Applicant argues “Claims 21-30 were rejected under 35 U.S.C. 103 as being unpatentable over Takegami et al. (JP20191845814, herein “Takegami”) in view of Nakanishi (US 20110227587 Al), Wampler (US 20200182937 Al), Khasanov et al. US 20140356733 Al, herein “Khasanov”) and Chen et al. (CN 102263299 A, herein "Chen").
These rejections, insofar as they may pertain to the presently pending claims, are traversed.
While not conceding the appropriateness of the Examiner's rejections, but merely to advance prosecution of the instant application, Applicant submits that independent claim 21 is amended to recite the following combination of elements:
[a] storage battery internal state estimation device that estimates an internal state of a storage battery to diagnose deterioration and predict replacement timing of the storage battery, the storage battery internal state estimation device comprising: …
the storage battery internal state estimation device is configured to output the estimated internal state, including a deterioration parameter, based on the estimated high-frequency element and the estimated low-frequency element, to diagnose a deterioration state of the storage battery and predict a replacement timing for the storage battery
Independent claim 26 is amended in a similar manner with varying scope.
Applicant respectfully submits the prior art of record fails to teach or suggest the newly recited limitations of independent claims 21 and 26”.
With further search and consideration, Examiner respectfully disagrees the above arguments. The prior art rejections made in the non-final office actions are proper. Examiner views the prior art Wampler (US 20200182937 Al) teaches the amended limitations, please see claims rejection below. Therefore, the combination of the prior arts of record teaches all and amended limitations in the independent claims. The new dependent claims are also taught by the combinations of the applied prior arts, see below in prior art rejection section. Therefore, claims 21-38 are not in condition for allowance in view of the applied prior arts.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: data generation circuitry and estimation circuitry in claims 21, 24, 25.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
Although the specification does not explicitly recite the structure of the data generation circuitry and estimation circuitry, a person having ordinary skill in the art would understand that these circuitry as a processor or equivalent. The specification recites estimation unit and data generation unit for these respective circuitries.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claim(s) 21-38 is/are rejected under 35 U.S.C. 103 as being unpatentable over Takegami et al. (JP2019184581A) herein after “Takegami” in view of Nakanishi (US 20110227587 A1), Wampler (US 20200182937 A1), Khasanov et al. (US 20140356733 A1) herein after “Khasanov” and Chen et al. (CN 102263299 A) herein after “Chen”
Regarding Claim 21 Takegami teaches A storage battery internal state estimation device that estimates an internal state of a storage battery to diagnose deterioration and predict replacement timing of the storage battery, (para [0001] The present invention relates to a storage battery diagnostic device and a storage battery diagnostic method for diagnosing deterioration of a storage battery, para [0002] Therefore, in order to grasp the replacement time of the storage battery and predict the life of the storage battery, a deterioration diagnosis technique for the storage battery is required.), the storage battery internal state estimation device comprising:
a data generation circuitry to generate time-series data for estimation from time-series data of a current value and a voltage value acquired from the storage battery (para [0024] As shown in FIG. 5 First, in step S 102, the detection currents Ii of the storage battery 101-i are detected by the N current detection units 102-1, ... , 102-N. Next, in step S103, the detection voltage Vi of the storage battery 101-i is detected by the N voltage detection units 103-1, ... , 103-N. Next, in step S 104, the data storage unit 104 stores the time-series data of the storage battery 101-I using the values of the detection current Ii and the detection voltage Vi.);
an estimation circuitry to estimate a model function of the storage battery on the basis of the time-series data for estimation (para [0024] Next, in step S 105, the optimization unit 105 generates a storage battery model function of the storage battery 101-i based on the time series data of the data storage unit 104, and executes the estimation calculation by the optimization method.), wherein
the time-series data for estimation includes at least one of:
Z Dvj-th (j=1,..., N Dv ) order differential voltage curves, the number of which is “N Dv” that is an integer of one or more (para [0034] The voltage differential curve (nd > 1) is calculated and output to the time series data storage unit 201.);
the estimation circuitry estimates
estimates at least the low-frequency element function from the Z Ivj -th order integral voltage curve or the Z Iqj -th order integral capacity curve ([0035] In general, the differential operation or the difference operation amplifies the high frequency noise of the original signal. Therefore, in order to reduce the high frequency noise, a lowpass filter that performs sample averaging before and after the differential operation may be used. good. Alternatively, a more sophisticated filter, such as a Savitzky-Goray filter, may be used.).
As the instant application suggests integral is included in low-pass filter [in, para 0158], the prior art also uses low pass filter (i.e., viewed as an estimator) to estimate the low frequency element function (voltage, current) through the integrated voltage, current data. The present invention would have been modified using a low-pass filter to obtain low frequency element from the integral voltage curve.
Takegami does not clearly teach Z Ivj-th (j=1,..., N Iv ) order integral voltage curves, the number of which is “N Iv” that is an integer of one or more
Nakanishi teaches Z Ivj-th (j=1,..., N Iv ) order integral voltage curves, the number of which is “N Iv” that is an integer of one or more (para [0021] a Fourier transform unit configured to perform Fourier transform on current data and voltage data output from the DUT using a transformation window having a time width that is an integral multiple of a period of the first signal, while sequentially shifting a start time of the transformation window, thereby obtaining a plurality of Fourier transform data strings of the voltage data and the current data);
Herein examiner views Fourier transforms (i.e., as an estimator) involves integration to estimate order integral of plurality of voltage or current data (i.e., viewed as curves).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have incorporated Nakanishi into Takegami for the purpose of estimating an integral voltage curve by using a Fourier transform technique so that a better estimation of a battery internal condition can be obtained.
Takegami and Nakanishi does not clearly teach the model function is represented by a sum of element functions including a high-frequency element function and a low-frequency element function; and
at least the high-frequency element function from the Z Dvj -th order differential voltage curve or the Z Dqj -th order differential capacity curve.
Wampler teaches the model function is represented by a sum of element functions including a high-frequency element function and a low-frequency element function (para [0054] A summation node 95 then outputs an estimated voltage (Vest) that accounts for the low-frequency and high-frequency effects.,) and
at least the high-frequency element function from the Z Dvj -th order differential voltage curve or the Z Dqj -th order differential capacity curve ( para [0051]… An ordinary differential equation may be used to determine ⇐, e.g., depending on the sign of the battery current (I), as will be appreciated, with hysteresis modeled for both charging and discharging modes of operation.
para [0054]… A high-pass filter (HPF) 91 may also be used, with the output of the HPF 91 (i.e., time-lagged cell current (I)) likewise passing through one of the basis functions 92 and multiplied by a corresponding calibrated resistance 94).
As the instant application suggests differential is included in high-pass filter [in, para 0158], the prior art also uses high pass filter (i.e., viewed as an estimator) to estimate the high frequency element function (voltage, current) from the differential voltage, current data.
Wampler also teaches the storage battery internal state estimation device is configured to output the estimated internal state, including a deterioration parameter (para [0060] If a PET model is applied to both electrodes… Also, resistances change as the battery 13 ages. An ordinary differential equation (ODE) for R.sub.ij can be taken…), based on the estimated high-frequency element and the estimated low-frequency element (para [0053] As noted above, the sensors 16 of FIG. 1 may periodically provide the actual cell voltage, current, and temperature readings to the controller 50 of FIG. 1. The controller 50 predicts the internal state of the battery cell 14 given the time-history of such measured values, with the high-frequency empirical model 55 using a bank of low-pass filters (LPF) 90, i.e., LPF1, LPF2, LPFN, with fixed time constants spread over a time scale of interest.), to diagnose a deterioration state of the storage battery and predict the replacement timing for the storage battery (para [0068] Optional control actions shown in FIG. 3 also include generation of a numeric state of health (SOH) indicative of the present health or remaining useful life of the battery 13. For instance, when the SOH is indicative of a degraded battery 13, the controller 50 may record a diagnostic code triggering replacement of the battery 13).
In above paragraphs examiner views the controller 50 (i.e., a battery internal state determination device, see Fig. 1 and 5) outputs an estimated internal state of a battery that includes the determination of resistance rate of change (i.e., in PET model) using an estimated high-frequency element and the estimated low-frequency element (i.e., in EMP model) to diagnose a deterioration state (i.e., from SOH) of the battery and predict the replacement timing for the storage battery.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have incorporated Wampler into Takegami for the purpose of estimating a higher frequency component using a high-pass filter and determining a sum of high frequency and low frequency element function. Therefore, an accuracy of determining the internal state of the battery can be improved using the high frequency and low frequency signal element information of the battery and battery replacement prediction can be accurately determined.
Takegami, Nakanishi and Wampler do not teach
Z Dqj-th (j= 1,..., N Dq ) order differential capacity curves, the number of which is “N Dq” that is an integer of one or more; and
Z Iqj-th (j=1..... N Iq ) order integral capacity curves, the number of which is “N Iq” that is an integer of one or more,
Khasanov teaches Z Dqj-th (j= 1,..., N Dq ) order differential capacity curves, the number of which is “N Dq” that is an integer of one or more ([0030] FIG. 5 is a differential capacity curve of the lithium batteries manufactured in Examples 19 and 20, and Comparative Example 8;)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have incorporated Khasanov into Takegami for the purpose of estimating differential capacity curve of a battery so that an electrochemical behavior of the battery during charging and discharging can be accurately determined.
Chen teaches Z Iqj-th (j=1..... N Iq ) order integral capacity curves, the number of which is “N Iq” that is an integer of one or more (para [0005] as shown in FIG. 1. FIG. 1 is a discharge characteristic curve diagram of a conventional hybrid battery device…. curve c is a lithium polymer battery pack and type 18650 lithium battery pack average capacity curve, namely the integral capacity curve of the conventional hybrid battery device.)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have incorporated Chen into Takegami for the purpose of estimating integral capacity curve of a battery so that a state of health of the battery can be accurately determined.
Regarding claim 22, the combination of Takegami, Nakanishi, Wampler, Kasanoy and Chen teach the storage battery internal state estimation device according to claim 21, Wampler wherein at least one of the high-frequency element function includes a position µ and a scale σ as parameters (para [0054]… A high-pass filter (HPF) 91 may also be used, with the output of the HPF 91 (i.e., time-lagged cell current (I)) likewise passing through one of the basis functions 92), and
at least one of the low-frequency element function includes a position µ and a scale σ as parameters (Takegami teaches -para [0004] The dV / dQ curve analysis method utilizes the fact that the voltage of the storage battery is expressed by the synthesis of the potentials of the positive electrode and the negative electrode, respectively, and the peak position and peak shape of the dV / dQ curve derived from each of the positive electrode and the negative electrode. By paying attention to, detailed deterioration diagnosis is performed.)
Herein examiner views the integration and differentiation help to determine signal (i.e., high and low frequency element function) peak position and scale.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have incorporated Wampler into Takegami for the purpose of estimating a higher frequency component using a high-pass filter and determining a sum of high frequency and low frequency element function. Therefore, an accuracy of determining the internal state of the battery can be improved using the high frequency and low frequency’s peak and position information of the battery.
Regarding claim 23, the combination of Takegami, Nakanishi, Wampler, Kasanoy and Chen teach the storage battery internal state estimation device according to claim 22, Takegami teaches wherein when "x" is a capacity of the storage battery, F(x; 1, µ, σ) is an arbitrary sigmoid function having the position p and the scale o as the parameters, and f(x; 1, µ, σ) is a peak function obtained by differentiating the sigmoid function, at least one of the high-frequency element function and the low-frequency element function is a skew sigmoid function expressed by the following expression (1) using a height "k" and a skew parameter "v", or a skew peak function expressed by the following expression (2) obtained by differentiating the skew sigmoid function (para [0094] As another example of the OCV element function, a hyperbolic tangent function may be used, or a function such that the curve becomes asymmetric when differentiated may be used. For example, Gompertz function, generalized logistic function and the like can be mentioned. In short, it may be a function that is monotonically non-decreasing and can express parameters so that it has a mountain-shaped peak when differentiated.).
Herein examiner views the logistic function as the sigmoid function for expressing function parameters such as peak position, scale, battery capacity.
Regarding claim 24, the combination of Takegami, Nakanishi, Wampler, Kasanoy and Chen teach the storage battery internal state estimation device according to claim 21, wherein
Takegami suggests repeats in order an operation of estimating the low-frequency element function of a lower frequency using the Zij-th order integral voltage curve or the Z1j-th order integral capacity curve of a higher order, (in para [0166] In the above description, an example in which the determination unit 403 repeatedly performs the determination process from 1 to rmax times has been described, but the present invention is not limited to this case.))
Wampler teaches the estimation circuitry repeats in order an operation of estimating the high-frequency element function of a higher frequency using the ZDj-th order differential voltage curve or the ZDj-th order differential capacity curve of a higher order (para [0055] Thereafter, a closed-loop estimation may be used to compare the estimated voltage (V.sub.est) to the measured cell voltage (V.sub.C) and adjust the internal state accordingly, e.g., using the example Kalman filter of FIG. 4 or a variation thereof such as extended Kalman, unscented Kalman, or another estimation technique, as will be appreciated by those of ordinary skill in the art. ),
Herein examiner views the closed loop estimation circuit repeats the operation of estimating of high or low frequency function element.
estimates the high-frequency element function or the low-frequency element function using the high-frequency element function and the low-frequency element function that have already been estimated ( para [0054] A summation node 95 then outputs an estimated voltage (Vest) that accounts for the low-frequency and high-frequency effects. Para [0055]) and
Examiner views a closed loop technique uses already estimated high or low frequency elements to obtain the new high or low frequency element.
estimates the model function on the basis of the time-series data for estimation and the high-frequency element function and the low-frequency element function estimated (para [0054] A summation node 95 then outputs an estimated voltage (Vest) that accounts for the low-frequency and high-frequency effects. Para [0055] Thereafter, a closed-loop estimation may be used to compare the estimated voltage (V.sub.est) to the measured cell voltage (V.sub.C) and adjust the internal state accordingly, e.g., using the example Kalman filter of FIG. 4 or a variation thereof such as extended Kalman, unscented Kalman, or another estimation technique, as will be appreciated by those of ordinary skill in the art).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have incorporated Wampler into Takegami for the purpose of estimating a higher frequency component using a high-pass filter and determining a sum of high frequency and low frequency element function. Therefore, an accuracy of determining the internal state of the battery can be improved using the high frequency and low frequency signal element information of the battery.
Regarding claim 25, the combination of Takegami, Nakanishi, Wampler, Kasanoy and Chen teach the storage battery internal state estimation device according to claim 21,
wherein the estimation circuitry when estimating the high-frequency element function using the ZDj-th order differential voltage curve or the ZDj-th order differential capacity curve, simultaneously estimates an approximate function that approximates the element function that has not yet been estimated other than the high-frequency element function to be estimated (Takegami in Fig. 4 shows an optimization and estimation be performed in simultaneously in a group estimation 7) and
when estimating the low-frequency element function using the Zij-th order integral voltage curve or the Z1j-th order integral capacity curve, simultaneously estimates an approximate function that approximates the element function that has not yet been estimated other than the low-frequency element function to be estimated Takegami in Fig. 4 shows an optimization and estimation be performed in simultaneously in a group estimation 7).
Takegami showed an optimization and estimation be performed in simultaneously in a group estimation 7. Accordingly, it would have been obvious that the present inventor has implemented or modified those simultaneous optimization and estimation for estimating both the high and low frequency element to accurately estimate the internal state of a battery by using the high and low frequency information related to the battery.
Claim 26 is rejected as claim 21 having same claim limitation/element.
Claim 27 is rejected as claim 22 having same claim limitation/element.
Claim 28 is rejected as claim 23 having same claim limitation/element.
Claim 29 is rejected as claim 24 having same claim limitation/element.
Claim 30 is rejected as claim 25 having same claim limitation/element.
Regarding claim 31, the combination of Takegami, Nakanishi, Wampler, Kasanoy and Chen teach the storage battery internal state estimation device according to claim 21. Takegami teaches wherein the data generation circuitry is configured to preprocess the time-series data of the current value and the voltage value by applying a noise reduction filter to generate the time-series data for estimation, the noise reduction filter including at least one of a low-pass filter or a Savitzky-Golay filter (para [0034]- That is, when the detected voltage Vi, k at time k is expressed as the value obtained by differentiating the detected voltage Vi, k by the amount of electricity Qi, k, the voltage derivative data calculation unit 203 sets the voltage derivative curve from the first order to the nd order in time series. Calculated as voltage derivative data and output. Here, j = 1 to nd. In addition, nd may be set appropriately.
[0035] In general, the differential operation or the difference operation amplifies the high frequency noise of the original signal. Therefore, in order to reduce the high frequency noise, a lowpass filter that performs sample averaging before and after the differential operation may be used. good. Alternatively, a more sophisticated filter, such as a Savitzky-Goray filter, may be used. Alternatively, after removing noise by function-approximing the time-series voltage data when the horizontal axis is an electric quantity by a kernel method or the like, a differential operation may be performed on the function.
Para [0036] Therefore, as the output of the data storage unit 104, the time-series data output from the time-series data storage unit 201 is. As described above, the time-series data output from the data storage unit 104 includes time-series current data, time-series electricity quantity data, time-series voltage data, and time-series voltage differential data.).
In above paragraphs 34 to 36, examiner views the data generation circuitry (i.e., data storage unit 201) is configured to preprocess the time-series data of the current value and the voltage value by applying a noise reduction filter (i.e., low pass filter) to generate the time-series data for estimation (i.e., low pass filtered data), the noise reduction filter includes at least one of a low-pass filter or a Savitzky-Golay filter.
Regarding claim 32, the combination of Takegami, Nakanishi, Wampler, Kasanoy and Chen teach the storage battery internal state estimation device according to claim 21, Wampler teaches wherein the estimation circuitry is configured to estimate the model function using an iterative estimation technique that minimizes an evaluation function comparing an estimated voltage output by the model function to a measured voltage of the storage battery and adjusts parameters of the high-frequency element function and the low-frequency element function (abstract: The empirical model includes low-pass/band-pass filters and a high-pass filter.
para [0055] Thereafter, a closed-loop estimation may be used to compare the estimated voltage (V.sub.est) to the measured cell voltage (V.sub.C) and adjust the internal state accordingly, e.g., using the example Kalman filter of FIG. 4 or a variation thereof such as extended Kalman, unscented Kalman, or another estimation technique, as will be appreciated by those of ordinary skill in the art. For this reason, the term “state” as used herein with respect to the empirical model 55 includes the calibrated resistances applied to the various basis functions 92.
In above paragraphs examiner views the estimation circuitry (i.e., control with closed-loop estimation) is configured to estimate the model function using an iterative estimation technique that minimizes (i.e., using a Kalman filter) an evaluation function comparing an estimated voltage output by the model function to a measured voltage of the storage battery and adjusts parameters of the high-frequency element function and the low-frequency element function (i.e., adjusting the internal state- empirical model 55 which includes the high-frequency element function and the low-frequency element function using a Kalman filter)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have incorporated Wampler into Takegami for the purpose of iterative estimation technique that minimizes an evaluation function comparing an estimated voltage output by the model function to a measured voltage of the storage battery and adjusts parameters of the high-frequency element function and the low-frequency element function by using a Kalman filter, so that the internal state of the battery can be accurately monitored.
Regarding claim 33, the combination of Takegami, Nakanishi, Wampler, Kasanoy and Chen teach the storage battery internal state estimation device according to claim 21, Wampler teaches wherein the high-frequency element function is derived from a differential operation applied to the time-series data for estimation (see above in claim 32, where Kalman filter is viewed perform a differential operation to time-series (i.e., from closed loop) data to derive a high frequency element), and
the low-frequency element function is derived from an integral operation applied to the time-series data for estimation (see above in claim 32, where Kalman filter is viewed to perform an integral operation to time-series (i.e., from closed loop) data to derive a low frequency element), the differential and integral operations being performed in a sequence determined by a frequency range of interest (para [0056] Relative to the LPFs 90, the HPF 91 may have a shorter time constant, or merely u.sub.H=1, i.e., a straight pass-through of the battery current (I) signal as noted above. In an alternative arrangement, some or all of the low-pass filters could be replaced with band-pass filters, with the cut-off frequencies of these band-pass filters being arranged to collectively cover the entire frequency range of interest.
[0069] As set forth above, the high-frequency empirical model 55 with its use of LPFs 90, basis functions 92 and resistances 94 as shown in FIG. 6, may be used to account for high-frequency effects in a battery cell 14 of FIGS. 1 and 2…Thus, model 55 enables improved accuracy estimation via structure that covers a wider frequency range.).
Examiner views the low pass filtration (i.e., integration) and high pass filtration (i.e., differentiation) are performed in sequence (i.e., by a band pass filtration) for a signal analysis to cover a frequency range of interest.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have incorporated Wampler into Takegami for the purpose of deriving time series high frequency element and low frequency element using Kalman filter that implements derivative and integration operation in a sequence by a bandpass filtration in a frequency range of interest, so that the state of the battery is performed accurately for a given range of frequency.
Regarding claim 34, the combination of Takegami, Nakanishi, Wampler, Kasanoy and Chen teach the storage battery internal state estimation device according to claim 21, Takegami teaches wherein the deterioration parameter includes at least one of a positive electrode capacity retention, a negative electrode capacity retention, a deviation due to lithium consumption, or a resistance increase rate (para [0128] However, θp, i, θn, i, θs, and i represent the positive electrode capacity retention rate, the negative electrode capacity retention rate, and the positive / negative electrode SOC shift amount of the storage battery 101-i, respectively, and Ri [Ω] represents the storage battery 101. Represents the internal resistance of -i)., and
the storage battery internal state estimation device is configured to compare the deterioration parameter with reference storage battery data to diagnose the deterioration state (Para [0016] Here, the storage battery diagnostic device and the storage battery diagnostic method according to the present invention diagnose the deterioration state of the storage battery based on the charge /discharge data of the storage battery.
para [0051] Here, the capacity parameter group refers to three values of a positive electrode capacity retention rate θp, a negative electrode capacity retention rate θn, and a SOC shift amount between positive and negative electrodes when compared with a reference storage battery.).
Examiner views a storage battery diagnostic device (i.e., the storage battery internal state estimation device) compares the deterioration parameter (i.e., electrodes capacity retention rates, SOC and resistances) with reference storage battery data to diagnose the deterioration state using charge/discharge data of the battery.
Claims 35-38 are rejected as claims 31-34 respectively having the same claim limitations.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Jin et al. (US 20100280777 A1) discusses estimating state of charge of battery.
Palladino (US 20070236181 A1) discusses modeling energy for battery diagnostics.
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).
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/SHARAD TIMILSINA/ Examiner, Art Unit 2863
/Catherine T. Rastovski/ Supervisory Primary Examiner, Art Unit 2863