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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Evaluating whether a claim is eligible subject matter under 35 U.S.C. 101 adheres to the following eligibility analysis procedure:
Step 1: The examiner determines whether then claim belongs to a statutory category. See MPEP § 2016(III).
Step 2A, prong 1: The examiner evaluates whether the claim recites a judicial exception. As explained in MPEP § 2106.04(II), a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
Step 2A, prong 2: The examiner evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. This evaluation is performed by:
identifying whether there are any additional elements recited in the claim beyond the judicial exception, and
evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application.
Step 2B: The examiner evaluates whether the claim provides an inventive concept, also referred to as “significantly more”. This evaluation is performed by:
identifying whether there are any additional elements recited in the claim beyond the judicial exception, and
evaluating those additional elements individually and in combination to determine whether they amount to significantly more.
Claims 1-17 are rejected under 35 U.S.C. 101 because these claims are directed to an abstract idea without significantly more.
Regarding claim 1 (a method claim)
Step 2A-I: This claim recites the abstract idea of a mathematical algorithm for estimating the state of charge value of a battery.
Step 2A-II: The judicial exception is not integrated into a practical application because the mere performance of the algorithm offers no improvement to the battery itself.
Step 2B: This claim does not include additional elements that amount to significantly more. While the claim language does recite one additional element, “measuring an initial voltage and an initial current of the battery”, this is an essential data-gathering step necessary for the performance of the rest of the algorithm and therefore does not amount to significantly more.
Furthermore, the dependent claims 2-8 are also rejected because they merely further expand upon the aforementioned mathematical algorithm and do not recite any further additional elements.
Regarding claim 9 (an apparatus claim)
Step 2A-I: This claim recites the abstract idea of a mathematical algorithm for estimating the state of charge of a battery.
Step 2A-II: The judicial exception is not integrated into a practical application because the mere performance of the algorithm offers no improvement to the battery itself.
Step 2B: This claim does not include elements that amount to significantly more. While the claim language does recite additional elements, namely a “measurer” and “a battery management system”, neither of these amount to significantly more as a “measurer” is some generic device configured to perform an essential data-gathering step necessary for the performance of the rest of the algorithm and a “battery management system”, which is composed of several processors, is merely generic computer hardware configured to perform the algorithm by carrying out generic computer operations.
Furthermore, dependent claims 10-17 are also rejected because they merely further expand upon how the battery management system, which is generic computer hardware, is further configured to perform the algorithm by carrying out generic computer operations and do not recite any further additional elements.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3 and 9-12 are rejected under 35 U.S.C. 103 as being unpatentable over Mu et al. (A Novel Multi-Model Probability Based Battery State-of-charge Fusion Estimation Approach, Elsevier, 2016; hereinafter Mu) in view of Naik, Sanjeev M. (US 20090058367 A1, hereinafter Naik).
Regarding claim 1:
The examiner respectfully points out that Mu teaches a method of estimating a state of charge (SOC) value of a battery, the method comprising:
measuring an initial voltage and an initial current of the battery (Section 2.3 — “…u =
I
L
is the system input,
U
o
c
is the open circuit voltage…”);
estimating, by a battery management system, resistance parameters of respective battery models (see Fig. 1, first layer — “Thevenin model, DP model, [RC] model”; it is inherent that each of these models has a resistance parameter. For example, see the R-values in Equation 2; also, the R in “RC Model” stands for resistor) based on the measured initial voltage and the measured initial current (a person having ordinary skill in the art would have understood that the models are produced using voltage and current measurements as seen in Section 2.3 with regards to the Thevenin model);
However, Mu fails to teach converting, by the battery management system, the estimated resistance parameters by comparing an actually measured voltage value with open-circuit voltage (OCV)s determined through the estimated resistance parameters.
Naik teaches converting, by the battery management system, the estimated resistance parameters by comparing an actually measured voltage value with open-circuit voltage (OCV)s determined through the estimated resistance parameters (see Fig. 2 — The method discloses determining a battery open-circuit voltage 24 and then computing battery internal resistance 28; the resistance parameter is then used to estimate battery terminal voltage 30; the covariance 40 between the estimated and measured OCV is then computed and used to convert the resistance parameter 42, see [0037] — “After updating the covariance, the adaptive battery estimator 34 calculates an update to the battery internal resistance…”). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to convert the resistance using covariance as taught by Naik in combination with the method taught by Mu to improve model accuracy to the real world.
The combination of Mu and Naik then further teaches:
determining, by the battery management system, probabilities that the battery corresponds to the respective battery models (see Mu, Equation 11 —
f
U
t
k
p
j
) based on difference values between voltage values of the battery models estimated based on the estimated resistance parameters of the respective battery models and the actually measured voltage value (see Naik, Fig. 2 — The covariance 40 computed by Naik is a difference value between the estimated and measured OCVs).
determining, by the battery management system, a fused SoC (see Mu, Section 3.1 — “…the fusion estimation
S
o
C
^
f
s
can be obtained by:
S
o
C
^
f
s
=
ω
1
S
o
C
^
1
+
ω
2
S
o
C
^
2
+
ω
3
S
o
C
^
3
”; the subscript n denotes the specific model;
ω
n
are the weight values determined by Equation 11) by applying weights for the respective battery models (see Mu, Equation 11 —
Pr
p
j
U
t
k
-
1
; this term is a weight from the previous timestep as shown by
ω
j
k
=
Pr
p
j
U
t
k
when evaluated for k-1) to the determined probabilities that the battery corresponds to the respective battery models (see Mu, Equation 11 —
f
U
t
k
p
j
) based on model OCV information (see the U-values in Fig. 1) of the respective battery models determined based on the converted resistance parameters (see Naik, Fig. 2 — the updated battery internal resistance 42 is used to predict battery terminal voltage 44; the new estimation of SoC is made using the converted resistance parameters); and
estimating, by the battery management system, the SOC value of the battery based on the fused SoC (see Mu, Section 3.1 — “…the fusion estimation
S
o
C
^
f
s
can be obtained by:
S
o
C
^
f
s
=
ω
1
S
o
C
^
1
+
ω
2
S
o
C
^
2
+
ω
3
S
o
C
^
3
”).
The examiner notes that Mu does not disclose that the fused value is a fused OCV. However, it would have been obvious to substitute OCV for SoC because the relationship between the two is well-understood by one having ordinary skill in the art and the two are readily interchangeable (see Mu, Section 2.3 — “…the curve of OCV-SoC…”). Doing so would have advantageously allowed for an indication of the corresponding OCV.
Regarding claim 2:
Mu fails to teach wherein the converting of the estimated resistance parameters includes converting the estimated resistance parameters based on difference values between the OCVs determined through the estimated resistance parameters of the respective battery models and the actually measured voltage value and determining OCVs of the respective battery models based on the converted resistance parameters.
Naik further teaches wherein the converting of the estimated resistance parameters includes converting the estimated resistance parameters based on difference values between the OCVs determined through the estimated resistance parameters of the respective battery models and the actually measured voltage value (see Fig. 2 — The method discloses determining a battery open-circuit voltage 24 and then computing battery internal resistance 28; the resistance parameter is then used to estimate battery terminal voltage 30; the covariance 40 between the estimated and measured OCV is then computed and used to convert the resistance parameter 42, see [0037] — “After updating the covariance, the adaptive battery estimator 34 calculates an update to the battery internal resistance…”; the covariance 40 computed by Naik is a difference value between the estimated and measured OCVs) and determining OCVs of the respective battery models based on the converted resistance parameters (see Fig. 2 — the updated battery internal resistance 42 is used to predict battery terminal voltage 44; the new estimation of OCV is made using the converted resistance parameters). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to include determining the OCVs of the respective battery models based on the converted resistance parameters as taught by Naik into the method taught by Mu and Naik to improve model accuracy over time.
Regarding the corresponding apparatus, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to further configure the OCV processor taught by Mu and Naik to execute the method step
Regarding claim 3:
Mu fails to teach wherein the converting of the estimated resistance parameters based on the difference values between the OCVs determined through the estimated resistance parameters of the respective battery models and the actually measured voltage value includes converting the estimated resistance parameters by determining error covariances and applying weights to the difference values between the OCVs through the estimated resistance parameters of the respective battery models determined and the actually measured voltage value.
Naik further teaches wherein the converting of the estimated resistance parameters based on the difference values between the OCVs determined through the estimated resistance parameters of the respective battery models and the actually measured voltage value (see Fig. 2 — The method discloses determining a battery open-circuit voltage 24 and then computing battery internal resistance 28; the resistance parameter is then used to estimate battery terminal voltage 30; the covariance 40 between the estimated and measured OCV is then computed and used to convert the resistance parameter 42, see [0037] — “After updating the covariance, the adaptive battery estimator 34 calculates an update to the battery internal resistance…”; the covariance 40 computed by Naik is a difference value between the estimated and measured OCVs) includes converting the estimated resistance parameters by determining error covariances (a person having ordinary skill in the art would understand covariance may be interpreted as an error value) and applying weights to the difference values between the OCVs through the estimated resistance parameters of the respective battery models determined and the actually measured voltage value (see [0037], Equation 8 — The resistance parameter is updated using a plurality of weighted covariance expressions). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to convert the estimated resistance parameters as taught by Naik in the method taught by Mu and Naik to better understand how the model estimations differ from measured values.
Regarding claim 9:
The examiner respectfully points out that Mu teaches an apparatus for estimating an SoC value of a battery (see Fig. 2 — The method disclosed in Fig. 1 has been performed in a physical test, disclosing the existence of an apparatus for its implementation, see Section 4 — “The Federal Urban Driving Schedule (FUDS) test was used to verify the proposed method.”), the apparatus comprising:
A measurer configured for measuring a voltage and a current of the battery (see Fig. 1 — “Real-time measurements of battery current and voltage”; that these measurements are made in the performance of the method discloses a measuring device)
A battery management system (see Fig. 1 — The disclosed apparatus for the performance of this method constitutes a battery management system) configured for estimating open-circuit voltage (OCV)s of respective battery models (see the U-values in Fig. 1) based on a measured initial voltage and a measured initial current of the battery (Section 2.3 — “…u =
I
L
is the system input,
U
o
c
is the open circuit voltage…”), and for estimating the SOC value of the battery based on the estimated OCVs (see Fig. 1 — A fused estimate of the SoC value of the battery is made based on weights and model SoC values; the weights are based on the estimated OCVs, see the U-values sent to the “Fusion rules” node).
The examiner also notes that the method taught by Mu discloses determining OCV probabilities of the respective battery models (see Mu, Equation 11 —
f
U
t
k
p
j
) based on the measured voltage and the measured current (see Fig. 1 — The real time voltage and current measurements are fed into the equivalent circuit models) and estimating the SoC value of the battery based on the determined OCV probabilities of the respective battery models (see Fig. 1 — A fused estimate of the SoC value of the battery is made based on weights and model SoC values; the weights are based on the determined SoC probabilities, see Equation 11).
Mu fails to teach wherein the battery management system includes a model OCV processor configured for determining OCV probabilities of the respective battery models based on the measured voltage and the measured current and an estimation processor configured for estimating the SOC value of the battery based on the determined OCV probabilities of the respective battery models.
Naik teaches a battery management system (Abstract — “An adaptive battery estimation control system…”) including a plurality of processors ([0013] — “The adaptive battery estimator includes a plurality of modules that cooperate to process input signals… As used herein the term "module" or "modules" is defined as one or more units capable of processing or evaluating signals…”). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to substitute the battery management system taught by Mu for the multi-processor battery management system taught by Naik, wherein one of the plurality of processors would be configured for determining OCV probabilities of the respective battery models based on the measured voltage and the measured current and another would be configured for estimating the SOC value of the battery based on the determined OCV probabilities of the respective battery models to improve processing time by distributing the processing load between one processor for voltage calculations and another for charge calculations.
Regarding claim 10:
The examiner respectfully points out that the combination of Mu and Naik further teaches wherein the model OCV processor (all of the following steps would be carried out on the OCV processor since they are voltage calculations) is configured for estimating resistance parameters of the respective battery models (see Mu, Fig. 1, first layer — “Thevenin model, DP model, [RC] model”; it is inherent that each of these models has a resistance parameter. For example, see the R-values in Equation 2; also, the R in “RC Model” stands for resistor) based on the voltage and current measured by the measurer (a person having ordinary skill in the art would have understood that the models are produced using the voltage and current measurements made by the measurer as seen in Mu, Section 2.3 with regards to the Thevenin model) and for converting the estimated resistance parameters by comparing an actually measured voltage value with voltage values estimated through the estimated resistance parameters (see Naik, Fig. 2 — The method discloses determining a battery open-circuit voltage 24 and then computing battery internal resistance 28; the resistance parameter is then used to estimate battery terminal voltage 30; the covariance 40 between the estimated and measured OCV is then computed and used to convert the resistance parameter, see Naik, [0037] — “After updating the covariance, the adaptive battery estimator 34 calculates an update to the battery internal resistance…”).
Regarding claim 11:
The examiner respectfully points out that the combination of Mu and Naik discloses wherein in converting the estimated resistance parameters, the model OCV processor (all of the following steps would be carried out on the OCV processor since they are voltage calculations) is further configured for converting the estimated resistance parameters based on difference values between the OCVs determined through the estimated resistance parameters of the respective battery models and the actually measured voltage value (see Naik, Fig. 2 — The covariance 40 computed by Naik is a difference value between the estimated and measured OCVs) and determining OCVs of the respective battery models based on the converted resistance parameters (see Naik, Fig. 2 — the updated battery internal resistance 42 is used to predict battery terminal voltage 44).
Regarding claim 12:
The examiner respectfully points out that the combination of Mu and Naik further teaches wherein in converting the estimated resistance parameters based on the difference values between the OCVs determined through the estimated resistance parameters of the respective battery models and the actually measured voltage value , the model OCV processor (all of the following steps would be carried out on the OCV processor since they are voltage calculations) is further configured for converting the estimated resistance parameters by determining error covariances (a person having ordinary skill in the art would understand covariance may be interpreted as an error value) and applying weights to the difference values between the OCVs through the estimated resistance parameters of the respective battery models determined and the actually measured voltage value (see Naik, [0037], Equation 8 — The resistance parameter is updated using a plurality of weighted covariance expressions).
Claims 4-7 and 13-16 are rejected under 35 U.S.C. 103 as being unpatentable over Mu and Naik in view of Hidai et al. (US 20120072141 A1, hereinafter Hidai).
Regarding claim 4:
Mu fails to teach wherein the determining of the probabilities that the battery corresponds to the respective battery models includes determining normal distribution probabilities based on the difference values between the voltage values of the battery models estimated based on the estimated resistance parameters of the respective battery models and the actually measured voltage value and determining the weights for the respective battery models based on the determined normal distribution probabilities.
Hidai teaches determining normal distribution probabilities based on covariance (see Hidai, [0093] — “The observation probability
P
(
Y
t
|
S
t
)
in Equation (4) is calculated by a multivariate normal distribution with an observation average
μ
t
and a covariance matrix C.”). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to determine normal distribution probabilities as taught by Hidai based on the difference values between the voltage values of the battery models estimated based on the estimated resistance parameters of the respective battery models and the actually measured voltage value as taught by Mu and Naik (recall Naik, Fig. 2 — the difference value is covariance 40) and determine the weights for the respective battery models (see Mu, Equation 11 —
Pr
p
j
U
t
k
-
1
) based on the determined normal distribution probabilities (the model probabilities in Mu, Equation 11 would be calculated as normal distribution probabilities as taught by Hidai) in order to ensure the model weights adhere to the distribution of measured voltage values.
Regarding claim 5:
The examiner respectfully points out that Mu further teaches wherein the determining of the weights for the respective battery models includes determining the weights for the respective battery models by dividing probabilities that the actually measured voltage value is included in the respective battery models (see Equation 11 —
f
U
t
k
p
j
Pr
p
j
U
t
k
-
1
) by a sum of the probabilities that the actually measured voltage value is included in the respective battery models (see Equation 11 —
∑
i
=
1
N
f
U
t
k
p
i
P
r
(
p
i
|
U
t
(
k
-
1
)
)
), respectively.
Regarding claim 6:
The examiner respectfully points out that Mu further teaches wherein the determining of the fused SoC (see Mu, Section 3.1 — “…the fusion estimation
S
o
C
^
f
s
can be obtained by:
S
o
C
^
f
s
=
ω
1
S
o
C
^
1
+
ω
2
S
o
C
^
2
+
ω
3
S
o
C
^
3
”; the subscript n denotes the specific model;
ω
n
are the weight values determined by Equation 11) includes determining the fused SoC by applying the weights for the respective battery models (see Mu, Equation 11 —
Pr
p
j
U
t
k
-
1
) to the probabilities that the actually measured voltage value is included in the respective battery models (see Mu, Equation 11 —
f
U
t
k
p
j
).
The examiner notes that Mu does not disclose that the fused value is a fused OCV. However, it would have been obvious to substitute OCV for SoC because the relationship between the two is well-understood by one having ordinary skill in the art and the two are readily interchangeable (see Mu, Section 2.3 — “…the curve of OCV-SoC…”). Doing so would have advantageously allowed for an indication of the corresponding OCV.
Regarding claim 7:
The examiner respectfully points out that Mu further teaches wherein the determining of the fused SoC further includes determining the fused SoC as a sum of values (see Mu, Section 3.1 — “…the fusion estimation
S
o
C
^
f
s
can be obtained by:
S
o
C
^
f
s
=
ω
1
S
o
C
^
1
+
ω
2
S
o
C
^
2
+
ω
3
S
o
C
^
3
”) obtained by applying the weights for the respective battery models (
ω
n
are the weight values determined by Equation 11) to model SoCs determined from the respective battery models (the subscript n denotes the specific model).
The examiner notes that Mu does not disclose that the fused value is a fused OCV. However, it would have been obvious to substitute OCV for SoC because the relationship between the two is well-understood by one having ordinary skill in the art and the two are readily interchangeable (see Mu, Section 2.3 — “…the curve of OCV-SoC…”). Doing so would have advantageously allowed for an indication of the corresponding OCV.
Regarding claim 13:
The examiner respectfully points out that the combination of Mu and Naik teaches an apparatus wherein the model OCV processor is configured for determining difference values between the voltage values estimated through the estimated resistance parameters and the actually measured voltage value (see Naik, Fig. 2 — the updated battery internal resistance 42 is used to predict battery terminal voltage 44).
The combination of Mu and Naik fails to teach wherein the model OCV processor is configured for determining probabilities that the battery corresponds to the respective battery models based on normal distribution probabilities determined based on the determined difference values and is configured for determining a fused OCV by applying weights for the respective battery models to the determined probabilities that the battery corresponds to the respective battery models based on determined model OCV information of the respective battery models.
While Mu and Naik do not teach the OCV processor being configured for determining a fused OCV by applying weights for the respective battery models to the determined probabilities that the battery corresponds to the respective battery models based on determined model OCV information of the respective battery models, they teach the estimation processor being configured for determining a fused SoC (see Mu, Fig. 1 — A fused estimate of the SoC value of the battery is made based on weights and model SoC values) by applying weights for the respective battery models to the determined probabilities that the battery corresponds to the respective battery models (The fused model weights are determined by applying weights to the determined probabilities, see Mu, Equation 11 —
f
U
t
k
p
j
Pr
p
j
U
t
k
-
1
) based on determined model OCV information of the respective battery models (The probabilities are based on model information, see Mu, Section 3.2 — “…
p
j
represents the certain parameters set of models…”). Note that Mu and Naik do not disclose that the fused value is a fused OCV. However, it would have been obvious to substitute OCV for SoC because the relationship between the two is well-understood by one having ordinary skill in the art and the two are readily interchangeable (see Mu, Section 2.3 — “…the curve of OCV-SoC…”). Doing so would have advantageously allowed for an indication of the corresponding OCV. Therefore, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to move determining a fused OCV from the estimation processor to the OCV processor, since it is a voltage calculation, in order to reduce the processing load on the estimation processor.
Hidai teaches determining normal distribution probabilities based on covariance (see Hidai, [0093] — “The observation probability
P
(
Y
t
|
S
t
)
in Equation (4) is calculated by a multivariate normal distribution with an observation average
μ
t
and a covariance matrix C.”). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to have the apparatus determine normal distribution probabilities as taught by Hidai based on the difference values between the voltage values of the battery models estimated based on the estimated resistance parameters of the respective battery models and the actually measured voltage value as taught by Mu and Naik (recall Naik, Fig. 2 — the difference value is covariance 40) and determine the weights for the respective battery models (see Mu, Equation 11 —
Pr
p
j
U
t
k
-
1
) based on the determined normal distribution probabilities (the model probabilities in Mu, Equation 11 would be calculated as normal distribution probabilities as taught by Hidai) in order to ensure the model weights in the battery management system adhere to the distribution of measured voltage values.
Regarding claim 14:
The examiner respectfully points out that Mu, Naik, and Hidai further teach wherein the model OCV processor is configured for determining the weights for the respective battery models (The OCV processor is already configured to perform this step in determining a fused SoC, see Mu, Fig. 1 — A fused estimate of the SoC value of the battery is made based on weights and model SoC values) by dividing probabilities that the actually measured voltage value is included in the respective battery models (see Mu, Equation 11 —
f
U
t
k
p
j
) by a sum of the probabilities that the actually measured voltage value is included in the respective battery models (see Mu, Equation 11 —
∑
i
=
1
N
f
U
t
k
p
i
P
r
(
p
i
|
U
t
(
k
-
1
)
)
), respectively.
Regarding claim 15:
The examiner respectfully points out that Mu, Naik, and Hidai further teach wherein the model OCV processor is configured for determining the fused SoC by applying the weights for the respective battery models to model SoCs determined from the respective battery models (see Mu, Fig. 1 — A fused estimate of the SoC value of the battery is made by applying on weights to model SoC values, see Mu, Equation 10 —
S
o
C
^
f
s
=
ω
1
S
o
C
^
1
+
ω
2
S
o
C
^
2
+
ω
3
S
o
C
^
3
) in response to the measured voltage (see Mu, Equation 11 —
U
t
(
k
)
is measured voltage).
The examiner notes that Mu does not disclose that the fused value is a fused OCV. However, it would have been obvious to substitute OCV for SoC using the OCV processor because the relationship between the two is well-understood by one having ordinary skill in the art and the two are readily interchangeable (see Mu, Section 2.3 — “…the curve of OCV-SoC…”). Doing so would have advantageously allowed for an indication of the corresponding OCV.
Regarding claim 16:
The examiner respectfully points out that Mu, Naik, and Hidai further teach wherein the model OCV processor is configured for determining the fused SoC as a sum of values obtained by applying the determined weights for the respective battery models to model SoCs determined from the respective battery models (see Mu, Section 3.1 — “…the fusion estimation
S
o
C
^
f
s
can be obtained by:
S
o
C
^
f
s
=
ω
1
S
o
C
^
1
+
ω
2
S
o
C
^
2
+
ω
3
S
o
C
^
3
”; the subscript n denotes the specific model;
ω
n
are the weight values determined by Equation 11).
The examiner notes that Mu does not disclose that the fused value is a fused OCV. However, it would have been obvious to substitute OCV for SoC using the OCV processor because the relationship between the two is well-understood by one having ordinary skill in the art and the two are readily interchangeable (see Mu, Section 2.3 — “…the curve of OCV-SoC…”). Doing so would have advantageously allowed for an indication of the corresponding OCV.
Claim 8 is rejected under 35 U.S.C. 103 as unpatentable over Mu, Naik, and Hidai in view of Plett, Gregory L. (Extended Kalman Filtering for Battery Management Systems of LiPB-Based HEV Battery Packs: Part 2. Modeling and Identification, Elsevier, 2004; hereinafter Plett).
Regarding claim 8:
Mu fails to teach converting, by the battery management system, the fused OCV into a matrix type to be applied to a Kalman filter; and estimating the SOC value of the battery through the Kalman filter.
Plett teaches converting, by the battery management system, the system state of the battery into a matrix to be applied to a Kalman filter (Section 3 — “In order to use Kalman-based methods for a battery management system, we must first have a cell model in a discrete-time state-space form. Specifically, we assume the form:
PNG
media_image1.png
73
159
media_image1.png
Greyscale
where xk is the system state vector at discrete-time index k, where the “state” of a system comprises in summary form the total effect of past inputs on the system operation so that the present output may be predicted solely as a function of the state and present input.”; a vector is a matrix; the system state vector may include SoC values, see Section 3 — “Our method constrains the state vector xk to include SOC as one component.”) and estimating the SoC value of the battery through the Kalman filter (see Section 2.5 — “The direct benefit of this approach is that the Kalman filter automatically gives a dynamic estimate of the SOC and its uncertainty…”). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to convert, by the battery management system, the fused SoC taught by Mu into a system state matrix to be applied to a Kalman filter and then estimate the SoC value of the battery through the Kalman filter as taught by Plett to better estimate the SoC over time.
The examiner notes that Mu does not disclose that the fused value is a fused OCV. However, it would have been obvious to substitute OCV for SoC because the relationship between the two is well-understood by one having ordinary skill in the art and the two are readily interchangeable (see Mu, Section 2.3 — “…the curve of OCV-SoC…”). Doing so would have advantageously allowed for an indication of the corresponding OCV.
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Mu and Naik in view of Plett.
Regarding claim 17:
Mu and Naik fail to teach wherein the estimation processor is configured for estimating the SOC value of the battery by applying the fused OCV to a Kalman filter.
Plett teaches converting the system state of the battery into a matrix to be applied to a Kalman filter (Section 3 — “In order to use Kalman-based methods for a battery management system, we must first have a cell model in a discrete-time state-space form. Specifically, we assume the form:
PNG
media_image1.png
73
159
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where xk is the system state vector at discrete-time index k, where the “state” of a system comprises in summary form the total effect of past inputs on the system operation so that the present output may be predicted solely as a function of the state and present input.”; a vector is a matrix; the system state vector may include SoC values, see Section 3 — “Our method constrains the state vector xk to include SOC as one component.”) and estimating the SoC value of the battery through the Kalman filter (see Section 2.5 — “The direct benefit of this approach is that the Kalman filter automatically gives a dynamic estimate of the SOC and its uncertainty…”). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to configure the estimation processor to convert the fused SoC taught by Mu into a system state matrix to be applied to a Kalman filter and then estimate the SoC value of the battery through the Kalman filter as taught by Plett to better estimate the SoC over time.
Prior Art
The prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure:
Ding et al. (US 20260003003 A1), METHOD AND SYSTEM FOR ESTIMATING STATE OF CHARGE IN BATTERY CLUSTERS, ELECTRONIC DEVICE, AND STORAGE MEDIA
Matsuda, Shigeru (US 20250216470 A1), BATTERY ANALYSIS SYSTEM, BATTERY ANALYSIS METHOD, AND NON-TRANSITORY COMPUTER-READABLE RECORDING MEDIUM
Li et al. (CN 118281382 A), Electrochemical Device Management Method and Device Thereof, Electrochemical Device and Electric Device
Andersson, Helge (US 20220032805 A1), METHOD TO DETECT VEHICLE BATTERY TYPE BEFORE CHARGE
Tang et al. (CN 113935222 A), Power Battery Multi-model Fusion Estimation Method based on Ordered Weighted Average Operator
Naskali, Matti (US 20190383880 A1), METHOD, CONTROLLLING UNIT AND ELECTRONIC CHARGING ARRANGEMENT FOR DETERMINING STATE OF CHARGE OF A BATTERY DURING CHARGING OF THE BATTERY
Xiong et al. (CN 106842045 A), A Self-Adapting Weight Method Based on Model Cell Fusion Modelling Method and Battery Management System
Uchino et al. (US 20170146609 A1), DEGRADATION STATE ESTIMATING DEVICE, STATE-OF-CHARGE ESTIMATING DEVICE, OCV CURVE CALCULATING/GENERATING DEVICE, AND POWER STORAGE DEVICE
The prior art listed above was used by the examiner to better understand and contextualize the claimed invention within the art.
Conclusion
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/RYAN JAMES STEAR/Examiner, Art Unit 2857
/ARLEEN M VAZQUEZ/Supervisory Patent Examiner, Art Unit 2857