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 .
Status of Claims
Claims 1-17 are currently pending in the application. Claims 1 and 8-10 have been amended.
Response to Arguments
Rejections under 35 U.S.C. § 101
Regarding the rejections of claims 1-17, the applicant argued the following:
“Applicant respectfully submits that the rejection of claims 1-17 is overcome by amendment thereto, which amount to significantly more than the abstract idea and is practical application of the presently claimed invention. In particular, Applicant respectfully submits that the "Fused OCV" calculation process of the present disclosure is not a simple mathematical averaging but it assigns dynamic weights to individual models based on the real-time errors between actually measured voltages and model- estimated voltages, thereby tracking unknown battery characteristics and physically improving the accuracy of the SOC estimation. Furthermore, the amended claims further clarify structural relationship between a sensor, the model OCV processor and the estimation processor within the BMS to interact with each other, for the concrete implementation for explicitly reciting the physical configurations, including the sensor for measuring voltage and current values, and the specific hardware processor configurations configured to execute each step. Accordingly, since the claim limitations of Claims 1 and 9 are not simply abstract ideas but are concrete methods closely coupled with a physical system, Applicant respectfully submits that aforementioned feature of the present invention amounts to significantly more and practical application of the presently claimed invention. Based on the above reason, Applicant respectfully submits that the rejection of claims under 35 U.S.C. § 101 should be withdrawn.”
The examiner has fully considered the above arguments but does not find them persuasive.
The applicant submits that the “Fused OCV” calculation process of the present disclosure is not a simple mathematical averaging but it assigns dynamic weights to individual models based on the real-time errors between actually measured voltages and model-estimated voltages, thereby tracking unknown battery characteristics and physically improving the accuracy of the SOC estimation. The examiner respectfully responds that a more complex abstract mathematical process that yields a more accurate estimation of SOC than a simpler abstract mathematical process (such as a simple mathematical averaging, as referenced exempli gratia by the applicant) is still itself an abstract idea, namely a mathematical algorithm.
The applicant further submits that the amended claims further clarify structural relationship between a sensor, the model OCV processor and the estimation processor within the BMS to interact with each other, for the concrete implementation for explicitly reciting the physical configurations, including the sensor for measuring voltage and current values, and the specific hardware processor configurations configured to execute each step. The examiner respectfully responds that the aforementioned elements all constitute generic computer components configured together to perform generic computer operations for the execution of the recited mathematical steps.
However, the amendments set forth by the applicant necessitate further consideration under 35 USC 101. Accordingly, new grounds for rejection under 35 USC 101 are presented below; see Claim Rejections - 35 USC § 101.
Rejections under 35 U.S.C. § 102 and 103
The examiner has considered the applicant’s arguments pertaining to the rejections of claims 1-17 under 35 USC 103 and finds them persuasive. However, the amendments set forth by the applicant necessitate novel consideration under 35 USC 103. Accordingly, new grounds for rejection under 35 USC 103 are presented below; see Claim Rejections – 35 USC § 103.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 9 is rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. In particular, claim 9 recites the amended limitation “…convert the estimated resistance parameters…”; there is insufficient antecedent basis for this limitation.
Claim Rejections - 35 USC § 101
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-17 are rejected under 35 U.S.C. 101 because they are directed to an abstract idea without significantly more.
Claim 1
Step 1: Claim 1 falls into the statutory category of method claims.
Step 2A-I: The claim recites an abstract idea, namely a mathematical algorithm comprising the following steps:
Estimating, by a processor, resistance parameters of respective battery models based on the measured initial voltage and the measured initial current;
Converting, by the processor, the estimated resistance parameters by comparing an actually measured voltage value with open-circuit voltage (OCV)s determined through the estimated resistance parameters wherein the converting is performed by determining error covariances between the OCVs and the measured voltage value;
Determining, by the processor, probabilities that the battery corresponds to the respective battery models 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;
Determining, by the processor, 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 model OCV information of the respective battery models determined based on the converted resistance parameters; and
Estimating, by the processor, the SOC value of the battery based on the fused OCV.
Step 2A-II: The claim language does not integrate the recited mathematical algorithm into a practical application because the mere performance thereof does not itself provide any improvement to lifespan or efficiency of the batteries managed by the battery management system.
Step 2B: The claim recites the following additional elements:
A sensor
A processor
Measuring, by a sensor, an initial voltage and an initial current of the battery.
However, additional elements (a) and (b) are generic computer components configured to perform generic computer functions for the execution of the recited mathematical algorithm and therefore do not amount to significantly more. Additional element (c) constitutes a mere data gathering step required for the performance of the mathematical algorithm and thus also does not amount to significantly more.
Furthermore, claims 2-8 are also rejected by virtue of their dependence from claim 1 and because they do not set forth any further additional elements that would integrate the recited mathematical algorithm into a practical application or that would amount to significantly more.
Claim 9
Step 1: Claim 9 falls into the statutory category of apparatus claims.
Step 2A-I: The claim recites an abstract idea, namely a mathematical algorithm comprising the following steps:
Estimate open-circuit voltage (OCV)s of respective battery models based on a measured initial voltage and a measured initial current of the battery, and for estimating the SOC value of the battery based on the estimated OCVs;
Convert the estimated resistance parameters by comparing an actually measured voltage value with OCVs determined through the estimated resistance parameters, wherein the converting is performed by determining error covariances between the OCVs and the measured voltage value
Step 2A-II: The claim language does not integrate the recited mathematical algorithm into a practical application because the mere performance thereof does not itself provide any improvement to lifespan or efficiency of the batteries managed by the battery management system.
Step 2B: The claim recites the following additional elements:
A sensor configured for measuring a voltage and a current of the battery
A processor communicatively coupled to the sensor
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
However, additional element (a) is a generic computer component configured to perform generic computer functions to execute a mere data gathering step, additional element (b) is a generic computer component configured to perform generic computer functions to execute the recited mathematical algorithm, and additional elements (c) and (d) are both generic computer components, each configured to perform generic computer functions to execute the recited mathematical algorithm; none of these additional elements amount to significantly more.
Furthermore, claims 10-17 are also rejected by virtue of their dependence from claim 9 and because they do not set forth any further additional elements that would integrate the recited mathematical algorithm into a practical application or that would amount to significantly more.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
Claims 1-3 and 9-12 are rejected under 35 USC 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 (US 20090058367 A1) and Sayegh (US 20200025828 A1).
Claim 1
Mu discloses a method of estimating a state of charge (SOC) value of a battery, the method comprising: measuring, by a sensor, an initial voltage and an initial current of the battery (Section 2.3 — “…y = Ut is the observed value, u =
I
L
is the system input,
U
o
c
is the open circuit voltage…”; that OCV y is an observed (measured) value discloses a sensor to perform the observing (measuring); that OCV y is a function of time t discloses observing (measuring) an initial voltage corresponding to t = 0, y = U0) and estimating, by a processor (the models were calculated using a computer, which discloses a processor), resistance parameters of respective battery (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) models 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).
Mu fails to disclose converting, by the processor, the estimated resistance parameters by comparing an actually measured voltage value with open-circuit voltage (OCV)s determined through the estimated resistance parameters wherein the converting is performed by determining error covariances between the OCVs and the measured voltage value; determining, by the processor, probabilities that the battery corresponds to the respective battery models 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; determining, by the processor, 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 model OCV information of the respective battery models determined based on the converted resistance parameters; and estimating, by the processor, the SOC value of the battery based on the fused OCV.
Naik discloses converting, by a processor, 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 disclosed by Naik in combination with the method disclosed by Mu to improve model accuracy to the real world.
Mu and Naik then together disclose: determining, by the processor, 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 processor, 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 processor, 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
”).
Mu and Naik still fail to teach wherein the converting is performed by determining error covariances between the OCVs and the measured voltage value.
Sayegh discloses determining error covariances between the OCVs and the measured voltage value ([0043] — “The method for determining the matrix Q thus consists in producing a covariance matrix on the basis of the standard deviation of the errors between the measured voltage and the predicted voltage for various operating points of the battery.”; covariance values determined on the basis of standard deviation of the errors constitute error covariances). 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 error covariances between the OCVs and the measured voltage value as disclosed by Sayegh with the method disclosed by Mu and Naik for the advantage of improving model accuracy to the real world by better accounting for model errors.
The examiner notes that Mu does not disclose that the fused value is a fused OCV. However, it would have been obvious to substitute using OCV instead of SoC in the method disclosed by Mu, Naik, and Sayegh because the transition between the two is merely an algebraic transformation (an algebraic equivalence relationship) well-understood by one having ordinary skill in the art, meaning 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 actual battery OCV.
Claim 2
Mu fails to disclose 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 discloses 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 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 disclosed by Naik into the method disclosed by Mu, Naik, and Sayegh to improve model accuracy over time.
Claim 3
Mu fails to disclose 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 discloses 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 is a difference value between the estimated and measured OCVs) includes converting the estimated resistance parameters by determining error covariances (as disclosed by Sayegh in claim 1) 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 disclosed by Naik in the method disclosed by Mu, Naik, and Sayegh to better understand how the model estimations differ from measured values.
Claim 9
Mu discloses an apparatus for estimating a state of charge (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 sensor configured for measuring a voltage and a current of the battery (see Fig. 1 — “Real-time measurements of battery current and Voltage”; real-time measurement capability discloses a sensor for performing the measurement); and a processor communicatively coupled to the sensor (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 performs calculations on measured quantities which discloses a processor communicatively coupled to the sensor), wherein the processor is configured to: estimate open-circuit voltage (OCV)s of respective battery models based on a measured initial voltage and a measured initial current of the battery (see Fig. 1 — the OCV estimations produced by the observers are dependent on the model outputs, which are dependent on the real-time measurements of battery current and voltage; real-time measurements disclose a first (initial) battery current and first (initial) battery voltage measured), and for estimating the SOC value of the battery based on the estimated OCVs (Section 3.1-3.2 — The SOC estimate of Equation 10 is dependent on the weights of Equation 11, which are dependent on estimated OCV (the U parameters)), estimate resistance parameters (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); wherein the processor includes: a model OCV processor (see Fig. 1 — That the processor determines model OCVs means it is a model OCV processor) configured for determining OCV probabilities of the respective battery models based on the measured voltage and the measured current (Equation 11 —
f
U
t
k
p
j
; Fig. 1 — The parameter U in Equation 11 is the model OCV, which is dependent on the real-time measurements of battery current and voltage); and an estimation processor (see Equation 10, Fig. 1 — That the processor estimates an SoC means it is an estimation processor) configured for estimating the SOC value of the battery based on the determined OCV probabilities of the respective battery models (Equation 10-11 — The SoC estimate of Equation 10 is dependent on the weights of Equation 11, which are dependent on the OCV probabilities).
Mu fails to disclose wherein the processor is configured to convert the estimated resistance parameters by comparing an actually measured voltage value with OCVs determined through the estimated resistance parameters, wherein the converting is performed by determining error covariances between the OCVs and the measured voltage value.
Naik discloses converting the estimated resistance parameters by comparing an actually measured voltage value with OCVs 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 disclosed by Naik in combination with the method disclosed by Mu to improve model accuracy to the real world.
Mu and Naik still fail to teach wherein the converting is performed by determining error covariances between the OCVs and the measured voltage value.
Sayegh discloses determining error covariances between the OCVs and the measured voltage value ([0043] — “The method for determining the matrix Q thus consists in producing a covariance matrix on the basis of the standard deviation of the errors between the measured voltage and the predicted voltage for various operating points of the battery.”; covariance values determined on the basis of standard deviation of the errors constitute error covariances). 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 error covariances between the OCVs and the measured voltage value as disclosed by Sayegh with the apparatus disclosed by Mu and Naik for the advantage of improving model accuracy to the real world by better accounting for model errors.
Claim 10
Mu discloses wherein the model OCV processor 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 the current measured by the sensor (voltage and current are used as seen in Mu, Section 2.3 with regards to the Thevenin model).
Mu fails to disclose converting the estimated resistance parameters by comparing an actually measured voltage value with voltage values estimated through the estimated resistance parameters.
Naik discloses disclose converting the estimated resistance parameters by comparing an actually measured voltage value with voltage values estimated through the estimated resistance parameters (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…”). 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 by comparing an actually measured voltage value with voltage values estimated through the estimated resistance parameters as further disclosed by Naik for the apparatus disclosed by Mu, Naik, and Sayegh in order to improve model accuracy to real-world conditions.
Claim 11
Mu fails to disclose wherein in converting the estimated resistance parameters, the model OCV processor 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; and determining OCVs of the respective battery models based on the converted resistance parameters.
Naik discloses wherein in converting the estimated resistance parameters, the model OCV processor 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). 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 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 determine OCVs of the respective battery models based on the converted resistance parameters as disclosed by Naik for the apparatus disclosed by Mu, Naik, and Sayegh in order to better understand how the models compare to the real-life battery.
Claim 12
Mu fails to disclose 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 is further configured for: 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.
Sayegh discloses determining error covariances ([0043] — “The method for determining the matrix Q thus consists in producing a covariance matrix on the basis of the standard deviation of the errors between the measured voltage and the predicted voltage for various operating points of the battery.”; covariance values determined on the basis of standard deviation of the errors constitute error covariances). 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 determine error covariances as disclosed by Sayegh with the apparatus disclosed by Mu, Naik, and Sayegh for the advantage of improving model accuracy.
Mu and Sayegh still fail to disclose 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 discloses 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 is further configured for converting the estimated resistance parameters by 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). 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 in the process of converting the estimated resistance parameters by 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 as disclosed by Naik, a step of determining error covariances as disclosed by Mu and Sayegh in order to better account for model error in adjusting model parameters.
Claims 4-7 and 13-16 are rejected under 35 USC 103 as being unpatentable over Mu, Naik, and Sayegh in view of Hidai et al. (US 20120072141 A1, hereinafter Hidai).
Claim 4
Mu fails to disclose 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 discloses 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, Naik, and Sayegh 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.
Claim 5
Mu discloses 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.
Claim 6
Mu discloses 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 using OCV instead of SoC in the method disclosed by Mu, Naik, and Sayegh because the transition between the two is merely an algebraic transformation (an algebraic equivalence relationship) well-understood by one having ordinary skill in the art, meaning 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 actual battery OCV.
Claim 7
Mu discloses 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 using OCV instead of SoC in the method disclosed by Mu, Naik, and Sayegh because the transition between the two is merely an algebraic transformation (an algebraic equivalence relationship) well-understood by one having ordinary skill in the art, meaning 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 actual battery OCV.
Claim 13
Mu fails to disclose 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, 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.
Naik discloses determining difference values between the voltage values estimated through the estimated resistance parameters and the actually measured voltage value (Fig. 2 — the updated battery internal resistance 42 is used to predict battery terminal voltage 44). 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 difference values as disclosed by Naik with the apparatus disclosed by Mu, Naik, and Sayegh in order to better understand how the models differ from the physical battery.
Mu and Naik still fail to disclose 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.
Mu and Naik do not disclose 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. However, together they disclose 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…”). However, it would have been obvious to substitute using OCV instead of SoC in the apparatus disclosed by Mu, Naik, and Sayegh because the transition between the two is merely an algebraic transformation (an algebraic equivalence relationship) well-understood by one having ordinary skill in the art, meaning 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 actual battery 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 determine a fused OCV in order to better understand how the measured voltage of the battery compares to the behavior of idealized models.
Hidai discloses determining normal distribution probabilities based on covariance ([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 disclosed 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 disclosed by Mu, Naik, and Sayegh 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.
Claim 14
Mu discloses 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.
Claim 15
Mu discloses 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 using OCV instead of SoC in the apparatus disclosed by Mu, Naik, and Sayegh because the transition between the two is merely an algebraic transformation (an algebraic equivalence relationship) well-understood by one having ordinary skill in the art, meaning 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 actual battery OCV.
Claim 16
Mu discloses 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 using OCV instead of SoC in the apparatus disclosed by Mu, Naik, and Sayegh because the transition between the two is merely an algebraic transformation (an algebraic equivalence relationship) well-understood by one having ordinary skill in the art, meaning 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 actual battery OCV.
Claim 8 is rejected under 35 USC 103 as unpatentable over Mu, Naik, Sayegh, 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).
Mu fails to disclose 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 discloses 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 disclosed 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 disclosed 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 using OCV instead of SoC in the apparatus disclosed by Mu, Naik, and Sayegh because the transition between the two is merely an algebraic transformation (an algebraic equivalence relationship) well-understood by one having ordinary skill in the art, meaning 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 actual battery OCV.
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Mu, Naik, and Sayegh in view of Plett.
Mu fails to disclose wherein the estimation processor is configured for estimating the SOC value of the battery by applying the fused OCV to a Kalman filter.
Plett discloses 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
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 configure the estimation processor to convert the fused SoC disclosed 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 disclosed by Plett to better estimate the SoC over time.
Conclusion
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RYAN JAMES STEAR whose telephone number is (571)272-8334. The examiner can normally be reached 7:30-5:30 EST/EDT.
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/RYAN JAMES STEAR/Examiner, Art Unit 2857
/ARLEEN M VAZQUEZ/Supervisory Patent Examiner, Art Unit 2857
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