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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/16/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings are objected to because “Power regulator monitoring means” in Fig.2 should be “Power generator monitoring means”. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The abstract of the disclosure is objected to because the abstract contains equations 1 and 2, which should be removed. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
The disclosure is objected to because of the following informalities:
Para[0004] recites “batter” which should be “battery”.
Para[0025] and Para[0032] recite “float component capacity retention rate” which should be “float capacity retention rate”.
Para[0025] and Para[0032] recite “the cycle capacity retention rate from the Weibull coefficients mf” which should be “the float capacity retention rate from the Weibull coefficients mf”.
Para[0043] recites “control unit 30” which should be “control unit 40”.
Para[0047] recites “receiver” which is not mentioned in the specification or drawings.
Appropriate correction is required.
Claim Objections
Claims 1, 6, and 11 objected to because of the following informalities:
Claims 1, 6, and 11 recite “the following formula (1)” and “the following formula (2)” , which should be “formula (1)” and “formula (2)”.
Claims 1, 6, and 11 have two periods, which should be corrected.
Claim 6 recites “the program” which should be “a program”.
Appropriate correction is required.
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.
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.
Claim 11 contains the following functional limitations with identified corresponding structure:
Storage means has corresponding structure of a memory (see instant application [0053])
Data acquisition means has corresponding structure of a CPU (see instant application [0053])
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.
Claims 2, 5, 6-10, 12, and 15 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 2, 6-7, and 12 recite “the capacity retention rate”. There is insufficient antecedent basis for this limitation in these claims.
Claims 2, 6-7, and 12 recite “the capacity retention rate” as being obtained from the float test and being obtained from the cycle test separately, but the abstract recites “the capacity retention rate in a period t or at a cycle number N is estimated from the float capacity retention rate and the cycle capacity retention rate of the secondary battery” and the specification in Para[0059] recites “The float capacity retention rate to be estimated is designated as the capacity retention rate based on the measurement values of a float test. The cycle capacity retention rate to be estimated is designated as the capacity retention rate based on the measurement values of a cycle test.” Therefore, “the capacity retention rate” has two separate definitions in the disclosure, making all instances of “the capacity retention rate” as indefinite. Examiner will interpret “the capacity retention rate” in the context of the claims where it is recited and in light of the specification in Para[0059].
Claims 5, 10, and 15 recite equations 3, 6, and 9 respectively. However, since Claims 4, 9, and 14 already recite equations 2, 5, and 8 for the SOH in terms of a product of two Weibull distributions, it is not clear how the SOH can also be the sum of the same two Weibull distributions. Although the specification in Para[0074] recites that Equations 2,5, and 8 are used for the case when the float and cycle deterioration are strongly correlated and to use Equations 3,6, and 9 when they are not strongly correlated, it is not clear how the degree of correlation translates to the equations in Claims 5, 10 and 15, rendering equations 3, 6, and 9 as indefinite.
Claims that depend on the above rejected claims are also rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-15 are rejected under 35 U.S.C. 101. The claimed invention is directed to the abstract concept of performing mental steps without significantly more. The claim(s) recite(s) the following abstract concepts in BOLD of
With regards to Claim 1,
A secondary battery state of health (SOH) estimation method, which estimates a state of health (SOH) of a secondary battery by use of a Weibull law, comprising:
obtaining Weibull coefficients mf, ηf corresponding to a float capacity retention rate, and the float capacity retention rate represented by the following formula (1), from measurement values of a float test for determining the state of health;
obtaining Weibull coefficients mc, ηc corresponding to a cycle capacity retention rate, and the cycle capacity retention rate represented by the following formula (2), from measurement values of a cycle test for determining the state of health; and
estimating the State of Health (SOH) in a period t or at a cycle number N from the float capacity retention rate and the cycle capacity retention rate of the secondary battery.
[Equation 1]
Float capacity retention rate =
e
x
p
{
-
(
t
η
f
)
m
f
}
... (1)
Cycle capacity retention rate =
e
x
p
{
-
(
N
η
c
)
m
c
}
... (2).
With regards to Claim 6,
A secondary battery state of health estimation program, which estimates a state of health (SOH) of a secondary battery by use of a Weibull law,
the program comprising making a computer work to perform:
a step of obtaining Weibull coefficients mf, ηf corresponding to a float capacity retention rate, and the float capacity retention rate represented by the following formula (1), from measurement values of a float test for determining the capacity retention rate;
a step of obtaining Weibull coefficients mc, ηc corresponding to a cycle capacity retention rate, and the cycle capacity retention rate represented by the following formula (2), from measurement values of a cycle test for determining the capacity retention rate;
and a step of estimating the state of health in a period t or at a cycle number N from the float capacity retention rate and the cycle capacity retention rate of the secondary battery.
[Equation 4]
Float capacity retention rate =
e
x
p
{
-
(
t
η
f
)
m
f
}
... (1)
Cycle capacity retention rate =
e
x
p
{
-
(
N
η
c
)
m
c
}
... (2).
With regards to Claim 11,
A secondary battery state of health estimation apparatus, which performs a secondary battery state of health estimation method to estimate a state of health (SOH) of a secondary battery,
the apparatus comprising:
storage means storing data on a cycle test and a float test; and
data acquisition means for acquiring data on the secondary battery in operation, a period t, and a cycle number N from the secondary battery;
the apparatus executing
a step of obtaining Weibull coefficients mf, ηf corresponding to a float capacity retention rate, and the float capacity retention rate represented by the following formula (1), from measurement values of the float test;
a step of obtaining Weibull coefficients mc, ηc corresponding to a cycle capacity retention rate, and the cycle capacity retention rate represented by the following formula (2), from measurement values of the cycle test; and
a step of estimating the state of health in the period t or at the cycle number N from the float capacity retention rate and the cycle capacity retention rate of the secondary battery.
[Equation 7]
Float capacity retention rate =
e
x
p
{
-
(
t
η
f
)
m
f
}
... (1)
Cycle capacity retention rate =
e
x
p
{
-
(
N
η
c
)
m
c
}
... (2).
Under step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter. The above claims are considered to be in a statutory category.
Under Step 2A, Prong One, we consider whether the claims recite a judicial exception (abstract idea). In the above claim, the highlighted portions constitute abstract ideas because, under a broadest reasonable interpretation, they recite limitations that fall into/recite abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, they fall into the grouping of subject matter that, when recited as such in a claim limitation, cover performing mathematics or mental steps. Additionally, the clam limitations merely indicate a field of use or technological environment in which the judicial exception is performed, which is the estimation of the state of health of a secondary battery.
Next, under Step 2A, Prong Two, we consider whether the claims that recite a judicial exception are integrated into a practical application. In this step, we evaluate whether the claims recite additional elements that integrate the exception into a practical application of that exception.
The judicial exceptions are not integrated into a practical application because there is no improvement to another technology or technical field; improvements to the functioning of the computer itself; a particular machine; effecting a transformation or reduction of a particular article to a different state or thing. Examiner notes that even though the claimed method is tied to a particular machine or apparatus (i.e. the secondary battery), it does not represent an improvement to another technology or technical field as the secondary battery was already produced before the mental and mathematical steps listed in BOLD of Claims 1, 6, and 11, which do not indicate an improvement upon the component. Similarly, there are no other meaningful limitations linking the use to a particular technological environment. Finally, there is nothing in the claim that indicates an improvement to the functioning of the computer itself or transform a particular article to a new state.
Finally, under Step 2B, we consider whether the additional elements are sufficient to amount to significantly more than the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exceptions because “obtaining Weibull coefficients mf, ηf corresponding to a float capacity retention rate from measurement values of a float test for determining the state of health” and “obtaining Weibull coefficients mc, ηc corresponding to a cycle capacity retention rate from measurement values of a cycle test for determining the state of health” in Claims 1, 6, and 11 amount to nothing more than necessary data gathering as recited in MPEP section 2106.05(g). Necessary data gathering (i.e. receiving data) is considered extra solution activity in light of Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092- 93 (Fed. Cir. 2015).
In addition, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exceptions because “the program comprising making a computer work to perform” and “the apparatus, the storage means storing data on a cycle test and a float test, and the data acquisition means for acquiring data on the secondary battery in operation, a period t, and a cycle number N from the secondary battery” in Claims 1, 6, and 11 are further directed to generic computer elements and are rejected under 35 U.S.C. 101 as generic computer elements are not considered significantly more than the abstract idea. As recited in the MPEP, 2106.05(b), merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359-60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94.
Claims 2-5, 7-10, and 12-15 are further directed to abstract ideas and are rejected under 35 U.S.C. 101.
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) 1-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Harris (“Failure statistics for commercial lithium ion batteries: A study of 24 pouch cells”) in view of Börger (US 20210249704 A1).
With regards to Claim 1, Harris teaches
obtaining Weibull coefficients mf, ηf corresponding to a float capacity retention rate (See Page 591 column 2, the equation
S
t
=
e
-
(
t
-
γ
η
)
β
(hereinafter referred to as equation A1) contains β which corresponds to mf, and η corresponds to ηf. See also Page 591 Column 1 “A statistical analysis of failure [51] can begin by defining four time-dependent (or cycle number-dependent) functions.” Therefore, t in equation A1 can be a cycle or a time (i.e. making S(t) a float capacity retention rate.).), and the float capacity retention rate represented by the following formula (1) (See equation A1, where β corresponds to mf, and η corresponds to ηf, and t corresponds to the time),
obtaining Weibull coefficients mc, ηc corresponding to a cycle capacity retention rate (See Page 591 column 2, the equation A1, which contains β which corresponds to mc, and η which corresponds to ηc. See also Page 591 column 1 “the fraction of all of the cells in a population that fail on cycle t (i.e. t in equation A1 is the cycle number N)”. See also Page 591 Column 1 “A statistical analysis of failure [51] can begin by defining four time-dependent (or cycle number-dependent) functions.” Therefore t in equation A1 can be a cycle or a time (i.e. making S(t) a cycle capacity retention rate.).), and the cycle capacity retention rate represented by the following formula (2) (See equation A1, where β corresponds to mc, and η corresponds to ηc, and t (i.e. the cycle number) corresponds to N), from measurement values of a cycle test (See Page 590 column 2 “Both capacity and internal resistance (~1 kHz) were measured (i.e. measurement values) after each cycle (i.e. a cycle test).”) for determining the state of health (See Page 591 column 2 “we will define a failed battery as one that has lost 20% of its initial capacity” and Page 592 column 1 “A Weibull analysis might involve using median ranks [56] (MR) to estimate the values of F at the measured failure times”. In other words, the measurement values of the capacitances are used as a reference to determine the failure times (i.e. which indicate a SOH) by implementing a Weibull analysis.); and
estimating the State of Health (SOH) in a period t or at a cycle number N (Examiner notes an option is presented by “or”, and Examiner chooses “cycle number N”) from the cycle capacity retention rate of the secondary battery (See Page 591 column 2 “we will define a failed battery (i.e. a secondary battery) as one that has lost 20% of its initial capacity” and Page 592 column 1 “A Weibull analysis might involve using median ranks [56] (MR) to estimate the values of F at the measured failure times”. See also Page 591 column 1 “
F
t
=
∫
0
t
f
t
'
d
t
'
, the failure function, the cumulative fraction of cells that have failed by cycle t.” In other words, the failure function is estimated at a cycle number t, which is a measure of the health of the secondary battery. Note that the failure function is related to the survival function, i.e. the cycle capacity retention rate, as S(t) = 1- F(t) (See Page 591 column 1). Therefore, the cycle capacity retention rate is used to estimate the SOH.).
[Equation 1]
Float capacity retention rate =
e
x
p
{
-
(
t
η
f
)
m
f
}
... (1) (See equation A1, where β corresponds to mf, and η which corresponds to ηf, and t corresponds to the time)
Cycle capacity retention rate =
e
x
p
{
-
(
N
η
c
)
m
c
}
... (2) (See equation A1, where β corresponds to mc, and η corresponds to ηc, and t (i.e. the cycle number) corresponds to N).
Harris is silent to the language of
from measurement values of a float test for determining the state of health;
estimating the State of Health (SOH) in a period t or at a cycle number N from the float capacity retention rate and the cycle capacity retention rate.
Börger teaches
from measurement values of a float test (See Para[0044] “t: is the measured (i.e., in particular, presently accrued) operating duration (i.e. the operating duration is the float test, and it is measured for measurement values) of the battery cell”) for determining the state of health (See Para[0041] “This adapted Weibull distribution describes the probability of a failure of a battery cell in percent. (i.e. a State of Health)”, where the adapted Weibull distribution is equation 11, which contains the time t.);
estimating the State of Health (SOH) in a period t or at a cycle number N from the float capacity retention rate and the cycle capacity retention rate (See Para[0042], equation 11, where t is the period (See Para[0044] “t: is the measured (i.e., in particular, presently accrued) operating duration (i.e. the duration is the period)”) and n is the cycle number (See Para[0047] “n: is the measured (in particular, accrued) number of cycles of the battery cell”), the float capacity retention rate is the exponential factor
e
α
(
t
-
t
0
T
-
t
0
)
b
, and the cycle capacity retention rate is the exponential factor
e
β
(
n
-
n
0
N
-
n
0
)
b
, which is obtained after expanding the exponent in equation 11. See also Para[0041] “This adapted Weibull distribution describes the probability of a failure of a battery cell in percent. (i.e. a State of Health)”, so the State of Health is estimated by the period t, the cycle number n, the float capacity retention rate, and the cycle capacity retention rate).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Harris wherein measurement values of a float test for determining the state of health and estimating the State of Health (SOH) in a period t or at a cycle number N from the float capacity retention rate and the cycle capacity retention rate is done like in Börger in order to estimate the State of Health using more than just the cycle capacity retention rate, which will yield more accurate representations of the performance of the secondary battery.
With regards to Claim 2, Harris and Börger teach the limitations of Claim 1. Harris further teaches
taking the capacity retention rate obtained from the test as a measured capacity retention rate (See Page 590 column 2 “Both capacity and internal resistance (~1 kHz) were measured (i.e. measurement values) after each cycle (i.e. a test).” See also Page 591 column 2 “we will define a failed battery as one that has lost 20% of its initial capacity” and Page 592 column 1 “A Weibull analysis might involve using median ranks [56] (MR) to estimate the values of F at the measured failure times”. In other words, the measurement values of the capacitances are used as a reference to determine the values of F (i.e. which is directly related to the capacity retention rate S(t) (i.e. equation A1) as S(t) = 1- F(t) (See Page 591 column 1), so a measured capacity retention rate is obtained.) by implementing a Weibull analysis. See also Page 591 Column 1 “A statistical analysis of failure [51] can begin by defining four time-dependent (or cycle number-dependent) functions.” Therefore t in equation A1 can be a cycle or a time (i.e. making S(t) a float capacity retention rate when t corresponds to a period/time), and Weibull plotting the measured capacity retention rate in relation to ln(cycle) and ln(ln(l/ capacity retention rate)) to prepare a Weibull plot of the capacity retention rate (See Fig. 3b (i.e. the points plotted on 3b constitutes Weibull plotting the measured capacity retention rate values), where the x-axis is the ln(cycles to failure) (i.e ln(cycle number)) and the y-axis is the ln(-ln(1-F)) (i.e. ln(ln(l/ capacity retention rate) and 1-F = S (See Page 591 column 1) which is the capacity retention rate));
estimating a deterioration prediction line, represented by a straight-line equation, from the Weibull plot of the capacity retention rate (See Fig. 3b (i.e. the Weibull plot of the capacity retention rate 1 - F = S (See Page 591 column 1) ) “OLS fit using the MRR technique for (a) 3-Weibull and (b) 2-Weibull distributions. The fits (i.e. estimating a deterioration prediction line (i.e. the straight-line through the data points in Fig. 3b)) appear to provide strong constraints on the slopes, but this perception is overly optimistic.” See also Page 592 column 1, where the equation
ln
-
ln
1
-
F
t
=
βln
t
-
βln
η
(hereinafter referred to as equation A2) constitutes a straight-line equation. Examiner notes equation A2 can be used for both cycle and float tests, as it is derived from S(t) and Page 591 Column 1 recites “A statistical analysis of failure [51] can begin by defining four time-dependent (or cycle number-dependent) functions (i.e. including S(t)).” Therefore, t in equations A1 and A2 can be a cycle or a time (i.e. making S(t) a float capacity retention rate when t corresponds to a period/time);
obtaining the Weibull coefficients m and η from a slope and an intercept of the deterioration prediction line (See equation A2, which describes the deterioration prediction line, the slope is
β
and the intercept is
-
βln
η
, from which the Weibull coefficients
m
=
β
and
η
can be obtained);
determining the capacity retention rate from the Weibull coefficients m and η and the formula (1) (See equation A2 (which is formula (1) rearranged when t is the time/period), which describes the deterioration prediction line with the Weibull coefficients
m
=
β
and
η
, and solve for the capacity retention rate 1-F(t) = S(t) (See Page 591 column 1));
taking the capacity retention rate obtained from the cycle test as a measured cycle capacity retention rate (See Page 590 column 2 “Both capacity and internal resistance (~1 kHz) were measured (i.e. measurement values) after each cycle (i.e. a cycle test).” See also Page 591 column 2 “we will define a failed battery as one that has lost 20% of its initial capacity” and Page 592 column 1 “A Weibull analysis might involve using median ranks [56] (MR) to estimate the values of F at the measured failure times”. In other words, the measurement values of the capacitances are used as a reference to determine the values of F (i.e. which is directly related to the cycle capacity retention rate S(t) as S(t) = 1- F(t) (See Page 591 column 1)) by implementing a Weibull analysis.), and Weibull plotting the measured cycle capacity retention rate in relation to ln(cycle number) and ln(ln(l/ capacity retention rate)) to prepare a Weibull plot of the cycle capacity retention rate (See Fig. 3b (i.e. the points plotted on 3b constitutes Weibull plotting the measured cycle capacity retention rate values), where the x-axis is the ln(cycles to failure) (i.e ln(cycle number)) and the y-axis is the ln(-ln(1-F)) (i.e. ln(ln(l/ capacity retention rate) and 1-F = S (See Page 591 column 1) which is the cycle capacity retention rate));
estimating a cycle deterioration prediction line, represented by a straight-line equation, from the Weibull plot of the cycle capacity retention rate (See Fig. 3b (i.e. the Weibull plot of the cycle capacity retention rate 1 - F = S (See Page 591 column 1) ) “OLS fit using the MRR technique for (a) 3-Weibull and (b) 2-Weibull distributions. The fits (i.e. estimating a cycle deterioration prediction line (i.e. the straight-line through the data points in Fig. 3b)) appear to provide strong constraints on the slopes, but this perception is overly optimistic.” See also Page 592 column 1, where the equation A2 constitutes a straight-line equation);
obtaining the Weibull coefficients mc and ηc from a slope and an intercept of the cycle deterioration prediction line (See equation A2, which describes the cycle deterioration prediction line, the slope is
β
and the intercept is
-
βln
η
, from which the Weibull coefficients
m
c
=
β
and
η
c
=
η
can be obtained); and
determining the cycle capacity retention rate from the Weibull coefficients mc and ηc and the formula (2) (See equation A2 (which is formula (2) rearranged when t is the cycle), which describes the cycle deterioration prediction line with the Weibull coefficients
m
c
=
β
and
η
c
=
η
, and solve for the cycle capacity retention rate 1-F(t) = S(t) (See Page 591 column 1)).
Harris is silent to the language of
a float test.
Börger teaches
a float test (See Para[0044] “t: is the measured (i.e., in particular, presently accrued) operating duration (i.e. the operating duration is the float test, and it is measured for measurement values) of the battery cell”. Examiner notes that the limitations of “float capacity retention rate”, “measured float capacity retention rate”, “ln(period)”, “ln(ln(l/capacity retention rate))”, “float deterioration prediction line”, “mf”, and “
η
f
” follow from a substitution of the cycle test in Harris with the float test from Börger.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Harris wherein a float test is used like in Börger in order to have two separate tests for the capacity retention rates to calculate the state of health in a more informed manner.
With regards to Claim 3, Harris and Börger teach the limitations of Claim 1. Harris is silent to the language of
determining the state of health from four arithmetic operations on the float capacity retention rate and the cycle capacity retention rate.
Börger teaches
determining the state of health from four arithmetic operations on the float capacity retention rate and the cycle capacity retention rate (See equation 11, where the SOH is obtained from equation 11 by doing 4 operations: i) moving the “1” to the left side of equation 11 ii) multiplying equation 11 by -1 on both sides iii) expanding the exponent on the right hand side and iv) factorizing the sum of the exponent into a product of exponentials, thereby identifying the float capacity retention rate
e
α
(
t
-
t
0
T
-
t
0
)
b
and the cycle capacity retention rate
e
β
(
n
-
n
0
N
-
n
0
)
b
. See also Para[0041] “This adapted Weibull distribution describes the probability of a failure of a battery cell in percent. (i.e. a State of Health)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Harris wherein determining the state of health from four arithmetic operations on the float capacity retention rate and the cycle capacity retention rate is done like in Börger in order to systematically calculate the state of health from two separate sources of capacity retention rates. Examiner notes that Harris already relates the state of health to S(t) = 1-F(t) (See Page 591 column 1 in Harris and See Page 591 column 2 “we will define a failed battery as one that has lost 20% of its initial capacity” and Page 592 column 1 “A Weibull analysis might involve using median ranks [56] (MR) to estimate the values of F at the measured failure times”. In other words, the measurement values of the capacitances are used as a reference to determine the values of F (i.e. which is directly related to the cycle capacity retention rate S(t) as S(t) = 1- F(t) (See Page 591 column 1)) by implementing a Weibull analysis. Therefore, the failure rate (which can function as a state of health) is related to the S(t), which is equal to the Weibull distribution (and the equation 11 in Börger is also a Weibull distribution), see equation A1.)
With regards to Claim 4, Harris and Börger teach the limitations of Claim 1. Harris is silent to the language of
estimating the state of health as the state of health in the period t or at the cycle number N from the following formula (A):
S
t
a
t
e
o
f
h
e
a
l
t
h
S
O
H
=
exp
-
t
η
f
m
f
×
exp
-
N
η
c
m
c
…
A
.
Börger teaches
estimating the state of health as the state of health in the period t or at the cycle number N from the following formula (A) (See equation 11 where the product of the float capacity retention rate
e
α
(
t
-
t
0
T
-
t
0
)
b
and the cycle capacity retention rate
e
β
(
n
-
n
0
N
-
n
0
)
b
can be obtained by expanding the exponent (set b = 2, which is possible because in Para[0060] “For wear and fatigue failures, a form parameter b>1 is selected”)on the right hand side and looking at cross terms of exponents where the float capacity retention rate
e
α
(
t
-
t
0
T
-
t
0
)
b
and the cycle capacity retention rate
e
β
(
n
-
n
0
N
-
n
0
)
b
appear. ):
S
t
a
t
e
o
f
h
e
a
l
t
h
S
O
H
=
exp
-
t
η
f
m
f
×
exp
-
N
η
c
m
c
…
A
.
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Harris wherein estimating the state of health as the state of health in the period t or at the cycle number N from the following formula (A)
S
t
a
t
e
o
f
h
e
a
l
t
h
S
O
H
=
exp
-
t
η
f
m
f
×
exp
-
N
η
c
m
c
…
A
.
is done like in Börger in order to provide a concise formulation for calculating the State of health by combining two separate capacity retention rates.
With regards to Claim 6, Harris teaches
a step of obtaining Weibull coefficients mf, ηf corresponding to a float capacity retention rate (See Page 591 column 2, the equation A1 which contains β which corresponds to mf, and η corresponds to ηf. See also Page 591 Column 1 “A statistical analysis of failure [51] can begin by defining four time-dependent (or cycle number-dependent) functions.” Therefore, t in equation A1 can be a cycle or a time (i.e. making S(t) a float capacity retention rate.).), and the float capacity retention rate represented by the following formula (1) (See equation A1, where β corresponds to mf, and η corresponds to ηf, and t corresponds to the time),
a step of obtaining Weibull coefficients mc, ηc corresponding to a cycle capacity retention rate (See Page 591 column 2, the equation A1, which contains β which corresponds to mc, and η which corresponds to ηc. See also Page 591 column 1 “the fraction of all of the cells in a population that fail on cycle t (i.e. t in equation A1 is the cycle number N)”. See also Page 591 Column 1 “A statistical analysis of failure [51] can begin by defining four time-dependent (or cycle number-dependent) functions.” Therefore t in equation A1 can be a cycle or a time (i.e. making S(t) a cycle capacity retention rate.).), and the cycle capacity retention rate represented by the following formula (2) (See equation A1, where β corresponds to mc, and η corresponds to ηc, and t (i.e. the cycle number) corresponds to N), from measurement values of a cycle test (See Page 590 column 2 “Both capacity and internal resistance (~1 kHz) were measured (i.e. measurement values) after each cycle (i.e. a cycle test).”) for determining the capacity retention rate (See Page 591 column 2 “we will define a failed battery as one that has lost 20% of its initial capacity” and Page 592 column 1 “A Weibull analysis might involve using median ranks [56] (MR) to estimate the values of F at the measured failure times”. In other words, the measurement values of the capacitances are used as a reference to determine the values of F (i.e. which is directly related to the cycle capacity retention rate S(t) as S(t) = 1- F(t) (See Page 591 column 1)) by implementing a Weibull analysis.);
and a step of estimating the state of health in a period t or at a cycle number N (Examiner notes an option is presented by “or”, and Examiner chooses “cycle number N”) from the cycle capacity retention rate of the secondary battery (See Page 591 column 2 “we will define a failed battery (i.e. a secondary battery) as one that has lost 20% of its initial capacity” and Page 592 column 1 “A Weibull analysis might involve using median ranks [56] (MR) to estimate the values of F at the measured failure times”. See also Page 591 column 1 “
F
t
=
∫
0
t
f
t
'
d
t
'
, the failure function, the cumulative fraction of cells that have failed by cycle t. ”In other words, the failure function is estimated at a cycle number t, which is a measure of the health of the secondary battery. Note that the failure function is related to the survival function, i.e. the cycle capacity retention rate, as S(t) = 1- F(t) (See Page 591 column 1). Therefore, the cycle capacity retention rate is used to estimate the SOH.).
[Equation 4]
Float capacity retention rate =
e
x
p
{
-
(
t
η
f
)
m
f
}
... (1) (See equation A1, where β corresponds to mf, and η corresponds to ηf, and t corresponds to the time)
Cycle capacity retention rate =
e
x
p
{
-
(
N
η
c
)
m
c
}
... (2) (See equation A1, where β corresponds to mc, and η corresponds to ηc, and t (i.e. the cycle number) corresponds to N).
Harris is silent to the language of
from measurement values of a float test for determining the capacity retention rate;
a step of estimating the state of health in a period t or at a cycle number N from the float capacity retention rate and the cycle capacity retention rate.
Börger teaches
from measurement values of a float test for determining the capacity retention rate (See Para[0044] “t: is the measured (i.e., in particular, presently accrued) operating duration (i.e. the operating duration is the float test, and it is measured for measurement values) of the battery cell”. See also Para[0042] equation 11, where the capacity retention rate (interpreted here as the float capacity retention rate) is the exponential factor
e
α
(
t
-
t
0
T
-
t
0
)
b
);
a step of estimating the state of health in a period t or at a cycle number N from the float capacity retention rate and the cycle capacity retention rate (See Para[0042], equation 11, where t is the period (See Para[0044] “t: is the measured (i.e., in particular, presently accrued) operating duration (i.e. the duration is the period)”) and n is the cycle number (See Para[0047] “n: is the measured (in particular, accrued) number of cycles of the battery cell”), the float capacity retention rate is the exponential factor
e
α
(
t
-
t
0
T
-
t
0
)
b
, and the cycle capacity retention rate is the exponential factor
e
β
(
n
-
n
0
N
-
n
0
)
b
, which is obtained after expanding the exponent in equation 11. See also Para[0041] “This adapted Weibull distribution describes the probability of a failure of a battery cell in percent. (i.e. a State of Health)”, so the State of Health is estimated by the period t, the cycle number n, the float capacity retention rate, and the cycle capacity retention rate).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Harris wherein measurement values of a float test for determining the capacity retention rate and a step of estimating the state of health in a period t or at a cycle number N from the float capacity retention rate and the cycle capacity retention rate is done like in Börger in order to estimate the state of health using more than just the cycle capacity retention rate, which will yield more accurate representations of the performance of the secondary battery.
With regards to Claim 7, Harris and Börger teach the limitations of Claim 6. Harris further teaches
a step of taking the capacity retention rate obtained from the test as a measured capacity retention rate (See Page 590 column 2 “Both capacity and internal resistance (~1 kHz) were measured (i.e. measurement values) after each cycle (i.e. a test).” See also Page 591 column 2 “we will define a failed battery as one that has lost 20% of its initial capacity” and Page 592 column 1 “A Weibull analysis might involve using median ranks [56] (MR) to estimate the values of F at the measured failure times”. In other words, the measurement values of the capacitances are used as a reference to determine the values of F (i.e. which is directly related to the capacity retention rate S(t) (i.e. equation A1) as S(t) = 1- F(t) (See Page 591 column 1), so a measured capacity retention rate is obtained.) by implementing a Weibull analysis. See also Page 591 Column 1 “A statistical analysis of failure [51] can begin by defining four time-dependent (or cycle number-dependent) functions.” Therefore t in equation A1 can be a cycle or a time (i.e. making S(t) a float capacity retention rate when t corresponds to a period/time), and Weibull plotting the capacity retention rate in relation to ln(cycle) and ln(ln(l / capacity retention rate)) to prepare a Weibull plot of the capacity retention rate (See Fig. 3b (i.e. the points plotted on 3b constitutes Weibull plotting the capacity retention rate values), where the x-axis is the ln(cycles to failure) (i.e. ln(cycle number)) and the y-axis is the ln(-ln(1-F)) (i.e. ln(ln(l/ capacity retention rate) and 1-F = S (See Page 591 column 1) which is the cycle capacity retention rate);
a step of estimating a deterioration prediction line, represented by a straight-line equation, from the Weibull plot of the capacity retention rate (See Fig. 3b (i.e. the Weibull plot of the capacity retention rate 1 - F = S (See Page 591 column 1) ) “OLS fit using the MRR technique for (a) 3-Weibull and (b) 2-Weibull distributions. The fits (i.e. estimating a deterioration prediction line (i.e. the straight-line through the data points in Fig. 3b)) appear to provide strong constraints on the slopes, but this perception is overly optimistic.” See also Page 592 column 1, where the equation A2 constitutes a straight-line equation. Examiner notes equation A2 can be used for both cycle and float tests, as it is derived from S(t) and Page 591 Column 1 recites “A statistical analysis of failure [51] can begin by defining four time-dependent (or cycle number-dependent) functions (i.e. including S(t)).” Therefore, t in equations A1 and A2 can be a cycle or a time (i.e. making S(t) a float capacity retention rate when t corresponds to a period/time);
a step of obtaining the Weibull coefficients m and η from a slope and an intercept of the deterioration prediction line (See equation A2, which describes the deterioration prediction line, the slope is
β
and the intercept is
-
βln
η
, from which the Weibull coefficients
m
=
β
and
η
can be obtained);
a step of determining the capacity retention rate from the Weibull coefficients m and η and the formula (1) (See equation A2 (which is formula (1) rearranged when t is the time/period), which describes the deterioration prediction line with the Weibull coefficients
m
=
β
and
η
, and solve for the capacity retention rate 1-F(t) = S(t) (See Page 591 column 1));
a step of taking the capacity retention rate obtained from the cycle test as a measured cycle capacity retention rate (See Page 590 column 2 “Both capacity and internal resistance (~1 kHz) were measured (i.e. measurement values) after each cycle (i.e. a cycle test).” See also Page 591 column 2 “we will define a failed battery as one that has lost 20% of its initial capacity” and Page 592 column 1 “A Weibull analysis might involve using median ranks [56] (MR) to estimate the values of F at the measured failure times”. In other words, the measurement values of the capacitances are used as a reference to determine the values of F (i.e. which is directly related to the cycle capacity retention rate S(t) as S(t) = 1- F(t) (See Page 591 column 1)) by implementing a Weibull analysis.), and Weibull plotting the cycle capacity retention rate in relation to ln(cycle number) and ln(ln(l/ capacity retention rate)) to prepare a Weibull plot of the cycle capacity retention rate (See Fig. 3b (i.e. the points plotted on 3b constitutes Weibull plotting the measured cycle capacity retention rate values), where the x-axis is the ln(cycles to failure) (i.e ln(cycle)) and the y-axis is the ln(-ln(1-F)) (i.e. ln(ln(l/ capacity retention rate) and 1 - F = S (See Page 591 column 1) which is the cycle capacity retention rate));
a step of estimating a cycle deterioration prediction line, represented by a straight-line equation, from the Weibull plot of the cycle capacity retention rate (See Fig. 3b (i.e. the Weibull plot of the cycle capacity retention rate 1 - F = S (See Page 591 column 1) ) “OLS fit using the MRR technique for (a) 3-Weibull and (b) 2-Weibull distributions. The fits (i.e. a step of estimating a cycle deterioration prediction line (i.e. the straight-line through the data points in Fig. 3b)) appear to provide strong constraints on the slopes, but this perception is overly optimistic.” See also Page 592 column 1, where the equation A2 constitutes a straight-line equation );
a step of obtaining the Weibull coefficients mc and ηc from a slope and an intercept of the cycle deterioration prediction line (See equation A2, which describes the cycle deterioration prediction line, the slope is
β
and the intercept is
-
βln
η
, from which the Weibull coefficients
m
c
=
β
and
η
c
=
η
can be obtained); and
a step of determining the cycle capacity retention rate from the Weibull coefficients mc and ηc and the formula (2) (See equation A2 (which is formula (2) rearranged when t is the cycle), which describes the cycle deterioration prediction line with the Weibull coefficients
m
c
=
β
and
η
c
=
η
, and solve for the cycle capacity retention rate 1-F(t) = S(t) (See Page 591 column 1)).
Harris is silent to the language of
a float test.
Börger teaches
a float test (See Para[0044] “t: is the measured (i.e., in particular, presently accrued) operating duration (i.e. the operating duration is the float test, and it is measured for measurement values) of the battery cell”. Examiner notes that the limitations of “float capacity retention rate”, “measured float capacity retention rate”, “ln(period)”, “ln(ln(l/capacity retention rate))”, “float deterioration prediction line”, “mf”, and “
η
f
” follow from a substitution of the cycle test in Harris with the float test from Börger.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Harris wherein a float test is used like in Börger in order to have two separate tests for the capacity retention rates to calculate the state of health in a more informed manner.
With regards to Claim 8, Harris and Börger teach the limitations of Claim 6. Harris is silent to the language of
a step of determining the state of health from four arithmetic operations on the float capacity retention rate and the cycle capacity retention rate.
Börger teaches
a step of determining the state of health from four arithmetic operations on the float capacity retention rate and the cycle capacity retention rate (See equation 11, where the SOH is obtained from equation 11 by doing 4 operations: i) moving the “1” to the left side of equation 11 ii) multiplying equation 11 by -1 on both sides iii) expanding the exponent on the right hand side and iv) factorizing the sum of the exponent into a product of exponentials, thereby identifying the float capacity retention rate
e
α
(
t
-
t
0
T
-
t
0
)
b
and the cycle capacity retention rate
e
β
(
n
-
n
0
N
-
n
0
)
b
. See also Para[0041] “This adapted Weibull distribution describes the probability of a failure of a battery cell in percent. (i.e. a State of Health)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Harris wherein determining the state of health from four arithmetic operations on the float capacity retention rate and the cycle capacity retention rate is done like in Börger in order to systematically calculate the state of health from two separate sources of capacity retention rates. Examiner notes that Harris already relates the state of health to S(t) = 1-F(t) (See Page 591 column 1 in Harris and See Page 591 column 2 “we will define a failed battery as one that has lost 20% of its initial capacity” and Page 592 column 1 “A Weibull analysis might involve using median ranks [56] (MR) to estimate the values of F at the measured failure times”. In other words, the measurement values of the capacitances are used as a reference to determine the values of F (i.e. which is directly related to the cycle capacity retention rate S(t) as S(t) = 1- F(t) (See Page 591 column 1)) by implementing a Weibull analysis. Therefore, the failure rate (which can function as a state of health) is related to the S(t), which is equal to the Weibull distribution (and the equation 11 in Börger is also a Weibull distribution), see equation A1.)
With regards to Claim 9, Harris and Börger teach the limitations of Claim 6. Harris is silent to the language of
a step of estimating the state of health as the state of health in the period t or at the cycle number N from the following formula (A):
S
t
a
t
e
o
f
h
e
a
l
t
h
S
O
H
=
exp
-
t
η
f
m
f
×
exp
-
N
η
c
m
c
…
A
.
Börger teaches
a step of estimating the state of health as the state of health in the period t or at the cycle number N from the following formula (A) (See equation 11 where the product of the float capacity retention rate
e
α
(
t
-
t
0
T
-
t
0
)
b
and the cycle capacity retention rate
e
β
(
n
-
n
0
N
-
n
0
)
b
can be obtained by expanding the exponent (set b = 2, which is possible because in Para[0060] “For wear and fatigue failures, a form parameter b>1 is selected”)on the right hand side and looking at cross terms of exponents where the float capacity retention rate
e
α
(
t
-
t
0
T
-
t
0
)
b
and the cycle capacity retention rate
e
β
(
n
-
n
0
N
-
n
0
)
b
appear. ):
S
t
a
t
e
o
f
h
e
a
l
t
h
S
O
H
=
exp
-
t
η
f
m
f
×
exp
-
N
η
c
m
c
…
A
.
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Harris wherein a step of estimating the state of health as the state of health in the period t or at the cycle number N from the following formula (A)
S
t
a
t
e
o
f
h
e
a
l
t
h
S
O
H
=
exp
-
t
η
f
m
f
×
exp
-
N
η
c
m
c
…
A
.
is done like in Börger in order to provide a concise formulation for calculating the State of health by combining two separate capacity retention rates.
With regards to Claim 11, Harris teaches
a step of obtaining Weibull coefficients mf, ηf corresponding to a float capacity retention rate (See Page 591 column 2, the equation A1 which contains β which corresponds to mf, and η corresponds to ηf. See also Page 591 Column 1 “A statistical analysis of failure [51] can begin by defining four time-dependent (or cycle number-dependent) functions.” Therefore, t in equation A1 can be a cycle or a time (i.e. making S(t) a float capacity retention rate.).), and the float capacity retention rate represented by the following formula (1) (See equation A1, where β corresponds to mf, and η corresponds to ηf, and t corresponds to the time);
a step of obtaining Weibull coefficients mc, ηc corresponding to a cycle capacity retention rate (See Page 591 column 2, the equation A1, which contains β which corresponds to mc, and η which corresponds to ηc. See also Page 591 column 1 “the fraction of all of the cells in a population that fail on cycle t (i.e. t in equation A1 is the cycle number N)”. See also Page 591 Column 1 “A statistical analysis of failure [51] can begin by defining four time-dependent (or cycle number-dependent) functions.” Therefore t in equation A1 can be a cycle or a time (i.e. making S(t) a cycle capacity retention rate.).), and the cycle capacity retention rate represented by the following formula (2) (See equation A1, where β corresponds to mc, and η corresponds to ηc, and t (i.e. the cycle number) corresponds to N), from measurement values of the cycle test (See Page 590 column 2 “Both capacity and internal resistance (~1 kHz) were measured (i.e. measurement values) after each cycle (i.e. a cycle test).”); and
a step of estimating the state of health in the period t or at the cycle number N (Examiner notes an option is presented by “or”, and Examiner chooses “cycle number N”) from the cycle capacity retention rate of the secondary battery (See Page 591 column 2 “we will define a failed battery (i.e. a secondary battery) as one that has lost 20% of its initial capacity” and Page 592 column 1 “A Weibull analysis might involve using median ranks [56] (MR) to estimate the values of F at the measured failure times”. See also Page 591 column 1 “
F
t
=
∫
0
t
f
t
'
d
t
'
, the failure function, the cumulative fraction of cells that have failed by cycle t. ”In other words, the failure function is estimated at a cycle number t, which is a measure of the health of the secondary battery. Note that the failure function is related to the survival function, i.e. the cycle capacity retention rate, as S(t) = 1- F(t) (See Page 591 column 1). Therefore, the cycle capacity retention rate is used to estimate the SOH.).
[Equation 7]
[Equation 4]
Float capacity retention rate =
e
x
p
{
-
(
t
η
f
)
m
f
}
... (1) (See equation A1, where β corresponds to mf, and η corresponds to ηf, and t corresponds to the time)
Cycle capacity retention rate =
e
x
p
{
-
(
N
η
c
)
m
c
}
... (2) (See equation A1, where β corresponds to mc, and η corresponds to ηc, and t (i.e. the cycle number) corresponds to N).
Harris is silent to the language of
storage means storing data on a cycle test and a float test,
data acquisition means for acquiring data on the secondary battery in operation, a period t, and a cycle number N,
from measurement values of the float test;
a step of estimating the state of health in the period t or at the cycle number N from the float capacity retention rate and the cycle capacity retention rate.
Börger teaches
storage means storing data on a cycle test and a float test (See Fig. 2 the database 20 and Para[0099] “ For redundancy or outsourcing of the registered operating variables (i.e. where the operating variables include those from a cycle test and a float test, See Para[0041] “the failure prognosis is ascertained specifically on the basis of a Weibull distribution in the second operation for at least one battery cell of the battery by the lifetime-relevant operating variables” and See Para[0042], equation 11, where t is the period (See Para[0044] “t: is the measured (i.e., in particular, presently accrued) operating duration (i.e. the duration is the period, and the operating during this duration constitutes a float test)”) and n is the cycle number (See Para[0047] “n: is the measured (in particular, accrued) number of cycles (i.e. the act of measuring the number of cycles comprises a cycle test of the battery) of the battery cell”)) and optionally also the categorization, the battery management system 14 is configured to transmit the above-described sensor data and operating variables and optionally also the categories of the cells 4 to a database 20 (see FIG. 2).”); and
data acquisition means for acquiring data on the secondary battery in operation, a period t, and a cycle number N (See Para[0099] “ For redundancy or outsourcing of the registered operating variables (i.e. where the operating variables include those from a cycle test and a float test, See Para[0041] “the failure prognosis is ascertained specifically on the basis of a Weibull distribution in the second operation for at least one battery cell (i.e. a secondary battery) of the battery by the lifetime-relevant operating variables” and See Para[0042], equation 11, where t is the period (See Para[0044] “t: is the measured (i.e., in particular, presently accrued) operating duration (i.e. the duration is the period, and the operating during this duration constitutes a float test)”) and n is the cycle number (See Para[0047] “n: is the measured (in particular, accrued) number of cycles (i.e. the act of measuring the number of cycles comprises a cycle test of the battery) of the battery cell”)) and optionally also the categorization, the battery management system 14 is configured to transmit the above-described sensor data and operating variables and optionally also the categories of the cells 4 to a database 20 (see FIG. 2). This database 20 is implemented here on a server 22 (i.e. a data acquisition means, as the database obtains the data from the secondary battery, the period t, and the cycle number N, and the database is inside the server 22 )”);
the apparatus (The entire Fig. 2 is the apparatus) executing
from measurement values of the float test (See Para[0044] “t: is the measured (i.e., in particular, presently accrued) operating duration (i.e. the operating duration is the float test, and it is measured for measurement values) of the battery cell”);
a step of estimating the state of health in the period t or at the cycle number N from the float capacity retention rate and the cycle capacity retention rate (See Para[0042], equation 11, where t is the period (See Para[0044] “t: is the measured (i.e., in particular, presently accrued) operating duration (i.e. the duration is the period)”) and n is the cycle number (See Para[0047] “n: is the measured (in particular, accrued) number of cycles of the battery cell”), the float capacity retention rate is the exponential factor
e
α
(
t
-
t
0
T
-
t
0
)
b
, and the cycle capacity retention rate is the exponential factor
e
β
(
n
-
n
0
N
-
n
0
)
b
, which is obtained after expanding the exponent in equation 11. See also Para[0041] “This adapted Weibull distribution describes the probability of a failure of a battery cell in percent. (i.e. a State of Health)”, so the State of Health is estimated by the period t, the cycle number n, the float capacity retention rate, and the cycle capacity retention rate).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Harris wherein storage means storing data on a cycle test and a float test, data acquisition means for acquiring data on the secondary battery in operation, a period t, and a cycle number N, measurement values of a float test for determining the state of health and a step of estimating the state of health in the period t or at the cycle number N from the float capacity retention rate and the cycle capacity retention rate is done like in Börger in order to have an efficient means to store and access data via the storage means and the data acquisition means respectively, and to estimate the State of Health using more than just the cycle capacity retention rate, which will yield more accurate representations of the performance of the secondary battery.
With regards to Claim 12, Harris and Börger teach the limitations of Claim 11. Harris further teaches
a step of taking the capacity retention rate obtained from the test as a measured capacity retention rate (See Page 590 column 2 “Both capacity and internal resistance (~1 kHz) were measured (i.e. measurement values) after each cycle (i.e. a test).” See also Page 591 column 2 “we will define a failed battery as one that has lost 20% of its initial capacity” and Page 592 column 1 “A Weibull analysis might involve using median ranks [56] (MR) to estimate the values of F at the measured failure times”. In other words, the measurement values of the capacitances are used as a reference to determine the values of F (i.e. which is directly related to the capacity retention rate S(t) (i.e. equation A1) as S(t) = 1- F(t) (See Page 591 column 1), so a measured capacity retention rate is obtained.) by implementing a Weibull analysis. See also Page 591 Column 1 “A statistical analysis of failure [51] can begin by defining four time-dependent (or cycle number-dependent) functions.” Therefore t in equation A1 can be a cycle or a time (i.e. making S(t) a float capacity retention rate when t corresponds to a period/time), and Weibull plotting the capacity retention rate in relation to ln(cycle) and ln(ln(l/ capacity retention rate)) to prepare a Weibull plot of the capacity retention rate (See Fig. 3b (i.e. the points plotted on 3b constitutes Weibull plotting the capacity retention rate values), where the x-axis is the ln(cycles to failure) (i.e. ln(cycle number)) and the y-axis is the ln(-ln(1-F)) (i.e. ln(ln(l/ capacity retention rate) and 1-F = S (See Page 591 column 1) which is the capacity retention rate));
a step of estimating a deterioration prediction line, represented by a straight-line equation, from the Weibull plot of the capacity retention rate (See Fig. 3b (i.e. the Weibull plot of the capacity retention rate 1 - F = S (See Page 591 column 1) ) “OLS fit using the MRR technique for (a) 3-Weibull and (b) 2-Weibull distributions. The fits (i.e. estimating a deterioration prediction line (i.e. the straight-line through the data points in Fig. 3b)) appear to provide strong constraints on the slopes, but this perception is overly optimistic.” See also Page 592 column 1, where the equation A2 constitutes a straight-line equation. Examiner notes equation A2 can be used for both cycle and float tests, as it is derived from S(t) and Page 591 Column 1 recites “A statistical analysis of failure [51] can begin by defining four time-dependent (or cycle number-dependent) functions (i.e. including S(t)).” Therefore, t in equations A1 and A2 can be a cycle or a time (i.e. making S(t) a float capacity retention rate when t corresponds to a period/time);
a step of obtaining the Weibull coefficients m and η from a slope and an intercept of the deterioration prediction line (See equation A2, which describes the deterioration prediction line, the slope is
β
and the intercept is
-
βln
η
, from which the Weibull coefficients
m
=
β
and
η
can be obtained);
a step of determining the capacity retention rate from the Weibull coefficients m and η and the formula (1) (See equation A2 (which is formula (1) rearranged when t is the time/period), which describes the deterioration prediction line with the Weibull coefficients
m
=
β
and
η
, and solve for the capacity retention rate 1-F(t) = S(t) (See Page 591 column 1))
a step of taking the capacity retention rate obtained from the cycle test as a measured cycle capacity retention rate (See Page 590 column 2 “Both capacity and internal resistance (~1 kHz) were measured (i.e. measurement values) after each cycle (i.e. a cycle test).” See also Page 591 column 2 “we will define a failed battery as one that has lost 20% of its initial capacity” and Page 592 column 1 “A Weibull analysis might involve using median ranks [56] (MR) to estimate the values of F at the measured failure times”. In other words, the measurement values of the capacitances are used as a reference to determine the values of F (i.e. which is directly related to the cycle capacity retention rate S(t) as S(t) = 1- F(t) (See Page 591 column 1)) by implementing a Weibull analysis.), and Weibull plotting the cycle capacity retention rate in relation to ln(cycle number) and ln(ln(l/ capacity retention rate)) to prepare a Weibull plot of the cycle capacity retention rate (See Fig. 3b (i.e. the points plotted on 3b constitute Weibull plotting the measured cycle capacity retention rate values), where the x-axis is the ln(cycles to failure) (i.e. ln(cycle number)) and the y-axis is the ln(-ln(1-F)) (i.e. ln(ln(l/ capacity retention rate) as 1 - F = S (See Page 591 column 1) which is the cycle capacity retention rate));
a step of estimating a cycle deterioration prediction line, represented by a straight-line equation, from the Weibull plot of the cycle capacity retention rate (See Fig. 3b (i.e. the Weibull plot of the cycle capacity retention rate 1 - F = S (See Page 591 column 1) ) “OLS fit using the MRR technique for (a) 3-Weibull and (b) 2-Weibull distributions. The fits (i.e. a step of estimating a cycle deterioration prediction line (i.e. the straight-line through the data points in Fig. 3b)) appear to provide strong constraints on the slopes, but this perception is overly optimistic.” See also Page 592 column 1, where the equation A2 constitutes a straight-line equation );
a step of obtaining the Weibull coefficients mc and ηc from a slope and an intercept of the cycle deterioration prediction line (See equation A2, which describes the cycle deterioration prediction line, the slope is
β
and the intercept is
-
βln
η
, from which the Weibull coefficients
m
c
=
β
and
η
c
=
η
can be obtained); and
a step of determining the cycle capacity retention rate from the Weibull coefficients mc and ηc and the formula (2) (See equation A2 (which is formula (2) rearranged when t is the cycle), which describes the cycle deterioration prediction line with the Weibull coefficients
m
c
=
β
and
η
c
=
η
, and solve for the cycle capacity retention rate 1-F(t) = S(t) (See Page 591 column 1)).
Harris is silent to the language of
a float test.
Börger teaches
a float test (See Para[0044] “t: is the measured (i.e., in particular, presently accrued) operating duration (i.e. the operating duration is the float test, and it is measured for measurement values) of the battery cell”. Examiner notes that the limitations of “float capacity retention rate”, “measured float capacity retention rate”, “ln(period)”, “ln(ln(l/capacity retention rate))”, “float deterioration prediction line”, “mf”, and “
η
f
” follow from a substitution of the cycle test in Harris with the float test from Börger.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Harris wherein a float test is used like in Börger in order to have two separate tests for the capacity retention rates to calculate the state of health in a more informed manner.
With regards to Claim 13, Harris and Börger teach the limitations of Claim 11. Harris is silent to the language of
a step of determining the state of health from four arithmetic operations on the float capacity retention rate and the cycle capacity retention rate.
Börger teaches
a step of determining the state of health from four arithmetic operations on the float capacity retention rate and the cycle capacity retention rate (See equation 11, where the SOH is obtained from equation 11 by doing 4 operations: i) moving the “1” to the left side of equation 11 ii) multiplying equation 11 by -1 on both sides iii) expanding the exponent on the right hand side and iv) factorizing the sum of the exponent into a product of exponentials, thereby identifying the float capacity retention rate
e
α
(
t
-
t
0
T
-
t
0
)
b
and the cycle capacity retention rate
e
β
(
n
-
n
0
N
-
n
0
)
b
. See also Para[0041] “This adapted Weibull distribution describes the probability of a failure of a battery cell in percent. (i.e. a State of Health)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Harris wherein determining the state of health from four arithmetic operations on the float capacity retention rate and the cycle capacity retention rate is done like in Börger in order to systematically calculate the state of health from two separate sources of capacity retention rates. Examiner notes that Harris already relates the state of health to S(t) = 1-F(t) (See Page 591 column 1 in Harris and See Page 591 column 2 “we will define a failed battery as one that has lost 20% of its initial capacity” and Page 592 column 1 “A Weibull analysis might involve using median ranks [56] (MR) to estimate the values of F at the measured failure times”. In other words, the measurement values of the capacitances are used as a reference to determine the values of F (i.e. which is directly related to the cycle capacity retention rate S(t) as S(t) = 1- F(t) (See Page 591 column 1)) by implementing a Weibull analysis. Therefore, the failure rate (which can function as a state of health) is related to the S(t), which is equal to the Weibull distribution (and the equation 11 in Börger is also a Weibull distribution), see equation A1.)
With regards to Claim 14, Harris and Börger teach the limitations of Claim 11. Harris is silent to the language of
a step of estimating the state of health as the state of health in the period t or at the cycle number N from the following formula (A):
S
t
a
t
e
o
f
h
e
a
l
t
h
S
O
H
=
exp
-
t
η
f
m
f
×
exp
-
N
η
c
m
c
…
A
.
Börger teaches
a step of estimating the state of health as the state of health in the period t or at the cycle number N from the following formula (A) (See equation 11 where the product of the float capacity retention rate
e
α
(
t
-
t
0
T
-
t
0
)
b
and the cycle capacity retention rate
e
β
(
n
-
n
0
N
-
n
0
)
b
can be obtained by expanding the exponent (set b = 2, which is possible because in Para[0060] “For wear and fatigue failures, a form parameter b>1 is selected”)on the right hand side and looking at cross terms of exponents where the float capacity retention rate
e
α
(
t
-
t
0
T
-
t
0
)
b
and the cycle capacity retention rate
e
β
(
n
-
n
0
N
-
n
0
)
b
appear. ):
S
t
a
t
e
o
f
h
e
a
l
t
h
S
O
H
=
exp
-
t
η
f
m
f
×
exp
-
N
η
c
m
c
…
A
.
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Harris wherein a step of estimating the state of health as the state of health in the period t or at the cycle number N from the following formula (A)
S
t
a
t
e
o
f
h
e
a
l
t
h
S
O
H
=
exp
-
t
η
f
m
f
×
exp
-
N
η
c
m
c
…
A
.
is done like in Börger in order to provide a concise formulation for calculating the State of health by combining two separate capacity retention rates.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOSTOFA AHMED HISHAM whose telephone number is (571)272-8773. The examiner can normally be reached Monday - Friday, 7:00 a.m. - 4 p.m. ET.
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/MOSTOFA AHMED HISHAM/Examiner, Art Unit 2857
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2857
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