DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
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.
Claims 1-2, 4-5, 7-11, 13-14, 16-17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Imre (DE102020202307A1) in view of “Kalman filter” on Wikipedia.org (Accessed 15 April, 2026, with Wayback Machine Publication date of 4 July, 2022, having the link: https://web.archive.org/web/20220704201752/https://en.wikipedia.org/wiki/Kalman_filter; hereinafter “Wikipedia”).
Regarding claim 1, Imre discloses a system for estimating a state-of-charge of a battery assembly (¶13: “the invention relates to a method for determining the state of charge of a first electrochemical energy storage unit”; the “first electrochemical energy storage unit” is mapped to the battery assembly; ¶24: “the invention relates to a device for determining a state of charge and/or a self-discharge of an electrochemical energy storage unit of an electrical energy storage system according to the above descriptions”; that is, there is a system (the “device” and the “electrical energy storage system”) implementing the method), the system comprising:
a sensor cell (¶13: the “second electrochemical energy storage unit”; ¶7: “Electrochemical energy storage units include…battery cells”) coupled in series to the battery assembly (¶13: “The electrical energy storage system comprises at least two electrochemical energy storage units of different electrochemical types”; ¶12: “the at least two…electrochemical energy storage units of different electrochemical types are arranged alternately in series”), wherein the battery assembly has an assembly battery chemistry, the sensor cell has a sensor battery chemistry, and the assembly battery chemistry is different than the sensor battery chemistry (¶13: “the first electrochemical energy storage unit belongs to a first electrochemical type, in particular based on lithium iron phosphate, and the second electrochemical energy storage unit belongs to a second electrochemical type, in particular based on lithium nickel manganese oxide”); and
an estimator circuit coupled to the battery assembly and the sensor cell (¶24: “The device comprises at least one means, in particular an electronic control unit, which is configured to perform all steps of the disclosed method for determining a state of charge”; ¶25: “The minimum one means can include, for example, a battery management control unit and/or current sensors and/or voltage sensors and/or temperature sensors”; Imre discloses measuring storage unit voltages, so the device would be coupled to the battery assembly and sensor cell; see also Fig. 1 where device 13 is connected to the different electrochemical energy storage units 11, 12; ¶31 says units 11 and 12 have different electrochemical types), wherein the estimator circuit is operational to:
acquire a current sensor cell state-of-charge of the sensor cell based on a sensor cell model of the sensor cell and a sensor voltage across the sensor cell (¶15: “the state of charge [SOC] of the second electrochemical energy storage unit is determined as a function of the terminal voltage of the second electrochemical energy storage unit”; that is, the SOC is based on the voltage across the unit, and since there is a function relating terminal voltage to unit SOC, the SOC is based on a model of some kind); and
calculate a current battery assembly state-of-charge of the battery assembly based on the current sensor cell state-of-charge (¶16: “The state of charge of the first electrochemical energy storage unit is then determined as a function of the state of charge of the second electrochemical energy storage unit.”).
Imre does not explicitly disclose acquiring a sequence of current sensor cell state-of-charges of the sensor cell based on a sensor cell model of the sensor cell and a sequence of sensor voltages across the sensor cell; and
calculating a sequence of current battery assembly state-of-charges of the battery assembly based on the sequence of current sensor cell state-of-charges. However, this simply describes estimating SOC over time, and it would have been obvious to one of ordinary skill in the art practicing the invention of Imre to do so in order to track SOC as Imre’s electrical energy storage system operates.
Imre still does not explicitly disclose calculating an estimated battery assembly state-of-charge of the battery assembly and an estimated sensor cell state-of-charge of the sensor cell by filtering in parallel the sequence of current battery assembly state-of-charges and the sequence of current sensor cell state-of-charges.
However, Imre does disclose using a Kalman filter to determine the SOC of the second electrochemical energy storage unit (¶18).
Wikipedia teaches that Kalman filters are applied on time series measurement data to estimate unknown variables (Pg. 1, first paragraph). Kalman filters work in a two-phase process where a prediction of a current state variable is determined, then once a next measurement is observed, a new estimate is produced (Pg. 1, last paragraph). Kalman filters are recursive (Pg. 5, paragraph under “Technical description and context”). This recursion means that each updated state estimate is a function of a sequence of previous states (see Pg. 7, under “Details”: the a posteriori state estimate
x
^
k
|
k
at time
k
is a function of observations up to and including time
k
; this is the general case for recursive predictions). Finally, Wikipedia teaches that a state estimate may be a vector of values, where the vector includes a variable and a function of that variable (Pg. 10: in the example application, the state to be estimated by the Kalman filter is a vector of a truck’s position and velocity; velocity is a function of position). In the context of Imre, Imre teaches that the battery assembly’s SOC is a function of the sensor cell’s SOC (see ¶16 of Imre quoted above).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of Wikipedia with the invention of Imre by calculating an estimated battery assembly state-of-charge of the battery assembly and an estimated sensor cell state-of-charge of the sensor cell (the “Update” step of the Kalman filter) by filtering in parallel the sequence of current battery assembly state-of-charges and the sequence of current sensor cell state-of-charges. This would represent applying a known method for implementing a Kalman filter, and filtering the battery assembly and sensor cell SOCs in parallel would enable one to use the Kalman filter to estimate battery assembly SOC as well.
Regarding claim 10, the limitations of claim 10 are found in claim 1 and are rejected for the same reasons.
Regarding claim 19, many of the limitations of claim 19 are found in claim 1 and are rejected for the same reasons. Claim 19 also recites a vehicle which comprises the system of claim 1. While Imre doesn’t disclose a vehicle, Imre does describe that the context of the invention relates to vehicles (see ¶2-4, for example). Therefore, it would have been obvious to one of ordinary skill practicing the invention of Imre in view of Wikipedia to cause the system of claim 1 to be implemented in a vehicle. Doing so would enable one to estimate SOCs of vehicle battery units.
Regarding claims 2 and 11, Imre in view of Wikipedia teaches the limitations of claims 1 and 10. Furthermore, Wikipedia teaches modeling state variables as augmented state variables (bottom of Pg. 6 and top of Pg. 7: the Kalman filter assumes that the true state of a system is a function of a model including a state transition model
F
k
, and the model is also augmented with process noise
w
k
).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of Wikipedia with the invention of Imre in view of Wikipedia by causing the sensor cell model of the sensor cell (that is, the model used in the Kalman filter for filtering the sensor cell and battery assembly; see rejection of claim 1 and state transition model referenced above) to include the sequence of current battery assembly state-of-charges as an augmented state variable. Doing so would enable one to account for noise in the Kalman filter.
Regarding claims 4 and 13, Imre in view of Wikipedia teaches the limitations of claims 1 and 10, respectively. Furthermore, Wikipedia teaches that Extended Kalman filters do not require assumptions of linearity (Pg. 23, the second and third paragraphs under “Nonlinear filters”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of Wikipedia with the invention of Imre in view of Wikipedia by causing the filtering to utilize an Extended Kalman Filter because the Extended Kalman filter can handle nonlinear relationships, and is therefore more generally applicable.
Regarding claims 5 and 14, Imre in view of Wikipedia teaches the limitations of claims 4 and 13, respectively. While Imre in view of Wikipedia does not explicitly teach that the Extended Kalman Filter is a fast Extended Kalman Filter to calculate the estimated sensor cell state-of-charge, it would have been obvious to set the Extended Kalman Filter to be fast so as to use all observation data when making SOC estimates (note ¶66 of Applicant’s specification, which describes a fast Extended Kalman Filter as one for which “the observations in the elapsed time interval are used in each filter cycle”) (see also rejection of claims 1 and10, where the filtering performed by the Kalman filter is to estimate sensor cell SOC).
Regarding claims 7 and 16, Imre in view of Wikipedia teaches the limitations of claims 1 and 10, respectively, and further teaches that the assembly battery chemistry is a lithium iron phosphate chemistry (Imre, ¶13: “the first electrochemical energy storage unit belongs to a first electrochemical type, in particular based on lithium iron phosphate”).
Regarding claims 8 and 17, Imre in view of Wikipedia teaches the limitations of claims 1 and 10, respectively, and further teaches that the sensor battery chemistry is a nickel manganese cobalt chemistry (Imre, ¶9: “the at least two electrochemical energy storage units of different electrochemical types comprise…at least one second battery cell based on a different electrochemical basis, in particular on lithium nickel manganese cobalt oxide.”).
Regarding claim 9, Imre in view of Wikipedia teaches the limitations of claim 1, and further teaches that the battery assembly is a battery pack or a battery module (an electrochemical energy storage unit is a battery pack or module; see Imre, ¶7, noting that a battery comprises one or more cells).
Regarding claim 20, Imre in view of Wikipedia teaches the limitations of claim 19, but does not explicitly teach the limitations of claim 20. However, it would have been obvious to one of ordinary skill in the art practicing the invention of Imre in view of Wikipedia to cause the estimated battery assembly state-of-charge to have an accuracy within 3 percent. The additional limitation of claim 20 describes a result, not a method or system which naturally produces the result. One would be motivated to operate the system taught by Imre in view of Wikipedia to achieve such a result, because results with higher accuracy are more reliable than those with lower accuracy. See MPEP 2144.05(II)(A).
Examiner’s Statement Regarding Distinguishability Over Prior Art
Claims 3, 6, 12, 15, and 18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Regarding claims 3 and 12, Imre in view of Wikipedia teaches the limitations of claims 2 and 11, respectively, but does not teach that the sequence of current battery assembly state-of-charges is represented by the equation in claims 3 and 12. The examiner finds that it would not have been obvious to represent the sequence of current battery assembly state-of-charges with this particular equation. Therefore claims 3 and 12 are distinguishable over the prior art of record.
Regarding claims 6 and 15, Imre in view of Wikipedia teaches the limitations of claims 5 and 13, respectively, but does not teach the limitations of claims 6 and 15 (Imre teaches in ¶20 determining the self-discharge of the second electrochemical energy storage unit; while this could be termed a capacity degradation coefficient, this is not the same as
q
in Applicant’s specification defining the capacity degradation coefficient as the coulombic efficiency). Furthermore, the prior art does not fairly suggest estimating a capacity degradation coefficient using the Extended Kalman Filter executed at a slower rate than the fast Extended Kalman Filter. Therefore claims 6 and 15 are distinguishable over the prior art of record.
Regarding claim 18, Imre in view of Wikipedia teaches the limitations of claim 10 but does not teach the limitations of claim 18.
Plett (“Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 2. Modeling and identification”) discloses an extended Kalman filtering (EKF) method to estimate battery pack parameters including SOC (Abstract). Plett teaches developing a model to capture battery cell dynamics, the final model including “terms that describe the dynamic contributions due to open-circuit voltage, ohmic loss, polarization time constants, electro-chemical hysteresis, and the effects of temperature” (Abstract). Plett teaches that an EKF can be applied with the model to determine unknown model parameters given test data (Pg. 263, column 1, third paragraph). Plett teaches modeling terminal voltage (Pg. 266, column 1, paragraph above Section 3.3: “we add dynamics to the state to model cell terminal voltage relaxation.”).
However, the prior art does not fairly suggest a sensor cell model including a hysteresis transit component, a plurality of lagged currents component, a plurality of resistances component, and a terminal voltage component. Therefore claim 18 is distinguishable over the prior art of record.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Zhou ("A low-complexity state of charge estimation method for series-connected lithium-ion battery pack used in electric vehicles") discloses a method of estimating the SOC of a battery pack using the SOC of a few representative cells, and further implements a Kalman filter (Abstract).
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ETHAN WESLEY EDWARDS
Examiner
Art Unit 2857
/E.W.E./Examiner, Art Unit 2857
/ANDREW SCHECHTER/Supervisory Patent Examiner, Art Unit 2857
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