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 Status
Claims 1-20 are pending for examination.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3, 10-14 and 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Casasnovas (Pub. No.: US 2025/0244732 A1).
Regarding claim 1, Casasnovas teaches a method for monitoring and managing battery performance (Abstract, system and method for predicating the state of Li-ion battery) comprising:
deriving a representation of diffusion overpotential behavior for a lithium-ion battery (abstract “Methods are disclosed of controlling operation of a Li-ion battery system. Such methods include obtaining a predicted state of the Li-ion battery system from a reduced order model either with degradation (in first methods) or without degradation (in second methods),” and para [0114], “Further operational data that may represent and, therefore, be included in predicted state may include, e.g., concentrations at different points of the battery, potentials, voltage, outer temperature, etc. which may be physically interpretable and, hence, may also be useful to correct predicted state (by e.g. correction module 105), to control Li-ion battery system 101 (by e.g. control module 106), etc.”) according to a discrete-time state-space approximation of a convolution-defined diffusion (CDD) model for the lithium-ion battery; and
determining behavior of the lithium-ion battery according to the discrete-time state-space approximation of the convolution-defined diffusion (CDD) model for the lithium-ion battery (para [0026], “In examples of first and second methods, the obtaining, calculation or pre-calculation of the reduced order model (with degradation in first methods and without degradation in second methods) may include applying a discrete realization algorithm (DRA), to obtain the reduced order model as a reduced-order discrete-time state-space model (SSM) further depending on predefined operational states or conditions that are known to be experienced by the Li-ion battery system during operation. Such a discrete realization algorithm (DRA) may include a convolution quadrature method based on a Linear Multistep Method.”. The system estimates a state of the battery by using a reduced ordered model. The reduced order model applies discrete-time state-space model and convolution model for the estimation.).
Regarding claim 2, Casasnovas teaches the method of claim 1, wherein determining the behavior of the lithium-ion battery comprises:
applying input current to the lithium-ion battery (Fig. 3, the control module 106 outputs a control signal to the Li-ion battery 101);
capturing voltage response of the lithium-ion battery resulting from applying the input current (Fig. 3, para [0030], “Battery measurements (to be processed by Kalman filter) may include a battery voltage, or an average battery temperature, or an average ambient temperature, or a pressure, or any combination thereof. Battery voltage (potential difference between cell ends) may result from determining a difference between solid phase potential at cathode-current collector interface and anode-current collector interface.”); and
determining diffusion overpotential for the lithium-ion battery based on analysis of the voltage response according to the discrete-time state-space approximation of the convolution-defined diffusion (CDD) model (Fig. 2, steps 201-203, and para [0026], the system determines the state of the battery based on the applies discrete-time state-space model and convolution model).
Regarding claim 3, Casasnovas teaches the method of claim 1, wherein deriving the representation of the diffusion overpotential behavior comprises:
deriving the discrete-time state-space approximation based on a recursive formulation using diffusion state data computed by the model extending back to a pre-determined number of instances defining a finite time horizon (Fig. 1, para [0036], “The prediction module (in iterative module) is configured to determine a predicted state of the Li-ion battery system by selecting a version of the reduced order model depending on a previous corrected state of the Li-ion battery system from previous iteration of the iterative loop, and by calculating the predicted state based on the selected version of the reduced order model depending on the previous corrected state, a previous corrected current demanded to the Li-ion battery system from previous iteration of the iterative loop, and a present current demanded to the Li-ion battery system.”. The system uses previous data to estimate the current state of the battery.).
Regarding claim 10, Casasnovas teaches the method of claim 1, further comprising:
deriving based on the discrete-time state-space approximation one or more battery performance metrics and/or battery degradation data (Abstract and para [0026], the system uses discrete-time state-space model to predict the state of the battery.).
Regarding claim 11, Casasnovas teaches the method of claim 10, further comprising:
determining based on the battery degradation data one or more of: state of health (SoH) of the lithium-ion battery, or state of charge (SoC) for the lithium-ion battery (para [0010], “Apart from the model-state and the model-output, the STATE of the Li-ion battery system may further include any operational data that is directly derivable from the model-state and/or model-output. Examples of said further operational data may be, e.g., state of charge (SoC) of the battery, temperature of the battery, anode porosity, etc. which may be directly derived from the model-state and/or output through known calculations.”).
Regarding claim 12, recites a system that performs the method of claim 1, therefore it is rejected for the same reasons.
Casasnovas further teaches the system includes a processor and memory to perform the method. See para [0038].
Regarding claim 13, recites a system that performs the method of claim 2, therefore it is rejected for the same reasons.
Regarding claim 14, recites a system that performs the method of claim 3, therefore it is rejected for the same reasons.
Regarding claim 20, recites a CRM comprising instructions to perform the method of claim 1, therefore it is rejected for the same reasons.
Allowable Subject Matter
Claims 4-9 and 15-19 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.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZHEN Y WU whose telephone number is (571)272-5711. The examiner can normally be reached Monday-Friday, 10AM-6PM, EST.
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/ZHEN Y WU/Primary Examiner, Art Unit 2685