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 .
Under 35 USC § 101
Claims 1-10 are directed to patent-eligible subject matter under 35 U.S.C. § 101.
Although the claims may recite mathematical concepts in the form of a battery-model calculations and state-estimation operations, the claims, when considered as a whole, integrate such calculations into a practical application. In particular, independent claim 1 applies the state-estimation operations to sensor measurements obtained from a physical battery using a physical battery model, determines the battery state on a processor collocated with the battery, and determines parameters of the physical battery model using a second state estimator operating on a processor remote from the battery. Thus, the claimed mathematical operations are applied as part of a physical battery rather than merely being performed as calculations on a generic computer. Accordingly, the claim integrates any recited mathematical concept into a practical application and is not directed to a judicial exception under Step 2A.
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.
Claims 1, 5, and 9 are rejected under 35 U.S.C. 103 as being unpatentable over
Ravi et al. (Pub. No. US 2018/0083461) (hereinafter Ravi) in view of Klein et al. (Pub. No. US 2017/0288414) (hereinafter Klein).
As pe claim 1, Ravi teaches a battery 110 communicatively connected to a local battery management system (BMS) 104 (see ¶ [0016]). Ravi further teaches that measured battery characteristics, including voltage, current, and/or temperature, are obtained and supplied for estimation (see ¶ [0029]). Thus, Ravi teaches receiving a set of sensor measurements associated with the battery.
Ravi further teaches that a local BMS 104 includes state estimator 130 and a processor or a microcontroller (see ¶¶ [0016]-[0018]). Ravi teaches physics-based electrochemical models, including the Newman model, for modeling physical processes occurring within a Li-ion battery (see ¶ [0022]), and explains that the model requires identification of physical parameters (see ¶ [0023]). Ravi further teaches distributing the identification and calculation of model states and parameters between the local BMS and a remote calculating system (see ¶ [0024]). In the method of Fig. 2, the local BMS implementing state estimator 130 calculates the battery state based on measured battery characteristics and remotely calculated parameters (see ¶ [0029]). Accordingly, Ravi teaches the claimed local determination of battery state using a state estimator and a physical battery model.
Ravi further teaches that local BMS 104 is communicatively connected to remote parameter estimator 160, which may be implemented by a mainframe, server, desktop computer, tablet, smartphone, or other device having a processor and memory (see ¶¶ [0019]-[0021]). Ravi explicitly teaches that parameters may be estimated remotely, for example in the cloud, permitting use of more accurate and computationally intensive models than would normally execute on the local BMS (see ¶ [0025]). Ravi further teaches that the remote parameter estimator calculates battery parameters from measured characteristics and sends the calculated parameters to the local BMS for use by the state estimator 130 (see ¶¶ [0026] and [0029]).
Ravi therefore teaches the claimed local/remote processing architecture and remote determination of physical-battery-model parameters, but Ravi identifies the remote estimator as “parameter estimator” rather than explicitly identifying it as a “second state estimator”.
Klein, however, explicitly supplies state-estimator implementation for parameter determination. Klein teaches that state and parameter estimator 220 includes a first state observer 310, a second state observer 320, and a third observer 330 (see ¶ [0024]). The first state observer uses a detailed battery model, including an electrochemical model, whose parameters change over the battery lifespan (see ¶¶ [0025] and [0027]). Klein then explicitly teaches that second state observer 320 receives measured signals and estimate thermodynamic parameters Pt using a second battery model, and that the second battery model shares those thermodynamic parameters with the first battery model, and that the second state observer estimates internal states in addition to the parameters Pt (see ¶ [0028]). Kein further teaches that the thermodynamic parameters Pt are part of the state vector of the second state observer and that the second state observer provides the estimated thermodynamic parameters to the first state observer (see ¶ [0028]).
Klein further teaches that the second and third state observers update estimated parameter values using measured voltage, current, and temperature and estimated internal states (see ¶ [0031]); second state observer 320 includes a battery model and an estimation algorithm, such as Kalman filter, Moving Horizon Estimation, or Least Square Estimation, usable to estimate states and/or parameters (see ¶ [0033]); and that second state observer 320 estimates one or more thermodynamic parameters included in the battery model of first state observer 310 and sends those estimates to the first observer (see ¶ [0036]). Klein additionally teaches that state/parameter-estimation methods include Extended Kalman Filter and Unscented Kalman Filter techniques (see ¶ [0044]); and that an EKF may estimate both rapidly varying battery states and slowly varying battery parameters (see ¶ [0045]).
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to implement Ravi’s remotely located parameter estimator 160 using the second-state-observer/state-estimator architecture taught by Klein, such that a second state estimator operating on Ravi’s remote processor determines parameters of Ravi’s physical battery model, because Klein explicitly teaches using a second state observer to estimate parameters that are included in the battery model used by a first state observer, while Ravi explicitly teaches performing battery-model parameter estimation remotely to accommodate more accurate and computationally intensive estimation, thereby enabling Ravi’s remotely determined battery-model parameters to be generated using a known state-estimation architecture and supplied to Ravi’s local state estimator for determining battery state. The modification would amount to use of known estimation technique for the same parameter-estimation function already performed by Ravi’s remote estimator and would predictably preserve Ravi’s distributed local-state/remote-parameter architecture.
As per claim 5, the combination of Ravi and Klein teaches the system as stated above. Ravi further teaches that a physics-based electrochemical model requires identification of a large set of physical parameters (see ¶ [0023]), that identification an calculation of model states and parameters may be distributed between the local BMS and remote calculating system (see ¶¶ [0006] and [0024]), and the remotely calculated parameters are provided as a parameter set to the local state estimator for use in estimating battery states (see ¶¶ [0025]-[0026]). Klein likewise teaches a first electrochemical battery model having model parameters (see ¶¶ [0025] and [0027]) and explicitly teaches second state observer 320 determining thermodynamic parameters shared with that first battery model (see ¶ [0028]) and providing those parameter estimates to the first state observer (see ¶ [0036]).
As per claim 9, the combination of Ravi and Klein teaches the system as stated above.
Ravi explicitly teaches that battery states are rapidly varying whereas battery parameters are slowly varying, and that the state and parameter estimation algorithms may be separated because the states and parameters change on different time scales (see ¶ [0025]). Ravi teaches periodic remote parameter updates based on the applicable calculation time scale and states that the update interval may depend on how fast the parameters are expected to change on estimation complexity (see ¶ [0026]). Ravi further explains that parameters typically vary more slowly than battery states (see ¶ [0027]).
Ravi additionally teaches that battery states may be calculated or updated multiple times using the same stored parameter set before a new parameter set is received, and explicitly states that the frequency of calculating estimated battery states and the frequency of remotely calculating battery parameters are independently governed (see ¶[0031]). Ravi further teaches implementations in which simplified local calculations are executed more frequently while advanced or complex remote parameters calculations are executed less frequently (see ¶ [0032]).
Klein independently supports the differing estimation dynamics by teaching that the first, second, and third state observers are tuned with progressively slower speeds of convergence (see ¶¶ [0030] and [0036]) and that an EKF may estimate rapidly varying battery states and slowly varying battery parameters (see ¶ [0045]).
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to determine Ravi’s battery state at a first frequency greater than the second frequency used to determine the remotely estimated model parameters because Ravi explicitly recognizes that battery states vary more rapidly than battery parameters and that remote parameter estimation may employ more computationally intensive algorithms, thereby maintaining responsive battery-state estimation while avoiding unnecessary repetition of slower remote parameter calculations.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Ravi in view
of Klein and further in view of Basu et al. (NPL: “Coupled electrochemical thermal modelling of a novel Li-ion battery pack thermal management system” Applied Energy 181 (2016) pages 1-13 (hereinafter Basu).
As per claim 2, the combination of Ravi and Klein teaches the system as stated above except that the physical battery model comprises a cell model, a pack geometry model, an electrochemical model, and a thermal transport model.
Basu teaches a coupled three-dimensional electrochemical-thermal physical model of a Li-ion battery pack comprising individual 18650 cells. Basu models individual-cell electrochemical and thermal behavior, including electrochemical reactions, lithium transport, cell heat generation, and thermal conduction within the cells (see §3.1), Basu further explicitly describes the “Pack geometry” (see §2), including the spatial arrangement and dimensions of the cells, conduction elements, and coolant channel, and incorporates such geometry into a three-dimensional computational model of the battery pack and cooling system (see §3 and Figs. 1 and 3). Basu explicitly provides and “Electrochemical model” (see §3.1) and a “Conjugate heat transfer model” (see §3.2), the latter modeling transport of heat generated in the cells through the conduction elements and flow channel to the coolant.
Claims 3, 4 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over
Ravi in view of Klein and further in view of Basu and Li et al. (Pub. No. US 2015/0066405) (hereinafter Li).
As per claim 3, the combination of Ravi, Klein and Basu teaches the system as stated above except that the cell model comprises an equivalent circuit model.
Li, however, teaches that battery power capability may be estimated using a battery equivalent circuit model and that the circuit-model parameters may be determined using an EKF (see ¶ [0002]). Li’s Fig. 2 and ¶ [0017] explicitly disclose battery equivalent circuit model 30 having a first series resistance r1, a second resistance r2 forming an RC network 32, capacitance C, open-circuit voltage, terminal voltage, and current. Li further teaches estimating r1, r2, C, and the RC-network voltage using an EKF (see ¶ [0018]).
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to include Li’s equivalent circuit model in the individual-cell modeling of Basu’s battery pack because Li teaches that an equivalent circuit model provides a computationally efficient electrical representation of lithium-ion battery behavior using resistance, capacitance, voltage, and current parameters, thereby supplementing Basu’s heat-generation and thermal-transport modeling with a known electrical representation useful for estimating battery electrical behavior. Applying the known equivalent circuit model (ECM) at the cell-model level would have been a predictable use of conventional battery modeling technique in Basu’s individual-cell-based pack model.
As per claim 4, the combination of Ravi, Klein and Basu teaches the system as stated above except that the equivalent circuit model is parameterized as a function of at least one of temperature or state of charge (SOC).
Li, however, teaches that beginning-of-life ECM parameters r1, r2, and C are shown as functions of battery temperature and states that the resistances and capacitance of the ECM vary with temperature (see ¶ [0019]). Li further teaches that the variation of r1, r2, and C is also true for SOC and that those parameters may be set based on battery SOC (see ¶ [0020]).
Li further teaches that the upper and lower limits of r1 may be functions of both battery temperature and SOC (see ¶ [0022]).; that the corresponding r2 and C parameter limits may likewise be set for temperature and SoC (see ¶ [0023]); and explicitly writes the r1 parameter limits as r1 max (temperature, SOC) and r1 min (temperature, SOC) (see ¶ [0024]). ¶ [0025] additionally states that the parameters for a particular battery may vary relative to temperature and SOC. ¶¶ [0026]-[0028] teaches producing parameter limits for ranges of temperature and /or SOC and using look-up tables indexed by input temperature, SOC, or both. Thus, Li explicitly teaches an ECM whose resistance and capacitance parametrization depends upon temperature and /or SOC.
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to parameterize the equivalent circuit model of the combined system as a function of temperature and/or SOC as taught by Li because Li explicitly recognizes that the ECM resistance and capacitance parameters vary with battery temperature and SOC, thereby providing parameter values representative of the battery’s current operating condition and improving convergence and accuracy of the model-based estimation.
As per claim 7, the combination of Ravi, Klein and Basu teaches the system as stated above. Klein further teaches identifying an Unscented Kalman Filter (UKF), together with an Extended Kalman Filter, Sigma Point Kalman Filter, Iterated Kalman Filter, Moving Horizon Estimation, and Least Squares techniques, as methods that may be employed for state and parameter estimation (see ¶ [0044]). Thus, Klein teaches employing a UKF as the estimation algorithm of a state observer that determines battery-model parameters supplied for use by another state observer.
Li teaches a battery equivalent circuit model containing model parameters including a first resistance r1, a second resistance r2, and a capacitance C (see ¶ [0017]), and teaches estimating the values of r1, r2, C, and the RC-network voltage V2 using a Kalman-filter-based state estimator (see ¶ [0018]). Li teaches that the equivalent circuit model parameters vary as functions of battery temperature and SOC (see ¶¶ [0019]-[0020]). Li further provides equations employing temperature and SOC dependent battery model parameter values, including equations relating V2max and V2main to OCV, battery terminal voltage V, current I, and the parameter values r1max(temperature, SOC) and r1min(temperature, SOC) (see ¶¶ [0023]-[0024]). Li explicitly teaches that the upper limit and lower limit determiners 64 and 66 may comprise look-up tables containing values of the battery model parameters as functions of temperature, SOC, or both, including two-dimensional look-up tables, and explains that the data are stored in the look-up tables for “easy, quick access during calculations (see ¶ [0028]). Li further teaches supplying measured battery terminal voltage, current, and temperature to a Kalman-filter estimator to estimate the battery-model parameter Z (see ¶ [0029]) and teaches storing battery model parameters in look-up tables in the vehicle controller (see ¶ [0035]).
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify Ravi’s local/remote state and parameter estimation arrangement in view of Klein and Li such that the remotely operating second state estimator employs and Unscented Kalman Filter to determine battery model parameter values and the determined parameter values are used as elements of a look-up table employed by the local state estimator together with equations relating or operating on the look-up parameter information, because Klein teaches using a second state observer to estimate changing parameters of a battery model and explicitly teaches a UKF as known technique for performing state and parameter estimation, while Li teaches representing battery model parameter information in look-up tables for easy and quick access during calculations and using the corresponding temperature and SOC dependent parameter values in equations of the battery model, thereby permitting computational intensive parameter estimation to be performed by the second estimator while providing the local state estimator with an efficiently retrievable representation of the determined battery model parameters for performing battery state calculations.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Ravi in view
of Klein and further in view of Lundgren et al. (Pub. No. US 2017/0077717) (hereinafter Lundgren).
As per claim 6, the combination of Ravi and Klein teaches the system as stated above. Klein further teaches that the parameters P of its detailed battery model are assumed to be known at the beginning of life, but that those parameters change over the lifespan of the battery cell because of aging phenomena (see ¶ [0025]). Klein further teaches that the model parameters continuously change and that parameter-estimation algorithms are employed to track the changing parameters (see ¶ [0027]). Klein further teaches that the second and third state observers may incorporate knowledge of parameter changes based on first principle aging models (see ¶ [0030]) and those observers update parameter values using measured voltage, current, and temperature and estimated internal states (see ¶ [0031]). Thus, Klein teaches updating the battery-model parameterization to account for age-related changes in the battery.
The combination of Ravi and Klein, however, fails to explicitly teach conditioning that update on the battery exceeding a predetermined threshold age.
Lundgren, however, teaches monitoring battery age as a battery-related parameter (see ¶ [0020]) and teaches that battery-related variable passing a threshold can cause its weighting to be adjusted and can cause new thresholds to be set (see ¶ [0021]). Lundgren further teaches sensing/measuring the age of the battery and using that information in computing or selecting a charging profile (see ¶ [0026]) and storing the battery’s date of manufacture for use in battery-management processing (see ¶ [0027]). More specifically, Lundgren teaches calculating battery age from the battery’s date of manufacture and/or the manufacture date of the device (see ¶ [0033]) and comparing the determined battery age to a “threshold age”, if the threshold age has been passed, the battery age is assigned a weight for subsequent processing, whereas if the threshold age has not been passed, battery age is determined again (see ¶ [0037]) . ¶ [0037] further teaches that after a new charging profile is set, new thresholds using the condition that an actual battery age has passed a predetermined threshold age as a criterion for changing battery-management processing.
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the age-responsive parameter updating of Ravi as modified by Klein so that the physical-battery-model parameterization is updated when the battery age exceeds a predetermined threshold age, as taught by Lundgren, because Kein explicitly recognizes that physical battery-model parameters that are valid at the beginning of battery life change over the battery lifespan due to aging and therefore teaches tracking and updating those parameters, while Lundgren teaches comparing battery age against a predetermined threshold age as a known criterion for changing battery-management processing, thereby causing model re-parameterization after aging has progressed to a predetermined level at which the prior parameterization may no longer accurately represent the battery while avoiding an age-triggered update before that predetermined aging condition occurs. Such use of known threshold criterion to determine when to perform Klein’s known aging-related parameter update would have been a predictable use of prior-art elements according to their established functions.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Ravi in view
of Klein and further in view of Lee (Pub. No. US 2015/0355283).
As per claim 8, the combination of Ravi and Klein teaches the system as stated above.
Further, Ravi teaches that the local state estimator does not wait for completion of the remote parameter calculation but instead continues determining battery state using the most recently stored parameter values while the remote parameter calculation proceeds asynchronously (see ¶ [0030]). Ravi further teaches that battery state may be calculated multiple times using the same parameter set before a newly calculated parameter set is received from the remote system (see ¶ [0031]).
Klein further teaches the second state estimator architecture relied upon with respect to a state and parameter estimator having first and second state observers (see ¶ [0024]); teaches that the second state observer estimates parameters using a second battery model and provides those state observer estimates parameters to the first state observer (see ¶ [0028]); and teaches that the second state observer estimates parameters included in the battery model of the first state observer and sends the estimated parameters to the first state observer (see ¶ [0036]). Klein therefore supports the claimed arrangement in which parameters used by the first/local state estimator are determined by the second state estimator.
However, the combination of Ravi and Klein fails to explicitly disclose that the processor updates the physical battery model with the parameters determined by the second state estimator with less than a threshold downtime during which the battery state cannot be determined (emphasis underlined).
Lee, however, teaches updating a physical battery model with newly estimated battery-model parameters while continuing battery state estimation. Lee teaches a controller configured to estimate battery model parameters and to estimate battery SOC in real time using a reduced order electrochemical model (see ¶ [0015]). Lee further teaches estimating parameters of the reduced-order battery model so that the model more accurately reflects the battery over its lifetime and explains that parameter adaptation improves the estimated battery-state variables and associated performance variables (see ¶ [0016]). More particularly, Lee identifies as objective: estimating battery model parameters to update model-parameter changes depending on operating conditions or battery life, designing an Extended Kaman Filter framework to estimate state variables and model parameters concurrently; and estimating battery state variables and associated performance variables from measured battery voltage in real time using the updated battery model (see ¶ [0017]). Lee further teaches that an EKF based on the reduced-order battery model estimates effective Li-ion concentration profiles representing battery dynamics, from which battery performance variables including SOC are computed (see ¶ [0020]). Lee additionally teaches that the estimated battery model parameters are used to update the battery model that the estimator itself uses in estimating battery SOC (see ¶ [0066]). Lee illustrates the model parameters as being updated from current input and measured voltage profiles using EKF, while EKF estimates both battery model parameters and state variables from which SOC and available power limits are determined (see ¶ [0068]).
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to configure the local processor of Ravi as modified by Klein to update the physical battery model with the parameters determined by the second state estimator with downtime less than a predetermined acceptable threshold during witch battery state cannot be determined, as suggested by Lee’s concurrent parameter/state estimation and real time battery state estimation using updated battery model, because Ravi already teaches an asynchronous architecture in which local battery state determination continues using the existing parameter set while updated parameters are determined remotely, and Lee teaches updating battery model parameters while estimating state variables concurrently and determining battery state in real time using the updated model (the examiner notes that concurrent parameter/state estimation and real time state estimation implies the claimed “less than threshold downtime”), thereby permitting newly determined battery model parameters to be incorporated into the physical battery model without an operationally significant interruption in the availability of battery state determination.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Ravi in view
of Klein and further in view of Miftakhov (Pub. No. US 2019/0061546).
As per claim 10, the combination of Ravi and Klein teaches the system as stated above except that the battery state is determined in response to receiving an application program interface request.
Miftakhov, however, teaches an electric or hybrid electric vehicle having a vehicle battery and a battery management system (BMS) configured to manage the state of the vehicle battery, wherein the battery, battery charger, and/or BMS provides an application programming interface (API) (see Abstract and ¶ [0005]). Miftakhov further teaches that the BMS manages the battery by, inter alia, monitoring its state, calculating secondary data, and reporting that data, and the vehicle battery, charger, and BMS exchange data and commands with the cloud control server through the API (see ¶ [0033]), Miftakhov teaches that the BMS provides and API accessible by the cloud control server and that the API incorporates API function calls designed to enable software applications executing on the cloud control server to access functions performed by the BMS, including conventional BMS functions such as monitoring battery state and calculating an explicitly associates API function calls secondary data (see ¶ [0036]). Thus, Miftakhov explicitly associates API function calls with access to battery-management functions that include monitoring the state of the battery and performing battery-related calculations. Miftakhov further provides an on-demand request/response example in which the cloud control server uses the BMS API to request an estimated battery-related quantity from the BMS before executing a proposed event, Miftakhov explains that a predetermined API function call is made by the cloud control server to the BMS in response to that API function call (see ¶ [0033] and [0036]-[0037]). Accordingly, Miftakhov teaches battery-specific architecture which a remote application invokes a BMS function by an API request/function call and receives battery-related information in response.
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to configure the battery-state estimator of Ravi as modified by Klein such that battery state is determined in response to receiving an application programming interface request, as suggested by Miftakhov, because Miftakhov teaches providing remote application with an API function call, thereby enabling a remote application to invoke Ravi’s battery-state determination on demand and obtain the resulting battery-state information through a standardized programming interface.
Prior art
The prior art made record and not relied upon is considered pertinent to applicant’s
disclosure:
Kadirvel et al. [‘992] discloses a battery management unit may measure a battery voltage of a battery across a first pair of nodes of the battery management unit to produce a first battery measurement and a current of the battery based on a voltage across a second pair of nodes of the battery management unit. Then, the battery management unit may generate a threshold current for a comparator in the battery management unit based on the current, where the threshold current is a sum of the current and a predetermined reference current associated with a predetermined operating profile of an application. Next, the battery management unit may measure the first voltage when the current equals the threshold current to produce a second battery measurement. Moreover, the battery management unit may calculate a model parameter in a model of the battery based on the first voltage measurement, the second voltage measurement, and the predetermined reference current.
Koch et al. [‘254] discloses a battery assembly includes a battery having first and second electrodes and an electrolyte. A controller is operatively connected to the battery. The controller includes a processor and tangible, non-transitory memory on which is recorded instructions for executing a method for estimating a charge acceptance capability of the battery. Interaction of the electrolyte and at least one of the first and second electrodes produces a discharge product. The controller is programmed to quantize and track both accumulation and removal of the discharge product when at least one enabling condition is met. The controller is programmed to initialize a timer when the enabling condition is met and increment the timer by a calculation time interval (Δt).
Contact information
Any inquiry concerning this communication or earlier communications from the
examiner should be directed to MOHAMED CHARIOUI whose telephone number is (571)272-2213. The examiner can normally be reached Monday through Friday, from 9 am to 6 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Schechter can be reached on (571) 272-2302. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Mohamed Charioui
/MOHAMED CHARIOUI/Primary Examiner, Art Unit 2857
