US-20260126490-A1 - BATTERY PARAMETER ESTIMATION USING REDUCED-ORDER AND/OR FULL-ORDER ELECTROCHEMICAL MODELS

US20260126490A1US 20260126490 A1US20260126490 A1US 20260126490A1US-20260126490-A1

Abstract

This disclosure relates to systems, methods, and techniques for estimating battery parameters using a battery analysis system. In certain embodiments, the system receives current data for a battery cell, preprocesses the data to reduce frequency and amplitude, and executes a shallow estimation function using a reduced-order electrochemical model to estimate battery parameters based on the preprocessed data. In some embodiments, a deep estimation function, which utilizes a full-order electrochemical model, may be utilized to further refine the battery parameter estimates if higher accuracy is desired. Other embodiments are disclosed herein as well.

Inventors

Wei Lu
TIANHAN GAO
Dong-Wook KOH
Gwang Hoon JUN
You Na Kim

Assignees

LG ENERGY SOLUTION, LTD.
THE REGENTS OF THE UNIVERSITY OF MICHIGAN

Dates

Publication Date: 20260507
Application Date: 20241101

Claims (20)

1 . A method for estimating one or more battery parameters: receiving, by a battery analysis system, current data corresponding to a battery cell; generating, by a preprocessing function of the battery analysis system, preprocessed current data based on the current data, the preprocessed current data having a reduced frequency and a reduced amplitude relative to the current data; receiving, by a shallow estimation function of the battery analysis system, the preprocessed current data; executing the shallow estimation function to estimate one or more battery parameters corresponding to the battery cell, at least in part, by applying the preprocessed current data as an input to a simulation executed by a reduced-order electrochemical model; and determining a diagnostic assessment corresponding to the battery cell based, at least in part, on the one or more battery parameters.
2 . The method of claim 1 , wherein the shallow estimation function utilizes an optimization function that cooperates with the reduced-order electrochemical model to estimate one or more battery parameters, and generating the preprocessed current data prior to execution of the shallow estimation function operates to narrow down a parameter range that is utilized by the optimization function in estimating the one or more battery parameters and utilized by the reduced-order electrochemical model in executing the simulation.
3 . The method of claim 1 , further comprising: determining if the one or more battery parameters estimated using the shallow estimation function are sufficiently accurate; and in response to determining that the one or more battery parameters are not sufficiently accurate, executing a deep estimation function that utilizes a full-order electrochemical model to refine the one or more battery parameters corresponding to the battery cell.
4 . The method of claim 3 , wherein the one or more battery parameters estimated by the shallow estimation function are applied to narrow a parameter range for the deep estimation function.
5 . The method of claim 1 , further comprising: receiving reference voltage data comprising charging/discharging data derived from one or more battery-powered devices; determining a benchmark terminal voltage profile based, at least in part, on the reference voltage data; generating, using the reduced-order electrochemical model, a simulated terminal voltage profile based on the preprocessed current data; and comparing the simulated terminal voltage profile with the benchmark terminal voltage profile to assess a sufficiency of the preprocessed current data.
6 . The method of claim 5 , wherein: the preprocessed current data is generated according to a reduction metric that quantifies a degree to which a frequency and an amplitude of the current data is reduced; and in response to determining that a difference between the simulated terminal voltage profile and the benchmark terminal voltage profile satisfies an error threshold, the preprocessed current data generated according to the reduction metric is determined to be acceptable for usage in estimating the one or more battery parameters corresponding to the battery cell.
7 . The method of claim 5 , wherein: the preprocessed current data is generated according to a first reduction metric that quantifies a degree to which a frequency and an amplitude of the current data is reduced; and in response to determining that a difference between the simulated terminal voltage profile and the benchmark terminal voltage profile does not satisfy an error threshold, new preprocessed current data is generated according to a second reduction metric that reduces the frequency and the amplitude of the current data to a lesser extent relative to the first reduction metric.
8 . The method of claim 7 , wherein the preprocessed current data is iteratively refined according to a new reduction metric until the difference between the simulated terminal voltage profile and the benchmark terminal voltage profile satisfies the error threshold.
9 . The method of claim 1 , wherein: the battery analysis system is configured to estimate the one or more battery parameters for one or more battery cells included in an electric vehicle; and the battery analysis system is integrated directly into the electric vehicle or is integrated into a cloud environment that is in communication with the electric vehicle over a network.
10 . The method of claim 1 , wherein the one or more battery parameters include at least one of: a degradation parameter corresponding to the battery cell; a thermal parameter corresponding to the battery cell; a model parameter corresponding to the battery cell; or a state-of-health (SOH) parameter corresponding to the battery cell.
11 . A system for estimating one or more battery parameters comprises: one or more processing devices; and one or more non-transitory storage devices storing computing instructions that, when executed by the one or more processing devices, cause the one or more processing devices to perform operations comprising: receiving, by a battery analysis system, current data corresponding to a battery cell; generating, by a preprocessing function of the battery analysis system, preprocessed current data based on the current data, the preprocessed current data having a reduced frequency and a reduced amplitude relative to the current data; receiving, by a shallow estimation function of the battery analysis system, the preprocessed current data; executing the shallow estimation function to estimate one or more battery parameters corresponding to the battery cell, at least in part, by applying the preprocessed current data as an input to a simulation executed by a reduced-order electrochemical model; and determining a diagnostic assessment corresponding to the battery cell based, at least in part, on the one or more battery parameters.
12 . The system of claim 11 , wherein the shallow estimation function comprises an optimization function that works in conjunction with the reduced-order electrochemical model to estimate one or more battery parameters, and generating the preprocessed current data prior to execution of the shallow estimation function operates to narrow down a parameter range that is utilized by the optimization function in estimating the one or more battery parameters and utilized by the reduced-order electrochemical model in executing the simulation.
13 . The system of claim 11 , wherein execution of the computing instructions further causes the one or more processing devices to perform operations comprising: determining if the one or more battery parameters estimated using the shallow estimation function are sufficiently accurate; and in response to determining that the one or more battery parameters are not sufficiently accurate, executing a deep estimation function that utilizes a full-order electrochemical model to refine the one or more battery parameters corresponding to the battery cell.
14 . The system of claim 13 , wherein the one or more battery parameters estimated by the shallow estimation function are applied to narrow a parameter range for the deep estimation function.
15 . The system of claim 11 , wherein execution of the computing instructions further causes the one or more processing devices to perform operations comprising: receiving reference voltage data comprising charging/discharging data derived from one or more battery-powered devices; determining a benchmark terminal voltage profile based, at least in part, on the reference voltage data; generating, using the reduced-order electrochemical model, a simulated terminal voltage profile based on the preprocessed current data; and comparing the simulated terminal voltage profile with the benchmark terminal voltage profile to assess a sufficiency of the preprocessed current data.
16 . The system of claim 15 , wherein: the preprocessed current data is generated according to a reduction metric that quantifies a degree to which a frequency and an amplitude of the current data is reduced; and the battery analysis system is configured to evaluate the preprocessed current data such that: in response to determining that a difference between the simulated terminal voltage profile and the benchmark terminal voltage profile satisfies an error threshold, the preprocessed current data generated according to the reduction metric is determined to be acceptable for usage in estimating the one or more battery parameters corresponding to the battery cell; or in response to determining that the difference between the simulated terminal voltage profile and the benchmark terminal voltage profile does not satisfy the error threshold, the preprocessed current data is iteratively refined according to a new reduction metric until the difference between the simulated terminal voltage profile and the benchmark terminal voltage profile satisfies the error threshold.
17 . The system of claim 11 , wherein the battery analysis system is configured to estimate the one or more battery parameters for one or more battery cells included in an electric vehicle, and the battery analysis system is integrated directly into the electric vehicle or is integrated into a cloud environment that is in communication with the electric vehicle.
18 . A method for estimating one or more battery parameters comprising: receiving, by a shallow estimation function, current data corresponding to a battery cell; executing the shallow estimation function to estimate one or more battery parameters corresponding to the battery cell, at least in part, by applying the current data as an input to a simulation executed by a reduced-order electrochemical model; receiving, by a deep estimation function, the one or more battery parameters estimated using the shallow estimation function; executing the deep estimation function to refine the one or more battery parameters corresponding to the battery cell, wherein: the deep estimation function utilizes a full-order electrochemical model to refine the one or more battery parameters corresponding to the battery cell; and the one or more battery parameters estimated by the shallow estimation function are applied to narrow a parameter range for the deep estimation function; and determining a diagnostic assessment corresponding to the battery cell based, at least in part, on the one or more battery parameters.
19 . The method of claim 18 , wherein the current data is preprocessed prior to execution of the shallow estimation function or the deep estimation function to reduce a frequency and an amplitude of the current data.
20 . The method of claim 19 , wherein: the current data is preprocessed according to a reduction metric that quantifies a degree to which a frequency and an amplitude of the current data are reduced; and the current data is iteratively refined according to a new reduction metric until an error threshold is satisfied.

Description

TECHNICAL FIELD This disclosure is related to systems, methods, and techniques for estimating battery parameters. In certain embodiments, the systems, methods, and techniques described herein can be executed to rapidly estimate battery parameters of one or more battery cells with high accuracy utilizing reduced-order and/or full-order electrochemical models. BACKGROUND Battery management systems (BMSs) monitor and control the charging and discharging of rechargeable batteries. For example, a BMS may measure and regulate various parameters, such as voltage, current, temperature, and state of charge, for individual battery cells or entire battery packs. In some cases, the BMS also may perform functions like cell balancing, thermal management, and communication with external systems to optimize battery performance and longevity. The rapid advancement of electric vehicles, and other battery-powered systems, has significantly increased the demands on battery performance and reliability. These vehicles and systems often require batteries to operate under diverse and challenging conditions, necessitating more sophisticated BMS technologies. Modern BMSs are expected to not only ensure safe operation, but also maximize battery efficiency, extend lifespan, and provide accurate real-time data for optimal system performance. As such, there is a growing need for advanced BMS solutions that can handle complex battery configurations, adapt to varying operational requirements, and integrate seamlessly with electric vehicles and/or other battery-powered systems. The increasing complexity of battery applications and their operational environments has led to the development of various modeling approaches, including some approaches that rely on an equivalent circuit model (ECM). ECMs may represent battery behavior using electrical components, such as resistors and capacitors, to simulate the electrochemical processes within the battery. These models may provide a simplified representation of battery dynamics in an effort to perform calculations of battery states and performance characteristics. While ECMs provide a simplified representation of battery dynamics, they often struggle to accurately capture the complex physical behaviors and electrochemical processes occurring within battery cells during real-world operation, particularly under varying conditions or as the battery ages. Consequently, the estimations generated by ECMs often do not have sufficient accuracy or reliability to be used in electric vehicle systems and/or other battery-powered systems. Another potential approach to estimate or measure parameters of batteries may be to apply a pseudo-two-dimensional (P2D) electrochemical model. However, traditional P2D models have several disadvantages. These models typically involve solving partial differential equations (PDEs), which often rely on finite element methods, leading to slower simulation speeds. Moreover, these models are computationally intensive, often requiring significant processing power and time to estimate parameters, which may limit their applicability in real-time battery management systems. The background description provided herein is for the purpose of generally presenting context of the disclosure. The materials described in this section are not prior art to the claims in this application and are not admitted to be prior art, or suggestions of the prior art, by inclusion in this section. SUMMARY The present disclosure relates to systems, methods, apparatuses, computer program products, and techniques for estimating battery parameters. In certain embodiments, a battery analysis system utilizes a combination of reduced-order and full-order electrochemical models, along with data preprocessing algorithms and machine learning-based optimization techniques, to rapidly and accurately estimate various battery parameters for one or more battery cells. In certain embodiments, the battery analysis system can execute a multi-stage estimation approach, initially leveraging a shallow estimation function to quickly narrow down parameter ranges, followed by a deep estimation function for final refinement if higher accuracy is desired. This combined approach allows for efficient parameter estimation across various operating scenarios, balancing both speed and precision considerations, and enables real-time parameter estimates for battery management systems in applications such as electric vehicles. In certain embodiments, a shallow estimation function may initially be executed that utilizes one or more reduced-order electrochemical models to perform rapid battery parameter estimation. If further accuracy is desired, a deep estimation function can subsequently be employed to further hone the accuracy of the battery parameters. In scenarios where the deep estimation function is utilized to refine the accuracy of the battery parameters, the estimates output by the shallow estimation function can be leveraged to redu