Battery parameter estimation system and battery parameter estimation method using reduced-order and / or full-order electrochemical models

Abstract

Provided are a system, a method, and technology for estimating battery parameters by using a battery analysis system. In certain embodiments, the system receives current data for a battery cell, pre-processes the data to reduce the frequency and the amplitude, and executes a shallow estimation function using a reduced-order electrochemical model to estimate the battery parameters on the basis of the pre-processed data. In some examples, if higher accuracy is required, a deep estimation function utilizing a full-order electrochemical model can be used to further refine an estimate of the battery parameters.

Description

System and method for estimating battery parameters using a reduced-order electrochemical model and / or a full-order electrochemical model

[0001] The present invention relates to a system, method, and technology for estimating battery parameters.

[0002] This application claims priority to U.S. Application No. 18 / 935,345 filed November 1, 2024, all of which are incorporated by reference into this application.

[0003] A battery management system (BMS) monitors and controls the charging and discharging of rechargeable batteries. For example, a BMS can measure and regulate various parameters such as voltage, current, temperature, and state of charge for individual battery cells or the entire battery pack. In some cases, a BMS can perform functions such as cell balancing, thermal management, and communication with external systems to optimize battery performance and lifespan.

[0004] The rapid advancement of electric vehicles and other battery-powered systems has led to a significant increase in demands regarding battery performance and reliability. Since these vehicles and systems often require batteries to operate under diverse and demanding conditions, more sophisticated BMS technology is required. Modern BMSs must not only ensure safe operation but also maximize battery efficiency, extend lifespan, and provide accurate real-time data for optimal system performance. Consequently, there is a growing demand for advanced BMS solutions capable of handling complex battery configurations, adapting to various operational requirements, and seamlessly integrating with electric vehicles and / or other battery-powered systems.

[0005] As the complexity of battery application and operating environments increases, various approaches have been developed, including those relying on equivalent circuit models (ECMs). ECMs can represent battery behavior by simulating electrochemical processes within the battery using electrical components such as resistors and capacitors. These models can provide a simplified representation of battery dynamics to perform calculations of battery state and performance characteristics. While ECMs provide a simplified representation of battery dynamics, this often makes it difficult to accurately capture the complex physical behavior and electrochemical processes occurring within battery cells under various conditions during actual operation or during battery aging. Consequently, estimates generated by ECMs often lack sufficient accuracy and reliability for use in electric vehicles and / or other battery-powered systems.

[0006] Another potential approach for estimating or measuring battery parameters is to apply pseudo-two-dimensional (P2D) electrochemical models. However, conventional P2D models have several drawbacks. These models generally involve calculating partial differential equations (PDEs), which often rely on the finite element method, resulting in slow simulation speeds. Furthermore, these models are computationally intensive, frequently requiring significant processing power and time to estimate parameters, which may limit their application in real-time battery management systems.

[0007] The background description provided in this specification is intended to present the general context of the present invention. The contents described in the background are not prior art for the claims of this application, and being included in the background is not to be recognized as prior art or an implied prior art.

[0008] The present invention is devised to solve the above-mentioned problems and aims to provide a battery parameter estimation system and method capable of rapidly estimating battery parameters of one or more battery cells with high accuracy using a reduced-order electrochemical model and / or a full-order electrochemical model.

[0009] Other objects and advantages of the present invention may be understood from the following description and will become more clearly apparent from the embodiments of the present invention. Furthermore, it will be readily apparent that the objects and advantages of the present invention can be realized by the means and combinations thereof set forth in the claims.

[0010] A method for estimating one or more battery parameters according to an embodiment of the present invention is provided, the method comprising: a step in which a battery analysis system receives current data corresponding to a battery cell; a step in which a preprocessing function of the battery analysis system generates preprocessed current data based on the current data, wherein the preprocessed current data has a reduced frequency and a reduced amplitude compared to the current data; a step in which a shallow estimation function of the battery analysis system receives the preprocessed current data; a step in which the preprocessed current data is applied as input to a simulation executed by a reduced-order electrochemical model to at least partially estimate one or more battery parameters corresponding to the battery cell; and a step in which a diagnostic evaluation corresponding to the battery cell is determined at least partially based on the one or more battery parameters.

[0011] The shallow estimation function utilizes an optimization function that cooperates with the reduced-order electrochemical model to estimate one or more battery parameters, and the step of generating the preprocessed current data before the shallow estimation function is executed can operate to narrow the parameter range utilized by the optimization function in estimating the one or more battery parameters and the parameter range utilized by the reduced-order electrochemical model in executing the simulation.

[0012] The above method may further include the step of determining whether the one or more battery parameters estimated using the shallow estimation function are accurate; and, if the one or more battery parameters are determined to be inaccurate, the step of executing a deep estimation function utilizing a full-order electrochemical model to refine the one or more battery parameters corresponding to the battery cell.

[0013] The one or more battery parameters estimated by the above shallow estimation function can be applied to narrow the parameter range for the above deep estimation function.

[0014] The above method may further include: receiving reference voltage data including charge / discharge data derived from one or more battery drive devices; determining a reference terminal voltage profile based at least partially on the reference voltage data; generating a simulated terminal voltage profile based on the preprocessed current data using the reduced-order electrochemical model; and comparing the simulated terminal voltage profile with the reference terminal voltage profile to evaluate the sufficiency of the preprocessed current data.

[0015] The above-mentioned preprocessed current data is generated according to a reduction metric that quantifies the degree to which the frequency and amplitude of the above-mentioned current data are reduced, and if the difference between the above-mentioned simulated terminal voltage profile and the above-mentioned reference terminal voltage profile satisfies an error threshold, the above-mentioned preprocessed current data generated according to the reduction metric can be determined to be suitable for use in estimating one or more battery parameters corresponding to the battery cell.

[0016] The above preprocessed current data is generated according to a first reduction metric that quantifies the degree to which the frequency and amplitude of the current data are reduced, and if the difference between the simulated terminal voltage profile and the reference terminal voltage profile does not satisfy an error threshold, new preprocessed current data may be generated according to a second reduction metric that reduces the frequency and amplitude of the current data to a smaller degree than the first reduction metric.

[0017] The preprocessed current data can be iteratively refined according to a new reduction metric until the difference between the simulated terminal voltage profile and the reference terminal voltage profile satisfies the error threshold.

[0018] The battery analysis system is configured to estimate one or more battery parameters for one or more battery cells included in an electric vehicle, and the battery analysis system may be directly integrated into the electric vehicle or integrated into a cloud environment that communicates with the electric vehicle through a network.

[0019] The above one or more battery parameters may 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 an SOH parameter corresponding to the battery cell.

[0020] A system for estimating one or more battery parameters according to another embodiment of the present invention is provided, wherein the system comprises one or more processing devices; and one or more non-transient storage devices for storing computing instructions, wherein the one or more processing devices execute the computing instructions to perform the steps of: receiving current data corresponding to a battery cell through a battery analysis system; generating preprocessed current data based on the current data through a preprocessing function of the battery analysis system, wherein the preprocessed current data has a reduced frequency and reduced amplitude compared to the current data; receiving the preprocessed current data through a shallow estimation function of the battery analysis system; executing the shallow estimation function to at least partially estimate one or more battery parameters corresponding to the battery cell by applying the preprocessed current data as input to a simulation executed by a reduced-order electrochemical model; and determining a diagnostic evaluation corresponding to the battery cell based on at least a portion of the one or more battery parameters.

[0021] The shallow estimation function includes an optimization function that operates with the reduced-order electrochemical model to estimate one or more battery parameters, and the step of generating the preprocessed current data before the shallow estimation function is executed may operate to narrow the parameter range utilized by the optimization function in estimating the one or more battery parameters and the parameter range utilized by the reduced-order electrochemical model in executing the simulation.

[0022] The above one or more processing devices may further perform the step of executing the computing instruction to determine whether the one or more battery parameters estimated using the shallow estimation function are accurate; and, if the one or more battery parameters are determined to be inaccurate, the step of executing a deep estimation function utilizing a full-order electrochemical model to refine the one or more battery parameters corresponding to the battery cell.

[0023] The one or more battery parameters estimated by the above shallow estimation function can be applied to narrow the parameter range for the above deep estimation function.

[0024] The above one or more processing devices may further perform the steps of: executing the computing instruction to receive reference voltage data including charge / discharge data derived from one or more battery driving devices; determining a reference terminal voltage profile based at least partially on the reference voltage data; generating a simulated terminal voltage profile based on the preprocessed current data using the reduced-order electrochemical model; and comparing the simulated terminal voltage profile with the reference terminal voltage profile to evaluate the sufficiency of the preprocessed current data.

[0025] The above-mentioned preprocessed current data is generated according to a reduction metric that quantifies the degree to which the frequency and amplitude of the above-mentioned current data are reduced, and the battery analysis system evaluates that the above-mentioned preprocessed current data generated according to the reduction metric is suitable for use in estimating one or more battery parameters corresponding to the battery cell when the difference between the above-mentioned terminal voltage profile and the above-mentioned reference terminal voltage profile satisfies an error threshold, and when the difference between the above-mentioned terminal voltage profile and the above-mentioned reference terminal voltage profile does not satisfy the error threshold, the above-mentioned preprocessed current data can be repeatedly refined according to a new reduction metric until the difference between the above-mentioned terminal voltage profile and the above-mentioned reference terminal voltage profile satisfies the error threshold.

[0026] The battery analysis system above estimates one or more battery parameters for one or more battery cells included in an electric vehicle and can be directly integrated into the electric vehicle or integrated into a cloud environment that communicates with the electric vehicle.

[0027] A method for estimating one or more battery parameters according to another embodiment of the present invention is provided, the method comprising: a shallow estimation function receiving current data corresponding to a battery cell; executing the shallow estimation function to at least partially estimate one or more battery parameters corresponding to the battery cell by applying the current data as input to a simulation executed by a reduced-order electrochemical model; a deep estimation function receiving 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 the parameter range for the deep estimation function; and determining a diagnostic evaluation corresponding to the battery cell based at least partially on the one or more battery parameters.

[0028] Before the above shallow estimation function or the above deep estimation function is executed, the current data may be preprocessed to reduce the frequency and amplitude of the current data.

[0029] The above current data is preprocessed according to a reduction metric that quantifies the degree to which the frequency and amplitude of the above current data are reduced, and the above current data can be repeatedly refined according to a new reduction metric until an error threshold is satisfied.

[0030] According to at least one embodiment of the present invention, a battery analysis system can rapidly and accurately estimate various battery parameters for one or more battery cells by utilizing a combination of a reduced-order electrochemical model and / or a full-order electrochemical model together with a data preprocessing algorithm and a machine learning-based optimization technique.

[0031] In addition, according to at least one embodiment of the present invention, the battery analysis system may execute a multi-stage estimation approach, utilize a shallow estimation function to rapidly narrow the parameter range initially, and utilize a deep estimation function for final refinement when higher accuracy is required. This combined approach enables efficient parameter estimation across various operating scenarios while balancing both speed and precision, and enables real-time parameter estimation for battery management systems in applications such as electric vehicles.

[0032] Additionally, according to at least one embodiment of the present invention, a shallow estimation function utilizing one or more reduced-order electrochemical models may be executed initially to perform rapid battery parameter estimation. Subsequently, if higher accuracy is required, a deep estimation function may be used to further fine-tune the accuracy of the battery parameters. In a scenario where a deep estimation function is used to improve the accuracy of the battery parameters, the estimate output by the shallow estimation function may be utilized to reduce the parameter range for the deep estimation function, thereby significantly reducing the convergence time and computational resources required to generate the final estimate for battery parameter estimation.

[0033] In addition, according to at least one embodiment of the present invention, one or more preprocessing functions are executed on input data provided to a shallow estimation function and / or a deep estimation function to further improve efficiency for generating battery parameter estimates and reduce computation time.

[0034] Furthermore, according to at least one of the embodiments of the present invention, a preprocessing process for input current data can significantly reduce the computational requirements typically associated with processing high-frequency current data, thereby enabling more efficient battery parameter estimation with high accuracy. Additionally, the preprocessing function executed on the input current data can improve the efficiency of the electrochemical model and / or optimization function by reducing the computational complexity of the input current data, thereby enabling faster convergence for parameter estimation. Furthermore, during the deep estimation step, estimates from the shallow estimation step can be utilized to narrow the selection range for the optimization function used in the deep estimation process, and the number of iterations required to converge to the final parameter value can be significantly reduced.

[0035] The effects of the present invention are not limited to those mentioned above, and other unmentioned effects will be clearly understood by those skilled in the art from the description in the claims.

[0036] Exemplary embodiments that are not limiting are described with reference to the drawings below. For easier explanation of the embodiments, the drawings below are provided, and the same reference numerals indicate the same or corresponding configurations.

[0037] FIG. 1 is an exemplary block diagram of a battery parameter estimation system according to specific embodiments.

[0038] FIG. 2 is a block diagram illustrating exemplary features of a battery analysis system according to specific embodiments.

[0039] FIG. 3 is a flowchart illustrating an exemplary method of battery parameter estimation according to specific embodiments.

[0040] FIG. 4 is a flowchart illustrating another exemplary method of a battery parameter estimation system according to specific embodiments.

[0041] FIG. 5 is a graph showing a comparison of current data before and after preprocessing based on a first reduction metric according to specific embodiments.

[0042] FIG. 6 is a graph showing a comparison of current data before and after preprocessing based on a second reduction metric according to specific embodiments.

[0043] FIG. 7 is a graph showing a comparison of current data before and after preprocessing based on a third reduction metric according to specific embodiments.

[0044] FIG. 8 is a graph showing a comparison of current data before and after preprocessing based on the fourth reduction metric according to specific embodiments.

[0045] FIG. 9 is a graph showing a comparison of current data before and after preprocessing based on the fifth reduction metric according to specific embodiments.

[0046] FIG. 10 is a graph showing a comparison of current data before and after preprocessing based on the sixth reduction metric according to specific embodiments.

[0047] FIG. 11 is a graph showing exemplary raw current data measured for two cycles of a battery cell according to specific embodiments.

[0048] FIG. 12 is a graph showing exemplary terminal voltage data measured for two cycles of a battery cell according to specific embodiments.

[0049] FIG. 13 is a graph showing exemplary raw current data measured for a battery cell according to specific embodiments.

[0050] FIG. 14 is a graph showing exemplary terminal voltage data measured for a battery cell according to specific embodiments.

[0051] FIG. 15 is a graph showing exemplary raw current data measured for three cycles of a battery cell according to specific embodiments.

[0052] FIG. 16 is a graph showing exemplary terminal voltage data measured for three cycles of a battery cell according to specific embodiments.

[0053] FIG. 17 is a graph showing a comparison between a simulated terminal voltage profile and a reference terminal voltage profile for estimated model parameters according to specific embodiments.

[0054] FIG. 18 is a graph showing a reduced selection area of ​​the first battery parameter according to specific embodiments.

[0055] FIG. 19 is a graph showing a reduced selection area of ​​the second battery parameter according to specific embodiments.

[0056] FIG. 20 is a graph showing a reduced selection area of ​​a third battery parameter according to specific embodiments.

[0057] FIG. 21 is a graph showing a reduced selection area of ​​the first battery parameter according to specific embodiments.

[0058] FIG. 22 is a graph showing a comparison between a simulated terminal voltage profile and a reference terminal voltage profile at a first test point according to specific embodiments.

[0059] FIG. 23 is a graph showing a comparison between a simulated terminal voltage profile and a reference terminal voltage profile at a second test point according to specific embodiments.

[0060] FIG. 24 is a graph showing a comparison between a simulated terminal voltage profile and a reference terminal voltage profile at a third verification point according to specific embodiments.

[0061] FIG. 25 is a graph showing a comparison between a simulated terminal voltage profile and a reference terminal voltage profile at a fourth verification point according to specific embodiments.

[0062] FIG. 26 is a graph showing a comparison between a simulated terminal voltage profile and a reference terminal voltage profile at a fifth verification point according to specific embodiments.

[0063] FIG. 27 is a graph showing a comparison of a simulated terminal voltage profile and a reference terminal voltage profile at a sixth verification point according to specific embodiments.

[0064] FIG. 28 is a graph showing a comparison between a simulated terminal voltage profile and a reference terminal voltage profile at a seventh verification point according to specific embodiments.

[0065] FIG. 29 is a graph showing a comparison of a simulated terminal voltage profile and a reference terminal voltage profile at the eighth verification point according to specific embodiments.

[0066] FIG. 30 is a graph showing a comparison between a simulated terminal voltage profile and a reference terminal voltage profile at a ninth verification point according to specific embodiments.

[0067] FIG. 31 is a graph showing a comparison between a simulated terminal voltage profile and a reference terminal voltage profile at a 10th verification point according to specific embodiments.

[0068] FIG. 32 is a graph showing an exemplary change pattern of battery model parameters during operation according to specific embodiments.

[0069] FIG. 33 is a graph showing another exemplary change pattern of battery model parameters during operation according to specific embodiments.

[0070] FIG. 34 is a graph showing another exemplary change pattern of battery model parameters during operation according to specific embodiments.

[0071] FIG. 35 is a graph showing another exemplary change pattern of battery model parameters during operation according to specific embodiments.

[0072] FIG. 36 is a graph showing another exemplary change pattern of battery model parameters during operation according to specific embodiments.

[0073] FIG. 37 is a graph showing another exemplary change pattern of battery model parameters during operation according to specific embodiments.

[0074] FIG. 38 is a graph showing exemplary current data according to specific embodiments.

[0075] FIG. 39 is a graph showing an exemplary reference terminal voltage profile according to specific embodiments.

[0076] FIG. 40 is a graph showing a comparison between a reference terminal voltage profile and a simulated terminal voltage profile according to specific embodiments.

[0077] FIG. 41 is a graph showing another comparison of a reference terminal voltage profile and a simulated terminal voltage profile according to specific embodiments.

[0078] FIG. 42 is a network diagram illustrating an exemplary system for hosting a battery analysis system in a server or cloud-based environment according to specific embodiments.

[0079] FIG. 43 is a block diagram showing a battery analysis system integrated into a battery-driven device according to specific embodiments.

[0080] FIG. 44 is a schematic diagram illustrating a battery analysis system integrated into a vehicle according to specific embodiments.

[0081] The following description illustrates exemplary aspects of the present disclosure. However, it should be recognized that such description is not intended to limit the scope of the present disclosure. Rather, the description includes combinations and variations of the exemplary aspects described herein.

[0082] The embodiments described herein may be combined in various ways. Any aspect or feature described for one embodiment may be included in other embodiments mentioned herein. Furthermore, the embodiments described herein may be hardware-based, software-based, or preferably a combination of hardware and software elements. Therefore, it should be noted that even if some examples, features, or components are described in this specification as being implemented in software or hardware, all embodiments, features, and / or components mentioned herein may be implemented in hardware and / or software.

[0083] FIG. 1 illustrates an exemplary system (100A) for estimating battery parameters. The system includes one or more battery cells (105) and a battery analysis system (100) that measures or estimates one or more battery parameters (110) corresponding to one or more battery cells (105).

[0084] In certain embodiments, the battery analysis system (100) may be configured to estimate or measure one or more battery parameters (110) for each battery cell (105). The battery analysis system (100) may estimate or measure one or more battery parameters (110) using any of the techniques described herein. In certain embodiments, the battery analysis system (100) may estimate one or more battery parameters (110) more quickly and efficiently by utilizing a combination of a reduced-order electrochemical model, a data preprocessing algorithm, and a machine learning-based optimization technique. These techniques serve to rapidly narrow the parameter range before applying a full-order electrochemical model for final correction. Details regarding these parameter estimation techniques are described below.

[0085] The battery analysis system (100) may be implemented in software, hardware, or a combination thereof. In some examples, the battery analysis system (100) may include a software-based model or system comprising computer instructions or logic that implement some or all parameter estimation techniques described herein. Such computer instructions or logic may be stored in one or more storage devices and may be executed by one or more processing units. Additionally or alternatively, the battery analysis system (100) may include one or more application-specific integrated circuits (ASICs), one or more field-programmable gate arrays (FPGAs), and / or other hardware components designed to perform some or all parameter estimation techniques described herein.

[0086] The battery analysis system (100) may be configured to estimate or measure battery parameters (110) for any type of battery cell (105). In some examples, the battery cell (105) may be a lithium-ion battery cell. Additionally or alternatively, the battery cell (105) may be a lithium-metal battery cell, a sodium-ion battery cell, a semi-solid-state battery cell, an all-solid-state battery cell, a zinc-ion battery cell, a lithium-sulfur battery cell, a flow battery cell, a proton exchange membrane fuel cell (PEMFC), and / or other types of electrochemical battery cells.

[0087] The battery parameter estimation technique described herein may be performed for any number of battery cells (105). In certain embodiments, the parameter estimation technique may be applied to estimate or measure battery parameters (110) for a plurality of battery cells (105) (e.g., two or more battery cells (105) connected in series or in parallel). In other embodiments, the battery parameter estimation technique may be applied to estimate or measure battery parameters (110) for a single battery cell (105).

[0088] In certain embodiments, the battery analysis system (100) receives input data (120) from each battery cell (105), and this input data (120) is used by the battery analysis system (100) to estimate or measure one or more battery parameters (110) for each battery cell (105). In some examples, the input data (120) may include current data (121) representing or measuring a current value corresponding to each battery cell (105). In certain embodiments, the input data (120) may additionally or alternatively include data representing or measuring other attributes of the battery cell (105) (e.g., voltage, resistance, and / or other references or conditions for each battery cell (105).

[0089] The types of battery parameters (110) estimated or measured by the battery analysis system (100) may vary. In certain embodiments, the battery parameters (110) estimated or measured by the battery analysis system (100) may include one or more degradation parameters (110A), one or more model parameters (110B), one or more thermal parameters (110C), and / or one or more state-of-health (SOH) parameters (110D). Additionally, the battery analysis system (100) may be configured to estimate or measure other types of battery parameters (110), including but not limited to all other parameters mentioned herein.

[0090] The degradation parameter (110A) may generally include any metric, measurement, and / or indicator that can be utilized to characterize the degradation or aging of the battery cell (105), or the performance over time and / or during use. In certain embodiments, the degradation parameter (110A) may represent the reaction rate of change of the solid-electrolyte interphase (SEI) layer of the anode and cathode, lithium plating, particle dissolution, and / or electrolyte decomposition. Additionally or alternatively, the degradation parameters (110A) may represent the anode SEI formation rate, cathode SEI formation rate, cathode film formation rate, lithium-plating rate, electrolyte decomposition rate, transitional metal dissolution rate, film resistance, capacity fade rate, internal resistance increase, self-discharge rate, electrode material dissolution rates, and / or cycling efficiency loss for each battery cell (105). The battery analysis system (100) may also estimate various other types of degradation parameters (110A).

[0091] Model parameters (110B) may generally include variables, parameters, setpoints, and / or other criteria used to perform simulations and / or estimate parameters of the battery cell (105) by electrochemical models such as the reduced-order and full-order electrochemical models described herein. Generally, model parameters (110B) may include variables, criteria, or setpoints related to the physical and / or chemical properties of the battery cell (105), and / or variables, criteria, or setpoints related to the behavior and performance of the battery cell (105). In some examples, model parameters (110B) may represent the volume fraction of the solid and liquid phases, the electrode particle radius, the tortuosity of the electrode and electrolyte, and / or the kinetic reaction rates of the active material. Additionally or alternatively, model parameters (110B) may represent the initial state of charge (SOC) of the anode and cathode, the initial salt concentration in the electrolyte, the kinetic reaction rate of the particle surface, and / or other adjustment factors. Other adjustment factors may modify variables of the electrochemical model that are affected by specific mechanisms, such as electrode volume changes and changes in active surface area due to particle compaction gaps. The battery analysis system (100) may also estimate various other types of model parameters (110B).

[0092] Thermal parameters (110C) may generally include any metric, measurement, and / or indicator related to the thermal characteristics, behavior, and / or performance of the battery cell (105). Thermal parameters (110C) may represent the activation energy of the specific heat and thermal conductivity of the electrode and electrolyte associated with the battery cell (105), as well as the diffusivity and conductivity of the electrode and electrolyte. Additionally or alternatively, thermal parameters (110C) represent the heat generation rate during the charging and discharging process, as well as the heat transfer coefficient between the battery and its surrounding environment. The battery analysis system (100) may also estimate other types of thermal parameters (110C).

[0093] Battery SOH parameters (110D) may generally include metrics and indicators that reflect the health, condition, and / or performance capability associated with the initial or ideal state of the battery cell (105) and / or the aging of the battery cell (105). In some examples, battery SOH parameters (110D) may be derived at least partially from an evaluation of degradation parameters (110A) over time.

[0094] As described throughout this specification, the battery analysis system (100) may utilize enhanced techniques for estimating the aforementioned battery parameters (110) and / or other types of battery parameters.

[0095] FIG. 2 illustrates exemplary features, functions, and / or components of a battery analysis system (100) according to a specific embodiment. For clarity and ease of explanation, these features, functions, and / or components of the battery analysis system (100) may be illustrated or described as individual or separate components in some parts of this specification. However, it should be understood that these features, functions, and / or components may be combined or integrated in various ways.

[0096] The battery analysis system (100) can be stored in one or more storage devices (101) that communicate with one or more processing devices (102).

[0097] One or more storage devices (101) may include (i) non-volatile memory, such as read-only memory (ROM), and / or (ii) volatile memory, such as random access memory (RAM). The non-volatile memory may be removable and / or non-removable non-volatile memory. The RAM may include dynamic RAM (DRAM), static RAM (SRAM), etc. Additionally, the ROM may include masked programmed ROM, programmable ROM (PROM), one-time programmable ROM (OTP), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) (e.g., electrically changeable ROM (EAROM) and / or flash memory, etc.). In certain embodiments, one or more storage devices (101) include a physical and non-transitory medium.

[0098] One or more processing units (102) may include one or more central processing units (CPUs), one or more microprocessors, one or more microcontrollers, one or more controllers, one or more Complex Instruction Set Computing (CISC) microprocessors, one or more Reduced Instruction Set Computing (RISC) microprocessors, one or more Very Long Instruction Word (VLIW) microprocessors, one or more graphics processing units (GPUs), one or more digital signal processors, one or more application-specific integrated circuits (ASICs), and / or other types of processors or processing circuits capable of performing the required functions.

[0099] One or more storage devices (101) contain data and instructions related to implementing any or all of the functionalities of the battery analysis system (100) and its corresponding components (e.g., input acquisition unit (125), preprocessing function (130), shallow estimation function (145), deep estimation function (155), optimization function (170) and / or instructions related to any other functionalities related to the battery analysis system (100). One or more processing devices (102) may be configured to execute instructions stored in one or more storage devices (101). Exemplary configurations for each of these components are described in more detail below.

[0100] The battery analysis system (100) includes an input acquisition unit (125) configured to receive, access, and / or store input data (120) corresponding to each battery cell (105). In certain embodiments, the input data (120) may include current data (121) representing a current value or measurement value for each battery cell (105). In certain embodiments, the current data (121) may be measured, calculated, or estimated using a hardware-based and / or software-based current measurement unit, and in some cases may include one or more current sensors and / or one or more shunt resistors. The current measurement unit may be part of the battery analysis system (100) (e.g., part of the input acquisition unit (125)) and / or may be an external component communicating with the battery analysis system (100).

[0101] A battery analysis system (100) can estimate battery parameters (110) for a battery cell (105) by executing a shallow estimation function (145) (also referred to herein as a “fast estimation function” or “reduced-order estimation function”), a deep estimation function (155) (also referred to herein as a “full-order estimation function”), or a combination thereof, at least partially based on input data (120) (e.g., current data (121)) obtained from a battery cell (105). The shallow estimation function (145) can estimate battery parameters (110) for a battery cell (105) by utilizing one or more reduced-order electrochemical models (140), and the deep estimation function (155) can estimate battery parameters (110) for a battery cell (105) by utilizing one or more full-order electrochemical models (150). In certain embodiments, the shallow estimation function (145) and the deep estimation function (155) may each include and execute one or more optimization functions (170), and the optimization functions operate to estimate battery parameters (110) for a battery cell (105) in combination with an electrochemical model.

[0102] In certain embodiments, each full-order electrochemical model (150) may include a detailed physics-based model (e.g., a pseudo-2D or P2D model) that simulates the electrochemical processes occurring within the battery cell (105). In some embodiments, these models may utilize partial differential equations (PDEs) to represent phenomena such as charge transfer, mass transport, and reaction kinetics across multiple spatial and temporal scales. Additionally, the full-order electrochemical model (150) may utilize the finite element method and / or other numerical techniques to solve these equations and represent battery behavior with high precision. The full-order electrochemical model (150) may consider various physical and chemical parameters of the battery cell (105), including electrode thickness, particle size distribution, electrolyte properties, and reaction rates. While these models can provide high accuracy, they generally consume significant computational resources and have relatively long simulation times.

[0103] Generally, each reduced-order electrochemical model (140) may include a more simplified or streamlined model for simulating the electrochemical processes occurring within the battery cell (105) compared to the full-order electrochemical model (150). For example, compared to the full-order electrochemical model (150), the reduced-order electrochemical model (140) may estimate battery parameters (110) more quickly and may require a smaller input database. In some embodiments, the reduced-order electrochemical model (140) may represent a modified and / or optimized single-particle model to reduce computational resources and / or simulation time. In certain embodiments, the reduced-order electrochemical model (140) may be constructed using order reduction techniques that serve to reduce the number of state variables and / or equations solved during battery simulation. In some cases, the reduced-order electrochemical model (140) may utilize approximations and assumptions to reduce computational complexity while maintaining high accuracy for specific operating conditions. The reduced-order electrochemical model (140) can be calibrated and verified against experimental data and / or higher-fidelity models to ensure that the prediction remains sufficiently accurate within the intended operating range.

[0104] In certain embodiments, the reduced-order electrochemical model (140) may utilize specific physical and chemical parameters of the battery cell (105), but may use simplified mathematical formulas compared to the precision electrochemical model (150). In some embodiments, the reduced-order electrochemical model (140) may undergo a detailed calibration procedure that eliminates the need to calculate partial differential equations (PDEs), yet the reduced-order electrochemical model (140) enables accurate simulation of the electrochemical performance of the battery cell (105) having specific C-rate profiles (e.g., higher C-rate profiles and / or C-rates of 2.5C or higher). The simplified nature of the reduced-order electrochemical model (140) enables faster computation times, making it particularly suitable for real-time applications and / or rapid parameter estimation processes.

[0105] As described throughout this specification, the battery analysis system (100) may estimate battery parameters (110) using a multi-stage approach that utilizes the strengths and advantages of the two types of electrochemical models mentioned above (i.e., a reduced-order model and a full-order model). In some examples, the battery analysis system (100) may first execute a shallow estimation function (145) that relies on at least one reduced-order electrochemical model (140) to quickly narrow down the parameter range for the desired battery parameters (110) (taking advantage of faster computation time and reduced computational complexity), and if higher accuracy is required, may then execute a deep estimation function (155) that relies on at least one full-order electrochemical model (150) to finally refine the battery parameters (110) (and take advantage of higher accuracy). When a deep estimation function (155) is applied to improve the accuracy of battery parameters (110), the battery parameter estimates output by the shallow estimation function (145) can be used to narrow the parameter selection range of the deep estimation function (155), thereby significantly reducing the computational resources and simulation time required to perform the deep estimation function (155). This combined approach can allow the system to efficiently estimate parameters across various operating scenarios while balancing speed and precision considerations in estimating battery parameters (110). Additionally, in some embodiments, this technique can generate parameter estimates for real-time systems and applications.

[0106] In certain embodiments, electrochemical models (both the reduced-order electrochemical model (140) and the precision electrochemical model (150)) may include, utilize, or communicate with one or more optimization functions (170) to estimate battery parameters (110). The design and / or configuration of one or more optimization functions (170) may vary. In certain embodiments, each optimization function (170) may correspond to a machine-learning (ML) model pre-trained for battery parameter optimization. In some examples, the machine-learning model may execute a pruner / sampler algorithm designed to rapidly narrow the selection area of ​​each battery parameter being estimated by sampling from a parameter range and removing non-optimal solutions. Additionally, the ML model may iteratively refine the battery parameter estimates, thereby enabling rapid convergence to optimal values ​​and significantly reducing computational time and resources for battery parameter estimation. In some embodiments, the techniques described herein may be applied to narrow the parameter range used by the optimization function (170) to estimate each battery parameter (110), thereby significantly reducing the number of iterations and / or computation time required for the optimization function (170) to derive an optimized value of the battery parameter (110). Other types of optimization functions (170) may also be used by the electrochemical models described herein.

[0107] In certain embodiments, electrochemical models may cooperate with or communicate with an optimization function (170) to estimate desired battery parameters (110). In some examples, the electrochemical model may be applied to simulate battery behavior based on input data and initial parameter estimates, while the optimization function (170) may cooperatively refine the battery parameter estimates by iteratively adjusting these parameters to minimize the difference between the simulation output and the measurement data. This applies to both the reduction-order electrochemical model (140) and the precision electrochemical model (150).

[0108] Before estimating battery parameters (110) for a battery cell (105), one or more preprocessing functions (130) may be executed on input data (120) (e.g., input current data (121)) derived from each battery cell (105) being analyzed. In certain embodiments, the preprocessing function (130) may be configured to perform feature distillation or extraction on the current data (121) (or other input data (120)), and the preprocessed current data (121) may be provided as input to the shallow estimation function and / or deep estimation function described herein. The feature distillation or extraction techniques applied to the current data (121) and / or input data (120) ensure that the estimates are produced with sufficient accuracy while further enhancing the speed at which the battery analysis system (100) derives estimates of the battery parameters (110).

[0109] In some examples, the initial current data (121) derived from the battery cell (105) may correspond to raw current data having high frequency and amplitude, which requires extensive simulation time for a physics-based model to accurately capture frequency characteristics. Therefore, the preprocessing function (130) may apply one or more feature distillation techniques to reduce the frequency and / or amplitude of the current data (121), thereby significantly reducing the computational requirements typically associated with processing raw current data. Various techniques may be applied to reduce the frequency and / or amplitude of the input current data (121). In certain embodiments, the preprocessing function (130) may apply Gaussian filtering, Kalman filtering, and / or a specially designed convolution kernel to reduce the frequency and / or amplitude of the input current data (121).

[0110] In certain embodiments, the preprocessing functions (130) may also be applied to quantify the degree of preprocessing performed on the input data (120) or to determine or select a reduction metric (131) that quantifies the degree to which the frequency and amplitude of the current data (121) are reduced. In certain embodiments, the preprocessing functions (130) may execute an evaluation process to select an optimized reduction metric (131) that ensures sufficient accuracy of the model simulation while maximizing the reduction of the frequency and amplitude of the current data (121) to improve simulation efficiency.

[0111] The way the preprocessing functions (130) identify or select the reduction metric (131) may vary. In certain embodiments, at least one reduction-order electrochemical model (140) and / or at least one full-order electrochemical model (150) (or a combination thereof) may be utilized to evaluate and / or select the reduction metric (131). In some examples, a full-order electrochemical model (150) may be utilized, and certain initial parameters of the full-order electrochemical model (150) (e.g., SOC values ​​of the anode and cathode, initial salt concentration, initial solid phase and electrolyte volume fractions, reaction rate constant of the electrode, etc.) may be initially set to arbitrarily selected values. These values ​​may not be particularly important at this stage and may be re-estimated in a subsequent processing step.

[0112] Reference voltage data (132) can be input into a precision electrochemical model (150) to generate or determine a reference terminal voltage profile (133). In some cases, the reference voltage data (132) may correspond to one or more initial voltage data sets or profiles derived from one or more battery-powered devices. The reference voltage data (132) may include arbitrary and dynamic charge / discharge data derived during the use of one or more battery-powered devices (e.g., during charge and discharge cycles). In some examples, the reference voltage data (132) may include experimental voltage data that can be used as a basis for generating the reference terminal voltage profile (133).

[0113] The full-order electrochemical model (150) can process reference voltage data (132) to derive a reference terminal voltage profile (133). In certain embodiments, the reference terminal voltage profile (133) may include a reference voltage curve representing terminal voltage over a period of time and / or a series of voltage measurements, and may be used as a standard for evaluating the quality or sufficiency of other simulated voltage profiles generated by the electrochemical model described herein.

[0114] After the reference terminal voltage profile (133) is determined, the reference voltage data (132) may be preprocessed according to a selected reduction metric (131) (e.g., using a Gaussian filter, Kalman filter, convolution kernel, or other distillation means), and the preprocessed current data (121) may be provided as input to either a reduction-order electrochemical model (140) or a full-order electrochemical model (150) to generate a simulated terminal voltage profile (134). The simulated terminal voltage profile (134) may include a voltage cuff and / or a series of voltage measurements derived through a simulation performed using the preprocessed current data (121).

[0115] The simulated terminal voltage profile (134) can be compared with the reference terminal voltage profile (133) identified in the previous preprocessing operation. The reference terminal voltage profile (133) can be used to evaluate the quality or sufficiency of the preprocessed current data. For example, if the difference between the simulated terminal voltage profile and the reference terminal voltage profile (133) satisfies an error threshold (135) (e.g., if the difference is sufficiently small and / or is below or within the error threshold (135) range), the preprocessed input current data can be considered sufficiently refined, which indicates that the selected reduction metric (131) used to generate the preprocessed current data is appropriate. Conversely, if the difference between the simulated terminal voltage profile (134) and the reference terminal voltage curve does not satisfy the error threshold (135) (e.g., if the difference is too large or exceeds the error threshold (135)), the preprocessed input current data may be considered oversimplified (e.g., indicating that the selected reduction metric (131) is not acceptable and / or that the value is too large). In the latter scenario, the value of the reduction metric (131) may be reduced to a certain degree (e.g., reduced by 50% in some cases), and the evaluation process may be re-executed using the modified reduction metric (131). This process may be repeated until a suitable reduction metric (131) is identified. In this way, the preprocessing function (130) can identify an optimal or suitable reduction metric (131) that maintains simulation accuracy while improving simulation efficiency.

[0116] The error threshold (135) used to compare the simulated terminal voltage profile (134) and the reference terminal voltage profile (133) may vary and may be adjusted or customized according to the level of accuracy required in the battery parameter estimation process. In some embodiments, the error threshold (135) may be set to a higher value to prioritize computational efficiency, while in other cases, it may be set to a lower value to achieve higher precision in parameter estimation. In some examples, the error threshold (135) may be set to 15%, whereby the average error or deviation between the simulated terminal voltage profile and the reference terminal voltage profile falls below 15%, and the estimation process is considered sufficiently accurate. In other implementations, the error threshold (135) may be set to various values ​​such as 5%, 10%, 20%, or 25%, depending on the required balance between accuracy and computational speed.

[0117] In certain embodiments, the battery analysis system (100) may be configured to generate or determine a diagnostic assessment (180) for each battery cell (105) analyzed by the system. The diagnostic assessment (180) for the battery cell (105) may be determined or generated at least partially based on one or more battery parameters (110) estimated or calculated for the battery cell (105) (e.g., one or more degradation parameters (110A), one or more model parameters (110B), one or more thermal parameters (110C), and / or one or more SOH parameters (110D) estimated or determined for the battery cell (105).

[0118] In some examples, the diagnostic evaluation (180) for the battery cell (105) may include a positive diagnostic evaluation, and if one or more battery parameters (110) (e.g., some or all of the battery parameters (110)) are determined to be in line with an expected value or expected range, it may indicate that the battery cell (105) is operating under normal conditions. In other examples, the diagnostic evaluation (180) for the battery cell (105) may include a negative diagnostic evaluation, and if one or more battery parameters (110) are detected to be out of line with an expected value or expected range, it may indicate that the battery cell (105) is operating under abnormal conditions.

[0119] In certain embodiments, one or more mitigation functions (185) may be executed in response to a negative diagnostic evaluation of one or more battery cells (105). The mitigation functions (185) may be initiated or executed by a battery analysis system (100) and / or a battery management system communicating with the battery analysis system (100). In certain embodiments, these mitigation functions (185) may be implemented to address issues identified by the negative diagnostic evaluation and / or to optimize the performance, safety, and lifespan of the affected battery cell (105) (or device including the battery cell (105)). The type of mitigation function (185) executed may vary and, in some cases, may depend on specific battery parameters identified as deviating from expected operating conditions.

[0120] In certain embodiments, executing the mitigation function (185) may adjust or modify operating parameters, performance and / or settings corresponding to one or more affected battery cells (105). This may include modifying the voltage, current, temperature and / or resistance corresponding to one or more battery cells (105). Additionally, or alternatively, this may include modifying one or more parameters or settings to adjust the charge rate, discharge rate, state of charge, depth of discharge, charge time, discharge time, power output, number of cycles, internal pressure, cooling rate, thermal management settings, balancing parameters, maximum charge voltage, minimum discharge voltage, maximum charge current, maximum discharge current, charge protocol, discharge protocol, idle time, load distribution, power allocation, cell grouping, bypass settings, fault tolerance level and / or sensitivity level corresponding to one or more battery cells (105). Other parameters and / or settings may also be modified or adjusted. In some scenarios, modifying or adjusting parameters, performance and / or settings corresponding to one or more affected battery cells (105) can modify and / or mitigate conditions that caused or generated a negative diagnostic evaluation.

[0121] In certain embodiments, executing the mitigation function (185) may implement isolation procedures for one or more battery cells (105) in response to the detection of a negative diagnostic evaluation corresponding to one or more battery cells (105). In some examples, the isolation procedures may include electrically isolating or bypassing (e.g., isolating from a battery pack or system containing the battery cell (105)) the affected battery cell (105) to prevent potential safety hazards (e.g., overheating or fire hazards) or further degradation. In certain embodiments, isolation of the battery cell (105) may be achieved through the activation of a switch, relay, or other circuit breaker mechanism integrated within a battery management system or device corresponding to the battery cell (105).

[0122] In certain embodiments, mitigation functions (185) may trigger the transmission or dispatch of an alert when a negative diagnostic evaluation is detected for one or more battery cells (105). Such alert may be transmitted to a vehicle or device (and / or a maintenance person, technical service provider, or device operator) containing the affected battery cells, and may identify details related to the negative evaluation or indicate that one or more battery cells (105) need to be replaced.

[0123] The battery analysis system may execute or implement other types of relaxation functions (185) in addition to those mentioned in the present disclosure.

[0124] FIG. 3 is a flowchart of an exemplary method (200A) of a battery parameter estimation method according to specific embodiments. Method 200A is merely exemplary and is not limited to the embodiments presented herein. Method 200A may be applied to various embodiments or examples not specifically depicted or described herein. In some embodiments, the steps of Method 200A may be performed in the order presented. In other embodiments, the steps of Method 200A may be performed in any appropriate order. In yet other embodiments, one or more steps of Method 200A may be combined or omitted. In many embodiments, a battery analysis system (100) may be configured to perform Method 200A and / or one or more steps of Method 200A. In these embodiments or other embodiments, one or more steps of method 200A may be implemented as one or more computer instructions configured to be executed in one or more processing units (102) and configured to be stored in one or more non-volatile storage devices (101). Such non-volatile memory storage devices (101) may be part of a computer system such as system 100A and / or battery analysis system 100.

[0125] In this exemplary method 200A, steps 210A and 220A may be performed as part of the fast estimation step 201A (which may also be called the shallow estimation step or the reduced-order estimation step), and steps 230A and 240A may be performed as part of the deep estimation step 202A (which may also be called the full-order estimation step).

[0126] In step 210A, one or more preprocessing functions (130) are executed on input data (120). The input data (120) may include current data (121) obtained from one or more battery cells (105) (e.g., raw current data having a relatively high frequency and amplitude). One or more preprocessing functions (130) may be executed to reduce the frequency and amplitude of the current data (121) based on a selected reduction metric (131). In some embodiments, the degree of preprocessing performed on the current data (121) may be determined by the reduction metric (131).

[0127] In step 220A, one or more battery parameters (110) are estimated using a reduction-order electrochemical model (140) and / or an optimization function (170). The reduction-order electrochemical model (140) and the optimization function (170) may cooperate to jointly estimate one or more battery parameters (110). In some examples, the one or more battery parameters (110) estimated in this step may include one or more battery degradation parameters (110A), one or more battery model parameters (110B), one or more thermal parameters (110C), one or more SOH parameters (110D) and / or other types of battery parameters (110).

[0128] In step 225A, it is determined whether one or more estimated battery parameters (110) need to be finely tuned and / or estimated with higher accuracy. If it is determined that additional tuning and / or accuracy is not needed or required, method 200A proceeds to the termination block and terminates. In this case, the battery parameter estimation performed in step 220A can be used to represent the final battery parameters (110) for one or more battery cells (105). On the other hand, if it is determined that additional refinement and / or accuracy is needed or required, method 200A proceeds to step 230A.

[0129] In step 230A, one or more preprocessing functions (130) may be executed again on the input data (120). For example, one or more preprocessing functions (130) may be executed on the input data (120) using a second reduction metric (131) different from the reduction metric (131) used in step 210A (e.g., a second reduction metric (131) that reduces the frequency and amplitude of the current data (121) less than the first reduction metric (131)).

[0130] In some embodiments, step 230A may be optional and / or omitted. For example, instead of re-executing one or more preprocessing functions (130), input data (120) (e.g., raw current data (121)) may be used to estimate one or more battery parameters (110) in step 240A, and thus no preprocessing may be performed.

[0131] In step 240A, one or more battery parameters (110) are estimated using a full-order electrochemical model (150) and / or an optimization function (170). The full-order electrochemical model (150) and the optimization function (170) may cooperate to jointly estimate one or more battery parameters (110). In other words, one or more battery parameters (110) may include one or more battery degradation parameters (110A), one or more battery model parameters (110B), one or more thermal parameters (110C), one or more SOH parameters (110D), and / or other types of battery parameters. The battery parameter estimation performed in step 240A may be used to represent the final battery parameters (110) for one or more battery cells (105). After the final battery parameters (110) for one or more battery cells (105) are determined, method 200A proceeds to a termination block and terminates.

[0132] In certain embodiments, the method 200A of FIG. 3 may further include a step of determining a diagnostic evaluation (180) for one or more battery cells (105) corresponding to input data. This diagnostic evaluation step may be performed after step 220A, after step 240A and / or after both steps.

[0133] In certain embodiments, the method 200A of FIG. 3 may further include the step of executing a mitigation function (185) in response to the detection of a negative or abnormal diagnostic evaluation for one or more battery cells (105).

[0134] FIGS. 5–10 include graphs comparing current data (121) before and after the application of a preprocessing function (130). In these graphs, the y-axis represents the current value, and the x-axis represents time (in units of 1 × 10⁴ seconds). Each graph displays the raw current data (121) before preprocessing and the preprocessed current data (121). Different reduction metrics (131) are used in each graph, which are indicated by “A” at the top of each graph. In this example, a larger value of A (or reduction metric (131)) results in a greater reduction in frequency and amplitude.

[0135] Figure 5 is a graph showing a comparison of raw current data before and after preprocessing when the first reduction metric is set to 10. Figure 6 is a graph showing a comparison of raw current data before and after preprocessing when the second reduction metric is set to 50. Figure 7 is a graph showing a comparison of raw current data before and after preprocessing when the third reduction metric is set to 100. Figure 8 is a graph showing a comparison of raw current data before and after preprocessing when the fourth reduction metric is set to 200 (200A). Figure 9 is a graph showing a comparison of raw current data before and after preprocessing when the fifth reduction metric is set to 400. Figure 10 is a graph showing a comparison of raw current data before and after preprocessing when the sixth reduction metric is set to 800.

[0136] As previously explained, prior to estimating battery parameters (110) for one or more battery cells (105), one or more preprocessing functions (130) may be executed to perform feature distillation on input current data (121). As shown in FIGS. 5–10, raw current data has high-frequency characteristics, which means that a physics-based model generally requires a significant amount of simulation time to accurately reflect these frequency characteristics. Therefore, preprocessing of current data (121) can provide advantages, as it extracts data that allows the optimization function (170) used in the shallow estimation function (145) and / or deep estimation function (155) to accurately calculate parameter estimation results, while simultaneously reducing the computational load.

[0137] Various techniques may be applied to select an optimal or appropriate reduction metric (131) to ensure that the estimates generated by the models are sufficiently accurate while reducing the computational load of the reduction-order electrochemical model(s) (140) and / or full-order electrochemical model(s) (150).

[0138] FIG. 4 is a flowchart of an exemplary method (200B) that may be applied to identify or select a reduction metric (131) according to specific embodiments. Method 200B is merely exemplary and is not limited to the embodiments presented herein. Method 200B may be applied to various embodiments or examples not explicitly shown or described herein. In some embodiments, the steps of Method 200B may be performed in the order presented. In other embodiments, the steps of Method 200B may be performed in any suitable order. In yet other embodiments, one or more steps of Method 200B may be combined or omitted. In many embodiments, a battery analysis system (100) and / or a preprocessing function (130) may be configured to perform Method 200B and / or one or more of the steps thereof. In these or other embodiments, one or more steps of method 200B may be implemented as one or more computer instructions configured to be executed in one or more processing units (102) and configured to be stored in one or more non-transient storage devices (101). Such non-transient memory storage devices (101) may be part of a computer system such as system 100A and / or a battery analysis system (100).

[0139] In step 210B, one or more battery model parameters are set to randomly selected values. These randomly set battery model parameters may include parameters used in a reduction-order electrochemical model (140) and / or a full-order electrochemical model (150) to perform simulations for one or more battery cells (105). In some examples, some or all of the parameters representing the initial state of charge (SOC) for the positive and negative electrodes, the initial salt concentration, the initial solid phase and electrolyte volume fraction, and the reaction rate constant of the electrodes may be set to random values. Other parameters of the electrochemical model may also be randomly selected values. The parameters set randomly at this step may be re-estimated in a subsequent parameter estimation procedure and may not be particularly useful for identifying an appropriate reduction metric (131).

[0140] In step 220B, a reference terminal voltage profile (133) is determined by processing the initial current data (121) using at least one full-order electrochemical model (150). The initial current data (121) may correspond to reference voltage data (132), such as raw or experimental current data having a relatively high frequency and amplitude.

[0141] In step 230B, one or more preprocessing functions (130) are executed on the initial current data according to a reduction metric (131) (e.g., using a Gaussian filter or other frequency / amplitude reduction means). As previously described, the reduction metric (131) can quantify the extent to which preprocessing is performed and / or the extent to which the frequency and amplitude of the raw current data are reduced. One or more preprocessing functions (130) output preprocessed current data with reduced frequency and amplitude.

[0142] In step 240B, a simulated terminal voltage profile (134) is generated based at least partially on a simulation executed by at least one reduction-order electrochemical model (140) or at least one full-order electrochemical model (150) using the preprocessed current data. The simulated terminal voltage profile (134) may be generated based on the preprocessed current data, and the accuracy of this profile may be associated with the reduction metric (131) used to generate the preprocessed current data. As described throughout this specification, an optimization function (170) may operate in parallel with at least one reduction-order electrochemical model (140) or at least one full-order electrochemical model (150) to generate the simulated terminal voltage profile (134).

[0143] In step 250B, the reference terminal voltage profile (133) and the simulated terminal voltage profile (134) are compared.

[0144] In step 255B, it is determined whether the difference between the simulated terminal voltage profile (134) and the reference terminal voltage profile (133) satisfies an error threshold (135). The error threshold (135) may represent a numerical value or range used to determine whether the simulated terminal voltage profile is sufficiently accurate for estimation purposes. In certain embodiments, the average error (e.g., absolute average error or AAE) or deviation between the simulated profile and the reference profile may be calculated and compared to the error threshold (135). In some examples, the error threshold (135) may be set to 15%, and when the average error or deviation is less than 15%, it is determined that the simulated terminal voltage profile (134) is sufficiently close to the reference terminal voltage profile (133) (and, conversely, when the average error or deviation exceeds 15%, it is determined that it is too large). The error threshold (135) can be set to different values ​​(e.g., 1%, 5%, 10%, 20%, 25%, etc.) depending on the desired balance between accuracy and computation time.

[0145] If the difference between the simulated terminal voltage profile (134) and the reference terminal voltage profile (133) is sufficiently small (e.g., less than the error threshold (135)), method 200B proceeds to step 260B. In step 260B, the reduction metric (131) is determined or selected to be suitable, and the method then terminates in a termination block.

[0146] On the other hand, if the difference between the simulated terminal voltage profile (134) and the reference terminal voltage profile (133) is too large (e.g., exceeding an error threshold (135)), this may indicate that the preprocessed current data is oversimplified and / or unsuitable. In this scenario, method 200B returns to step 230B, and a new reduction metric (131) may be selected to generate the preprocessed current data. Steps 230B, 240B, 250B, and 255B may be repeated sequentially until a suitable reduction metric (131) capable of generating preprocessed current data with sufficient accuracy or quality is identified. For example, in each iteration, the reduction metric (131) used to generate the preprocessed current data may be reduced (e.g., reduced by half or another predetermined ratio), and accordingly, the frequency and amplitude of the preprocessed current data generated in the current iteration are increased. The quality or sufficiency of the preprocessed current data in each iteration may be re-evaluated by comparing the difference between the simulated terminal voltage profile (134) and the reference terminal voltage profile (133) until an iteration is reached in which it is determined that the preprocessed current data used to generate the corresponding simulated terminal voltage profile (134) is sufficient and / or satisfies the error threshold (135).

[0147] Returning to FIG. 2, the reduction metric (131) selected by the preprocessing function (130) can operate to rapidly narrow the range of values ​​that the optimization function (170) and / or electrochemical models utilize to estimate the battery parameters (110). In some embodiments, the reduction metric (131) used to derive the preprocessed current data (121) for the reduction order electrochemical model (140) may be larger than the reduction metric (131) used to derive the preprocessed current data (121) for the full order electrochemical model (150). In some examples, the reduction metric (131) used to derive the preprocessed current data (121) for the reduction order electrochemical model (140) may be within the range of 100 to 500 (e.g., may be set to 100, 150, 200, 300, 400, or 500), whereas the reduction metric (131) used to derive the preprocessed current data (121) for the full order electrochemical model (150) may be less than 100 (e.g., may be set to 10, 25, 50, 75, or 99).

[0148] The following description refers to the drawings (Figs. 11-14, 15-17, 18-21, 22-31, 32-37, and 38-41) and describes an exemplary procedure for estimating battery parameters (110) according to a specific embodiment, as well as test results related to executing said procedure. said procedure may initially include estimating an initial set (110B) of model parameters for one or more battery cells (105), which may be used to modify specific variables or settings of an electrochemical model (e.g., a reduced-order electrochemical model (140) and / or a full-order electrochemical model (150)) to account for specific physical changes occurring during battery operation, such as changes in electrode volume, particle shrinkage spacing, and / or other changes in one or more battery cells (105). After estimating these initial model parameters (110B), the procedure is then carried out to estimate one or more degradation parameters (110A) and / or one or more thermal parameters (110C) for one or more battery cells (105). In some cases, the procedure may be extended or adjusted to estimate one or more SOH parameters (110D).

[0149] The exemplary procedure described below utilizes two types of lithium-ion battery cells for demonstration purposes (these may be referred to as "battery (001)" and "battery (002)" respectively).

[0150] In the first step of the above procedure, specific model parameters (110B) corresponding to the battery cell (105) are estimated. In some examples, the model parameters (110B) estimated at this step may include the initial state of charge (SOC) of the anode and cathode, the initial salt concentration in the electrolyte, the tortuosity of the cathode and anode, the kinetic reaction rate of the particle surface, and / or other adjustment coefficients. As mentioned above, these model parameters (110B) can modify variables within an electrochemical model that are affected by specific mechanisms, such as changes in electrode volume and changes in the active surface area due to particle compression gaps.

[0151] FIG. 11 shows input current data of a battery (001) under dynamic operating conditions without preprocessing, and FIG. 12 shows the corresponding measured cell terminal voltage curve of the battery (001) (showing two cycles as an example). In FIG. 11 and 12, the x-axis represents time (the unit of the x-axis is 1×10⁻¹⁰). 4 It represents (seconds). The y-axis of FIG. 11 represents current (A), and the y-axis of FIG. 12 represents terminal voltage (V). The terminal voltage curve shown in FIG. 12 may represent a reference terminal voltage profile (133) that can be used as a reference for comparison in the later stages of the procedure.

[0152] Input current data of lithium-ion battery cells (battery 001 and battery 002) includes both charging and discharging phases, exhibiting various random amplitudes during this process. To estimate model parameters (110B), specific parameters of the model (e.g., degradation parameters and thermal parameters) may be maintained at fixed values. An initial selection area or range may be defined for each battery parameter, and the optimization function (170) selects a value within this range. Experimental current and voltage data for the initial 100,000 seconds may be used for processing for estimation. During this phase, other parameters may be maintained at a fixed state.

[0153] After data preprocessing is performed (e.g., in some cases, using 200 as the reduction metric (131) value), the input current data can be accessed and utilized by a shallow estimation function (145). As previously described, the shallow estimation function (145) can estimate model parameters (110B) by utilizing a reduction-order electrochemical model (140) (e.g., a revised or modified single particle model) and an optimization function (170). The reduction-order electrochemical model (140) can provide fast computation speeds and high accuracy within a specific charge-discharge C-rate range (e.g., 2.5C or higher). Using preprocessed input current data and battery parameters (including model parameters (110B) and / or other parameters), a reduction-order electrochemical model (140) simulates and outputs a terminal voltage data curve (e.g., a simulated terminal voltage profile (134)), which is compared with the reference terminal voltage profile (133) of FIG. 12 to calculate the Absolute Average Error (AAE). If the AAE exceeds a threshold error value ξ (e.g., 30 mV), the shallow estimation function (145) may be run again to continue optimizing the model parameters (110B) until the AAE falls below ξ. The optimized model parameters (110B) that yield the lowest AAE may be stored or recorded as the battery parameter estimation result of the shallow estimation function (145).

[0154] In some embodiments, a deep estimation function (155) may be applied to further refine the model parameters (110B). In this scenario, the battery parameter estimation results generated by the shallow or reduced-order estimation function (145) may serve to narrow the selection area for each parameter calculated by the optimization function (170) in the deep estimation step. Since exploring within a wide selection area significantly increases the number of iterations required for convergence and consumes more time, narrowing the selection area can be a significant driving force in reducing computation time. For example, estimating the initial positive SOC with a wide initial selection area (0.05 to 0.95) may require 100,000 iterations for the optimization algorithm to converge, assuming other battery parameters remain constant. However, after repeating the fast estimation process several times, it can be observed that when the anode SOC value is within the range of 0.5 to 0.75, the simulated terminal voltage curve is more likely to achieve an AAE below an error threshold (e.g., 50 mV) compared to the reference terminal voltage curve. Thus, the selection area for the anode SOC can be narrowed from 0.05–0.95 to 0.5–0.75, which reduces the number of iterations to about 2,000 and significantly reduces the optimization time. This reduction may be particularly advantageous for the deep estimation function (155), as the simulation time for the full-order electrochemical model (150) is longer than that of the reduced-order electrochemical model (140). Furthermore, the shallow estimation process can be repeated several times to further narrow the selection area for each model parameter (110B) and avoid local optimization traps. This iterative approach can increase the convergence speed of the optimization process by refining the parameter range for subsequent rounds of the fast estimation process.

[0155] In some embodiments, if it is determined that higher accuracy is required after a fast estimation step (e.g., when the mean absolute error (AAE) threshold ξ is set to 15 mV or less), a deep estimation function (155) may be executed. In some examples, if the charging or discharging rate is relatively low (e.g., when the C-rate is less than 2.5), additional refinement may be required through the deep estimation function (155).

[0156] In some embodiments, the input current data (121) may be reprocessed with a smaller reduction metric (131) (e.g., less than 50). Alternatively, the input current data (121) may omit any preprocessing. In both scenarios, the current data may be accessed or input by a deep estimation function (155) that utilizes a full-order electrochemical model (150) and an optimization function (170) for higher accuracy estimation. As mentioned above, the selection region for each parameter in the deep estimation process may be based on or derived from the results of the fast or reduction-order estimation step. The model parameter (110B) may be optimized to minimize the AAE between the simulated terminal voltage profile (134) generated by the full-order electrochemical model (150) and the reference terminal voltage profile (133). The most optimized parameter that yields the minimum AAE may be recorded as the final model parameter (110B) in the deep estimation process.

[0157] As previously explained, the model parameters (110B) corresponding to the battery cells (105) (e.g., battery (001) and battery (002)) can be verified by using only a shallow estimation function (145) or by combining a shallow estimation function (145) and a deep estimation function (155) depending on the desired level of accuracy.

[0158] After estimating the initial parameters of the battery cell (105), the degradation parameters (110A) and / or thermal parameters (110C) of the battery cell (105) can be estimated. Since the aging and degradation of the battery accumulate over a long period, estimating the degradation parameters (110A) (e.g., SEI formation and growth rates of the positive and negative electrodes, lithium plating rates, transition metal dissolution and deposition rates, and / or electrolyte decomposition rates, etc.) may involve the use of a significantly larger data set.

[0159] In some examples, as shown in FIGS. 13 and 14, 1×10 7 3×10 seconds 7 Experimentally measured current and terminal voltage data under dynamic operating conditions lasting for seconds can be used to estimate degradation parameters (110A). In this process, initial battery parameters (e.g., initial positive and negative SOC, initial salt concentration, kinetic reaction rates of positive and negative surfaces) and battery model parameters (e.g., particle radius, electrode and separator thickness, electrode porosity) can be kept constant. In FIGS. 13 and 14, the x-axis represents time (the unit of the x-axis is 1×10⁻¹⁰). 6 It represents seconds. The y-axis of FIG. 13 represents the current (A) of the battery (002), and the y-axis of FIG. 14 represents the terminal voltage (V) of the battery (002).

[0160] Initially, a fast or shallow estimation function (145) may be executed to quickly estimate the battery degradation parameter (110A) and subsequently to narrow the selection range of each parameter to be considered by the optimization function (170) during the deep estimation process. Preprocessed current data can be utilized by the reduction-order electrochemical model (140) by applying a relatively large reduction metric (131) (e.g., when the value is 200). The degradation parameter (110A) can be optimized in the same or similar manner as previously described until the average absolute error (AAE) of the simulated terminal voltage profile (134) is below a specific error threshold ξ (e.g., less than 50 mV). In other words, a deep estimation function (155) may be applied when higher accuracy is required. In this case, minimally preprocessed or no preprocessed input current data may be accessed or input by the full-order electrochemical model (150) and the optimization function (170). The optimization function (170) can derive the final estimated result of the degradation parameter (110A) when the AAE of the simulated terminal voltage curve is minimized (e.g., when it is 25mV or less).

[0161] Accordingly, similar to the model parameter estimation process, the degradation parameter (110A) for the battery cell (105) (e.g., battery (001) and battery (002)) can be estimated using only a shallow estimation function (145) or a combination of a shallow estimation function (145) and a deep estimation function (155), depending on the required level of accuracy.

[0162] The same or similar technology may be extended to the estimation of State of Health (SOH) parameters (110D) for a battery cell (105), including but not limited to SOH parameters (110D) indicating the aging of the battery cell (105). In certain embodiments, since estimating actual degradation parameters may take a very long time, the battery analysis system (100) may estimate the SOH parameters (110D) using specific model parameters (110B) that can be utilized to derive an aging assessment. In some examples, the SOH parameters (110D) of the battery cell (105) may be derived at least partially using model parameters (110B) indicating the tortuosity and porosity of the electrode, the active surface area of ​​the particle, and / or the equivalent kinetic rate of the particle surface. These model parameters (110B) allow the aging of the battery cell (105) to be determined more quickly and efficiently compared to techniques that rely on actual degradation parameters. The rapid aging evaluation technique described herein enables the battery analysis system (100) to estimate SOH parameters (110D) for on-board and / or real-time applications.

[0163] In certain embodiments, the battery analysis system (100) can derive an SOH parameter (110D) corresponding to the battery cell at various operating stages, such as an initial stage when the battery cell (or multiple battery cells) is manufactured or installed in a battery-powered device (e.g., when the SOH is expected to be 100% or near thereon), an early life stage (e.g., when the SOH is expected to decrease slightly after minimal use), an intermediate life stage (e.g., when the SOH is expected to decrease gradually), and an end-of-life stage (e.g., when the SOH of the battery cell is expected to be too low for actual use). In a scenario where the battery cell (105) is installed in a vehicle, the operating stages may additionally or alternatively correspond to other stages of vehicle use.

[0164] In certain embodiments, to evaluate the SOH parameter (110D) of a specific battery cell (105), the battery model parameter (110B) may be periodically updated or estimated at each inspection point of each operation step. To estimate the initial model parameter (110B), the same or similar process described above may be used at each inspection point. For example, experimental or raw input data (120) spanning 100,000 seconds from each inspection point may be selected or extracted, and the input current data (121) may first be preprocessed with a larger reduction metric (131) (e.g., a value of 200). Subsequently, a shallow estimation function (145) utilizing at least one reduction order electrochemical model (140) and an optimization function (170) is executed to rapidly estimate the equivalent battery model parameter (110B) at a given point in time. In other words, this process helps to narrow the selection area for the deep estimation function (155), and where higher accuracy is required, the deep estimation function (155) can be executed to further refine the battery model parameters (110B) at each inspection point. At this stage, the results of the shallow estimation function (145) can be corrected. When executing the deep estimation function (155), current data (121) (minimal or no preprocessing) can be input into the full-order electrochemical model (150) and the optimization function (170). The most optimized battery model parameters (110B) at each inspection point correspond to the parameters that generate a simulated terminal voltage profile (134) (calculated at the corresponding inspection point) having the minimum AAE compared to the reference terminal voltage profile (133). These optimized model parameters (110B) can subsequently be used to derive updated SOH parameters (110D) at each inspection point, which can be used to indicate or evaluate the aging of the battery cell (105) at a given point in time.

[0165] FIGS. 15-17 and 18-21 show exemplary test results generated according to specific embodiments of the battery analysis system (100).

[0166] FIG. 15 shows raw current data of a battery (001) input to a battery analysis system (100) during testing, and FIG. 16 shows a cell terminal voltage curve or profile corresponding to the first three cycles (over the first 90,000 seconds) of the battery (001).

[0167] According to a specific embodiment disclosed herein, a shallow estimation function (145) was used to identify initial model parameters (110B) for a battery cell (105). In this example, the shallow estimation function (145) identified the model parameters (110B) by including a reduced-order electrochemical model (140) (e.g., a modified single-particle model) and an optimization function (170) (e.g., including a machine learning-based pruner / sampler algorithm). These estimated model parameters (110B) include the initial SOC of the cathode (SOC0, neg ), initial SOC of the anode (SOC0, pos ), initial salt concentration (c e0 ), cathode curvature (τ neg ), anode curvature (τ pos ), kinetic reaction rate of the cathode (k neg ), bipolar kinetic reaction rate (k pos ), solid diffusion coefficient of cathode particles (D s,neg ), solid diffusion coefficient of anode particles (D s,pos ...was included. The identification of these parameters is based on three cycles of random field data over the first 90,000 seconds of raw current data. The initial selection area for each battery model parameter (110B) can be selected from the range shown in Table 1 reproduced below.

[0168] Battery Initial Parameters Lower Limit Upper Limit SOC O,neg 0.10.5SOC 0,pos0.50.9c e0 500 mol / m 3 1500 mol / m³τ neg 0.55τ pos 0.55k neg 1x10 -13 m / s1X10 -10 m / sk pos 1x10 -13 m / s1X10 -10 m / sD s.neg 1x10 -15 m 2 / s1Х10 -12 m 2 / sD s,pos 1x10 -15 m 2 / s1Х10 -12 m 2 / s

[0169] During the test, a total of 10 battery initial parameter estimation optimization groups were processed in parallel through a shallow estimation function (145), and each group was optimized for 1,000 epochs by an optimization function (170). After estimation was completed for all 10 groups, the most optimized battery initial parameters in each group were identified. These parameters were selected based on the minimum AAE between the simulated terminal voltage profile (134) and the reference terminal voltage profile (133) within each group. The 10 sets of optimized battery model parameters (110B) were then input back into a reduced-order electrochemical model (140), and the simulated terminal voltage profile (134) was compared with the reference terminal voltage profile (133).

[0170] FIG. 17 illustrates an example of one of the comparisons performed. In particular, FIG. 17 shows a comparison between a simulated terminal voltage profile (134) based on optimized battery model parameters (110B) and a reference terminal voltage profile (133). In this case, the average absolute error (AAE) of the simulated terminal voltage is 29.8 mV. It can be seen that the simulated voltage curve (Vt) matches the reference voltage curve very well overall. The values ​​of the optimized initial model parameters (110B) derived after the shallow estimation function (145) is executed are summarized in Table 2 reproduced below.

[0171] Battery initial parameter most optimized value SOC0, neg 0.4678SOC0, pos 0.5271c e0 1022.2149 mol / m 3 τ neg 1.37τ pos 1.43k neg 1.1646X10 -11 m / sk pos 6.7234X10 -11 m / sD s,neg 3.6466X10 -14 m 2 / sD s,pos 3.1511X10 -14 m 2 / s

[0172] Additionally, the value range of the selection area for each initial battery model parameter (110B) can be narrowed (as shown in FIGS. 18-21), which can be further used to accelerate the deep estimation function. FIG. 18 shows the narrowed parameter range for the kinetic rate parameter. FIG. 19 shows the narrowed parameter range for the diffusion coefficient parameter. FIG. 20 shows the narrowed parameter range for the SOC value parameter. FIG. 21 shows the narrowed parameter range for the tortuosity parameter.

[0173] To indicate the battery aging state for the SOH parameter (110D), the equivalent battery model parameter (110B) can be re-estimated for different inspection points. The initial SOC of the negative electrode (SOC0, neg ), initial SOC of the anode (SOC0, pos ), initial salt concentration (c e0 ), solid diffusion coefficient of cathode particles (D s,neg ), and solid diffusion coefficient of anode particles (D s,pos Initial battery model parameters, including ) (shown in FIG. 18-21), can be fixed, and other model parameters (110B) are re-estimated at each inspection point. The model parameter (110B) updated at each inspection point is the solid-phase volume fraction (ε) of the anode. neg ), solid phase volume fraction of the cathode (ε pos ), anode curvature (τ neg ), cathode curvature (τ pos ), kinetic reaction rate of the anode particle surface (k pos ), kinetic reaction rate of the cathode particle surface (k neg It may include an adjustment factor for the active surface area of ​​the anode and cathode.

[0174] After the shallow estimation function (145) is applied, the updated equivalent battery model parameters can be input into the reduced-order electrochemical model (140) along with the parameters that have not changed. Subsequently, the simulated terminal voltage profile (134) can be experimentally measured within each checking window or compared with the reference terminal voltage profile (133).

[0175] FIGS. 22-31 shows a comparison of terminal voltage profiles. In each test window, it is observed that the simulated terminal voltage profile (134) matches well with the experimentally measured or reference terminal voltage curve, which indicates that the updated battery model parameters (110B) at each test time, estimated by the shallow estimation function (145), are accurate.

[0176] FIGS. 32-37 shows the change pattern of each battery model parameter (110B) according to cycling after a rough-fitting procedure and a fine-fitting procedure based on the simulation and battery model parameter estimation above.

[0177] FIGS. 38-40 illustrate test results related to the estimation of degradation parameters (110A) of a battery cell (e.g., battery (002)). FIG. 38 illustrates experimental input current data, and FIG. 39 illustrates the corresponding reference terminal voltage profile (133). Model parameters (110B) are initially estimated using a fast or shallow estimation function (145). After optimization, a simulated terminal voltage profile (134) is generated using a reduced-order electrochemical model (140) based on the optimized model parameters (110B), which is then compared with the reference terminal voltage profile (133). As illustrated in FIG. 40, the comparison shows a good match, indicating that the estimated initial battery parameters are accurate.

[0178] Next, the model parameter (110B) can be set as a constant, and the cathode SEI formation rate (k) neg,SEI ), anode SEI formation rate (k pos,SEI ), cathode SEI formation resistance (λ neg,SEI ), positive SEI formation resistance (λ pos,SEI ), electrolyte decomposition rate (k decom ), anodic manganese dissolution rate (k Mn,disso), and anodic manganese ion deposition rate (k Mn,dep Battery degradation parameters including ) are experimental data (length 2×10 7 It is estimated based on seconds. When applying a fast or shallow estimation function (145) to estimate the degradation parameter (110A), the input current data can be preprocessed with a reduction metric value of 200. The preprocessed current data can then be input into a shallow estimation function (145) that includes a reduction order electrochemical model (140) and an optimization function (170) for subsequent estimation. Using the optimized battery degradation parameter (110A) (along with the battery model parameter (110B)), the reduction order electrochemical model (140) and the optimization function (170) can generate a simulated terminal voltage profile (134).

[0179] FIG. 41 shows the result of comparing a simulated terminal voltage profile (134) and a reference terminal voltage profile (133) based on the estimated degradation parameter (110A). In this figure, the reference terminal voltage profile and the simulated terminal voltage profile are shown. It can be seen that the simulated terminal voltage curve matches the reference terminal voltage curve well overall (AAE 45.6 mV), which indicates that the degradation parameter (110A) estimated by the shallow estimation function (145) has high accuracy. The degradation parameter (110A) of the battery cell (105) can be identified as shown in Table 3 below.

[0180] Battery degradation parameter, most optimized value k neg,SEI 4.108X10 -14 m / sk pos,SEI 3.016X10 -16 m / sλ neg,SEI 0.011970815511806185λ pos,SEI 0.01112722837747713k decom 4.814X10 -13 m / sk Mn,disso 2.163X10 -13m / sk Mn,dep 9.769X10 -10 m / s

[0181] The battery analysis system (100) and the related battery parameter estimation technology described herein may be integrated into various systems, devices, and / or devices. There are various ways in which the battery analysis system (100) is integrated into such systems, devices, and / or devices. In some embodiments, the battery analysis system (100) may be directly integrated into the system, device, and / or device to estimate battery parameters (110) for one or more battery cells (105) used to power the system, device, and / or device. Additionally or alternatively, the battery analysis system (100) may be stored remotely and may communicate with such systems, devices, and / or devices via a network to estimate battery parameters (110) for one or more battery cells (105) integrated into the system, device, and / or device.

[0182] FIGS. 42-44 shows an exemplary configuration for integrating a battery analysis system (100) with such a system, device and / or device.

[0183] FIG. 42 is a block diagram of an exemplary system (1000) according to a specific embodiment. This system represents an exemplary network environment for deploying a battery analysis system (100) according to a specific embodiment.

[0184] The system (1000) includes one or more servers (1120) that communicate with one or more battery-powered devices (1110) (e.g., may include one or more vehicles (1110A)) via a network (1105). The battery analysis system (100) is stored and executed on one or more servers (1120). The network (1105) may represent any type of communication network and may be a network including, for example, a local area network (e.g., Wi-Fi network), a private network (e.g., Bluetooth network), a wide area network, an intranet, the Internet, a cellular network, a television network, a satellite communication network, and / or other types of networks.

[0185] All components illustrated in FIG. 42, namely the battery-powered unit (1110), vehicle (1110A), server (1120), and battery analysis system (100), may be configured to communicate directly with each other and / or through a network (1105) via a wired or wireless communication link, or a combination of both. Each of the battery-powered unit (1110), vehicle (1110A), server (1120), and battery analysis system (100) may include one or more storage devices (101) (e.g., RAM, ROM, PROM, etc.), one or more processing devices (102) (e.g., CPU, GPC, ASIC, processing circuit, etc.), and / or one or more communication devices (1103).

[0186] Each of one or more communication devices (1103) may include wired and wireless communication devices and / or interfaces that enable communication using wired and / or wireless communication technology. Wired and / or wireless communication may be implemented using any one or a combination of wired and / or wireless communication network topologies (e.g., ring, line, tree, bus, mesh, star, daisy chain, hybrid, etc.) and / or protocols (e.g., Private Area Network (PAN) protocol, Local Area Network (LAN) protocol, Wide Area Network (WAN) protocol, Cellular Network protocol, Power Line Network protocol, etc.). Exemplary PAN protocols may include Bluetooth, Zigbee, Wireless Universal Serial Bus (Wireless USB), Z-Wave, etc. Exemplary LAN and / or WAN protocols may include IEEE (Institute of Electrical and Electronic Engineers) 802.3 (also known as Ethernet), IEEE 802.11 (also known as Wi-Fi), etc.Exemplary wireless cellular network protocols may include GSM (Global System for Mobile Communications), GPRS (General Packet Radio Service), CDMA (Code Division Multiple Access), EV-DO (Evolution-Data Optimized), EDGE (Enhanced Data Rates for GSM Evolution), UMTS (Universal Mobile Telecommunications System), DECT (Digital Enhanced Cordless Telecommunications), Digital AMPS (IS-136 / TDMA (Time Division Multiple Access)), iDEN (Integrated Digital Enhanced Network), HSPA+ (Evolved High-Speed ​​Packet Access), LTE (Long-Term Evolution), WiMAX, etc. Specific communication software and / or hardware may vary depending on the network topology and / or protocol implemented. In certain embodiments, exemplary communication hardware may include wired communication hardware including, but not limited to, one or more data buses, one or more Universal Serial Buses (USB), and one or more networking cables (e.g., one or more coaxial cables, fiber optic cables, twisted pair cables, and / or other cables). Further exemplary communication hardware may include, for example, one or more wireless transceivers, one or more infrared transceivers, etc. Further exemplary communication hardware may include one or more networking components (e.g., modulation-demodulation components, gateway components, etc.). In certain embodiments, one or more communication devices may include one or more transceiver devices, each including a transmitter and a receiver for wireless communication.One or more communication devices (1103) may also include one or more wired ports (e.g., Ethernet port, USB port, auxiliary port, etc.) and related cables and wires (e.g., Ethernet cable, USB cable, auxiliary wire, etc.).

[0187] In certain embodiments, one or more communication devices (1103) may additionally or alternatively include one or more modem devices, one or more router devices, one or more access points and / or one or more mobile hotspots. For example, a modem device enables the battery-powered device (1110), vehicle (1110A), server (1120), and battery analysis system (100) to be connected to the Internet and / or other networks. The modem device may allow bidirectional communication between the Internet (and / or other networks) and the battery-powered device (1110), vehicle (1110A), server (1120), and battery analysis system (100). In certain embodiments, one or more router devices and / or access points enable the battery-powered device (1110), vehicle (1110A), server (1120), and battery analysis system (100) to be connected to a LAN and / or other networks. In a specific embodiment, one or more mobile hotspots may be configured to establish a LAN (e.g., Wi-Fi network) connected to another network (e.g., cellular network). The mobile hotspots enable the battery-powered device (1110), vehicle (1110A), server (1120), and battery analysis system (100) to access the Internet and / or other networks.

[0188] In a specific embodiment, the battery-driven device (1110) may generally represent any system, device, or apparatus equipped with one or more battery cells (105) or powered by one or more battery cells (105). The types of battery-driven devices (1110) may vary widely.

[0189] In some examples, the battery-powered unit (1110) may include a vehicle (1110A). The vehicle (1110A) may include a land-based vehicle (e.g., a passenger car, a truck, a motorcycle, etc.), a water-based vehicle (e.g., a boat, a vessel, a jet ski, etc.), and / or an air vehicle (e.g., an airplane, a helicopter, a spacecraft, etc.). The vehicle (1110A) may include an electric or hybrid vehicle, such as a passenger car, a truck, an airplane, a boat, or other vehicle, which is entirely or primarily powered by a battery comprising one or more battery cells (105). The vehicle (1110A) may also include a combustion-powered vehicle that uses the battery to power onboard systems, such as in-vehicle electronic devices, displays, or equipment. Thus, in some cases, the battery cell (105) may power the propulsion or movement of the vehicle. Additionally or alternatively, the battery cell (105) may power electronic devices or equipment integrated into the vehicle (1110A).

[0190] In another example, the battery-powered device (1110) may also include other types of devices powered wholly or partially by one or more battery cells (105), such as a desktop computer, a laptop computer, a mobile device (e.g., a smartphone, a personal digital assistant, a tablet device, a vehicle computing device, a wearable device, or other devices that are essentially mobile), a game console, and / or other types of devices.

[0191] Each battery-driven unit (1110) may include a battery management system (BMS) (1150). The BMS (1150) may be configured to monitor and control various aspects of the battery cells (105) within the battery-driven unit (1110). In some embodiments, the BMS (1150) may perform functions such as measuring and controlling the voltage, current, temperature, and state of charge of individual battery cells (105) or the entire battery pack containing the battery cells (105). The BMS (1150) may also perform cell balancing operations to ensure a uniform charge distribution across multiple battery cells (105), implement thermal management strategies to maintain an optimal operating temperature, and communicate with an external system to provide battery status information. Additionally, the BMS (1150) may be responsible for protecting the battery cells (105) from operating outside a safe operating area, detecting and preventing potential fault conditions, and optimizing battery performance and lifespan.

[0192] One or more servers (1120) may generally represent any type of computing device capable of communicating with other devices via a network (1105). In some embodiments, one or more servers (1120) may include one or more mainframe computing devices, one or more virtual servers, one or more application servers, and / or one or more cloud-based servers. In some embodiments, one or more servers (1120) may include one or more cloud-based servers hosting a cloud environment (1130). The cloud environment (1130) may provide scalable and on-demand computing resources that enable the efficient processing and storage of large datasets related to battery parameter estimation, thereby enabling faster and more cost-effective analysis compared to local computing solutions.

[0193] A battery analysis system (100) stored in one or more servers (1120) and / or a cloud environment (1130) may be configured to communicate with a battery-driven device (1110) and / or a vehicle (1110A) via a network (1105). In a specific embodiment, the battery analysis system (100) may interface with the battery-driven device (1110) and / or a BMS (1150) integrated into the battery-driven device (1110) via the network (1105) to receive input data (120) (e.g., current data (121)) corresponding to a battery cell (105) included in the battery-driven device (1110), for example, and provide battery parameter estimates to the battery-driven device (1110) in real-time or near real-time.

[0194] In one example scenario, each battery-driven device (1110) may continuously or periodically transmit input data (120) (e.g., voltage, current, resistance, or other measurements) to a remotely stored battery analysis system (100), and in response to receiving the input data (120), the battery analysis system (100) may estimate battery parameters (110) corresponding to the battery cells (105) of each battery-driven device (1110) by utilizing any one of the parameter estimation techniques described herein. For example, the battery analysis system (100) may estimate battery parameters (110) corresponding to each battery cell (105) of each battery-driven device (1110) by executing a shallow estimation function (145), a deep estimation function (155), or a combination thereof. The estimated battery parameters (110) may be transmitted to each battery-driven device (1110) via a network (1105). The BMS (1150) and / or other onboard system of the battery drive unit (1110) can manage the operation of the battery cell (105) used to supply power to the battery drive unit (1110) by utilizing the estimated battery parameters (110).

[0195] Integrating the battery analysis system (100) into a server or cloud environment can provide several advantages in some embodiments. Cloud-based infrastructure provides scalable computing resources, enabling efficient simulation processing to derive battery parameter estimates. In certain scenarios, this approach can enable faster analysis and more cost-effective solutions compared to local computing implementations. Additionally, since the cloud-based system receives input data from various sources and utilizes available computing power to process it quickly, it facilitates real-time or near-real-time parameter estimation for multiple battery-powered devices simultaneously. Furthermore, the centralized nature of the cloud environment makes updating and maintaining the battery analysis system easier, ensuring that all connected devices can benefit from the latest improvements and optimizations of the parameter estimation algorithm. Moreover, the cloud-based implementation allows system components, such as the optimization function (170), to be continuously improved and refined based on aggregated data accumulated from multiple battery-powered devices (1110), potentially enhancing the accuracy and efficiency of the battery parameter estimation process over time.

[0196] In certain embodiments, the battery analysis system (100) may additionally or alternatively be stored and executed on one or more battery-powered devices (1110). Thus, in some embodiments, the battery analysis system (100) may be stored as one or more server applications by one or more servers (1120), and in other embodiments, the battery analysis system (100) may be stored directly as one or more local applications (or one or more onboard applications) on the battery-powered device (1110) itself.

[0197] FIGS. 43-44 illustrates an embodiment in which the battery analysis system (100) is directly integrated with the battery drive unit (1110) and / or the vehicle (1110A). In this embodiment, the function of the battery analysis system (100) may be directly integrated within the battery management system (BMS) (1150), or the battery analysis system (100) may be a separate component that communicates with the battery management system (1150).

[0198] Integrating the battery analysis system (100) directly into the battery-powered device can provide several advantages in certain scenarios. This approach enables real-time on-device parameter estimation without relying on external network connections, which can be particularly useful in regions where internet or network access is limited or unreliable. Additionally, in some cases, this direct integration can reduce the latency of parameter estimation, allowing for more immediate adjustments to battery management strategies. Furthermore, the implementation of on-device analysis can enhance data privacy and security by processing battery information locally instead of transmitting it to an external server. This localized approach may also reduce the computational load on a centralized server and minimize data transmission costs in some implementations.

[0199] Additionally, in some embodiments, the battery analysis system (100) may be implemented as a combination of a front-end application (e.g., stored in a battery-driven device (1110)) and a back-end application (e.g., stored in one or more servers (1120)). All functions of the battery analysis system (100) described herein may be executed by the front-end application, the back-end application, or a combination of both.

[0200] The battery analysis system (100) may also be stored and run on other devices. For example, in some cases, the battery analysis system (100) may be integrated into a diagnostic tool or device that is physically separated from the battery drive unit (1110), and this device may communicate with the battery drive unit (1110) to measure battery parameters (110) related to the battery cells (105) contained in the battery drive unit (1110).

[0201]

[0202] In certain embodiments, the battery analysis system can rapidly and accurately estimate various battery parameters for one or more battery cells by utilizing a combination of a reduced-order electrochemical model and / or a full-order electrochemical model in conjunction with data preprocessing algorithms and machine learning-based optimization techniques. In certain embodiments, the battery analysis system can execute a multi-stage estimation approach, utilizing a shallow estimation function to rapidly narrow the parameter range initially, and utilizing a deep estimation function for final refinement when higher accuracy is required. This combined approach enables efficient parameter estimation across various operating scenarios while balancing both speed and precision, and enables real-time parameter estimation for battery management systems in applications such as electric vehicles.

[0203] In certain embodiments, a shallow estimation function utilizing one or more reduced-order electrochemical models may be executed initially to perform rapid battery parameter estimation. Subsequently, if higher accuracy is required, a deep estimation function may be used to further fine-tune the accuracy of the battery parameters. In scenarios where a deep estimation function is used to improve the accuracy of battery parameters, the estimates output by the shallow estimation function may be utilized to reduce the parameter range for the deep estimation function, thereby significantly reducing the convergence time and computational resources required to generate the final estimates for battery parameter estimation.

[0204] In certain embodiments, one or more preprocessing functions may be executed on input data provided to a shallow estimation function and / or a deep estimation function to further improve efficiency for generating battery parameter estimates and reduce computation time. In some examples, the battery analysis system may receive raw current data, and the preprocessing function may be executed to reduce the frequency and amplitude of the current data. In some embodiments, the preprocessing function may apply Gaussian filtering, Kalman filtering, and / or a specially designed convolution kernel to reduce the frequency and amplitude of the input current data.

[0205] During the preprocessing operation, one or more preprocessing functions may be used to select an optimal reduction metric that quantifies the degree of preprocessing performed on the input current data. In certain embodiments, the preprocessing function may execute an evaluation process to identify an optimized reduction metric that maximizes the reduction of the frequency and amplitude of the current data. This selection process for identifying the reduction metric may include comparing a simulated terminal voltage profile generated using the preprocessed data with a reference voltage profile. The reduction metric may be iteratively adjusted and re-evaluated until an acceptable balance between computational efficiency and simulation accuracy is achieved. By identifying and applying the optimal reduction metric, the preprocessing function can significantly reduce the computational requirements typically associated with processing high-frequency current data, enabling more efficient battery parameter estimation with high accuracy.

[0206] In certain embodiments, the shallow and deep estimation functions may utilize enhanced optimization functions combined with an electrochemical model that generates battery parameter estimates. In some examples, these optimization functions may employ one or more machine learning-based pruner / sampler algorithms designed to rapidly narrow the selection region for each estimated battery parameter. In certain embodiments, a preprocessing function executed on the input current data may improve the efficiency of the electrochemical model and / or optimization functions by reducing the computational complexity of the input current data, thereby enabling faster convergence for parameter estimation. Additionally, during the deep estimation step, estimates from the shallow estimation step may be utilized to narrow the selection region for the optimization function used in the deep estimation process, and the number of iterations required to converge to the final parameter value may be significantly reduced.

[0207] A battery analysis system can be utilized to estimate various types of battery parameters. These parameters may include degradation parameters, thermal parameters, battery model parameters, and state-of-health (SOH). In certain embodiments, the battery analysis system may initially estimate model parameters using one or more techniques described herein, and these model parameters may subsequently be utilized as inputs or constraints in the subsequent estimation process of degradation, thermal, and / or SOH parameters.

[0208] In certain embodiments, the battery analysis system may be configured to generate or determine a diagnostic evaluation for each battery cell analyzed by the system. The diagnostic evaluation for the battery cells may be determined or generated based on one or more battery parameter estimates obtained or derived according to the techniques described herein. In some examples, when one or more battery parameters match an expected value or expected range, the diagnostic evaluation may indicate that the battery cell is operating under normal conditions or provide a positive evaluation. Conversely, when one or more parameters deviate from an expected value or expected range, the diagnostic evaluation may indicate that the battery cell is operating under abnormal conditions or provide a negative evaluation.

[0209] In certain embodiments, a battery analysis system (or a battery management system communicating therewith) may be configured to execute or implement one or more mitigation functions based at least partially on a diagnostic evaluation of one or more battery cells. For example, one or more mitigation functions may be executed or performed in response to a diagnostic evaluation indicating a negative evaluation or an abnormal operating condition.

[0210] The types of mitigation functions executed may vary and, in some cases, depend on specific battery parameters identified as deviating from expected operating conditions. In some examples, mitigation functions may adjust the operating parameters and / or settings of a battery cell in response to a negative diagnostic assessment. Such adjustments may include modifying voltage, current, resistance, temperature, thermal management, and / or other operating settings to address the identified problem. In additional examples, the system may also execute isolation procedures for one or more battery cells in response to the detection of a negative diagnostic assessment. In additional examples, when a negative diagnostic assessment is detected for one or more battery cells, the mitigation function may include sending a warning or notification. Such warnings may be sent to a vehicle or device containing the affected battery cells, or to a technical service provider, and may identify details related to the negative assessment or indicate that one or more battery cells need to be replaced. The battery analysis system may execute or implement other types of mitigation functions in response to the derived diagnostic assessment, aiming to resolve the identified problem and optimize overall battery performance and lifespan.

[0211] The battery analysis system can be deployed in various environments and configurations. In certain embodiments, the system may be hosted in a server or cloud-based environment and can simultaneously perform scalable processing and real-time parameter estimation for multiple devices. This approach enables the battery analysis system to rapidly calculate battery estimates by utilizing larger computational resources when performing electrochemical simulations, and allows for the continuous improvement of optimization functions using a wider range of input data derived from multiple battery-powered devices. Additionally, or alternatively, the battery analysis system can be directly integrated into battery-powered devices, including but not limited to electric vehicles. Such on-device implementations can provide immediate and local parameter estimation without relying on network connectivity, reduce latency associated with network connections, and enhance data security by keeping data local within the battery-powered device. Additionally, or alternatively, a hybrid approach combining both server-based and on-device components can be used to leverage the advantages of both configurations.

[0212] It should be recognized that all features and / or functions described for one embodiment in this specification may be incorporated into all other embodiments mentioned in this specification. Furthermore, the embodiments described in this specification may be combined in various ways. Additionally, while specific embodiments, features, or components may be described in this specification as being implemented in software or hardware, it should be recognized that all embodiments, features, or components described in this specification may be implemented in hardware, software, or a combination of both.

[0213] Although various novel features of the present invention have been illustrated, described, and noted as applicable to specific embodiments, it should be understood that various omissions, substitutions, and modifications to the forms and details of the described and illustrated systems and methods may be made by those skilled in the art without departing from the spirit of the invention. Among other things, the steps of the method may be performed in a different order in many appropriate cases. Those skilled in the art will recognize, based on an understanding of the above disclosure and the teachings of the present invention, that specific hardware and devices that are part of the system described herein, and the general functions provided by and integrated therein, may differ in different embodiments of the present invention. Accordingly, the description of system components is for illustrative purposes only to facilitate a complete and perfect understanding and evaluation of the various aspects and functions realized in the specific embodiments of the present invention and the system and method embodiments. Those skilled in the art will understand that the present invention may be practiced in ways other than the described embodiments presented for illustrative purposes. Variations, modifications, and other implementations of the content described herein may occur to those skilled in the art without departing from the spirit and scope of the present invention and its claims.

Claims

A step in which a battery analysis system receives current data corresponding to a battery cell; A preprocessing function of the battery analysis system generates preprocessed current data based on the current data - the preprocessed current data has a reduced frequency and reduced amplitude compared to the current data; A shallow estimation function of the battery analysis system receives the preprocessed current data; A step of executing the shallow estimation function to at least partially estimate one or more battery parameters corresponding to the battery cell by applying the preprocessed current data as input to a simulation executed by a reduced-order electrochemical model; and A step of determining a diagnostic evaluation corresponding to the battery cell based at least partially on the one or more battery parameters mentioned above. A method for estimating one or more battery parameters including In paragraph 1, The above shallow estimation function is, To estimate one or more battery parameters, utilize an optimization function that cooperates with the above-mentioned reduced-order electrochemical model, and The step of generating the preprocessed current data before the above shallow estimation function is executed is: A method for estimating one or more battery parameters, which operates to narrow the parameter range utilized by the optimization function in estimating one or more battery parameters and the parameter range utilized by the reduction-order electrochemical model in executing the simulation. In paragraph 1, A step of determining whether the one or more battery parameters estimated using the above shallow estimation function are accurate; and A method for estimating one or more battery parameters, further comprising the step of executing a deep estimation function utilizing a full-order electrochemical model to refine one or more battery parameters corresponding to the battery cell when one or more of the above battery parameters are determined to be inaccurate. In Paragraph 3, The one or more battery parameters estimated by the above shallow estimation function are, A method for estimating one or more battery parameters applied to narrow the parameter range for the above-mentioned deep estimation function. In paragraph 1, A step of receiving reference voltage data including charge / discharge data derived from one or more battery driving devices; A step of determining a reference terminal voltage profile based at least partially on the above reference voltage data; A step of generating a simulated terminal voltage profile based on the preprocessed current data using the above-described reduced-order electrochemical model; and A method for estimating one or more battery parameters, further comprising the step of comparing the simulated terminal voltage profile and the reference terminal voltage profile to evaluate the sufficiency of the preprocessed current data. In paragraph 5, The above preprocessed current data is, It is generated according to a reduction metric that quantifies the degree to which the frequency and amplitude of the above current data are reduced, and A method for estimating one or more battery parameters, wherein if the difference between the simulated terminal voltage profile and the reference terminal voltage profile satisfies an error threshold, the preprocessed current data generated according to the reduction metric is determined to be suitable for use in estimating one or more battery parameters corresponding to the battery cell. In paragraph 5, The above preprocessed current data is, It is generated according to a first reduction metric that quantifies the degree to which the frequency and amplitude of the above current data are reduced, and A method for estimating one or more battery parameters, wherein if the difference between the simulated terminal voltage profile and the reference 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 amplitude of the current data to a lesser extent than the first reduction metric. In Paragraph 7, A method for estimating one or more battery parameters, 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 reference terminal voltage profile satisfies the error threshold. In Paragraph 9, The above battery analysis system is, It is configured to estimate one or more battery parameters for one or more battery cells included in an electric vehicle, and The above battery analysis system is, A method for estimating one or more battery parameters, which is directly integrated into the electric vehicle or integrated into a cloud environment that communicates with the electric vehicle through a network. In paragraph 1, The above one or more battery parameters are, Degradation parameters corresponding to the above battery cell; Thermal parameters corresponding to the above battery cell; Model parameters corresponding to the above battery cell; or A method for estimating one or more battery parameters, comprising at least one SOH parameter corresponding to the battery cell. One or more processing units; and It includes one or more non-transient storage devices for storing computing instructions, The above one or more processing devices execute the computing instruction, A step in which a battery analysis system receives current data corresponding to a battery cell; A preprocessing function of the battery analysis system generates preprocessed current data based on the current data - the preprocessed current data has a reduced frequency and reduced amplitude compared to the current data; A shallow estimation function of the battery analysis system receives the preprocessed current data; A step of executing the shallow estimation function to at least partially estimate one or more battery parameters corresponding to the battery cell by applying the preprocessed current data as input to a simulation executed by a reduced-order electrochemical model; and A system for estimating one or more battery parameters, performing the step of determining a diagnostic evaluation corresponding to the battery cell based on at least some of the one or more battery parameters. In Paragraph 11, The above shallow estimation function is, It includes an optimization function that operates with the above-mentioned reduced-order electrochemical model to estimate one or more battery parameters, and The step of generating the preprocessed current data before the above shallow estimation function is executed is: A system for estimating one or more battery parameters, which operates to narrow the parameter range utilized by the optimization function in estimating one or more battery parameters and the parameter range utilized by the reduction order electrochemical model in executing the simulation. In Paragraph 11, The above one or more processing devices execute the computing instruction, A step of determining whether the one or more battery parameters estimated using the above shallow estimation function are accurate; and A system for estimating one or more battery parameters, further performing the step of executing a deep estimation function utilizing a full-order electrochemical model to refine the one or more battery parameters corresponding to the battery cell when the one or more battery parameters are determined to be inaccurate. In Paragraph 13, The one or more battery parameters estimated by the above shallow estimation function are, A system for estimating one or more battery parameters applied to narrow the parameter range for the above-mentioned deep estimation function. In Paragraph 11, The above one or more processing devices execute the computing instruction, A step of receiving reference voltage data including charge / discharge data derived from one or more battery driving devices; A step of determining a reference terminal voltage profile based at least partially on the above reference voltage data; A step of generating a simulated terminal voltage profile based on the preprocessed current data using the above-described reduced-order electrochemical model; and A system for estimating one or more battery parameters, further performing the step of comparing the simulated terminal voltage profile and the reference terminal voltage profile to evaluate the sufficiency of the preprocessed current data. In Paragraph 15, The above preprocessed current data is, It is generated according to a reduction metric that quantifies the degree to which the frequency and amplitude of the above current data are reduced, and The above battery analysis system is, If the difference between the simulated terminal voltage profile and the reference terminal voltage profile satisfies an error threshold, the preprocessed current data generated according to the reduction metric is evaluated as suitable for use in estimating the one or more battery parameters corresponding to the battery cell, and A system for estimating one or more battery parameters, wherein if the difference between the simulated terminal voltage profile and the reference terminal voltage profile does not satisfy the error threshold, the preprocessed current data is repeatedly refined according to a new reduction metric until the difference between the simulated terminal voltage profile and the reference terminal voltage profile satisfies the error threshold. In Paragraph 11, The above battery analysis system is, Estimating one or more battery parameters for one or more battery cells included in an electric vehicle, and A system for estimating one or more battery parameters, which is directly integrated into the electric vehicle or integrated into a cloud environment that communicates with the electric vehicle. A shallow estimation function receives current data corresponding to the battery cell; A step of executing the shallow estimation function to at least partially estimate one or more battery parameters corresponding to the battery cell by applying the current data as input to a simulation executed by a reduced-order electrochemical model; A step in which a deep estimation function receives one or more battery parameters estimated using the shallow estimation function; Step of executing the deep estimation function to refine the one or more battery parameters corresponding to the battery cell—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 the parameter range for the deep estimation function; and A step of determining a diagnostic evaluation corresponding to the battery cell based at least partially on the one or more battery parameters mentioned above. A method for estimating one or more battery parameters including In Paragraph 18, A method for estimating one or more battery parameters, wherein the current data is preprocessed to reduce the frequency and amplitude of the current data before the above shallow estimation function or the above deep estimation function is executed. In Paragraph 19, The above current data is, The above current data is preprocessed according to a reduction metric that quantifies the degree of reduction in frequency and amplitude, and The above current data is, A method for estimating one or more battery parameters, which are iteratively refined according to a new reduction metric until an error threshold is satisfied.

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