Lithium ion battery health state evaluation method and equipment

Abstract

The invention discloses a lithium ion battery health state evaluation method and equipment. The method comprises the following steps: presetting a cell attenuation rate matrix in a battery management system, wherein the matrix defines single-circle capacity attenuation rates under different temperature and discharge depth combinations; acquiring the duration time and the corresponding discharge depth of the target battery in different temperature intervals in the complete time period; calculating cumulative capacity attenuation according to actual operation data and the matrix; and determining the current SOH according to the initial capacity sum. According to the method, simulation and actual measurement are combined to construct a high-precision matrix, a dynamic look-up table accumulation algorithm is utilized, high-precision and low-cost online evaluation of the SOH of the battery under complex variable working conditions is achieved, and the problems that a traditional method is insufficient in precision and high in calculation complexity in a dynamic scene are effectively solved.

Description

Technical Field

[0001] This invention relates to the field of battery status assessment technology, specifically to a method and apparatus for assessing the health status of lithium-ion batteries. Background Technology

[0002] State of Health (SOH) assessment is a core component of battery management systems, directly impacting the safety, performance, and economics of energy storage systems. Aging cells are prone to thermal runaway and increased internal resistance; real-time SOH assessment can provide early warnings of potential failure risks, preventing fires or explosions. Furthermore, accurate SOH estimation can optimize charge / discharge strategies, slow capacity decay, extend battery life, and improve the service life of energy storage systems. In the current context of large-scale energy storage battery applications, high-precision, low-cost SOH assessment methods have become a key area for technological breakthroughs in the industry.

[0003] Existing cell state of health (SOH) assessment methods are mainly divided into three categories: model-based methods, which construct electrochemical or equivalent circuit models and combine parameters to identify and estimate capacity decay, but rely on model accuracy and are computationally complex; data-driven methods, which utilize machine learning (such as neural networks) or statistical models to analyze aging characteristics in historical charge-discharge data, have strong adaptability but require a large amount of training data; and direct measurement methods, which directly reflect cell performance through capacity testing, internal resistance detection, or impedance spectroscopy analysis, have high accuracy but are difficult to implement online. In addition to the challenges of engineering application, cell life assessment also faces difficulties such as complex operating conditions.

[0004] Therefore, providing a method and device for assessing the health status of lithium-ion batteries to solve the above problems is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] To address the aforementioned technical problems, the present invention aims to provide a method for assessing the health status of lithium-ion batteries. This method is logically clear and easy to operate. By combining simulation and actual measurement, it accurately obtains the cell's degradation amount under various operating conditions and forms a degradation matrix pre-placed in the BMS system. Combined with a health status assessment algorithm, it realizes the assessment of the cell's health status, reduces computational complexity, reduces training samples, and improves the accuracy of health status assessment.

[0006] The technical solution provided by this invention is as follows: A method for assessing the health status of a lithium-ion battery includes the following steps: A pre-set cell degradation rate matrix is ​​configured in the battery management system. The cell degradation rate matrix defines the single-cycle charge-discharge capacity degradation rate of the lithium-ion battery under different temperature and different discharge depth combinations. Acquire actual operating data of the target battery over a complete time period, including the duration of the target battery in different temperature ranges and the corresponding depth of discharge; Based on the actual operating data and the cell degradation rate matrix, the cumulative capacity degradation of the target battery during the complete time period is obtained; The current health status of the target battery is determined based on its initial capacity and the cumulative capacity decay.

[0007] Preferably, the cell attenuation rate matrix is ​​constructed in the following manner: Obtain the measured capacity decay curve of a first test battery of the same type as the target battery at a fixed temperature and a fixed depth of discharge. A lithium battery life simulation model is constructed, and the lithium battery life simulation model is calibrated based on the measured capacity decay curve. Based on the calibrated lithium battery life simulation model, the capacity decay data of the first test battery under various temperature and discharge depth combinations are obtained through simulation, forming the cell decay rate matrix.

[0008] Preferably, calibrating the lithium battery life simulation model based on the measured capacity decay curve includes the following steps: Based on the lithium battery life simulation model, the simulated capacity decay curve of the first test battery is obtained; Calculate the error between the simulated capacity decay curve and the measured capacity decay curve; Determine whether the error is greater than a preset accuracy threshold; If not, adjust the parameters in the lithium battery life simulation model until the error is less than the preset accuracy threshold.

[0009] Preferably, after determining whether the error is greater than a preset accuracy threshold, the method further includes the following steps: If so, the current lithium battery life simulation model is used as the calibrated lithium battery life simulation model.

[0010] Preferably, the cell attenuation rate matrix is ​​further constructed in the following manner: Obtain multi-sample measured capacity decay curves of multiple second test batteries of the same type as the target battery under different temperature and different depth of discharge combinations; Based on the measured capacity decay curves of the multi-sample test, an artificial intelligence fitting algorithm is used to establish a mapping relationship between the single-cycle charge and discharge capacity decay rate and temperature and discharge depth, and the mapping relationship is used as the cell decay rate matrix.

[0011] Preferably, the cumulative capacity degradation of the target battery over the complete time period is obtained based on the actual operating data and the cell degradation rate matrix, specifically calculated using the following formula: ; in, This is the cumulative capacity decay. The initial capacity of the target battery; Index for combined temperature and depth of discharge conditions; For the cell attenuation rate matrix and the first The single-cycle charge / discharge capacity decay rate corresponding to the various operating conditions. For the target battery in the first Cumulative runtime under various operating conditions; This refers to the time required for the target battery to be fully charged in its current healthy state.

[0012] Preferably, the determination of the current health status of the target battery based on its initial capacity and cumulative capacity decay is calculated using the following formula: ; in, This represents the current health status of the target battery. The initial capacity of the target battery. This represents the cumulative capacity decay.

[0013] Preferably, the battery management system includes: a battery management unit, a battery cluster management unit, and a battery stack management system; The cell degradation rate matrix is ​​pre-set in the battery cluster management unit; The acquisition of the target battery's actual operating data over a complete time period is performed by the battery management unit, and the actual operating data is uploaded to the battery cluster management unit. The acquisition of the cumulative capacity decay of the target battery over the complete time period and the determination of the current health status of the target battery are performed by the battery cluster management unit.

[0014] Preferably, it further includes: The battery cluster management unit uploads the current health status of the target battery to the battery stack management system for storage and display.

[0015] An electronic device includes at least one processor and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method as described above.

[0016] This invention discloses a method for assessing the health status of lithium-ion batteries. By pre-setting the cell attenuation matrix in the battery management system, actual operating data under different temperature ranges and different discharge depth combinations are obtained. The cumulative capacity attenuation is calculated using the actual operating data and the cell attenuation matrix. Combined with the initial capacity of the target battery obtained from the query, the health status of the target battery is assessed.

[0017] The present invention has the following technical effects: This invention achieves high-precision SOH assessment under dynamic and varying operating conditions: by introducing a "cell degradation rate matrix," a "aging map" covering all operating conditions is constructed for the first time at the methodological level. This matrix quantifies and pre-stores the degradation rate under different combinations of (temperature, DOD). Furthermore, by collecting the duration of different operating conditions during actual operation, the dynamic process is discretized. This invention fundamentally changes the paradigm of SOH assessment—shifting from relying on a single, average degradation model to cumulative calculation based on accurate matching of multiple operating conditions and real operating history. This significantly improves the assessment accuracy in complex real-world scenarios, making the results closer to the actual health state of the battery. It possesses strong adaptability to complex application scenarios: This invention does not pre-define any fixed charging and discharging process. Regardless of the complexity and variability of the target battery's actual operating trajectory, this method can process it through a general process of "data acquisition - matrix matching - cumulative calculation." This invention is not tied to any specific operating condition, exhibiting strong versatility and robustness. The same set of pre-defined matrices and evaluation processes can be applied to the SOH evaluation of the same battery model under various users, geographical environments, and operating strategies, demonstrating strong scenario adaptability. Achieving a balance between high accuracy and low computational overhead, this invention possesses high engineering feasibility: It employs an architecture of "precise offline modeling and efficient online table lookup." The most complex model building and parameter identification processes are moved to the laboratory stage and ultimately simplified to a single lookup table (matrix). During online runtime, the BMS only needs to perform simple lookup, multiplication, and addition operations. This makes real-time deployment of high-order SOH algorithms possible in low-cost, large-scale industrial applications. It perfectly balances the inherent contradiction between accuracy and real-time performance, transforming this method from "theoretically excellent" to "engineerably usable." This invention provides a data foundation for refined management and value assessment throughout the battery's entire lifecycle: Essentially, it records and quantifies the "contribution" of different operating conditions to battery life. By analyzing the composition of cumulative capacity degradation, it's possible to trace back whether high-temperature or deep-discharge conditions caused the primary capacity decay. This not only enables State of Health (SOH) assessment but also empowers refined battery management and value assessment.

[0018] The present invention also provides an electronic device that, since it shares the same technical concept as the lithium-ion battery health status assessment method, solves the same technical problem, and has the same beneficial effects, will not be described in detail here. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart of a lithium-ion battery health status assessment method provided in an embodiment of the present invention; Figure 2 This is a flowchart of constructing a cell attenuation rate matrix provided in an embodiment of the present invention; Figure 3 This is a flowchart of the calibration lithium battery life simulation model provided in the embodiments of the present invention; Figure 4 This is a flowchart of another method for constructing a cell attenuation rate matrix provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of the battery management system provided in an embodiment of the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] The embodiments of this invention are written in a progressive manner.

[0023] This invention provides a method and apparatus for assessing the health status of lithium-ion batteries. It primarily addresses the technical problems of existing technologies, such as cost, data dependence, and the inability to implement them online.

[0024] like Figure 1 As shown, a method for assessing the health status of a lithium-ion battery includes the following steps: S1. A pre-set cell degradation rate matrix is ​​placed in the battery management system. The cell degradation rate matrix defines the single-cycle charge-discharge capacity degradation rate of lithium-ion batteries under different temperature and different discharge depth combinations. S2. Obtain the actual operating data of the target battery within a complete time cycle. The actual operating data includes the duration of the target battery in different temperature ranges and the corresponding depth of discharge. S3. Based on actual operating data and the cell degradation rate matrix, obtain the cumulative capacity degradation of the target battery over the complete time period; S4. Determine the current health status of the target battery based on its initial capacity and cumulative capacity decay.

[0025] The cell degradation rate matrix in step S1 is a pre-built two-dimensional data table stored in the battery management system. This matrix establishes a precise mapping relationship between the two key operating parameters, temperature and depth of discharge, and the single-cycle capacity degradation rate of the battery. The single-cycle charge-discharge capacity degradation rate refers to the percentage (or proportion) of capacity lost by a lithium-ion battery relative to its initial capacity after completing one full 100% charge-discharge cycle under specific, constant temperature (T) and depth of discharge (DOD) conditions. The calculation formula is as follows: (Capacity loss in this cycle / Initial battery capacity) × 100%, where, It is the decay rate, which is a function of temperature T and depth of discharge DOD; The complete time period in step S2 refers to a time period selected for evaluation calculations. Within this time period, the Battery Management System (BMS) continuously collects and records the cumulative operating time of the target battery under various operating conditions (temperature, DOD). It is a "data acquisition window" and "calculation cycle," not a fixed physical time unit (such as 1 hour or 1 day), but a logical time period used for cumulative aging calculations. Actual operating data refers to the set of physical quantities generated by the target battery within the "complete time period" under real working conditions, reflecting its dynamic operating conditions. These data serve as a bridge connecting the preset, static degradation model (matrix) with the dynamic, real battery degradation. The cumulative capacity decay in step S3 refers to the total amount of capacity lost relative to the initial capacity due to charge-discharge cycles during the "complete time period" of the evaluation. The current health status in step S4 is one of the most critical health indicators in the battery management system. It quantitatively describes the ratio between the battery's actual usable capacity at the current moment and its initial capacity when it is in a brand new state.

[0026] Steps S1 to S4 are the specific implementation details of the lithium-ion battery health status assessment method. By pre-setting the cell degradation rate matrix in the battery management system, setting the time period, and obtaining the actual operating data of the target battery within the time period, the cumulative capacity degradation of the target battery within the complete time period is calculated based on the actual operating data and the cell degradation rate matrix. By querying the initial capacity of the target battery and combining it with the cumulative capacity degradation, the current health status of the target battery is determined.

[0027] The above scheme achieves high-precision, online SOH assessment under dynamic operating conditions by introducing an attenuation rate matrix, piecewise lookup, and weighted accumulation mechanism. This fundamentally solves the assessment challenge under dynamic conditions, enabling SOH values ​​with near-laboratory accuracy to be obtained in complex real-world applications (such as electric vehicles and energy storage power stations). It employs an architecture of "precise offline modeling and efficient online lookup": the most complex model building and parameter identification processes are completed offline and ultimately simplified to a single lookup table (matrix). During online runtime (S2-S4), the BMS only needs to perform basic operations such as data recording, table lookup, multiplication, and addition. This achieves high accuracy with extremely low computational load. This allows the high-order algorithm to run in real-time in low-cost, large-scale industrial BMS, solving the core engineering bottleneck of the difficulty in implementing advanced algorithms; and it does not require any pre-defined fixed operating trajectory. Regardless of the complexity and variability of actual operating conditions, the "collection-lookup-accumulation" process remains universal, thus possessing inherent adaptability. The calculation process itself records the "contribution" of different operating conditions to the total attenuation, which can be used for retrospective analysis to identify the main factors leading to aging (such as high-temperature operation and excessive deep discharge), empowering full lifecycle management. By moving from basic data (matrix) to real-time data (operation history), to the core algorithm (accumulation model), and finally to the application output (SOH), a self-consistent and complete solution is formed. The pre-set "cell attenuation rate matrix" can be continuously iterated and optimized through more advanced simulation technology and a larger amount of experimental data, enabling the accuracy of the entire evaluation system to continuously improve without changing the online evaluation architecture. This solution is not only an evaluation method but also a continuously optimized technical platform, reserving space for future integration of more influencing factors (such as rate) and improved accuracy.

[0028] In summary, the technical effects of this invention are systematic. Through a sophisticated "matrix + dynamic accumulation" model, it simultaneously overcomes the three core challenges of "accuracy," "feasibility," and "applicability" that have long plagued the field of battery management. It is not a simple improvement on existing technologies, but rather an innovation in technical approach, elevating SOH assessment from an "estimation" and "speculation" to a "precise calculation" based on accurate mapping, thus laying a solid foundation for its large-scale, high-reliability application in harsh industrial scenarios.

[0029] like Figure 2 As shown, preferably, the cell attenuation rate matrix is ​​constructed in the following manner: A1. Obtain the measured capacity decay curve of a first test battery of the same type as the target battery at a fixed temperature and a fixed depth of discharge; A2. Construct a lithium battery life simulation model and calibrate the lithium battery life simulation model based on the measured capacity decay curve; A3. Based on the calibrated lithium battery life simulation model, the capacity decay data of the first test battery under various temperature and discharge depth combinations are obtained through simulation, forming a cell decay rate matrix.

[0030] The measured capacity decay curve in step A1 refers to the trajectory of battery capacity decay as the number of cycles increases, which is recorded by conducting long-term charge-discharge cycle tests on a sample battery or a batch of sample batteries under specific and controllable test conditions (such as fixed temperature, fixed depth of discharge, and fixed charge-discharge rate) through real physical experiments. The lithium battery life simulation model in step A2 is a computer mathematical model that uses mathematical equations and algorithms to simulate, characterize and predict the key aging mechanisms (such as active lithium loss, failure of positive and negative electrode materials, SEI film growth, etc.) that occur inside the lithium-ion battery during long-term charge and discharge cycles, as well as the macroscopic performance degradation (mainly the continuous decrease in capacity and the increase in internal resistance) caused by these mechanisms. The capacity decay data in step A3 refers to a set of data points obtained through experimental measurement or actual operation that can quantitatively describe the decrease in the usable capacity of a lithium-ion battery as the usage time or number of cycles increases. Steps A1 to A3 are the specific implementation details for constructing the cell attenuation rate matrix in step S1: Step A1. Obtain the measured capacity decay curve: "Same type as the target battery" ensures the inherent accuracy of the model, guaranteeing the applicability of the matrix from the source. "Fixed temperature and fixed depth of discharge" constitute a benchmark calibration point. This point is like a "benchmark stake" in geodesy, the starting point of trust for all simulation extensions. Typically, a typical or moderately stressed operating condition is chosen so that the model can better capture aging patterns, providing an unshakable real data anchor for the entire virtual simulation system, ensuring that the model is not "imagined out of thin air," but rather a deduction based on real behavior in the physical world. Step A2. Construct and calibrate the simulation model: The "lithium battery life simulation model" is typically an electrochemical-empirical coupling model. It includes both mathematical equations describing the internal mechanisms (such as SEI film growth) and empirical parameters for fitting macroscopic behavior. By adjusting key parameters in the model (such as activation energy, reaction rate constant, etc.), the error (such as root mean square error RMSE) between the simulated capacity decay curve and the measured curve from step A1 is minimized until a preset accuracy threshold is met. Through precise calibration, the virtual model becomes a "digital twin" of the real battery in digital space, and its predictive behavior is highly reliable. The calibrated model not only fits the curve but also, to some extent, captures the core aging mechanism of this battery model, giving it extrapolation prediction capabilities under untested conditions. Step A3. Simulation Expansion to Form a Matrix: In the calibrated model, the input variables—temperature and depth of discharge (DOD)—are systematically changed. For each (temperature, DOD) combination, the simulation is run, and the "single-cycle charge-discharge capacity decay rate" under that combination is calculated. The decay rates of all combinations are organized into a two-dimensional lookup table, which is the final "cell decay rate matrix." Utilizing the low cost and high efficiency of computer simulation, "measured data from a single benchmark point" is expanded into a "complete database covering hundreds of operating conditions." This saves more than 90% of testing costs and time, making high-precision evaluation across all operating conditions feasible in engineering. A seamless, high-resolution "aging map" is generated, ensuring that in subsequent online evaluations, regardless of the actual operating conditions, the most matching and accurate decay rate can be found, fundamentally solving the problem of sharp accuracy drops caused by operating condition mismatch in traditional methods. The above solution successfully transforms a traditional technique relying on "engineer experience + extensive trial and error" into a modern digital engineering methodology based on "model and data-driven" approaches that is replicable and predictable. This not only brings a leap in evaluation accuracy but also an order-of-magnitude improvement in R&D efficiency and engineering feasibility, constituting the outstanding substantive features and significant progress of this invention.

[0031] like Figure 3 As shown, preferably, the lithium battery life simulation model is calibrated based on the measured capacity decay curve, including the following steps: B1. Based on the lithium battery life simulation model, obtain the simulated capacity decay curve of the first test battery; B2. Calculate the error between the simulated capacity decay curve and the measured capacity decay curve; B3. Determine whether the error is greater than the preset accuracy threshold; B4. If not, adjust the parameters in the lithium battery life simulation model until the error is less than the preset accuracy threshold.

[0032] Preferably, after determining whether the error is greater than a preset accuracy threshold, the following steps are also included: If so, the current lithium battery life simulation model will be used as the calibrated lithium battery life simulation model.

[0033] The simulated capacity decay curve in step B1 refers to the predicted trajectory of battery capacity decay as the number of cycles increases, generated by the lithium battery life simulation model through internal mathematical equations after receiving specific input parameters (such as temperature, depth of discharge, charge / discharge rate, etc.). Steps B1 to B4 are the specific implementation details of calibrating the lithium battery life simulation model in step A2: Step B1. Obtain the simulation curve and establish a comparison benchmark: It concretizes the predictive ability of the abstract simulation model under specific working conditions into a visible and quantifiable data curve, providing a target object for subsequent accurate comparison. Step B2. Implement quantitative diagnosis: Statistical indicators such as root mean square error (RMSE) and mean absolute percentage error (MAPE) are typically used. This ensures that the evaluation is objective, comprehensive, and repeatable, rather than based on subjective judgments of individual data points. It accurately pinpoints the "distance" between the model and reality, providing the sole data basis for determining whether the calibration was successful. Step B3. Determine the error and threshold, set the quality benchmark, and define the termination condition: The "preset accuracy threshold" is the core of this step. It is not an arbitrary value, but rather determined by working backward from the accuracy requirements of the final SOH evaluation application. For example, if the SOH evaluation error is required to be less than 2%, then the prediction error of the model itself must be even smaller (e.g., RMSE < 1%). This transforms the calibration process from a vague goal of "getting as close as possible" to a standardized, measurable industrial process with a clear endpoint. This ensures that the accuracy of the final simulation model meets the requirements of the upper-level application. Step B4. Iterative parameter adjustment, endowing the model with learning and evolution capabilities, is the core closed loop for ensuring accuracy: This is not random adjustment, but a systematic parameter tuning based on sensitivity and optimization algorithms. Engineers or optimization algorithms will selectively adjust the most sensitive parameters in the model (such as kinetic parameters related to SEI film growth and lithium-ion diffusion) based on error results, making the simulated curve "closer" to the measured curve. The effects of this step include iterative optimization: through the closed-loop feedback of "prediction-comparison-adjustment-re-prediction," it gradually approximates reality until the model accuracy meets the target. Mechanism correction: This process essentially forces the model's internal mathematical mechanisms to better fit the chemical processes of the real physical world, thereby improving the model's extrapolation and prediction capabilities, rather than just curve fitting.

[0034] The above approach collectively forged a "high-fidelity digital battery": a transformation from a "model" to a "precise model." An uncalibrated model is merely a theoretical framework. After this process, it becomes a digital twin that accurately reflects the real aging behavior of a specific battery model. This forms the foundation for the credibility of all subsequent simulation extensions; it bridges the final gap between "experimental data" and "engineering applications": solving the key problem of "how to maximize the value of valuable measured data by injecting it into the simulation model." This allows limited experimental data to be transformed into the model's unlimited predictive power; and it establishes a quality-controlled industrial process: this process is clearly defined, with objective judgment criteria and reproducible results. This transforms the process of constructing a high-precision attenuation matrix from an "art" relying on personal experience into a set of engineering techniques that can be mass-produced and maintain stable quality.

[0035] In one embodiment, the specific implementation process of constructing the cell attenuation rate matrix through simulation is as follows: 1. Perform charge and discharge tests on the battery cells. For example, in a constant temperature chamber, perform 500 charge and discharge cycles at 0.5P and 100% DOD, record the amount of charge and discharge in each cycle, and obtain the measured cycle decay curve of the battery cells. 2. Construct a lithium battery life simulation model; 3. Input the basic parameters of the battery cell, such as capacity, charge / discharge rate during testing, temperature, etc., to simulate the aging of the battery cell; 4. Obtain the simulated attenuation curve, compare it with the measured attenuation curve, calculate the error (such as root mean square error RMSE), and if the simulation results do not meet the accuracy requirements, change the model parameters until the accuracy meets the requirements. 5. Output the current simulation model; 6. Change the temperature in the current model to obtain the decay curves at various temperatures under the current DOD condition; 7. Changes in the model ,get Operating conditions, attenuation curves under various temperature environments; The decay rates at each DOD and temperature are summarized to form a cell decay rate matrix, as shown in Table 1 below.

[0036] Table 1: Cell attenuation rate matrix at various DOD and temperatures

[0037] like Figure 4 As shown, preferably, the cell attenuation rate matrix is ​​also constructed in the following manner: C1. Obtain multi-sample measured capacity decay curves of multiple second test batteries of the same type as the target battery under different temperature and different depth of discharge combinations; C2. Based on multi-sample measured capacity decay curves, an artificial intelligence fitting algorithm is used to establish a mapping relationship between the single-cycle charge and discharge capacity decay rate and temperature and discharge depth, and the mapping relationship is used as the cell decay rate matrix.

[0038] In this context, the artificial intelligence fitting algorithm in step C2 refers to using a machine learning model to learn from a limited number of sample batteries tested under different operating conditions to construct a generalized function model that can map from input variables (temperature, depth of discharge) to output variables (capacity decay rate per charge-discharge cycle). Steps C1 to C2 are another specific implementation detail for constructing the cell attenuation rate matrix in step S1: It completely bypasses the process of building and calibrating complex electrochemical models. It directly uses multi-sample measured data as input and learns the complex nonlinear mapping relationship from (temperature, DOD) to decay rate directly through AI algorithms (such as neural networks, Gaussian process regression, etc.). When the aging mechanism of a battery is exceptionally complex or not yet fully understood, the simulation approach encounters theoretical bottlenecks. The AI ​​approach does not rely on profound prior knowledge of mechanisms, but purely lets the data "speak," thus potentially building high-precision mapping models faster under specific conditions. Powerful AI algorithms (especially deep learning) can uncover implicit features and complex interactions from massive, high-dimensional data that are difficult for the human brain or traditional mathematical models to detect. It may capture subtle coupling relationships between temperature and DOD on aging that have not been revealed by existing theories, and theoretically, it can achieve higher fitting accuracy than simulation models based on simplified physical assumptions when the data is sufficient and of extremely high quality. Once the data is ready, the process of building AI models (training and validation) can be highly automated, reducing the subjectivity and workload of manually debugging mechanistic model parameters, and improving the automation level and efficiency of matrix construction processes, which is especially suitable for scenarios with abundant data accumulation. While possessing the above technical effects, it also has drawbacks such as "strong data dependence, high cost and risk," "the 'black box' characteristic leads to doubts about reliability and extremely high extrapolation risk," and "more complex model deployment and updates."

[0039] Therefore, constructing the cell degradation rate matrix using AI tools is essentially an alternative approach, especially in specific scenarios where mechanistic modeling is difficult but massive amounts of high-quality data are available. However, its high data costs, "black box" risks, and weak extrapolation capabilities typically make it a "suboptimal" or "supplementary" solution in most industrial applications. Furthermore, this approach can also serve as a validation sample or comparative reference for simulation construction methods.

[0040] Preferably, based on actual operating data and the cell degradation rate matrix, the cumulative capacity degradation of the target battery over a complete time period is obtained, specifically calculated using the following formula: ; in, This is the cumulative capacity decay. The initial capacity of the target battery; Index for combined temperature and depth of discharge conditions; For the cell attenuation rate matrix and the first The single-cycle charge / discharge capacity decay rate corresponding to the various operating conditions. For the target battery in the first Cumulative runtime under various operating conditions; This refers to the time required for the target battery to be fully charged in its current healthy state.

[0041] The above formula details the specific implementation of calculating the cumulative capacity decay, which is achieved through "interval accumulation" (…). The strategy of "" breaks down a complex and dynamic operating process into multiple short-lived, potentially constant "operating condition intervals" (index k). For each interval, the corresponding attenuation rate is obtained by looking up a table (cell attenuation rate matrix). Multiply by the duration of that interval. This formula successfully maps time-varying temperature-load (DOD) sequences to a precise cumulative capacity decay, thus achieving a crucial leap from "static model" to "dynamic evaluation." The formula includes the duration of each operating condition. Divide by the current full charge duration This step essentially normalizes physical time to achieve an "equivalent number of cycles" relative to the current battery's full charge-discharge capacity. This design allows the degradation calculation to adapt to the battery's current health level, ensuring the long-term accuracy of the evaluation model throughout the battery's lifespan. The calculation process is: data acquisition → querying a pre-defined matrix → performing multiplication and addition operations. The most complex part (building an accurate degradation model) has been completed offline through simulation and experimentation and is now embedded in the matrix. During online evaluation, the BMS only needs to perform simple arithmetic operations, significantly reducing the computational resource requirements while maintaining high accuracy. This makes it possible to deploy this high-order algorithm in real-time in low-cost, large-scale industrial scenarios. It perfectly balances the inherent contradiction between "accuracy" and "computing power." The formula essentially records and quantifies the "contribution" of different operating conditions to battery life. By analyzing the cumulative degradation... The composition (i.e., analyzing different k values) This allows us to trace back whether high-temperature or deep-discharge conditions caused the main capacity degradation. This not only enables SOH assessment but also empowers refined battery management and value evaluation.

[0042] The above formula is not a simple point-based improvement, but rather a systematic solution. Through an ingenious model of "table lookup + dynamic accumulation + time normalization," it simultaneously overcomes the three core challenges that have long plagued the field of battery management: accuracy, adaptability, and feasibility.

[0043] Preferably, the current health status of the target battery is determined by calculating the following formula based on the initial capacity and cumulative capacity decay: ; in, This represents the current health status of the target battery. The initial capacity of the target battery. This represents the cumulative capacity decay.

[0044] The above formula details the specific implementation of calculating the current health status of the target battery, and is obtained through... This operation will accumulate capacity decay. Normalization transforms the value relative to the initial capacity. The percentage (%) is visualized: the output is a value between 0% and 100%, which intuitively reflects the "newness" or "remaining lifespan" of the battery, making it easy for users at all levels (from engineers to end users) to understand and use. Standardization: SOH becomes a dimensionless, standardized health indicator, making batteries of different capacities and models comparable, which is crucial for battery reuse, residual value assessment, and asset management; this formula is the final output of the method chain of this invention. It condenses the results of all the previous innovative steps (preset matrix, dynamic data acquisition, cumulative attenuation calculation) into a single, key decision indicator—SOH. This forms a technical closed loop of "data acquisition → model matching → cumulative calculation → state determination." This formula is the finishing touch of this closed loop, ensuring that the entire methodology is not only innovative in process but also that the output results are directly usable; in this scheme, It was not obtained through coarse ampere-hour integration or simple cyclic counting, but rather calculated using a high-precision decay rate matrix and a dynamic accumulation model. This is precisely because the preceding steps provided unprecedented high precision. This is what gives the calculation results of this standard SOH formula an unprecedentedly high level of reliability. This, in turn, highlights the technical advantages of the overall solution of this invention—it enhances the accuracy of the front-end process, ultimately enabling a standardized output result; the SOH formula contains only one subtraction and one division, making it one of the least computationally demanding arithmetic operations. This ensures that after the potentially complex lookup and accumulation calculations in the preceding steps, the generation of the final SOH result is instantaneous and without delay. This guarantees the real-time performance of the entire evaluation algorithm, and due to its simplicity, there is virtually no risk of errors or numerical instability in the code implementation, meeting the stringent reliability requirements of automotive-grade or energy storage systems.

[0045] The technical advantages of the above formula are as follows: it completes the transformation from physical quantities to state quantities, outputting intuitive and standardized health indicators; it constitutes the endpoint of the technical closed loop, making the entire method a complete solution; its high-precision results become the final manifestation of the value of the preceding innovative steps; and its extreme simplicity ensures the real-time performance and reliability of the system's final output. Therefore, it is not merely an ordinary industry formula application, but rather an indispensable final link closely integrated with the core innovation of this invention, jointly achieving the overall technical effect of high-precision and high-reliability online SOH assessment.

[0046] like Figure 5 As shown, preferably, the battery management system includes: a battery management unit, a battery cluster management unit, and a battery stack management system; The cell degradation rate matrix is ​​pre-set in the battery cluster management unit; The acquisition of actual operating data of the target battery over a complete time cycle is performed by the battery management unit, and the actual operating data is uploaded to the battery cluster management unit. The cumulative capacity degradation of the target battery over a complete time period and the determination of the target battery's current health status are performed by the battery cluster management unit.

[0047] Preferably, it further includes: The battery cluster management unit uploads the current health status of the target battery to the battery stack management system for storage and display.

[0048] The above content describes the specific implementation details of the battery management system in step S1, and its technical effects are as follows: Optimized allocation of computational load ensures the system's real-time performance and efficiency: BMU (Battery Unit): As the data acquisition front-end, it is responsible for acquiring high-frequency, low-level raw data such as cell voltage and temperature, and preliminarily calculating key information such as operating duration. This reduces the data preprocessing load of the main control unit. BCMU (Battery Control Unit): As the intelligent evaluation core, it carries the pre-set attenuation matrix and performs complex accumulation calculations. It aggregates data from multiple subordinate BMUs for cluster-level precise calculations. BAMS (Battery Management System): As the data display and storage center, it does not participate in real-time calculations but is responsible for result display and historical data storage. This forms a pipeline operation of "acquisition-computation-management," avoiding computational bottlenecks, fully utilizing the computing power of hardware at all levels, and ensuring high efficiency and real-time performance across the entire chain from cell data acquisition to SOH (State of Health) result output. By fully utilizing the inherent resources of the BMS, optimal cost is achieved: a large-capacity decay rate matrix is ​​pre-installed in the BCMU, taking advantage of its more abundant storage resources compared to the BMU. The most critical computing tasks are assigned to the BCMU, leveraging its stronger processing capabilities compared to the BMU and its more focused control task processing capabilities compared to the BAMS. There is no need to upgrade hardware or add dedicated computing modules for new algorithms, maximizing the use of the redundant resources of the existing BMS architecture. High-order functions are implemented with near-zero marginal hardware cost, resulting in excellent engineering economics. The clearly defined responsibilities enhance the overall robustness of the system: The Battery Management Unit (BMU) has a clear role, responsible only for data acquisition and uploading; its failure impact is limited to a single battery module. The Battery Module Management Unit (BCMU) functions as an independent evaluation unit; even if one BMU fails, other BCMUs can still operate normally. Even if the Battery Management System (BAMS) indicates a failure, the BCMU's evaluation function remains unaffected. The system achieves "distributed data acquisition, centralized computation, and independent management," with good decoupling between functional modules. A single node failure will not cause the entire SOH evaluation system to collapse, significantly improving the system's fault tolerance and robustness. This solution perfectly aligns with the physical topology and logical management requirements of energy storage systems: Energy storage systems typically consist of "cells → battery modules → battery clusters → battery stacks." This solution assigns the evaluation task to the "battery cluster," a crucial layer bridging these layers. Upward: The BCMU can report the overall State of Health (SOH) status of the entire battery cluster to the BAMS. Downward: The BCMU can finely manage the state of each cell within the cluster. The evaluation architecture is highly consistent with the physical architecture, enabling the evaluation results to be directly used for cluster-level balance management, energy dispatch, and fault isolation, achieving a seamless transition from state assessment to control execution.

[0049] This invention achieves a system-level technical effect of "1+1>2" by embedding the evaluation method into a standard three-tier BMS architecture. It is not merely a simple deployment of the algorithm, but a precise systems engineering optimization. It ensures that the high-precision SOH evaluation algorithm of this invention can be executed in a real-time, reliable, economical hardware environment that is perfectly compatible with existing systems, thereby solidly transforming advanced algorithmic innovation into practical and scalable industrial product competitiveness. This directly reflects the high industrial application value of this invention.

[0050] An electronic device includes at least one processor and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform a method as described above.

[0051] One or more embodiments in this application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of this application. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments in this application should be included within the protection scope of this application.

[0052] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by program instructions and related hardware. The aforementioned program instructions can be stored in a computer-readable storage medium. When the program instructions are executed, they perform the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as mobile storage devices, read-only memory (ROM), magnetic disks, or optical disks.

[0053] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature.

[0054] If a flowchart is used in this application, it is used to illustrate the operations performed by the system according to embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0055] The foregoing provides a detailed description of a method and apparatus for assessing the health status of a lithium-ion battery. The above description of the disclosed embodiments enables those skilled in the art to implement or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for assessing the health status of a lithium-ion battery, characterized in that, Includes the following steps: A pre-set cell degradation rate matrix is ​​configured in the battery management system. The cell degradation rate matrix defines the single-cycle charge-discharge capacity degradation rate of the lithium-ion battery under different temperature and different discharge depth combinations. Acquire actual operating data of the target battery over a complete time period, including the duration of the target battery in different temperature ranges and the corresponding depth of discharge; Based on the actual operating data and the cell degradation rate matrix, the cumulative capacity degradation of the target battery during the complete time period is obtained; The current health status of the target battery is determined based on its initial capacity and the cumulative capacity decay.

2. The method for assessing the health status of a lithium-ion battery as described in claim 1, characterized in that, The cell attenuation rate matrix is ​​constructed in the following manner: Obtain the measured capacity decay curve of a first test battery of the same type as the target battery at a fixed temperature and a fixed depth of discharge. A lithium battery life simulation model is constructed, and the lithium battery life simulation model is calibrated based on the measured capacity decay curve. Based on the calibrated lithium battery life simulation model, the capacity decay data of the first test battery under various temperature and discharge depth combinations are obtained through simulation, forming the cell decay rate matrix.

3. The method for assessing the health status of a lithium-ion battery as described in claim 2, characterized in that, The calibration of the lithium battery life simulation model based on the measured capacity decay curve includes the following steps: Based on the lithium battery life simulation model, the simulated capacity decay curve of the first test battery is obtained; Calculate the error between the simulated capacity decay curve and the measured capacity decay curve; Determine whether the error is greater than a preset accuracy threshold; If not, adjust the parameters in the lithium battery life simulation model until the error is less than the preset accuracy threshold.

4. The method for assessing the health status of a lithium-ion battery as described in claim 3, characterized in that, After determining whether the error is greater than a preset accuracy threshold, the following steps are also included: If so, the current lithium battery life simulation model is used as the calibrated lithium battery life simulation model.

5. The method for assessing the health status of a lithium-ion battery as described in claim 1, characterized in that, The cell attenuation rate matrix is ​​also constructed in the following manner: Obtain multi-sample measured capacity decay curves of multiple second test batteries of the same type as the target battery under different temperature and different depth of discharge combinations; Based on the measured capacity decay curves of the multi-sample test, an artificial intelligence fitting algorithm is used to establish a mapping relationship between the single-cycle charge and discharge capacity decay rate and temperature and discharge depth, and the mapping relationship is used as the cell decay rate matrix.

6. The method for assessing the health status of a lithium-ion battery as described in claim 1, characterized in that, The cumulative capacity degradation of the target battery over the complete time period is obtained based on the actual operating data and the cell degradation rate matrix, specifically calculated using the following formula: ； in, This is the cumulative capacity decay. The initial capacity of the target battery; Index for combined temperature and depth of discharge conditions; For the cell attenuation rate matrix and the first The single-cycle charge / discharge capacity decay rate corresponding to the various operating conditions. For the target battery in the first Cumulative runtime under various operating conditions; This refers to the time required for the target battery to be fully charged in its current healthy state.

7. The method for assessing the health status of a lithium-ion battery as described in claim 1, characterized in that, The current health status of the target battery, based on its initial capacity and cumulative capacity decay, is determined using the following formula: ； in, This represents the current health status of the target battery. The initial capacity of the target battery. This represents the cumulative capacity decay.

8. The method for assessing the health status of a lithium-ion battery as described in claim 1, characterized in that, The battery management system includes: a battery management unit, a battery cluster management unit, and a battery stack management system; The cell degradation rate matrix is ​​pre-set in the battery cluster management unit; The acquisition of the target battery's actual operating data over a complete time period is performed by the battery management unit, and the actual operating data is uploaded to the battery cluster management unit. The acquisition of the cumulative capacity decay of the target battery over the complete time period and the determination of the current health status of the target battery are performed by the battery cluster management unit.

9. The method for assessing the health status of a lithium-ion battery as described in claim 8, characterized in that, Also includes: The battery cluster management unit uploads the current health status of the target battery to the battery stack management system for storage and display.

10. An electronic device comprising at least one processor and a memory communicatively connected to the at least one processor; the memory storing instructions executable by the at least one processor to enable the at least one processor to perform the method as described in any one of claims 1 to 7.

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