A method for determining the state of health of a power battery and related equipment

Abstract

The embodiment of the application provides a kind of power battery health state determination method and related equipment, belong to power battery technical field.The method comprises: collecting the first voltage data and the first capacity data of power battery under current charge-discharge cycle;Determine the first capacity increment of power battery based on the first voltage data and the first capacity data;Determine the characteristic curve matched with the first capacity increment, and the characteristic curve is used to indicate the capacity increment of power battery at specific charge-discharge cycle;Determine the current health state of power battery corresponding to the characteristic curve.In the embodiment, the first capacity increment of power battery is matched with the pre-stored curve, so as to determine the corresponding characteristic curve, which is used to indicate the capacity increment of battery at specific health state, and the health state of battery can be directly determined according to the characteristic curve.The embodiment scheme can realize the determination of battery health state in different scenarios, and improve the reliability of power battery.

Description

Technical Field

[0001] This application relates to the field of power battery technology, and in particular to a method and related equipment for determining the health status of a power battery. Background Technology

[0002] The State of Health (SOH) of a power battery characterizes its degradation and aging level, and accurate and reliable battery SOH estimation methods are of great significance for the safe and efficient application of battery packs. However, due to the strong time-varying and nonlinear characteristics of lithium batteries, and their susceptibility to external environmental influences, accurate and reliable SOH estimation presents a significant challenge.

[0003] Currently, the mainstream SOH estimation methods mainly include model-based methods, such as equivalent circuit models and fractional-order models, but the model parameters are difficult to identify and have poor adaptability.

[0004] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the Invention

[0005] The main objective of this application is to propose a method and related equipment for determining the health status of a power battery, so as to achieve a simple and efficient determination of the health status of the power battery.

[0006] To achieve the above objectives, one aspect of this application proposes a method for determining the health status of a power battery, comprising:

[0007] Collect the first voltage data and first capacity data of the power battery under the current charge-discharge cycle; Based on the first voltage data and the first capacity data, the first capacity increment of the power battery is determined; Determine a characteristic curve that matches the first capacity increment, the characteristic curve being used to represent the capacity increment of the power battery during a specific charge-discharge cycle; Determine the current health status of the power battery corresponding to the characteristic curve.

[0008] In some embodiments, determining the characteristic curve matching the first capacity increment includes: Obtain a set of feature curves, wherein the set of feature curves stores at least one feature curve; Based on the change curve of the first capacity increment, a target feature curve is determined in the set of feature curves, and the trajectory of the target feature curve matches the change curve.

[0009] In some embodiments, the method further includes: establishing the feature curve set, including: Obtain the second voltage data and the second capacity data of the power battery; Based on the second voltage data and the second capacity data, the second capacity increment of the power battery is determined; Determine the curve similarity between the second capacity increment curve and the baseline curve; Based on the correspondence between each second capacity curve and the actual health value, the actual health value of different second capacity curves is determined, and the similarity between the actual health value and the curve is a quadratic relationship. The characteristic curve value is established by using multiple second capacity increment curves and the actual health values ​​corresponding to the second capacity curves.

[0010] In some embodiments, determining the curve similarity between the second capacity increment curve and the reference curve includes: Based on the distance function between the second capacity curve and the reference curve; The distance function is used to determine the curve similarity between the second capacity increment curve and the baseline curve.

[0011] In some embodiments, obtaining the second voltage data and the second capacity data of the power battery includes: Obtain the third voltage data and third capacity data of the power battery under the previous charge-discharge cycle; Obtain the fourth voltage data and the fourth capacity data of the power battery in the next charge-discharge cycle; The difference between the third voltage data and the fourth voltage data is used as the second voltage data, and the difference between the third capacity data and the fourth capacity data is used as the second capacity data.

[0012] In some embodiments, the curve similarity is proportional to the actual health value.

[0013] To achieve the above objectives, another aspect of this application provides a power battery health status determination device, the device comprising: The data acquisition module is used to acquire the first voltage data and the first capacity data of the power battery under the current charge-discharge cycle; The calculation module is used to determine the first capacity increment of the power battery based on the first voltage data and the first capacity data; A matching module is used to determine a feature curve that matches the first capacity increment, the feature curve representing the capacity increment of the power battery during a specific charge-discharge cycle; The determination module is used to determine the current health status of the power battery corresponding to the characteristic curve.

[0014] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above.

[0015] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods described above.

[0016] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer program product, including a computer program that, when executed by a processor, implements the aforementioned method.

[0017] This application provides a method and related equipment for determining the health status of a power battery. The method includes: collecting first voltage data and first capacity data of the power battery under the current charge-discharge cycle; determining a first capacity increment of the power battery based on the first voltage data and first capacity data; determining a characteristic curve matching the first capacity increment, the characteristic curve representing the capacity increment of the power battery in a specific charge-discharge cycle; and determining the current health status of the power battery corresponding to the characteristic curve. In this embodiment, the first capacity increment of the power battery is matched with a pre-stored curve to determine the corresponding characteristic curve, which represents the capacity increment of the battery in a specific health state. The health status of the battery can be directly determined based on the characteristic curve. This embodiment can determine the battery health status in different scenarios, adapting to different operating conditions and improving the reliability of the power battery. Attached Figure Description

[0018] Figure 1 This is a flowchart of the feature curve establishment method provided in the embodiments of this application; Figure 2 This is a diagram showing the correspondence between capacity and DTW provided in an embodiment of this application; Figure 3 This is a flowchart of the method for determining the health status of a power battery provided in the embodiments of this application. Figure 4 This demonstrates the effectiveness of the power battery health status determination method provided in the embodiments of this application. Figure 1 ; Figure 5 This demonstrates the effectiveness of the power battery health status determination method provided in the embodiments of this application. Figure 2 ; Figure 6 This is a schematic diagram of the structure of the power battery health status determination device provided in the embodiments of this application; Figure 7This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0021] The core of this application lies in providing a simple, efficient, and robust method for determining the state of health (SOH) of a power battery. This method eliminates the complex filtering or smoothing steps of traditional methods on raw data, directly utilizing voltage-capacity data collected during battery charging and discharging. It calculates the capacity increment curve and innovatively introduces a Dynamic Time Warping (DTW) algorithm to quantify the similarity between the current curve and the health baseline curve. Based on extensive experimental data, this application discovers a stable and significant quadratic function relationship between the battery's SOH and IC curves' DTW similarity. By pre-calibrating this analytical relationship, the battery's SOH can be quickly and accurately estimated online based solely on the IC curve of the current cycle, without relying on complex machine learning model training processes. This significantly reduces computational complexity and dependence on data quality, making it particularly suitable for embedding into battery management systems (BMS) with limited computing resources.

[0022] This application provides an overall flowchart of a method for determining the health status of a power battery according to one embodiment. The method mainly includes two modules: an offline model building stage and an online health status estimation stage. The offline stage aims to establish a reliable set of SOH-similarity mapping feature curves; the online stage utilizes this model to perform rapid diagnosis of real-time data. See also... Figure 1 , Figure 1 The process of establishing the characteristic curve set is as follows: Step S101: Obtain the second voltage data and the second capacity data of the power battery; Step S102: Based on the second voltage data and the second capacity data, determine the second capacity increment of the power battery; Step S103: Determine the curve similarity between the second capacity increment curve and the baseline curve; Step S104: Based on the correspondence between each second capacity curve and the actual health value, determine the actual health value of different second capacity curves. The similarity between the actual health value and the curve is a quadratic relationship. Step S105: Establish characteristic curve values ​​using multiple second capacity increment curves and the actual health values ​​corresponding to the second capacity curves.

[0023] This step describes the process of establishing the characteristic curve set. To establish an accurate characteristic curve set, a complete life cycle test of the target model's power battery (cell, module, or pack) needs to be performed in a controlled laboratory environment. The test typically uses a standard charge and discharge regime, such as charging in constant current (CC) constant voltage (CV) mode and discharging in constant current (CC) mode at an ambient temperature of 25°C.

[0024] Throughout the battery aging experiment, it is necessary to continuously collect voltage-time data and cumulative charge capacity-time data during the constant current charging phase for each cycle (or every fixed cycle interval, such as every 50 cycles). Since the charge capacity is proportional to time during the constant current phase, the time series is usually converted into a voltage-capacity (QV) series, resulting in a series of discrete data points (Vi, Qi), where i = 1, 2, ..., N, and N is the number of sampling points. To enhance the model's adaptability to different initial health states (e.g., slight differences in battery capacity at the time of manufacture), data acquisition can begin early in the battery's lifespan and continue until its end (e.g., capacity decay to 80% of rated capacity).

[0025] For each voltage-capacity sequence (Vi, Qi) acquired in a cycle, its capacity increment (IC) is calculated directly using the differential method. The calculation formula is: (1).

[0026] After calculation, the IC curve for this cycle is obtained, which is a sequence of (Vi, dQ / dVi) data points. This calculation process does not include any low-pass filtering, moving average, or smoothing algorithms, aiming to fully preserve the detailed features reflecting changes in the internal electrochemical state of the battery in the original data, including some minor fluctuations. These fluctuations may carry information related to the aging mechanism, which traditional filtering might treat as noise. The sound is filtered out, thus reducing the accuracy of the estimation. The resulting curve is the second capacity increment curve.

[0027] Select an IC curve from a healthy cycle (typically the first cycle or the average of the first few cycles) as a baseline curve. This curve represents the standard IC characteristics of the battery in a healthy state.

[0028] For each additional cycle of IC curve acquired during the offline phase, the DTW distance between it and the baseline curve R is calculated. DTW is a classic algorithm for measuring the similarity between two time series of different lengths. It finds the optimal alignment path between two sequences by locally stretching or compressing the time axis, thus effectively handling slight shifts or deformations of the charging curve on the voltage axis due to aging, which is something that simple Euclidean distance cannot do.

[0029] The specific calculation process is as follows: Calculate the local distance between each point on the baseline curve R and each point on the test curve T, forming an M x L ​​distance matrix D.

[0030] In matrix D, starting from (1,1) and ending at (M, L), find a path W = w1, w2, ..., wp, where wi = (ki, li), such that the sum of the local distances of all points on the path is minimized. The path must satisfy boundary, continuity, and monotonicity constraints. The cumulative distance γ(k, l) is usually calculated recursively using dynamic programming. (2) The minimum cumulative distance corresponding to the optimal path W is the DTW distance between the two curves: (3) The smaller the DTW distance, the more similar the curves. To obtain an indicator positively correlated with SOH (higher SOH indicates better health, and the more similar it should be to the baseline curve), the distance is converted into a similarity score S. A commonly used and effective conversion method is: (4) The value of S ranges from (0, 1]. The larger the value of S, the higher the similarity and the better the battery health.

[0031] For each cycle of data collected during the offline phase, two key variables are: The true state of health (DSW) value is typically defined by the ratio of the actual discharge capacity of the cycle to the battery's rated capacity (or initial capacity), and the DTW similarity calculated from the IC curve of that cycle and the baseline curve. A schematic diagram illustrating the relationship between the two is shown below. Figure 2 As shown.

[0032] There is a clear non-linear relationship between health status values ​​and similarity, and it can be fitted using a quadratic polynomial: (5) Where a, b, and c are the model coefficients to be calibrated.

[0033] The Least Squares Method is used to fit the offline dataset to determine the optimal values ​​of coefficients a, b, and c. The goal of the fit is to minimize the sum of squared residuals between the fitted values ​​and the true values ​​for all data points.

[0034] When it is necessary to estimate the current health status of the battery, perform the following operations: During a constant current charging process of the battery, the current voltage sequence and the cumulative charging capacity sequence are collected to obtain the voltage-capacity data of the current cycle, namely the first voltage data and the first capacity data.

[0035] Using the unfiltered differential method, the capacity increment curve of the current cycle is calculated, which is the first capacity increment.

[0036] The current IC curve is used as the test curve, and its DTW similarity is calculated with the baseline curve pre-stored in the BMS to obtain the current similarity value. The matching process is the calculation of DTW similarity, and the matching result represents the health status level.

[0037] The calculated curve similarity is substituted into the calibrated quadratic analytical model in the offline stage to directly calculate the current battery health status estimate.

[0038] The estimated current health status is output for battery status display, lifespan warning, energy management strategy optimization, etc., and the estimated value and corresponding cycle number can be recorded in the BMS non-volatile memory.

[0039] like Figure 3 As shown, Figure 3 Methods for determining the health status of power batteries include: Step S301: Collect the first voltage data and the first capacity data of the power battery under the current charge-discharge cycle; Step S302: Based on the first voltage data and the first capacity data, determine the first capacity increment of the power battery; Step S303: Determine the characteristic curve that matches the first capacity increment. The characteristic curve is used to represent the capacity increment of the power battery during a specific charge-discharge cycle. Step S304: Determine the current health status of the power battery corresponding to the characteristic curve.

[0040] The battery module was placed in a constant temperature environment of 25±2°C for continuous charge-discharge cycle testing. The charge-discharge regime is as follows: Charging: Constant current charging is used to charge the module to the total voltage, then constant voltage charging is used until the charging current drops below the preset current, at which point charging stops.

[0041] Let stand: Let stand for 30 minutes after charging is complete.

[0042] Discharge: Constant current discharge is used to discharge to the total voltage of the module.

[0043] Resting: After the discharge is complete, let it rest for 30 minutes, and then start the next cycle.

[0044] The experiment consisted of over 2000 cycles until the actual discharge capacity of the battery module decayed to the preset capacity. Throughout the test, high-precision data acquisition equipment was used to synchronously record the module's total voltage and cumulative charging capacity (Ah) during the constant current charging phase at a frequency of at least one point per second. The data was stored and divided into files based on the number of cycles.

[0045] The data from the first cycle (healthy state) and the 1000th cycle (aging state) are selected for comparison and demonstration.

[0046] The voltage V and capacity Q sequences for the first cycle of constant current charging were read from the data file, each containing approximately 4000 data points.

[0047] To avoid differential errors at the voltage endpoints, the central difference method is used to calculate the IC value. For the first and last points, forward or backward difference is used.

[0048] Plotting the calculated (Vj, ICj) as a curve yields the unfiltered IC curve for that cycle. Comparing the IC curves of the 1st and 1000th cycles, it can be clearly seen that with aging, the height of the main peak of the IC curve (corresponding to the solid solution reaction plateau of the LFP battery) decreases, its width increases, and it shifts overall towards higher voltage, resulting in a significant change in the curve shape.

[0049] Choose the IC curve of the first cycle as the baseline curve R. To reduce the impact of random fluctuations in the initial data, the average value of the IC curves of the first 5 cycles can also be used as the baseline.

[0050] For the c-th iteration (c=2,3,...,2000), its normalized IC curve is used as the test curve Tc. The DTW distance between Tc and the normalized baseline curve is calculated using the standard DTW algorithm. Euclidean distance is used for the local distance calculation.

[0051] For each cycle, the true SOH is calculated from the actual constant current discharge capacity of that cycle.

[0052] Pair the similarity of each cycle with the true SOH.

[0053] 80% of the cyclic data (approximately 1600 points) was randomly selected as the training set for model fitting; the remaining 20% ​​of the cyclic data (approximately 400 points) was used as an independent test set to evaluate the model's generalization performance.

[0054] On the training set, a quadratic model is fitted using the least squares method. A set of optimal coefficients is obtained through numerical calculation.

[0055] Compare the actual SOH with the estimated SOH by plotting them on the same graph, such as... Figure 4 As shown in the figure, the estimated values ​​(scatter plots or dashed lines) closely track the trend of the true values ​​(solid lines), exhibiting good consistency throughout the entire capacity decay range.

[0056] Calculate the estimation error and plot the error distribution as follows: Figure 5 As can be seen from the figure, the estimation error of the vast majority of cycles is concentrated in the range of ±2%, which shows high estimation accuracy.

[0057] Please see Figure 6 This application also provides a power battery health status determination device, which can implement the above-described method. The device includes: The acquisition module 61 is used to acquire the first voltage data and the first capacity data of the power battery under the current charge-discharge cycle; Calculation module 62 is used to determine the first capacity increment of the power battery based on the first voltage data and the first capacity data; Matching module 63 is used to determine a feature curve that matches the first capacity increment, the feature curve being used to represent the capacity increment of the power battery during a specific charge-discharge cycle; The determination module 64 is used to determine the current health status of the power battery corresponding to the characteristic curve.

[0058] In some embodiments, the matching module 63 is configured to: Obtain a set of feature curves, wherein the set of feature curves stores at least one feature curve; Based on the change curve of the first capacity increment, a target feature curve is determined in the set of feature curves, and the trajectory of the target feature curve matches the change curve.

[0059] In some embodiments, the apparatus further includes: a setup module 60, configured to: Obtain the second voltage data and the second capacity data of the power battery; Based on the second voltage data and the second capacity data, the second capacity increment of the power battery is determined; Determine the curve similarity between the second capacity increment curve and the baseline curve; Based on the correspondence between each second capacity curve and the actual health value, the actual health value of different second capacity curves is determined, and the similarity between the actual health value and the curve is a quadratic relationship. The characteristic curve value is established by using multiple second capacity increment curves and the actual health values ​​corresponding to the second capacity curves.

[0060] In some embodiments, the establishment module 60 is used for: Based on the distance function between the second capacity curve and the reference curve; The distance function is used to determine the curve similarity between the second capacity increment curve and the baseline curve.

[0061] In some embodiments, the establishment module 60 is used for: Obtain the third voltage data and third capacity data of the power battery under the previous charge-discharge cycle; Obtain the fourth voltage data and the fourth capacity data of the power battery in the next charge-discharge cycle; The difference between the third voltage data and the fourth voltage data is used as the second voltage data, and the difference between the third capacity data and the fourth capacity data is used as the second capacity data.

[0062] In some embodiments, the curve similarity is proportional to the actual health value.

[0063] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0064] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0065] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0066] Please see Figure 7 , Figure 7 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 701 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 702 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 702 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 702 and is called and executed by the processor 701 using the methods described in the embodiments of this application. The input / output interface 703 is used to implement information input and output; The communication interface 704 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 705 transmits information between various components of the device (e.g., processor 701, memory 702, input / output interface 703, and communication interface 704); The processor 701, memory 702, input / output interface 703, and communication interface 704 are connected to each other within the device via bus 705.

[0067] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0068] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0069] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0070] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0071] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0072] This application provides a method and related equipment for determining the health status of a power battery. The method includes: collecting first voltage data and first capacity data of the power battery under the current charge-discharge cycle; determining a first capacity increment of the power battery based on the first voltage data and first capacity data; determining a characteristic curve matching the first capacity increment, the characteristic curve representing the capacity increment of the power battery during a specific charge-discharge cycle; and determining the current health status of the power battery corresponding to the characteristic curve. In this embodiment, the first capacity increment of the power battery is matched with a pre-stored curve to determine the corresponding characteristic curve. This characteristic curve represents the capacity increment of the battery under a specific health state, and the battery's health status can be directly determined based on the characteristic curve. This embodiment can determine the battery's health status in different scenarios, adapting to different operating conditions and improving the reliability of the power battery.

[0073] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0074] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0075] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0076] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0077] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0078] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0079] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0080] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0081] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0082] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0083] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for determining the health status of a power battery, characterized in that, The method includes: Collect the first voltage data and first capacity data of the power battery under the current charge-discharge cycle; Based on the first voltage data and the first capacity data, the first capacity increment of the power battery is determined; Determine a characteristic curve that matches the first capacity increment, the characteristic curve being used to represent the capacity increment of the power battery during a specific charge-discharge cycle; Determine the current health status of the power battery corresponding to the characteristic curve.

2. The method according to claim 1, characterized in that, The determination of the characteristic curve matching the first capacity increment includes: Obtain a set of feature curves, wherein the set of feature curves stores at least one feature curve; Based on the change curve of the first capacity increment, a target feature curve is determined in the set of feature curves, and the trajectory of the target feature curve matches the change curve.

3. The method according to claim 2, characterized in that, The method further includes: establishing the feature curve set, including: Obtain the second voltage data and the second capacity data of the power battery; Based on the second voltage data and the second capacity data, the second capacity increment of the power battery is determined; Determine the curve similarity between the second capacity increment curve and the baseline curve; Based on the correspondence between each second capacity curve and the actual health value, the actual health value of different second capacity curves is determined, and the similarity between the actual health value and the curve is a quadratic relationship. The characteristic curve value is established by using multiple second capacity increment curves and the actual health values ​​corresponding to the second capacity curves.

4. The method according to claim 3, characterized in that, Determining the curve similarity between the second capacity increment curve and the reference curve includes: Based on the distance function between the second capacity curve and the reference curve; The distance function is used to determine the curve similarity between the second capacity increment curve and the baseline curve.

5. The method according to claim 3, characterized in that, The acquisition of the second voltage data and second capacity data of the power battery includes: Obtain the third voltage data and third capacity data of the power battery under the previous charge-discharge cycle; Obtain the fourth voltage data and the fourth capacity data of the power battery in the next charge-discharge cycle; The difference between the third voltage data and the fourth voltage data is used as the second voltage data, and the difference between the third capacity data and the fourth capacity data is used as the second capacity data.

6. The method according to claim 3, characterized in that, The curve similarity is directly proportional to the actual health value.

7. A device for determining the health status of a power battery, characterized in that, The device includes: The data acquisition module is used to acquire the first voltage data and the first capacity data of the power battery under the current charge-discharge cycle; The calculation module is used to determine the first capacity increment of the power battery based on the first voltage data and the first capacity data; A matching module is used to determine a feature curve that matches the first capacity increment, the feature curve representing the capacity increment of the power battery during a specific charge-discharge cycle; The determination module is used to determine the current health status of the power battery corresponding to the characteristic curve.

8. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 6.

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