Battery health state evaluation method and device, storage medium and electronic equipment

Abstract

The application discloses a battery health state evaluation method and device, a storage medium and electronic equipment. The method comprises the following steps: obtaining charging data of a target battery in a current charging period; constructing a differential capacity model of the target battery based on the charging data, wherein the differential capacity model is used to represent the change relationship between the charging capacity and the charging voltage of the target battery in the current charging period; determining the target parameter values corresponding to the plurality of characteristic parameters respectively based on the differential capacity model, wherein the plurality of characteristic parameters at least include the peak value, the peak area and the peak-valley distance in the differential capacity model; and determining the health state evaluation result of the target battery according to the target parameter values corresponding to the plurality of characteristic parameters respectively. The application solves the technical problem of low accuracy of battery health state evaluation caused by the fact that the related art does not comprehensively consider factors in battery health state evaluation.

Description

Technical Field

[0001] This invention relates to the field of battery management system technology, and more specifically, to a battery health status assessment method, apparatus, storage medium, and electronic device. Background Technology

[0002] With the booming development of the new energy vehicle industry, the design and optimization of Battery Management Systems (BMS) have become a key focus of the industry. In particular, the accurate assessment of battery state of health (SOH) directly affects the driving range, safety, and lifespan of electric vehicles. Existing SOH assessment methods mainly rely on two basic strategies: statistical analysis based on historical cumulative charge-discharge cycles and calendar aging data, and direct estimation of SOH based on a single charge.

[0003] Traditional methods often focus on the impact of battery cycle count and resting time on state of equilibrium (SOH), calculating historical cumulative charge-discharge capacity through ampere-hour integration and then using a pre-established database for SOH lookup. However, these methods have significant drawbacks: they rely excessively on aging data under laboratory conditions, failing to adequately consider the complex and variable charging conditions and environmental factors encountered in actual use. Furthermore, because they cannot reflect subtle changes in battery aging status in real time, the SOH assessment results of these methods often deviate significantly from reality. Another online SOH estimation strategy attempts to adjust SOH in real time by monitoring capacity changes during specific charging stages. However, its limitation lies in relying solely on the capacity difference between the start and end points of charging, ignoring the impact of real-time environmental parameters such as temperature and charge-discharge rate on battery performance. It also places high demands on the accurate determination of the start and end points, which is difficult to consistently meet in practical applications, leading to instability and inaccuracy in SOH assessment.

[0004] In summary, current battery health status assessment technologies suffer from incomplete consideration of factors, especially when real-time monitoring and adaptation to complex environmental conditions, which significantly limits the accuracy of SOH assessment.

[0005] There is currently no effective solution to the above problems. Summary of the Invention

[0006] This invention provides a battery health status assessment method, apparatus, storage medium, and electronic device to at least solve the technical problem of low accuracy in battery health status assessment caused by incomplete consideration of factors in related technologies.

[0007] According to one aspect of the present invention, a battery health status assessment method is provided, comprising: acquiring charging data of a target battery during a current charging period, wherein the charging data includes the charge amount and charging voltage of the target battery collected at multiple sampling times included in the current charging period; constructing a differential capacity model of the target battery based on the charging data, wherein the differential capacity model is used to characterize the change relationship between the charge amount and charging voltage of the target battery during the current charging period; determining target parameter values ​​corresponding to multiple feature parameters based on the differential capacity model, wherein the multiple feature parameters include at least the peak value, peak area, and peak-valley distance in the differential capacity model; and determining the health status assessment result of the target battery according to the target parameter values ​​corresponding to the multiple feature parameters.

[0008] Optionally, based on the target parameter values ​​corresponding to each of the multiple feature parameters, the health status assessment result of the target battery is determined, including: determining a health status relationship table corresponding to each of the multiple feature parameters, wherein the health status relationship table is used to indicate the correspondence between the parameter values ​​of the corresponding feature parameters and the battery health status; querying the corresponding health status relationship table based on the target parameter values ​​corresponding to each of the multiple feature parameters to obtain multiple estimated battery health states, wherein the multiple estimated battery health states correspond one-to-one with the multiple feature parameters; and determining the health status assessment result of the target battery during the current charging period based on the multiple estimated battery health states.

[0009] In the above methods, the establishment of a health status relationship table corresponding to different characteristic parameters can quickly obtain the estimated health status assessment result (i.e., estimated) SOH value corresponding to each characteristic parameter by looking up the table, transforming complex battery aging characteristics into specific SOH assessment values; by integrating the assessment results of multiple characteristic parameters, the battery health status can be reflected more comprehensively and meticulously, reducing the dependence on a single characteristic parameter and improving the stability and accuracy of the battery health status assessment results.

[0010] Optionally, determining the health state relationship table corresponding to each of the multiple feature parameters includes: collecting charging data of the sample battery corresponding to multiple different battery health states during the process of charging the sample battery from an empty state to a fully charged state according to a predetermined current under a predetermined temperature environment; constructing a differential capacity model of the sample battery corresponding to each of the multiple different battery health states based on the charging data corresponding to each of the multiple different battery health states; determining the sample parameter set of the sample battery corresponding to each of the multiple different battery health states based on the differential capacity model corresponding to each of the multiple different battery health states, wherein the sample parameter set includes the sample parameter values ​​corresponding to each of the multiple feature parameters under the corresponding battery health states; and obtaining the health state relationship table corresponding to each of the multiple feature parameters based on the sample parameter set corresponding to each of the multiple different battery health states.

[0011] In the above methods, by assigning appropriate weights to different characteristic parameters, the evaluation results can more accurately reflect the current health status of the battery. Since different types of batteries, or at different stages of aging, have certain differences in the characteristic parameters that best reflect the battery's health status, reasonable target weights can highlight these key characteristics and improve the relevance of the evaluation. Furthermore, a single characteristic parameter may be affected by measurement errors, environmental factors, or individual battery differences, while weighted calculations, by integrating the evaluation results of multiple characteristic parameters, can offset these influences to some extent, reducing the error in the evaluation results.

[0012] Optionally, based on multiple estimated battery health states, the health status assessment result of the target battery during the current charging period is determined, including: determining the target weights corresponding to each of the multiple feature parameters; and performing a weighted calculation based on the multiple estimated battery health states and the target weights corresponding to each of the multiple feature parameters to obtain the health status assessment result of the target battery.

[0013] In the above methods, when the characteristic parameters generate multiple target parameter values, weighted calculations ensure that the evaluation results fully reflect the combined impact of these values, avoiding the bias that might result from selecting only one value. The introduction of weighting coefficients allows for a reasonable allocation of the contribution of each predicted SOH value, reducing the impact of outliers or non-representative values ​​on the evaluation results and improving their reliability. Especially when the battery is under complex operating conditions such as frequent charging or deep discharging, the characteristic parameters may generate multiple values. The above methods ensure that even under complex conditions, the SOH evaluation maintains high accuracy and stability.

[0014] Optionally, determining the target weights for each of the multiple feature parameters includes: collecting charging data for the sample battery at multiple different battery health states during the charging process from an empty state to a fully charged state at a predetermined current under a predetermined temperature environment; constructing differential capacity models for the sample battery at multiple different battery health states based on the charging data; determining sample parameter sets for the sample battery at multiple different battery health states based on the differential capacity models, wherein the sample parameter sets include sample parameter values ​​for each of the multiple feature parameters at the corresponding battery health states; determining Pearson correlation coefficients between the multiple feature parameters and the battery health states based on the multiple different battery health states and the sample parameter sets corresponding to the multiple different battery health states; and determining the target weights for each of the multiple feature parameters based on the Pearson correlation coefficients between the multiple feature parameters and the battery health states.

[0015] Among the methods described above, the Pearson correlation coefficient is a statistical tool used to quantitatively analyze the correlation between characteristic parameters and State of Health (SOH). This method provides an objective and scientific basis for determining weights, reducing the interference of human factors. Determining the target weights of characteristic parameters through the Pearson correlation coefficient not only enhances the scientific rigor and objectivity of battery health status assessment but also improves the accuracy and reliability of the assessment results.

[0016] Optionally, the health status assessment result of the target battery is determined based on the target parameter values ​​corresponding to each of the multiple feature parameters, including: obtaining the environmental parameters of the target battery during the current charging period; determining the weight correction coefficients corresponding to each of the multiple feature parameters based on the environmental parameters; correcting the target weights corresponding to each of the multiple feature parameters based on the weight correction coefficients corresponding to each of the multiple feature parameters to obtain the corrected weight coefficients corresponding to each of the multiple feature parameters; and determining the health status assessment result of the target battery based on the corrected weight coefficients corresponding to each of the multiple feature parameters.

[0017] The above methods dynamically adjust the weights of characteristic parameters based on the actual environmental conditions during battery charging, ensuring high accuracy and reliability of the assessment method under various environments. For example, battery performance is affected by cold or hot weather; correcting the weights captures these effects, making the assessment results more realistic. By correcting the weights for environmental parameters, environmental factors in battery health status assessment can be considered. This makes SOH assessment no longer limited to the characteristic parameters of the battery itself, but forms a multi-dimensional and refined assessment system. It also effectively reduces the impact of changes in environmental conditions on SOH assessment results, avoiding assessment errors caused by neglecting environmental factors.

[0018] Optionally, before acquiring the charging data of the target battery during the current charging period, the method further includes: detecting whether the target battery is in a preset charging state, wherein the preset charging state is at least used to indicate that the state of charge of the target battery is less than a preset first energy threshold; detecting whether the charging current of the target battery is within a preset current range; and when the target battery is in a preset charging state and the charging current is within the preset current range, determining the time period between the time corresponding to the preset charging state and the time when the target battery is fully charged as the current charging period, wherein the time when the target battery is fully charged is used to indicate the time when the target battery is fully charged.

[0019] In the above methods, by limiting the state of charge (SOH) and charging current range at the start of charging, cleaner and higher-quality charging data can be obtained under consistent operating conditions. Preset charging state and current ranges provide a stable evaluation environment, avoiding fluctuations in evaluation results caused by changes in charging conditions and ensuring the stability of SOH evaluations. Monitoring and limiting the charging current range also helps prevent adverse effects such as overcharging and over-discharging during the charging process, thereby protecting battery safety and extending battery life.

[0020] According to another aspect of the present invention, a battery health status assessment device is also provided, comprising: a charging data acquisition module, configured to acquire charging data of a target battery during a current charging period, wherein the charging data includes the charging amount and charging voltage of the target battery collected at multiple sampling times included in the current charging period; a model construction module, configured to construct a differential capacity model of the target battery based on the charging data, wherein the differential capacity model is used to characterize the change relationship between the charging amount and charging voltage of the target battery during the current charging period; a parameter value determination module, configured to determine target parameter values ​​corresponding to multiple feature parameters based on the differential capacity model, wherein the multiple feature parameters include at least the peak value, peak area, and peak-valley distance in the differential capacity model; and a battery status assessment module, configured to determine the health status assessment result of the target battery based on the target parameter values ​​corresponding to the multiple feature parameters.

[0021] According to another aspect of the present invention, a non-volatile storage medium is also provided, which stores a plurality of instructions adapted for a battery health status assessment method, any one of which can be loaded and executed by a processor.

[0022] According to another aspect of the present invention, an electronic device is also provided, including one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement any one of the battery health status assessment methods.

[0023] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements the steps of any one of the battery health status assessment methods.

[0024] In this embodiment of the invention, charging data of the target battery during the current charging period is acquired. This charging data includes the charge amount and charging voltage of the target battery collected at multiple sampling times within the current charging period. A differential capacity model of the target battery is constructed based on the charging data. This differential capacity model characterizes the relationship between the charge amount and charging voltage of the target battery during the current charging period. Based on the differential capacity model, target parameter values ​​corresponding to multiple characteristic parameters are determined. These multiple characteristic parameters include at least the peak value, peak area, and peak-to-trough distance in the differential capacity model. Based on the target parameter values ​​corresponding to these multiple characteristic parameters, the health status assessment result of the target battery is determined. This achieves the goal of accurately determining the battery health status by analyzing the differential capacity model of the target battery during the current charging period and extracting multiple characteristic parameters, including the peak value, peak area, and peak-to-trough distance. This improves the accuracy of battery health status determination and solves the technical problem of low accuracy in battery health status assessment caused by incomplete consideration of factors in related technologies. Attached Figure Description

[0025] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0026] Figure 1 This is a flowchart of a battery health status assessment method according to an embodiment of the present invention;

[0027] Figure 2 This is a schematic diagram illustrating the extraction of feature parameters from an optional dQ / dV curve according to an embodiment of the present invention.

[0028] Figure 3 This is a flowchart of an optional battery health status assessment method according to an embodiment of the present invention;

[0029] Figure 4 This is a schematic diagram of a battery health status assessment device according to an embodiment of the present invention. Detailed Implementation

[0030] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0031] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention 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 the invention 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 a 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.

[0032] According to an embodiment of the present invention, a method embodiment for assessing battery health status is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0033] Figure 1 This is a flowchart of a battery health status assessment method according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0034] Step S102: Obtain the charging data of the target battery during the current charging period, wherein the charging data includes the charging amount and charging voltage of the target battery collected at multiple sampling times included in the current charging period.

[0035] Optionally, the target battery can be a ternary lithium battery or a lithium iron phosphate battery. The current charging period can be the time from when the battery starts charging from a certain state of charge (SOC) until it reaches a fully charged state. A fully charged state refers to the battery's SOC being close to 100% or at least at a high level (e.g., 95%, 98%). The charging data includes the battery's charge quantity (Qc) and charging voltage (Vc) collected at multiple sampling points during this charging process. The charge quantity can be obtained by measuring the current through a current sensor in the battery management system (BMS) and then integrating the results; while the charging voltage can be obtained by directly measuring the battery voltage through a voltage sensor.

[0036] Multiple sampling points refer to collecting battery charge and voltage values ​​at a certain sampling frequency (such as every second, every 10 seconds, or other time intervals set as needed) during the charging process. This allows for the capture of minute changes in the battery's state during charging, thus enabling the construction of a more accurate differential capacity model. For example, assuming a sampling frequency of once per second, a series of charge and voltage values ​​are recorded from the start to the end of charging. These data points constitute a "time series" of the charging process, which can be used to subsequently construct a differential capacity model of the target battery.

[0037] In an optional embodiment, before acquiring the charging data of the target battery during the current charging period, the method further includes: detecting whether the target battery is in a preset charging state, wherein the preset charging state is at least used to indicate that the state of charge of the target battery is less than a preset first energy threshold; detecting whether the charging current of the target battery is within a preset current range; and when the target battery is in a preset charging state and the charging current is within the preset current range, determining the time period between the time corresponding to the preset charging state and the time when the target battery is fully charged as the current charging period, wherein the time when the target battery is fully charged is used to indicate the time when the target battery is fully charged.

[0038] Optionally, before starting any SOH assessment, it is required to first detect whether the target battery is in a preset state of charge. This state can be that the battery's State of Charge (SOC) is less than a preset first charge threshold. For example, the preset first charge threshold can be set to 5% or 10% to ensure that the battery is in a near-discharged state at the start of the assessment, eliminating the influence of residual battery charge on the subsequent charging process, ensuring a consistent starting point for the assessment, and thus improving the accuracy and comparability of the SOH assessment. The selection of the preset current range can take into account the type and capacity of the battery, as well as the battery's safety and performance during charging. For example, the current range can be set between 70% and 90% of the battery's maximum allowable charging current. The purpose of monitoring the charging current is to ensure the stability and controllability of the charging process, avoid irreversible damage to the battery caused by excessive charging current, and also ensure that the battery can fully exhibit its electrochemical characteristics during charging, thereby obtaining higher quality charging data. When the target battery is in the preset state of charge and the charging current is within the preset current range, the time period from the corresponding moment of the preset state of charge to the moment when the target battery is fully charged is determined as the current charging period. This means that charging data will only begin to be collected and processed when the battery meets specific initial state of charge and charging current conditions. A fully charged state is the point at which the battery is fully charged, at which point the battery's SOC is close to 100%.

[0039] In the above methods, by limiting the state of charge (SOH) and charging current range at the start of charging, cleaner and higher-quality charging data can be obtained under consistent operating conditions. Preset charging state and current ranges provide a stable evaluation environment, avoiding fluctuations in evaluation results caused by changes in charging conditions and ensuring the stability of SOH evaluations. Monitoring and limiting the charging current range also helps prevent adverse effects such as overcharging and over-discharging during the charging process, thereby protecting battery safety and extending battery life.

[0040] Step S104: Construct a differential capacity model of the target battery based on the charging data, wherein the differential capacity model is used to characterize the relationship between the charging amount and the charging voltage of the target battery during the current charging period.

[0041] Optionally, the differential capacity model can be in the form of a differential capacity dQ / dV curve. This model has different slopes at different stages of charging, and the absolute value of the slope represents dQ / dV (differential capacity), which can reflect the change in battery charging efficiency and is an important indicator for assessing battery health.

[0042] Step S106: Based on the differential capacity model, determine the target parameter values ​​corresponding to each of the multiple feature parameters, wherein the multiple feature parameters include at least the peak value, peak area and peak-to-trough distance in the differential capacity model.

[0043] Optionally, peak value refers to the local maximum value on the dQ / dV curve; peak area refers to the area covered below a peak on the dQ / dV curve, which can reflect the battery's charging capability within a specific voltage range; peak-valley distance refers to the voltage difference between a peak and its nearest valley to the left on the dQ / dV curve.

[0044] Step S108: Determine the health status assessment result of the target battery based on the target parameter values ​​corresponding to each of the multiple feature parameters.

[0045] Optionally, the complex battery aging phenomenon can be transformed into quantifiable characteristic parameters (peak value, peak area, and peak-to-trough distance), and the battery's health status can be assessed in real time through the measurement and calculation of these characteristic parameters. This approach not only provides immediate feedback on battery health status and considers more comprehensive factors, but also reduces reliance on a single assessment standard to some extent, improving the accuracy and reliability of battery health status assessment results.

[0046] In one optional embodiment, determining the health status assessment result of the target battery based on the target parameter values ​​corresponding to each of the multiple feature parameters includes: determining a health status relationship table corresponding to each of the multiple feature parameters, wherein the health status relationship table is used to indicate the correspondence between the parameter values ​​of the corresponding feature parameters and the battery health status; querying the corresponding health status relationship table based on the target parameter values ​​corresponding to each of the multiple feature parameters to obtain multiple estimated battery health states corresponding to the target battery, wherein the multiple estimated battery health states correspond one-to-one with the multiple feature parameters; and determining the health status assessment result of the target battery during the current charging period based on the multiple estimated battery health states.

[0047] Optionally, a health status relation table is a pre-prepared data lookup table built upon extensive experimental data, recording the relationship between the parameter values ​​of different characteristic parameters and the battery's state of health (SOH). For example, three different health status relation tables are set up to record the relationship between peak value and SOH, peak area and SOH, and peak-to-trough distance and SOH, respectively. These tables are established by testing batteries at different health states (from 100% to 70%), recording their differential capacity model characteristic parameters during charging, and then pairing them with the corresponding SOH values. Based on the target values ​​of characteristic parameters extracted from the current charging period data (e.g., peak value, peak area, peak-to-trough distance), the corresponding health status relation table for each characteristic parameter is queried to obtain multiple estimated battery health states (SOH1, SOH2, SOH3) for the target battery. The estimated SOH value for each characteristic parameter is independent, reflecting the individual impact of that characteristic parameter on the battery health status. The estimated SOH values ​​of all characteristic parameters are combined to determine the health status assessment result of the target battery during the current charging period.

[0048] In the above methods, the establishment of a health status relationship table corresponding to different characteristic parameters can quickly obtain the estimated health status assessment result (i.e., estimated) SOH value corresponding to each characteristic parameter by looking up the table, transforming complex battery aging characteristics into specific SOH assessment values; by integrating the assessment results of multiple characteristic parameters, the battery health status can be reflected more comprehensively and meticulously, reducing the dependence on a single characteristic parameter and improving the stability and accuracy of the battery health status assessment results.

[0049] Optionally, under actual charging conditions, the data rationality is verified, and the estimated SOH values ​​corresponding to the three characteristic parameters are calculated. Specifically:

[0050] In actual user charging conditions, charging data of the target battery during the current charging period is obtained, and a differential capacity model of the target battery is constructed based on this data. The target parameter values ​​corresponding to each characteristic parameter are calculated based on this model, and the usability and rationality of the target parameter values ​​corresponding to each characteristic parameter are checked. Taking peak values ​​as an example, after the actual calculated peak values ​​of the target battery during the current charging period include the first peak value P1′ and the second peak value P2′, if the estimated sub-SOH values ​​corresponding to the characteristic parameter peak values ​​obtained from the table, including SOH(P1′) and SOH(P2′), differ by more than 3% (during experimental data testing, multiple sample test results are compared to analyze the changing trend of a single variable in the same influencing parameter after aging, i.e., the differences between different samples of the peak values ​​P1 and P2 after aging, the differences between different samples of the peak area S1 and S2 after aging, and the differences between different samples of the peak-valley distance L1 and L2 after aging, thereby establishing a deviation confidence interval that allows for correction), then the correction is abandoned by default.

[0051] In one optional embodiment, determining a health state relationship table corresponding to each of the multiple feature parameters includes: collecting charging data of the sample battery corresponding to multiple different battery health states during the process of charging the sample battery from an empty state to a fully charged state according to a predetermined current under a predetermined temperature environment; constructing a differential capacity model of the sample battery corresponding to each of the multiple different battery health states based on the charging data corresponding to each of the multiple different battery health states; determining a set of sample parameters corresponding to each of the multiple different battery health states based on the differential capacity model corresponding to each of the multiple different battery health states, wherein the set of sample parameters includes the sample parameter values ​​corresponding to each of the multiple feature parameters under the corresponding battery health states; and obtaining a health state relationship table corresponding to each of the multiple feature parameters based on the set of sample parameters corresponding to each of the multiple different battery health states.

[0052] Optionally, an experiment is conducted in a predetermined temperature environment to charge the sample battery from an empty state to a fully charged state using a predetermined current. By setting the predetermined temperature environment and predetermined current, the charging status of the battery under normal use conditions is simulated, thereby ensuring the representativeness of the experimental data. The selection of sample batteries covers different health states, allowing for the collection of sufficient data across the entire health degradation range. Next, based on the collected charging data, a differential capacity model of the sample battery under different health states is constructed. This model can intuitively display the curve of battery charge as a function of voltage, and its shape will vary significantly between batteries in different health states. Through analysis of these models, the set of sample parameters for the sample battery under different health states can be determined, including characteristic parameter values ​​such as peak value, peak area, and peak-to-trough distance. Finally, based on the sample parameter set obtained from the above analysis, a health state relationship table corresponding to each of the multiple characteristic parameters is generated. These relationship tables can serve as a "benchmark" for evaluating the future health state of the battery, with each data point representing the specific performance of a certain characteristic parameter under a specific health state.

[0053] In the above methods, conducting experiments under controlled environmental conditions ensures data quality, leading to more accurate relationship tables and thus improving the precision of battery health status assessments based on these tables. The experimental design covers batteries in different states, from healthy to aged, meaning the assessment method is applicable not only to brand-new batteries but also to batteries at varying degrees of aging, enhancing its practicality and broad applicability. The battery health status relationship tables established through these methods are based on extensive experimental data, making them more accurate and reliable, and providing strong data support for battery management and maintenance strategies.

[0054] Optionally, different SOH states are tested. The battery is discharged and then fully charged with a constant current, with the charging voltage Vc and charge amount Qc of each individual cell recorded in real time. Specifically: The charging data required to construct the health state relationship table is obtained. The sample battery is discharged at room temperature and allowed to stand, then charged with a constant current until fully charged. During this process, the charging voltage Vc and charge amount Qc of each individual cell are recorded in real time. The test is repeated at 1% SOH intervals, and the charging data under different SOH states is recorded. In this embodiment, the constant current used for testing can be determined based on the actual cell capacity. The test current can refer to the maximum available power (MAP) current for fast and slow charging of the cell. Generally, excessively high current will result in insufficient peak characteristics and high cell temperature rise, making temperature variables uncontrollable. Insufficiently low current is difficult to meet practical application requirements. Therefore, the current size needs to ensure a clear peak characteristic while minimizing cell temperature rise. Simultaneously, the experimental temperature is set close to the normal charging temperature. Temperatures that are too low or too high will trigger thermal management in actual vehicles, and significant temperature variations will have a greater impact on the results.

[0055] Plot the charging dQ / dV curves under different SOH states, and obtain the correspondence between the peak value, peak area, and peak-valley distance of the characteristic parameters and SOH. Based on this correspondence, establish a health state relationship table for each characteristic parameter. Specifically:

[0056] After recording the charging dQ / dV curves, differential capacity models (i.e., dQ / dV curves) for different battery health states (SOH) are plotted according to equal charging voltage dV intervals (ideally, the smaller the dV interval, the better; considering actual acquisition accuracy, the dV interval can be selected as 3-5mV). Figure 2 This is a schematic diagram illustrating the extraction of characteristic parameters from an optional dQ / dV curve according to an embodiment of the present invention. Characteristic parameter information (i.e., peak value, peak area, and peak-to-trough distance) is extracted from the curve. Based on the characteristic parameter information, three SOH values ​​(SOH1, SOH2, and SOH3) are calculated. Based on the characteristic parameter information and the corresponding three SOH values, health status relationship tables are constructed as shown in Tables 1 to 3 below. (It should be noted that there are differences in characteristics between ternary lithium batteries and lithium iron phosphate batteries. Ternary lithium batteries do not have a significant plateau period, but the correction method expressed in this embodiment is also applicable to ternary lithium batteries. This embodiment currently uses lithium iron phosphate battery parameters as an example.)

[0057] Table 1. Correspondence between peak value and first SOH value (SOH1)

[0058] 100％ P1_100 P2_100 99％ P1_99 P2_99 98％ P1_98 P2_98 97％ P1_97 P2_97 … … … 70％ P1_70 P2_70

[0059] Table 2. Correspondence between peak area 2 and second SOH value (SOH2)

[0060] 100％ S1_100 S2_100 99％ S1_99 S2_99 98％ S1_98 S2_98 97％ S1_97 S2_97 … … … 70％ S1_70 S2_70

[0061] Table 3. Correspondence between peak-trough distance and third SOH value (SOH3)

[0062] 100％ L1_100 L2_100 99％ L1_99 L2_99 98％ L1_98 L2_98 97％ L1_97 L2_97 … … … 70％ L1_70 L2_70

[0063] Each of the three estimated SOH values ​​(i.e., the first estimated battery health state SOH1, the second estimated battery health state SOH2, and the third estimated battery health state SOH3) has two corresponding sub-feature information. To obtain the corresponding SOH values, the sub-feature information is further weighted, specifically as follows:

[0064] SOH1 = a1*SOH(P1) + b1*SOH(P2); where the sub-feature information of SOH1 includes the corresponding first sub-SOH value SOH(P1) and second sub-SOH value SOH(P2), and the corresponding weighting coefficients are a1 and b1, respectively. a1 and b1 can be set by the user or obtained by test fitting, and a1+b1=1.

[0065] SOH2 = a2*SOH(S1) + b2*SOH(S2); where the sub-feature information of SOH2 includes the corresponding first sub-SOH value SOH(S1) and second sub-SOH value SOH(S2), and the corresponding weighting coefficients are a2 and b2, respectively. a2 and b2 can be set by the user or obtained by test fitting, and a2+b2=1.

[0066] SOH3 = a3*SOH(L1) + b3*SOH(L2), where the sub-feature information of SOH3 includes the corresponding first sub-SOH value SOH(L1) and second sub-SOH value SOH(L2), and the corresponding weighting coefficients are a3 and b3, respectively. a3 and b3 can be set by the user or obtained by test fitting, and a3 + b3 = 1.

[0067] Based on the above formula, after obtaining the three SOH values ​​(SOH1, SOH2, and SOH3), the weighting coefficients are calculated for fitting. The weighting coefficients (i.e., target weights) λ1, λ2, and λ3 corresponding to SOH1, SOH2, and SOH3 are calculated based on the experimental data, resulting in λ1 + λ2 + λ3 = 1. Interpolation or function fitting is used between adjacent data points in the table. Optionally, linear interpolation, polynomial interpolation, spline interpolation, etc., can be selected.

[0068] In one optional embodiment, the health status assessment result of the target battery during the current charging period is determined based on multiple estimated battery health states, including: determining the target weights corresponding to each of the multiple feature parameters; and performing a weighted calculation based on the multiple estimated battery health states and the target weights corresponding to each of the multiple feature parameters to obtain the health status assessment result of the target battery.

[0069] Optionally, different characteristic parameters may contribute differently to State of Health (SOH) when assessing battery health. Target weights refer to the relative importance coefficients assigned to each characteristic parameter in the final assessment calculation. These target weights reflect the sensitivity and importance of each characteristic parameter to battery aging and can be determined through experimental data analysis or theoretical models. For example, in lithium iron phosphate batteries, changes in peak value may be more sensitive to SOH assessment; therefore, the target weight corresponding to the peak value may be higher in the weighted calculation. In ternary lithium batteries, changes in peak-to-trough distance may be more critical, and the target weight corresponding to the peak-to-trough distance may be set larger. The selection of target weights can be based on the fitting results of experimental data to ensure that the weight allocation reflects the true state of battery aging to the greatest extent. After determining the target weights, each estimated battery health state (SOH1, SOH2, SOH3) is multiplied by the target weight of its corresponding characteristic parameter, and then summed to obtain the final battery health state assessment result.

[0070] In the above methods, by assigning appropriate weights to different characteristic parameters, the evaluation results can more accurately reflect the current health status of the battery. Since different types of batteries, or at different stages of aging, have certain differences in the characteristic parameters that best reflect the battery's health status, reasonable target weights can highlight these key characteristics and improve the relevance of the evaluation. Furthermore, a single characteristic parameter may be affected by measurement errors, environmental factors, or individual battery differences, while weighted calculations, by integrating the evaluation results of multiple characteristic parameters, can offset these influences to some extent, reducing the error in the evaluation results.

[0071] Optionally, SOH correction can be performed after parameter verification. Specifically, during use, the user determines whether the charging start conditions should meet the preset conditions to be as close to full discharge as possible if the preset charging state is in progress. The current magnitude should also be checked to ensure it conforms to the preset values; the actual current is allowed to fluctuate within a certain range of the preset current. The charging or discharging state must be maintained for a certain period before switching to the corresponding state to begin dQ / dV curve calculation, which is performed continuously in real time. After full charging, the three estimated battery health states (SOH1, SOH2, and SOH3) corresponding to the target battery are fully extracted. A weighted calculation is then performed based on these three estimated battery health states to obtain the final corrected SOH (i.e., the target battery health state assessment result).

[0072] Corrected SOH=λ1*SOH1+λ2*SOH2+λ3*SOH3

[0073] Wherein, λ1+λ2+λ3=1, and its value is obtained by fitting actual test data. Of course, if the proportion of the three components changes with aging, as obtained from experimental data, the three parameters can also be dynamically changed based on experimental data.

[0074] Optionally, the estimated SOH (i.e., the health status assessment result of the target battery) can be subject to appropriate correction limits to avoid errors introduced by a single correction result. At the same time, the estimated SOH can be used as the internal true SOH, and the SOH displayed on the client can approach the true SOH at a specific rate.

[0075] As an optional embodiment, when there are multiple target parameter values ​​corresponding to each of the multiple feature parameters, the estimated health status corresponding to any one of the multiple feature parameters is obtained in the following way: Based on the multiple target parameter values ​​corresponding to any one feature parameter, query the corresponding health status relationship table to obtain the estimated sub-SOH values ​​corresponding to each of the multiple target parameter values; determine the weight coefficients corresponding to each of the multiple target parameter values; based on the estimated sub-SOH values ​​corresponding to each of the multiple target parameter values ​​and the weight coefficients corresponding to each of the multiple target parameter values, obtain the estimated health status corresponding to any one feature parameter; using the method of obtaining the estimated health status corresponding to any one feature parameter, multiple estimated battery health states are obtained.

[0076] Optionally, for any characteristic parameter (e.g., peak value, peak area, or peak-to-trough distance), if it generates multiple target parameter values ​​during the current charging period, the corresponding health status relation table (e.g., Tables 1, 2, and 3 in weight 3) is first queried based on each target parameter value. This query allows for finding a corresponding estimated sub-SOH value for each target parameter value, representing the battery's expected health status under that specific parameter value. The weighting coefficients can be determined based on various criteria, such as the representativeness of the target parameter value, its order of appearance during charging, or fitting results from previous experimental data. The weighting coefficients quantify the contribution of each estimated sub-SOH value to the overall SOH assessment, ensuring the assessment results are both comprehensive and accurate. After determining the weighting coefficients, a weighted calculation is further used to obtain an estimated health status that integrates information from multiple target parameter values.

[0077] In the above methods, when the characteristic parameters generate multiple target parameter values, weighted calculations ensure that the evaluation results fully reflect the combined impact of these values, avoiding the bias that might result from selecting only one value. The introduction of weighting coefficients allows for a reasonable allocation of the contribution of each predicted SOH value, reducing the impact of outliers or non-representative values ​​on the evaluation results and improving their reliability. Especially when the battery is under complex operating conditions such as frequent charging or deep discharging, the characteristic parameters may generate multiple values. The above methods ensure that even under complex conditions, the SOH evaluation maintains high accuracy and stability.

[0078] In one optional embodiment, determining the target weights corresponding to each of the multiple feature parameters includes: collecting charging data of the sample battery at multiple different battery health states during the process of charging the sample battery from an empty state to a fully charged state according to a predetermined current under a predetermined temperature environment; constructing a differential capacity model of the sample battery at multiple different battery health states based on the charging data at multiple different battery health states; determining a set of sample parameters of the sample battery at multiple different battery health states based on the differential capacity model at multiple different battery health states, wherein the set of sample parameters includes the sample parameter values ​​corresponding to each of the multiple feature parameters at the corresponding battery health states; determining the Pearson correlation coefficients between the multiple feature parameters and the battery health states based on the multiple different battery health states and the set of sample parameters corresponding to the multiple different battery health states; and determining the target weights corresponding to each of the multiple feature parameters based on the Pearson correlation coefficients between the multiple feature parameters and the battery health states.

[0079] Optionally, the experiment involves charging the sample battery from an empty state to a fully charged state using a predetermined current at a predetermined temperature. This step ensures the consistency and repeatability of the experimental conditions. Using the collected charging data, differential capacity models (dQ / dV curves) of the sample battery under different health states are constructed. These models clearly demonstrate the relationship between the rate of capacity change and voltage during charging, contributing to a deeper understanding of the battery's electrochemical behavior. Subsequently, sample parameter sets are extracted from these differential capacity models, including sample values ​​of characteristic parameters such as peak value, peak area, and peak-to-trough distance. These values ​​reflect the characteristic performance of the battery under different health states. Next, based on the collected sample parameter sets and battery health states, the Pearson correlation coefficient between each characteristic parameter and the state of health (SOH) is calculated. The Pearson correlation coefficient is a statistic used to measure the linear correlation between two variables, revealing the strength of the association between each characteristic parameter and the battery health state; the larger the absolute value of the coefficient, the stronger the correlation. Based on the results of the Pearson correlation coefficient, the target weight for each characteristic parameter is determined. The logic for determining the weights is that the stronger the correlation of a feature parameter, the greater its weight in the final SOH assessment result, and vice versa. This means that feature parameters that can more accurately reflect changes in battery health status will play a greater role in the weighted calculation, thereby improving the overall accuracy of the SOH assessment.

[0080] Among the methods described above, the Pearson correlation coefficient is a statistical tool used to quantitatively analyze the correlation between characteristic parameters and State of Health (SOH). This method provides an objective basis for weight determination and reduces the interference of human factors. Determining the target weights of characteristic parameters through the Pearson correlation coefficient not only enhances the scientific rigor and objectivity of battery health status assessment but also improves the accuracy and reliability of the assessment results.

[0081] In one optional embodiment, the health status assessment result of the target battery during the current charging period is determined based on multiple estimated battery health states, including: acquiring environmental parameters of the target battery during the current charging period; determining weight correction coefficients corresponding to multiple feature parameters based on the environmental parameters; correcting the target weights corresponding to multiple feature parameters based on the weight correction coefficients corresponding to multiple feature parameters to obtain corrected weight coefficients corresponding to multiple feature parameters; and determining the health status assessment result of the target battery based on the corrected weight coefficients corresponding to multiple feature parameters and multiple estimated battery health states.

[0082] Optionally, environmental parameters include, but are not limited to, one or more of the following: temperature, humidity, and the battery's operating temperature range during charging. These parameters can affect the battery's core performance indicators such as electrochemical reaction rate, internal resistance, and energy density. Collecting environmental parameters allows for a more comprehensive understanding of the battery's operating conditions during the current charging period, leading to a more accurate assessment of the battery's health status. A weighting correction coefficient is calculated based on the collected environmental parameters to adjust the original target weights of each characteristic parameter (peak value, peak area, peak-to-trough distance). The weighting correction coefficient reflects the degree of influence of environmental conditions on the battery's characteristic parameters; that is, under different environmental conditions, the influence of certain characteristic parameters on SOH assessment may be enhanced or weakened. For example, under extreme low-temperature conditions, the change in peak area may be more significant than at room temperature. In this case, the weighting correction coefficient for peak area will be increased to reflect the change in the characteristic's importance under these environmental conditions. The process of adjusting the original target weights of the characteristic parameters based on the weighting correction coefficient results in weights that are called corrected weighting coefficients. Specifically, the original target weight of each feature parameter is multiplied by its corresponding weight correction coefficient to obtain the corrected weight; based on the corrected weight and the estimated SOH value of each feature parameter, the SOH evaluation result of the target battery is recalculated.

[0083] The above methods dynamically adjust the weights of characteristic parameters based on the actual environmental conditions during battery charging, ensuring high accuracy and reliability of the assessment method under various environments. For example, battery performance is affected by cold or hot weather; adjusting the weights captures these effects, making the assessment results more realistic. By adjusting the weights for environmental parameters, environmental factors in battery health status assessment can be considered. This makes SOH assessment no longer limited to the characteristic parameters of the battery itself, but forms a multi-dimensional and refined assessment system. It also effectively reduces the impact of changes in environmental conditions on SOH assessment results, avoiding assessment errors caused by neglecting environmental factors.

[0084] Through the above steps S102 to S108, the differential capacity model of the target battery during the current charging period can be analyzed, and multiple characteristic parameters, including peak value, peak area and peak-valley distance, can be extracted to accurately determine the battery health status. This achieves the technical effect of improving the accuracy of battery health status determination, and solves the technical problem of low accuracy of battery health status assessment caused by incomplete consideration of factors in battery health status assessment in related technologies.

[0085] Based on the above embodiments and optional embodiments, the present invention proposes an optional implementation method. Figure 3 This is a flowchart of an optional battery health status assessment method according to an embodiment of the present invention, such as... Figure 3As shown, the method includes:

[0086] S01: Characteristic parameter acquisition, testing different SOH states, charging the battery with a constant current after it has been discharged, and recording the individual cell charging voltage Vc and charging amount Qc in real time. Specifically:

[0087] To obtain the charging data needed to construct the health status relationship table, the sample battery was discharged and allowed to stand at room temperature before being charged with a constant current until fully charged. During the process, the charging voltage Vc and charging amount Qc of each individual battery were recorded in real time. The test was repeated at 1% SOH intervals, and charging data was recorded at different SOH levels. In this embodiment, the constant current used for testing can be determined based on the actual cell capacity. The test current can refer to the maximum available power (MAP) current for fast and slow charging of the cell. Generally, excessively high current will result in insufficient peak characteristics and high cell temperature rise, making temperature variables uncontrollable. Insufficiently high current is difficult to meet practical application requirements. Therefore, the current size needs to ensure a clear peak characteristic while minimizing cell temperature rise. Simultaneously, the experimental temperature is set close to the normal charging temperature. Temperatures that are too low or too high will trigger thermal management in actual vehicles, and significant temperature variations will have a substantial impact on the results.

[0088] S02: Extraction of dQ / dV curve features. Charging dQ / dV curves are plotted under different SOH states. The correspondence between the peak value, peak area, and peak-to-trough distance of the feature parameters and SOH is obtained. Based on this correspondence, a health status relationship table corresponding to each feature parameter is established. Specifically:

[0089] After recording the charging dQ / dV curves, differential capacity models for different battery health states (SOH) are plotted according to equal charging voltage dV intervals (ideally, the smaller the dV interval, the better; considering actual acquisition accuracy, the dV interval can be selected as 3-5mV). Figure 2 The dQ / dV curve shown is used to extract characteristic parameter information (i.e., peak value, peak area, and peak-to-trough distance). Based on the characteristic parameter information, three SOH values ​​(SOH1, SOH2, and SOH3) are calculated, and based on the characteristic parameter information and the corresponding three SOH values, a health status relationship table as shown in Tables 1 to 3 above is constructed. (It should be noted that there are differences in characteristics between ternary lithium batteries and lithium iron phosphate batteries. Ternary lithium batteries do not have a significant plateau period, but the correction method expressed in this embodiment is also applicable to ternary lithium batteries. Currently, lithium iron phosphate battery parameters are used as an example.)

[0090] Each of the three estimated SOH values ​​(i.e., the first estimated battery health state SOH1, the second estimated battery health state SOH2, and the third estimated battery health state SOH3) has two corresponding sub-feature information. To obtain the corresponding SOH values, the sub-feature information is further weighted, specifically as follows:

[0091] SOH1 = a1*SOH(P1) + b1*SOH(P2); where the sub-feature information of SOH1 includes the corresponding first sub-SOH value SOH(P1) and second sub-SOH value SOH(P2), and the corresponding weighting coefficients are a1 and b1, respectively. a1 and b1 can be set by the user or obtained by test fitting, and a1+b1=1.

[0092] SOH2 = a2*SOH(S1) + b2*SOH(S2); where the sub-feature information of SOH2 includes the corresponding first sub-SOH value SOH(S1) and second sub-SOH value SOH(S2), and the corresponding weighting coefficients are a2 and b2, respectively. a2 and b2 can be set by the user or obtained by test fitting, and a2+b2=1.

[0093] SOH3 = a3*SOH(L1) + b3*SOH(L2), where the sub-feature information of SOH3 includes the corresponding first sub-SOH value SOH(L1) and second sub-SOH value SOH(L2), and the corresponding weighting coefficients are a3 and b3, respectively. a3 and b3 can be set by the user or obtained by test fitting, and a3 + b3 = 1.

[0094] Based on the above formula, after obtaining the three SOH values ​​(SOH1, SOH2, and SOH3), the weighting coefficients are calculated for fitting. The weighting coefficients (i.e., target weights) λ1, λ2, and λ3 corresponding to SOH1, SOH2, and SOH3 are calculated based on the experimental data, resulting in λ1 + λ2 + λ3 = 1. Interpolation or function fitting is used between adjacent data points in the table. Optionally, linear interpolation, polynomial interpolation, spline interpolation, etc., can be selected.

[0095] S03: Write the health status relationship table corresponding to the three feature parameters into the software, and write the corresponding weighting coefficients at the same time;

[0096] S04: Screening of Actual Vehicle Charging Condition Data. Under actual charging conditions, the reasonableness of the data is verified, and the estimated SOH values ​​corresponding to the three characteristic parameters are calculated. Specifically:

[0097] The system acquires charging data of the target battery during the current charging period under user operating conditions, and constructs a differential capacity model of the target battery based on this data. Based on this model, it calculates the target parameter values ​​corresponding to each characteristic parameter, and checks the usability and rationality of the target parameter values ​​corresponding to each characteristic parameter. Taking peak values ​​as an example, after calculating the peak values ​​of the target battery during the current charging period, including the first peak value P1′ and the second peak value P2′, if the estimated sub-SOH values ​​corresponding to the characteristic parameter peak values ​​obtained from the table, including SOH(P1′) and SOH(P2′), differ by more than 3% (during experimental data testing, multiple sample test results are compared to analyze the changing trend of a single variable in the same influencing parameter after aging, i.e., the differences between different samples of the peak values ​​P1 and P2 after aging, the differences between different samples of the peak area S1 and S2 after aging, and the differences between different samples of the peak-valley distance and L2 after aging, thereby establishing a deviation confidence interval that allows for correction), then the correction is abandoned by default.

[0098] S05: Parameter verification passed, proceed with final SOH correction. Specifically, during use, the user determines whether the preset charging state is being used. The charging start conditions must meet the preset conditions to achieve or approach full discharge. The current magnitude must conform to the preset values; the actual current is allowed to fluctuate within a certain range of the preset current. The charging or discharging state must be maintained for a certain period before switching to the corresponding state to begin dQ / dV curve calculation, which is performed continuously in real time. After full charging, the three estimated battery health states (SOH1, SOH2, and SOH3) corresponding to the target battery are fully extracted. A weighted calculation is performed based on these three estimated battery health states to obtain the final corrected SOH (i.e., the target battery's health state assessment result). Of course, if the influence ratio of the three components changes with aging, based on experimental data, the three parameters can also be dynamically adjusted according to the experimental data.

[0099] It should be noted that the charging process of electric vehicles is a relatively stable operating condition for the battery. This embodiment is based on the charging scenario. Under certain operating conditions, the charging amount and battery voltage signal are recorded in real time during the charging process. The recorded data are plotted according to equal dV intervals to draw a differential capacity model (i.e., dQ / dV curve). The peak and trough data are obtained using the dQ / dV curve. The first estimated SOH value SOH1 is obtained based on the peak value and SOH relationship. This embodiment identifies multiple feature points and feature data of the curve after obtaining the dQ / dV-SOC curve. The peak value can better reflect the aging condition. At the same time, based on the curve change trend after aging, the peak area corresponding to the highest peak point is selected. The peak area is compared with the preset table to obtain the second estimated SOH value SOH2. In addition to identifying the peak value and peak area, the distance between the peak position and the trough position is also identified. Taking lithium iron phosphate as an example, there are two peaks due to the existence of the plateau period. Considering the distance changes between the two peaks and the corresponding troughs to their left, the distance values ​​of the two segments are compared with the preset table data to obtain the third estimated SOH value SOH3. This embodiment uses the idea of ​​feature data fusion to fuse the three estimated SOH values ​​and use weighted average to obtain the final SOH correction value as the battery health status assessment result. The value is fitted based on actual test data, so that the final battery health status assessment result can refer to more feature signals and prevent the error problem caused by a single feature quantity.

[0100] This embodiment also provides a battery health status assessment device, which is used to implement the above embodiments and preferred embodiments, and will not be repeated as already described. As used below, the terms "module" and "device" can refer to a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0101] According to an embodiment of the present invention, an apparatus embodiment for implementing the above-described battery health status assessment method is also provided. Figure 4 This is a schematic diagram of a battery health status assessment device according to an embodiment of the present invention, as shown below. Figure 4 As shown, the battery health status assessment device includes: a charging data acquisition module 400, a model construction module 402, a parameter value determination module 404, and a battery status assessment module 406, wherein:

[0102] The charging data acquisition module 400 is used to acquire the charging data of the target battery during the current charging period. The charging data includes the charging amount and charging voltage of the target battery collected at multiple sampling times during the current charging period.

[0103] The model building module 402 is connected to the charging data acquisition module 400 and is used to build a differential capacity model of the target battery based on the charging data. The differential capacity model is used to characterize the relationship between the charging amount and the charging voltage of the target battery during the current charging period.

[0104] The parameter value determination module 404 is connected to the model construction module 402 and is used to determine the target parameter values ​​corresponding to multiple feature parameters based on the differential capacity model. The multiple feature parameters include at least the peak value, peak area and peak-to-trough distance in the differential capacity model.

[0105] The battery status assessment module 406 is connected to the parameter value determination module 404 and is used to determine the health status assessment result of the target battery based on the target parameter values ​​corresponding to each of the multiple characteristic parameters.

[0106] In this embodiment of the invention, by setting up the charging data acquisition module 400, model construction module 402, parameter value determination module 404, and battery status assessment module 406, the purpose of analyzing the differential capacity model of the target battery during the current charging period and extracting multiple feature parameters, including peak value, peak area, and peak-valley distance, is achieved to accurately determine the battery health status. This improves the accuracy of battery health status determination and solves the technical problem of low accuracy in battery health status assessment caused by incomplete consideration of factors in related technologies.

[0107] It should be noted that the above modules can be implemented by software or hardware. For example, for the latter, it can be implemented in the following ways: the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0108] It should be noted that the charging data acquisition module 400, model construction module 402, parameter value determination module 404, and battery state evaluation module 406 mentioned above correspond to steps S102 to S108 in the embodiments. The instances and application scenarios implemented by the above modules and their corresponding steps are the same, but are not limited to the content disclosed in the above embodiments. It should be noted that the above modules, as part of the device, can run on a computer terminal.

[0109] It should be noted that the optional or preferred implementation methods of this embodiment can be found in the relevant descriptions in the embodiments, and will not be repeated here.

[0110] The aforementioned battery health status assessment device may also include a processor and a memory. The aforementioned charging data acquisition module 400, model construction module 402, parameter value determination module 404, battery status assessment module 406, etc., are all stored in the memory as program modules, and the processor executes the aforementioned program modules stored in the memory to realize the corresponding functions.

[0111] The processor contains a core that retrieves the corresponding program modules from memory. One or more cores may be configured. Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory includes at least one memory chip.

[0112] According to an embodiment of this application, an embodiment of a non-volatile storage medium is also provided. Optionally, in this embodiment, the non-volatile storage medium includes a stored program, wherein, when the program runs, it controls the device containing the non-volatile storage medium to execute any of the battery health status assessment methods described above.

[0113] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals, and the non-volatile storage medium includes stored programs.

[0114] Optionally, during program execution, the device containing the non-volatile storage medium may be controlled to perform any of the above-mentioned battery health status assessment methods.

[0115] According to an embodiment of this application, an embodiment of a processor is also provided. Optionally, in this embodiment, the processor is used to run a program, wherein the program executes any of the battery health status assessment methods described above.

[0116] According to an embodiment of this application, an embodiment of a computer program product is also provided, which, when executed on a data processing device, is adapted to execute a program that initializes the battery health status assessment method steps described above.

[0117] Optionally, when the above-mentioned computer program product is executed on a data processing device, it is suitable for executing a program that initializes the steps of any of the above-mentioned battery health status assessment methods.

[0118] This invention provides an electronic device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of any of the above-described battery health status assessment methods.

[0119] The order of the above embodiments of the present invention is merely for description and does not represent the superiority or inferiority of the embodiments.

[0120] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0121] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of modules described above can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules 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, or indirect coupling or communication connection between modules, and may be electrical or other forms.

[0122] The modules described above as separate components may or may not be physically separate. Similarly, the components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple modules. Some or all of the modules can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0123] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0124] If the aforementioned integrated modules are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable non-volatile storage medium. Based on this understanding, the technical solution of this invention, 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 non-volatile storage medium and includes several 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 invention. The aforementioned non-volatile storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0125] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for assessing battery health status, characterized in that, include: Acquire charging data of the target battery during the current charging period, wherein the charging data includes the charging amount and charging voltage of the target battery collected at multiple sampling times included in the current charging period; Based on the charging data, a differential capacity model of the target battery is constructed, wherein the differential capacity model is used to characterize the relationship between the charging amount and the charging voltage of the target battery during the current charging period, and the differential capacity model is in the form of a differential capacity dQ / dV curve; Based on the differential capacity model, target parameter values ​​corresponding to multiple feature parameters are determined, wherein the multiple feature parameters include at least the peak value, peak area, and peak-to-trough distance in the differential capacity model; The health status assessment result of the target battery is determined based on the target parameter values ​​corresponding to each of the plurality of feature parameters, including: determining a health status relationship table corresponding to each of the plurality of feature parameters, wherein the health status relationship table is used to indicate the correspondence between the parameter values ​​of the corresponding feature parameters and the battery health status; querying the corresponding health status relationship table based on the target parameter values ​​corresponding to each of the plurality of feature parameters to obtain a plurality of estimated battery health states corresponding to the target battery, wherein the plurality of estimated battery health states correspond one-to-one with the plurality of feature parameters; determining the target weights corresponding to each of the plurality of feature parameters; and performing a weighted calculation based on the plurality of estimated battery health states and the target weights corresponding to each of the plurality of feature parameters to obtain the health status assessment result of the target battery.

2. The method according to claim 1, characterized in that, The step of determining the health status relationship table corresponding to each of the plurality of feature parameters includes: Under a predetermined temperature environment, during the process of charging the sample battery from an empty state to a fully charged state according to a predetermined current from an empty state, charging data of the sample battery at multiple different battery health states are collected. Based on the charging data corresponding to the multiple different battery health states, construct the differential capacity model of the sample battery corresponding to the multiple different battery health states. Based on the differential capacity models corresponding to the multiple different battery health states, the sample parameter set corresponding to the sample battery in the multiple different battery health states is determined, wherein the sample parameter set includes the sample parameter values ​​corresponding to multiple feature parameters in the corresponding battery health states. Based on the sample parameter sets corresponding to the multiple different battery health states, a health state relationship table corresponding to each of the multiple feature parameters is obtained.

3. The method according to claim 1, characterized in that, Determining the target weights corresponding to each of the plurality of feature parameters includes: Under a predetermined temperature environment, during the process of charging the sample battery from an empty state to a fully charged state according to a predetermined current from an empty state, charging data of the sample battery at multiple different battery health states are collected. Based on the charging data corresponding to the multiple different battery health states, construct the differential capacity model of the sample battery corresponding to the multiple different battery health states. Based on the differential capacity models corresponding to the multiple different battery health states, the sample parameter set corresponding to the sample battery in the multiple different battery health states is determined, wherein the sample parameter set includes the sample parameter values ​​corresponding to multiple feature parameters in the corresponding battery health states. Based on the multiple different battery health states and the sample parameter sets corresponding to the multiple different battery health states, the Pearson correlation coefficients between the multiple feature parameters and the battery health states are determined. The target weights corresponding to each of the multiple feature parameters are determined based on the Pearson correlation coefficients between the multiple feature parameters and the battery health state.

4. The method according to claim 1, characterized in that, The step of determining the health status assessment result of the target battery during the current charging period based on the multiple estimated battery health states includes: Obtain the environmental parameters of the target battery during the current charging period; Based on the environmental parameters, determine the weight correction coefficients corresponding to each of the plurality of feature parameters; Based on the weight correction coefficients corresponding to each of the multiple feature parameters, the target weights corresponding to each of the multiple feature parameters are corrected to obtain the corrected weight coefficients corresponding to each of the multiple feature parameters. The health status assessment result of the target battery is determined based on the corrected weight coefficients corresponding to the multiple feature parameters and the multiple estimated battery health states.

5. The method according to any one of claims 1 to 4, characterized in that, Before acquiring the charging data of the target battery during the current charging period, the method further includes: Detect whether the target battery is in a preset charging state, wherein the preset charging state is at least used to indicate that the state of charge of the target battery is less than a preset first energy threshold. Detect whether the charging current of the target battery is within a preset current range; When the target battery is in the preset charging state and the charging current is within the preset current range, the time period between the time corresponding to the preset charging state and the time when the target battery is fully charged is determined as the current charging time period, wherein the time when the target battery is fully charged is used to indicate the time when the target battery is fully charged.

6. A battery health status assessment device, characterized in that, include: The charging data acquisition module is used to acquire the charging data of the target battery during the current charging period, wherein the charging data includes the charging amount and charging voltage of the target battery collected at multiple sampling times included in the current charging period. The model building module is used to build a differential capacity model of the target battery based on the charging data. The differential capacity model is used to characterize the relationship between the charging amount and the charging voltage of the target battery during the current charging period. The differential capacity model is in the form of a differential capacity dQ / dV curve. The parameter value determination module is used to determine the target parameter values ​​corresponding to each of the multiple feature parameters based on the differential capacity model, wherein the multiple feature parameters include at least the peak value, peak area and peak-to-trough distance in the differential capacity model; A battery status assessment module is used to determine the health status assessment result of a target battery based on the target parameter values ​​corresponding to each of the plurality of feature parameters. This includes: determining a health status relationship table corresponding to each of the plurality of feature parameters, wherein the health status relationship table indicates the correspondence between the parameter values ​​of the corresponding feature parameters and the battery health status; querying the corresponding health status relationship table based on the target parameter values ​​corresponding to each of the plurality of feature parameters to obtain multiple estimated battery health states corresponding to the target battery, wherein the multiple estimated battery health states correspond one-to-one with the plurality of feature parameters; determining the target weights corresponding to each of the plurality of feature parameters; and performing a weighted calculation based on the multiple estimated battery health states and the target weights corresponding to each of the plurality of feature parameters to obtain the health status assessment result of the target battery.

7. A non-volatile storage medium, characterized in that, The non-volatile storage medium stores multiple instructions, which are adapted to be loaded by a processor and executed by the battery health status assessment method according to any one of claims 1 to 5.

8. An electronic device, characterized in that, It includes one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the battery health status assessment method according to any one of claims 1 to 5.

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