SOH correction method, device, equipment and product

Abstract

The invention relates to an SOH correction method and device, equipment and a product. The method comprises the steps of obtaining an SOH value big data sample; based on the SOH value big data sample, determining a test SOH value of the battery of the selected vehicle in the SOH value big data sample and a dimensionless value corresponding to the SOH value of the battery of the to-be-corrected vehicle, and taking the test SOH value of the battery of the selected vehicle as a standard SOH value, the dimensionless value corresponding to the SOH value of the battery of the to-be-corrected vehicle is determined according to the test SOH value of the battery of the selected vehicle and the SOH value of the battery of the to-be-corrected vehicle; and according to the SOH value of the battery of the selected vehicle and the dimensionless value corresponding to the SOH value of the battery of the to-be-corrected vehicle, determining an SOH correction value of the battery of the to-be-corrected vehicle. According to the method provided by the invention, the evaluation accuracy of the SOH value is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power batteries, in particular to a SOH correction method, device, equipment and product. BACKGROUND

[0002] The state of health (SOH) of a battery is a core parameter of a power battery management system, and directly affects the remaining capacity, power output characteristics and safety performance of the battery. Accurate SOH evaluation is crucial for the calculation of the cruising range of an electric vehicle, power control and life management, and can avoid safety hazards caused by battery performance degradation. Currently, SOH evaluation techniques mainly rely on the analysis of battery parameters during charging or static processes, and are achieved through feature extraction and model calculation, but their accuracy and applicability still face challenges.

[0003] In the prior art, power battery SOH evaluation mainly adopts a modeling method based on data driving, usually in combination with feature extraction of charging and discharging cycles and machine learning algorithms. For example, static, dynamic and environmental data of a vehicle can be collected, single consistency features and battery health indicators can be calculated after charging and discharging cycle matching and data cleaning, and a GBDT model can be used to construct an SOH prediction model to deduce the SOH value. A reference relationship between SOH and cycle number can also be established through full charging and discharging experiments, charging and discharging segments can be extracted from historical operation data, and the average charging capacity gain of SOC and the SOH value can be standardized to construct a mapping model of time sequence features and SOH degradation, so as to deduce the real-time SOH value using the trained prediction model.

[0004] However, the existing method has the problem that the SOH value of the battery of the vehicle is inaccurate due to high algorithm complexity when evaluating the SOH. SUMMARY

[0005] The purpose of the present application is to provide a SOH correction method, device, equipment and product, which solves the technical problem of inaccurate SOH value evaluation in the prior art.

[0006] In order to achieve the above purpose, the technical scheme adopted by the present application is as follows:

[0007] In a first aspect, the present application provides a SOH correction method, the method comprising:

[0008] obtaining a SOH value big data sample;

[0009] The test SOH value of the battery of the selected vehicle in the SOH value big data sample and the dimensionless value corresponding to the SOH value of the battery of the vehicle to be corrected are determined based on the SOH value big data sample, the test SOH value of the battery of the selected vehicle is a standard SOH value, and the dimensionless value corresponding to the SOH value of the battery of the vehicle to be corrected is determined according to the test SOH value of the battery of the selected vehicle and the SOH value of the battery of the vehicle to be corrected;

[0010] The SOH correction value of the battery of the vehicle to be corrected is determined according to the SOH value of the battery of the selected vehicle and the dimensionless value corresponding to the SOH value of the battery of the vehicle to be corrected.

[0011] Optionally, the test SOH value of the battery of the selected vehicle in the SOH value big data sample and the dimensionless value corresponding to the SOH value of the battery of the vehicle to be corrected are determined based on the SOH value big data sample, and the test SOH value of the battery of the selected vehicle is a standard SOH value, and the dimensionless value corresponding to the SOH value of the battery of the vehicle to be corrected is determined according to the test SOH value of the battery of the selected vehicle and the SOH value of the battery of the vehicle to be corrected.

[0012] The use state corresponding to the vehicle in the SOH value big data sample is determined.

[0013] The first selected vehicle in a new vehicle state and the second selected vehicle in a high-usage state are determined according to the use state corresponding to the vehicle in the SOH value big data sample.

[0014] The test SOH value of the battery of the first selected vehicle is taken as a first standard SOH value, and the test SOH value of the battery of the second selected vehicle is taken as a second standard SOH value, and the dimensionless value corresponding to the SOH value of the battery of the vehicle to be corrected is determined; wherein the first standard SOH value represents the maximum SOH value in the SOH value big data sample, and the second standard SOH value represents the minimum SOH value in the SOH value big data sample.

[0015] Optionally, the vehicle to be corrected and the selected vehicle are of the same type.

[0016] And / or,

[0017] The method further comprises:

[0018] When there are more than two batteries of the second selected vehicle in the high-usage state, the SOH values of the batteries of the second selected vehicle in the high-usage state are weighted and averaged to obtain a second standard value representing the minimum SOH value in the SOH value big data sample.

[0019] Optionally, the test SOH value of the battery of the first selected vehicle is taken as a first standard value, and the test SOH value of the battery of the second selected vehicle is taken as a second standard value, and the dimensionless value corresponding to the SOH value of the battery of the vehicle to be corrected is determined.

[0020] determining a difference relationship between the SOH value of the battery of the vehicle to be corrected and the first standard SOH value, and a difference relationship between the SOH value of the battery of the vehicle to be corrected and the second standard SOH value;

[0021] According to the difference relationship between the SOH value of the battery of the vehicle to be corrected and the first standard SOH value, and the difference relationship between the SOH value of the battery of the vehicle to be corrected and the second standard SOH value, a dimensionless value corresponding to the SOH value of the battery of the vehicle to be corrected is determined.

[0022] Optionally, the method further comprises:

[0023] According to the charging segment data of the battery of the vehicle to be corrected, accumulated electric quantity of the battery of the vehicle to be corrected in the charging process is determined;

[0024] According to the accumulated electric quantity of the battery of the vehicle to be corrected in the charging process and the SOC change of the battery of the vehicle to be corrected, a dynamic SOH value of the battery of the vehicle to be corrected is determined;

[0025] According to the change trend of the dynamic SOH value of the battery of the vehicle to be corrected in the target time period, the SOH value of the battery of the selected vehicle is determined.

[0026] Optionally, when the charging segment data is acquired, the charging segment data at least meets one of the following conditions:

[0027] The time of the battery of the vehicle to be corrected in the resting state before charging meets the preset time condition, wherein the resting state represents that the output current of the battery of the vehicle to be corrected is less than the preset output current;

[0028] The charging starting temperature of the battery of the vehicle to be corrected meets the preset temperature condition;

[0029] The charging temperature of the battery of the vehicle to be corrected meets the preset temperature interval condition;

[0030] The current standard deviation of the charging segment data of the battery of the vehicle to be corrected meets the preset standard deviation condition.

[0031] In a second aspect, the present application further provides a SOH correction method, which comprises:

[0032] Sending the charging segment data of the battery of the vehicle to be corrected, the charging segment data being used to determine the SOH value of the battery of the vehicle to be corrected;

[0033] Receiving the SOH correction value of the battery of the vehicle to be corrected acquired by the method according to the first aspect;

[0034] Correcting the current SOH value of the battery of the vehicle to be corrected according to the SOH correction value of the battery of the vehicle to be corrected.

[0035] In a third aspect, the present application provides a SOH correction device, comprising:

[0036] an acquisition module, configured to acquire a SOH value big data sample;

[0037] a first determination module, configured to determine, based on the SOH value big data sample, a test SOH value of a battery of a selected vehicle in the SOH value big data sample and a dimensionless value corresponding to a SOH value of a battery of a vehicle to be corrected, the test SOH value of the battery of the selected vehicle being a standard SOH value, the dimensionless value corresponding to the SOH value of the battery of the vehicle to be corrected being determined according to the test SOH value of the battery of the selected vehicle and the SOH value of the battery of the vehicle to be corrected;

[0038] a second determination module, configured to determine, according to the SOH value of the battery of the selected vehicle and the dimensionless value corresponding to the SOH value of the battery of the vehicle to be corrected, a SOH correction value of the battery of the vehicle to be corrected.

[0039] In a fourth aspect, the present application provides a SOH correction device, comprising:

[0040] a sending module, configured to send charging segment data of the battery of the vehicle to be corrected, the charging segment data being used to determine the SOH value of the battery of the vehicle to be corrected;

[0041] a receiving module, configured to receive the SOH correction value of the battery of the vehicle to be corrected acquired according to the method of the first aspect;

[0042] a correction module, configured to correct a current SOH value of the battery of the vehicle to be corrected according to the SOH correction value of the battery of the vehicle to be corrected.

[0043] In a fifth aspect, the present application provides an electronic device, comprising a processor and a memory connected with the processor in communication;

[0044] the memory stores computer execution instructions;

[0045] the processor executes the computer execution instructions stored in the memory, so as to realize the various possible implementation manners described above.

[0046] In a sixth aspect, the present application provides a computer program product, comprising a computer program, which, when executed by a processor, realizes the various possible implementation manners described above.

[0047] The SOH correction method provided by the application, by obtaining a large sample of SOH values; based on the large sample of SOH values, determining the test SOH value of the battery of the selected vehicle in the large sample of SOH values, and the dimensionless value corresponding to the SOH value of the battery of the vehicle to be corrected, the test SOH value of the battery of the selected vehicle is the standard SOH value, and the dimensionless value corresponding to the SOH value of the battery of the vehicle to be corrected is determined according to the test SOH value of the battery of the selected vehicle and the SOH value of the battery of the vehicle to be corrected; according to the dimensionless value corresponding to the SOH value of the battery of the selected vehicle and the SOH value of the battery of the vehicle to be corrected, the SOH correction value of the battery of the vehicle to be corrected is determined, thereby, by obtaining a large amount of SOH value data as a large sample of SOH values, a representative selected vehicle is selected as a reference standard, and the SOH value of its battery is regarded as a standard value. For the vehicle to be corrected, its SOH value is converted into a dimensionless value for uniform comparison with the selected vehicle. By performing the dimensionless processing on the SOH value of the vehicle to be corrected, the deviation caused by different vehicle models, battery types or measurement conditions can be eliminated, and the standard SOH value of the selected vehicle and the dimensionless value of the vehicle to be corrected are combined, so as to obtain a more accurate SOH correction value of the vehicle to be corrected. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 The scene schematic diagram of the SOH correction method provided by the application;

[0049] Figure 2 The flowchart of the SOH correction method provided by the application Figure 1 ;

[0050] Figure 3 The flowchart of the SOH correction method provided by the application Figure 2 ;

[0051] Figure 4 The flowchart of the SOH correction method provided by the application Figure 3 ;

[0052] Figure 5 The structure schematic diagram of the SOH correction device provided by the application Figure 1 ;

[0053] Figure 6 The structure schematic diagram of the SOH correction method provided by the application Figure 2 ;

[0054] Figure 7 The structure schematic diagram of the electronic device provided by the embodiment of the application. DETAILED DESCRIPTION

[0055] The exemplary embodiments will be described in detail herein with reference to the attached drawings. In the following description, like reference numerals refer to like elements throughout the description. The following exemplary embodiments are not representative of all embodiments consistent with the present application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the present application as detailed in the appended claims.

[0056] Currently, there are three key problems in the evaluation of battery state of health (SOH): first, the algorithm relies heavily on experimental data correlation under specific working conditions, resulting in insufficient generalization ability in actual application; second, the calculation conditions are too harsh, requiring high-quality charging data or specific working conditions to trigger, and the actual applicable scenarios are limited; finally, the method based on experimental data is prone to produce occasional unreasonable calculation results, which may cause abnormal performance of vehicle control system and user complaints. These problems restrict the reliability and practicality of SOH evaluation methods.

[0057] The SOH correction method provided by the present application can adapt to various actual working conditions by obtaining a large amount of SOH value data samples, thereby breaking away from the dependence on specific experimental conditions. The SOH value of the to-be-corrected vehicle is calculated based on the charging segment data of the vehicle, and the SOH value is mapped to a unified reference interval through dimensionless processing, realizing effective comparison and correction between individuals. At the same time, the test SOH value of the battery of the selected vehicle is used as a boundary condition to establish a proportional mapping relationship, and a SOH correction value closer to the real aging state is obtained. In this way, while reducing the dependence on high-quality data, a dynamic adjustment mechanism is introduced to avoid unreasonable evaluation results caused by individual data point anomalies. Thus, the obtained SOH value is more accurate.

[0058] The technical solutions of the present application and how the technical solutions of the present application solve the above technical problems will be described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of the present application will be described below with reference to the drawings.

[0059] Figure 1 The scene schematic diagram of the SOH correction method provided by the present application is as follows: Figure 1As shown, the specific application scenario of the present application can include a communication connected server and a vehicle control system. The server can be a cloud platform, and the vehicle control system can be a vehicle-mounted computer or the like. The SOH correction method in the present application does not make special restrictions on the implementation mode of the execution subject, as long as the execution subject can obtain the SOH value big data sample; based on the SOH value big data sample, the test SOH value of the battery of the selected vehicle in the SOH value big data sample and the dimensionless value corresponding to the SOH value of the battery of the vehicle to be corrected are determined, the test SOH value of the battery of the selected vehicle is the standard SOH value, and the dimensionless value corresponding to the SOH value of the battery of the vehicle to be corrected is determined according to the test SOH value of the battery of the selected vehicle and the SOH value of the battery of the vehicle to be corrected; the SOH correction value of the battery of the vehicle to be corrected can be determined according to the dimensionless value corresponding to the SOH value of the battery of the selected vehicle and the SOH value of the battery of the vehicle to be corrected, or the charging segment data of the battery of the vehicle to be corrected is sent, and the charging segment data is used to determine the SOH value of the battery of the vehicle to be corrected; the SOH correction value of the battery of the vehicle to be corrected is received; and the current SOH value of the battery of the vehicle to be corrected can be corrected according to the SOH correction value of the battery of the vehicle to be corrected.

[0060] Figure 2 Flowchart of the SOH correction method provided by the present application Figure 1 As shown, Figure 2 The execution subject of the present method can be a cloud, as shown, Figure 2 The method can include:

[0061] S201, obtaining a SOH value big data sample.

[0062] The SOH value big data sample can refer to a comprehensive data set of a large number of battery state of health (SOH) values collected by the cloud. The sample can cover the SOH values calculated from the charging segment data of the batteries of various vehicles. The SOH value big data sample can include health status information of the batteries of different vehicles at different stages of their life cycles.

[0063] S202, based on the SOH value big data sample, determining the test SOH value of the battery of the selected vehicle in the SOH value big data sample and the dimensionless value corresponding to the SOH value of the battery of the vehicle to be corrected, the test SOH value of the battery of the selected vehicle being the standard SOH value, and the dimensionless value corresponding to the SOH value of the battery of the vehicle to be corrected being determined according to the test SOH value of the battery of the selected vehicle and the SOH value of the battery of the vehicle to be corrected.

[0064] The vehicle to be corrected can refer to a target vehicle to be corrected for SOH, i.e., a vehicle whose battery state of health needs to be accurately evaluated and adjusted during actual operation.

[0065] The SOH value of the battery of the selected vehicle can refer to the state of health of the battery calculated according to the charging segment data in the target time period, which can dynamically evaluate the battery aging by comparing the attenuation degree of the current actual charging capacity and the rated capacity. Unlike the static SOH in the prior art (such as laboratory full discharge test), the SOH value of the present application is a dynamic SOH value, which can reflect the capacity attenuation under actual working conditions, is affected by dynamic factors such as temperature, charging rate, standing history, and is more suitable for online tracking of battery performance changes by vehicle BMS.

[0066] The charging segment data can refer to the time series data recorded by the vehicle during the complete charging process, which can include battery voltage, current, temperature, SOC (State of Charge), and timestamp information. The charging segment data can reflect the charging characteristics of the vehicle battery (such as charging rate, energy input curve, temperature rise, etc.), so that the health status of the battery can be determined.

[0067] Optionally, the charging segment data can be collected and stored in real time by the vehicle BMS (Battery Management System), or recorded during the charging process through the charging pile communication interface (such as OBD-II, CAN bus).

[0068] The non-dimensional value corresponding to the SOH value of the battery of the selected vehicle can refer to the actual SOH value calculated by the selected vehicle based on the charging segment data, and a relative value representing the distribution of the actual SOH value in the SOH value big data sample after processing the test SOH value of the battery of the selected vehicle in the SOH value big data sample.

[0069] Optionally, the selected vehicle can refer to a specific vehicle selected as a reference standard, and the performance data of the battery of the vehicle can be regarded as a reference. That is, the battery of the vehicle usually has known good performance directly calibrated or can be accurately calibrated in the laboratory. In the embodiments of the present application, the test SOH value of the battery of the selected vehicle can be determined according to the SOH value in the SOH value big data sample, that is, the value can be the maximum SOH value and the minimum SOH value in the SOH value big data sample. Thus, the non-dimensional value corresponding to the SOH value of the battery of the selected vehicle can be determined by normalization through the maximum SOH value and the minimum SOH value, and the relative position of the battery health status of the vehicle to be corrected in the overall sample.

[0070] In the present application, the method for determining the SOH value of the battery of the selected vehicle according to the charging segment data can include:

[0071] According to the charging segment data of the battery of the vehicle to be corrected, the accumulated electric quantity of the battery of the vehicle to be corrected in the charging process is determined;

[0072] According to the accumulated electric quantity of the battery of the vehicle to be corrected during the charging process and the SOC change of the battery of the vehicle to be corrected, the dynamic SOH value of the battery of the vehicle to be corrected is determined.

[0073] According to the change trend of the dynamic SOH value of the battery of the vehicle to be corrected in the target time period, the SOH value of the battery of the selected vehicle is determined.

[0074] The accumulated electric quantity can refer to the total charging electric quantity calculated by time integration (ampere-hour integration method) of the charging current in the charging segment data. The accumulated electric quantity can satisfy:

[0075]

[0076] Wherein, I is the current data in the charging segment data, t0 is the charging start time, t1 is the charging end time, and the accumulated electric quantity can reflect the actual charging energy of the battery.

[0077] The SOC change of the battery of the vehicle to be corrected can refer to the SOC difference (i.e. △SOC) reported by the BMS at the start and end of charging. For example, when the SOC of the battery of the vehicle to be corrected is charged from 30% to 80%, the △SOC is 50%.

[0078] The SOH dynamic value of the battery of the vehicle to be corrected can refer to the real-time health status calculated in a single charging segment. The SOH dynamic value satisfies:

[0079]

[0080] The change trend of the SOH dynamic value of the battery of the vehicle to be corrected in the target time period can refer to the average performance or central trend of the battery health status in the target time period. Alternatively, the change trend can be an expected value obtained from the plurality of SOH dynamic values.

[0081] The target time period can refer to the time of obtaining the charging segment data. The time period can be the time of charging the battery of the vehicle to be corrected once, or the time of charging the battery of the vehicle to be corrected multiple times.

[0082] For example, when the target time period is 1 month, the SOH dynamic values of multiple charging in the month are 85%, 87%, 86%, 84% and 88% respectively, and the change trend of the SOH dynamic value of the battery of the vehicle to be corrected in the target time period is:

[0083] (85%+87%+86%+84%+88%) / 5=86%.

[0084] The SOH value of the battery of the vehicle to be corrected can refer to a representative estimate obtained by statistical analysis of each SOH dynamic value (based on ampere-hour integration and SOC change) calculated for multiple valid charging segments within the time period. This value can be integrated using methods such as moving average, weighted filtering, or probability distribution expectation value (such as Gaussian distribution mean) to eliminate random fluctuations of single charging data (such as temperature transient effects), thereby obtaining an SOH value reflecting the stable decay trend of the battery capacity within the time period.

[0085] In the present application, when acquiring the charging segment data, the charging segment data at least satisfies one of the following conditions:

[0086] The time for the battery of the vehicle to be corrected to be in a resting state before charging satisfies a preset time condition, wherein the resting state represents that the output current of the battery of the vehicle to be corrected is less than a preset output current;

[0087] The charging starting temperature of the battery of the vehicle to be corrected satisfies a preset temperature condition;

[0088] The charging temperature of the battery of the vehicle to be corrected satisfies a preset temperature interval condition;

[0089] The current standard deviation of the charging segment data of the battery of the vehicle to be corrected satisfies a preset standard deviation condition.

[0090] The resting state can refer to the battery of the vehicle to be corrected being in a non-working state before charging, with an output current lower than a preset output current, indicating that the battery is neither discharged nor charged, and is in a stable dormant state. Therefore, it can be ensured that the battery of the vehicle to be corrected reaches electrochemical equilibrium before charging, avoiding interference of residual current with the accuracy of subsequent charging data.

[0091] Optionally, the preset output current can be set arbitrarily. In order to improve the fault tolerance rate of screening while ensuring data effectiveness, the preset output current needs to cover BMS self-checking, background power consumption of low-voltage systems, etc. when the vehicle is in a dormant state, and exclude substantial discharge behaviors such as air conditioner pre-starting and remote wake-up, so as to avoid misjudgment caused by sensor noise or transient fluctuations. Therefore, the preset output current can be set to 5A.

[0092] The preset time condition can refer to the battery resting duration exceeding a set minimum threshold (such as 1 hour), which can exclude short parking or frequent start-stop working conditions. Therefore, it can be ensured that the battery temperature and the environment are fully balanced, and the polarization effect is basically eliminated, thereby obtaining more representative charging data.

[0093] The preset temperature condition can refer to the battery temperature at the beginning of charging being in a specific range (such as 10-30℃), which can avoid the influence of extreme low temperature (leading to lithium precipitation) or high temperature (accelerating side reactions) on the charging process.

[0094] The preset temperature range condition can refer to that the battery temperature needs to be continuously maintained within a limited range (such as 15-35℃) during the entire charging process, and if it is exceeded, it is considered as an abnormal segment. This condition can be used to screen the charging process with good temperature control, and exclude unreliable data caused by heat dissipation failure or environmental mutation.

[0095] The preset standard deviation condition can refer to that the fluctuation degree of the charging current needs to be lower than a set threshold value, and if the fluctuation degree of the charging current is higher than the set threshold value, it is determined that the data quality is poor and cannot be used for subsequent calculation. Optionally, the set threshold value can be 3A, and when the current standard deviation of the charging segment data is greater than 3A, it is represented that the charging segment data does not meet the preset standard deviation condition, and vice versa.

[0096] In the present application, the method for obtaining the charging segment data can include:

[0097] 1. Obtain full charging segment data when the charging starting temperature is less than 30, the charging temperature is located in 10-30℃, and the standing duration before charging is greater than 1 hour. Standing can be defined as the battery output current being less than 5A, and a static voltmeter is used to determine whether the charging starting temperature is available.

[0098] 2. Calculate the charging current standard deviation to evaluate the smoothness of the charging segment data. If the current standard deviation of the charging segment is less than or equal to 3A, it is determined that the obtained data can be used as the charging segment data.

[0099] S203, according to the dimensionless value corresponding to the SOH value of the battery of the selected vehicle and the SOH value of the battery of the vehicle to be corrected, determine the SOH correction value of the battery of the vehicle to be corrected.

[0100] The SOH value of the battery of the selected vehicle can include a maximum SOH value and a minimum SOH value, wherein the maximum SOH value can refer to the battery health state reference value calibrated by the selected vehicle at the factory or in the initial use stage, which can correspond to the ideal health state of the battery, such as 100%.

[0101] The minimum SOH value can refer to the actual health state value obtained by battery capacity test under laboratory conditions for a small number of high-year vehicles, as a reference benchmark for the end of battery aging of the vehicle or similar vehicles, such as 70%.

[0102] The SOH correction value can refer to taking the maximum SOH value and the minimum SOH value as boundary conditions, and determining the SOH correction value corresponding to the SOH value of the battery of the vehicle to be corrected by proportional mapping.

[0103] For example, if the dimensionless value is 0.733, the SOH correction value of the battery of the vehicle to be corrected can be:

[0104] 70% + (100% - 70%) x 0.733 = 70% + 30% x 0.733 = 70% + 21.99% = 91.99%.

[0105] In the present application, the first calibration SOH value is the SOH value of the battery of the calibration vehicle in a new vehicle state; the second calibration SOH value is the SOH value of the battery of the calibration vehicle in a high service life state;

[0106] The calibration vehicle is of the same type as the selected vehicle.

[0107] The high service life state can refer to a state in which the battery performance of the calibration vehicle has obviously degraded after long-term use, and usually corresponds to the health level in the later stage of the battery life, such as use for more than a certain number of years (such as 20 years or more) or driving mileage reaching a high value (such as more than 200,000 kilometers), and the actual SOH value is confirmed by laboratory capacity test as an aging reference benchmark.

[0108] The calibration vehicle being of the same type as the selected vehicle can mean that the two have high consistency in terms of battery chemical system, battery pack structure, vehicle power system configuration, and use case characteristics, such as the same brand, the same model or the same platform of electric vehicle, so as to ensure that the obtained first calibration SOH value and second calibration SOH value have sufficient reference and applicability for the selected vehicle.

[0109] The SOH correction method provided by the present application obtains a large amount of battery health state data of vehicles, constructs a SOH value big data sample with wide representativeness, and carries out dimensionless processing on the SOH value of the vehicle to be corrected based on the sample, so that it can be compared and corrected under a unified scale; combined with the SOH value (maximum SOH value and minimum SOH value) of the selected vehicle in the sample, a dynamic mapping relationship is established, which effectively improves the accuracy and individual adaptability of SOH evaluation; in addition, the method also introduces a mechanism for calculating dynamic SOH value based on charging segment data, and sets charging segment screening conditions to ensure the quality and consistency of the input data, thereby significantly reducing the dependence of traditional algorithms on specific working conditions and high-quality experimental data, and improving the robustness, stability and practicality of SOH evaluation in complex actual scenarios.

[0110] Figure 3 Flowchart of the SOH correction method provided by the present application Figure 2 As shown in the flowchart, the present embodiment is based on the embodiment Figure 3 and details the step of determining the dimensionless value corresponding to the SOH value of the battery of the selected vehicle in the SOH value big data sample based on the SOH value big data sample, which includes: Figure 2

[0111] ​S301, determine the use state corresponding to the vehicle in the SOH value big data sample;

[0112] S302, according to the use state corresponding to the vehicle in the SOH value big data sample, determine the first selected vehicle in the new car state and the second selected vehicle in the high life use state;

[0113] S303, the test SOH value of the battery of the first selected vehicle is taken as the first standard SOH value, and the test SOH value of the battery of the second selected vehicle is taken as the second standard SOH value, the dimensionless value corresponding to the SOH value of the battery of the vehicle to be corrected is determined; wherein the first standard SOH value represents the maximum SOH value in the SOH value big data sample, and the second standard SOH value represents the minimum SOH value in the SOH value big data sample.

[0114] Wherein, the use state corresponding to the vehicle can refer to the service life of the vehicle, when the service life of the vehicle is lower, the SOH of the vehicle obtained is larger and more accurate, and when the service life of the vehicle is higher, the SOH of the vehicle obtained is smaller and the error is larger. Based on this, the present application can select the first selected vehicle in the new car state and the second selected vehicle in the high life use state according to the service life of the vehicle.

[0115] Wherein, the high life use state can refer to the state that the battery of the vehicle has been used for a long time and has reached or is close to the state of being scrapped, for example, the vehicle whose battery has been used for more than 20 years is the vehicle in the high life use state. Since the vehicle can be conveniently recycled, the SOH value of the battery of the vehicle can be accurately obtained by laboratory measurement.

[0116] The new car state can refer to the state that the battery of the vehicle has been used for a short time, for example, the vehicle whose battery has been used for less than 1 year is the vehicle in the new car state. Since the battery of the vehicle is close to or equal to the factory setting, the SOH value of the battery of the vehicle can be directly calibrated.

[0117] In some embodiments, the first standard SOH value can correspond to the maximum value in the SOH value big data sample, and the second standard SOH value can correspond to the minimum value in the SOH value big data sample.

[0118] The test SOH value of the battery of the first selected vehicle is taken as the first standard SOH value, and the test SOH value of the battery of the second selected vehicle is taken as the second standard SOH value, and the dimensionless value corresponding to the SOH value of the battery of the vehicle to be corrected is determined by the maximum and minimum normalization.

[0119] In the present application, the test SOH value of the battery of the first selected vehicle is taken as the first standard value, and the test SOH value of the battery of the second selected vehicle is taken as the second standard value, and the non-dimensional value corresponding to the SOH value of the battery of the vehicle to be corrected is determined, comprising:

[0120] determining the difference relationship between the SOH value of the battery of the vehicle to be corrected and the first standard SOH value, and the difference relationship between the SOH value of the battery of the vehicle to be corrected and the second standard SOH value;

[0121] According to the difference relationship between the SOH value of the battery of the vehicle to be corrected and the first standard SOH value, and the difference relationship between the SOH value of the battery of the vehicle to be corrected and the second standard SOH value, the non-dimensional value corresponding to the SOH value of the battery of the vehicle to be corrected is determined.

[0122] wherein the non-dimensional value corresponding to the SOH value of the battery of the vehicle to be corrected satisfies:

[0123]

[0124] wherein, the non-dimensional value corresponding to the SOH value of the battery of the vehicle to be corrected, the second standard SOH value, the first standard SOH value.

[0125] For example, there are 100 samples in the SOH value big data sample, the minimum SOH value is 75%, and the maximum SOH value is 90%. When the SOH value of the battery of the selected vehicle is 86%, the relative health degree = (86%-75%) / (90%-86%)≈0.733.

[0126] In the present application, the vehicle type of the vehicle to be corrected is the same as that of the selected vehicle;

[0127] and / or,

[0128] The method further comprises:

[0129] When there are more than two batteries of the second selected vehicle in the high service life state, the SOH values of the batteries of the second selected vehicle in the high service life state are weighted and averaged to obtain the second standard value representing the minimum SOH value in the SOH value big data sample.

[0130] In order to ensure the accuracy of data measurement, the vehicle type of the vehicle to be corrected can be made the same as that of the selected vehicle, so as to ensure that the two have high similarity in terms of battery model, capacity attenuation characteristics, use conditions and thermal management strategy, thereby reducing the system error caused by vehicle type difference.

[0131] When the battery of the vehicle in the high service life state is two or more, the SOH values of the battery of the vehicle in the high service life state are weighted and averaged to obtain a second calibration value. For example, three vehicles of the same model and with long service life are selected, and the SOH values of the batteries of the three vehicles are measured as 72%, 68% and 70% respectively. The three values are weighted and averaged to obtain a second calibration SOH value of 70%. The average value is used as a representative health status of the same type of battery at the end of the aging stage, and is used as a lower limit reference in the subsequent SOH correction calculation of the selected vehicle, thereby improving the statistical reliability and applicability of the evaluation result.

[0132] The SOH correction method provided by the application realizes the relative positioning of the individual battery health status in the group data by determining the maximum SOH value and the minimum SOH value in the SOH value big data sample and taking them as the reference interval to convert the battery SOH value of the selected vehicle into a dimensionless value. This processing method can eliminate the dimensional differences caused by different battery types or use environments, so that the SOH values of different vehicles are comparable. At the same time, by normalizing the original SOH value to a unified interval, a standardized basis is provided for subsequent dynamic mapping and correction combined with the calibration value, which improves the stability and adaptability of the SOH evaluation result and enhances the accuracy of the algorithm in judging the aging trend of the individual battery.

[0133] Figure 4 The flowchart of the SOH correction method provided by the application Figure 3 As shown in Figure 4 The execution subject of the method can be a vehicle control system, as shown in Figure 4 The method can include:

[0134] S401, sending the charging segment data of the battery of the vehicle to be corrected, the charging segment data being used to determine the SOH value of the battery of the vehicle to be corrected;

[0135] S402, receiving the SOH correction value of the battery of the vehicle to be corrected obtained by the cloud according to the SOH correction method;

[0136] S403, correcting the current SOH value of the battery of the vehicle to be corrected according to the SOH correction value of the battery of the vehicle to be corrected.

[0137] The vehicle control system can send the charging segment data of the battery of the vehicle to the cloud platform through wireless transmission after obtaining the charging segment data of the battery of the vehicle. The cloud platform determines the SOH correction value of the battery of the vehicle to be corrected according to the charging segment data, and transmits the SOH correction value of the battery of the vehicle to be corrected back to the vehicle control system. The vehicle control system corrects the current SOH value of the battery of the vehicle to be corrected according to the SOH correction value of the battery of the vehicle to be corrected.

[0138] The embodiment provided in the present application is a correction method of SOH of a vehicle control system, and has similar implementation principles and technical effects, which will not be repeated here.

[0139] Figure 5 Structure diagram of the SOH correction device provided in the present application Figure 1 As shown in Figure 5 The SOH correction device 50 provided in the present application includes:

[0140] The acquisition module 501 is configured to acquire a SOH value big data sample.

[0141] The first determination module 502 is configured to determine, based on the SOH value big data sample, a test SOH value of a battery of a selected vehicle in the SOH value big data sample and a dimensionless value corresponding to a SOH value of a battery of a vehicle to be corrected, the test SOH value of the battery of the selected vehicle being a standard SOH value, and the dimensionless value corresponding to the SOH value of the battery of the vehicle to be corrected being determined according to the test SOH value of the battery of the selected vehicle and the SOH value of the battery of the vehicle to be corrected.

[0142] The second determination module 503 is configured to determine, according to the dimensionless value corresponding to the SOH value of the battery of the selected vehicle and the SOH value of the battery of the vehicle to be corrected, a SOH correction value of the battery of the vehicle to be corrected.

[0143] Optionally, the first determination module 502 can be specifically configured to:

[0144] determine a use state corresponding to a vehicle in the SOH value big data sample;

[0145] determine a first selected vehicle in a new vehicle state and a second selected vehicle in a high service life state according to the use state corresponding to the vehicle in the SOH value big data sample;

[0146] determine the dimensionless value corresponding to the SOH value of the battery of the vehicle to be corrected by taking a test SOH value of the battery of the first selected vehicle as a first standard SOH value and a test SOH value of the battery of the second selected vehicle as a second standard SOH value, wherein the first standard SOH value represents a maximum SOH value in the SOH value big data sample, and the second standard SOH value represents a minimum SOH value in the SOH value big data sample.

[0147] Optionally, the vehicle to be corrected and the selected vehicle in the second determination module 503 are of the same type.

[0148] Optionally, the second determination module 503 can be specifically configured to:

[0149] When the number of the battery of the second selected vehicle in the high service life state is more than two, the SOH values of the battery of the second selected vehicle in the high service life state are weighted and averaged to obtain a second standard value representing the minimum SOH value in the large data sample of SOH values.

[0150] Optionally, the first determining module 502 can be specifically configured to:

[0151] According to the charging segment data of the battery of the vehicle to be corrected, the accumulated electric quantity of the battery of the vehicle to be corrected in the charging process is determined.

[0152] According to the accumulated electric quantity of the battery of the vehicle to be corrected in the charging process and the SOC change of the battery of the vehicle to be corrected, the dynamic SOH value of the battery of the vehicle to be corrected is determined.

[0153] According to the change trend of the dynamic SOH value of the battery of the vehicle to be corrected in the target time period, the SOH value of the battery of the selected vehicle is determined.

[0154] Optionally, the first determining module 502 can be specifically configured to:

[0155] The time of the battery of the vehicle to be corrected in the resting state before charging satisfies the preset time condition, wherein the resting state represents that the output current of the battery of the vehicle to be corrected is less than the preset output current.

[0156] The charging starting temperature of the battery of the vehicle to be corrected satisfies the preset temperature condition.

[0157] The charging temperature of the battery of the vehicle to be corrected satisfies the preset temperature interval condition.

[0158] The current standard deviation of the charging segment data of the battery of the vehicle to be corrected satisfies the preset standard deviation condition.

[0159] The SOH correction device provided in the embodiment can execute the method provided in the method embodiment, and has similar implementation principles and technical effects, which will not be described here.

[0160] Figure 6 The SOH correction method provided in the embodiment Figure 2 As shown in Figure 6 The SOH correction device 60 provided in the embodiment includes:

[0161] The sending module 601 is configured to send the charging segment data of the battery of the vehicle to be corrected, and the charging segment data is used to determine the SOH value of the battery of the vehicle to be corrected.

[0162] The receiving module 602 is configured to receive the SOH correction value of the battery of the vehicle to be corrected.

[0163] The correction module 603 is configured to correct the current SOH value of the battery of the vehicle to be corrected according to the SOH correction value of the battery of the vehicle to be corrected.

[0164] The SOH correction device provided in the embodiment can execute the method provided in the method embodiment, and has similar implementation principles and technical effects. Details are not described herein again.

[0165] Figure 7 A structural schematic diagram of an electronic device provided in the embodiment is shown in FIG. 7. As shown in FIG. 7, the electronic device 70 provided in the embodiment includes at least one processor 701 and a memory 702. Optionally, the electronic device 70 further includes a communication component 703. The processor 701, the memory 702, and the communication component 703 are connected through a bus 704. Figure 7

[0166] In the implementation process, the at least one processor 701 executes the computer-executable instructions stored in the memory 702, so that the at least one processor 701 executes the method described above.

[0167] The specific implementation process of the processor 701 can refer to the method embodiments described above, and has similar implementation principles and technical effects. Details are not described herein again.

[0168] In the above-described embodiments, it should be understood that the processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor, etc. The steps of the method disclosed in the application can be directly embodied as the execution completed by the hardware processor, or executed by the combination of hardware and software modules in the processor.

[0169] The memory can include a random access memory (RAM) and can also include a non-volatile memory (NVM), for example, at least one disk memory.

[0170] ​The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the convenience of representation, the bus in the drawings of the present application does not limit to only one bus or one type of bus.

[0171] The present application also provides a computer program product, comprising a computer program, which, when executed by a processor, implements the method described above.

[0172] The present application also provides a computer readable storage medium, which stores computer execution instructions, and when a processor executes the computer execution instructions, the method described above is implemented.

[0173] The readable storage medium described above can be implemented by any type of volatile or non-volatile storage device or their combination, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special purpose computer.

[0174] An exemplary readable storage medium is coupled to the processor, so that the processor can read information from the readable storage medium, and can write information to the readable storage medium. Of course, the readable storage medium can also be an integral part of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the readable storage medium can also exist as discrete components in the device.

[0175] The division of units is only a logical functional division, and in actual implementation, there can be another division manner, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0176] The units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, i.e., may be located in one place, or may be distributed to multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0177] In addition, each functional unit in various embodiments of the application can be integrated into one processing unit, or each unit can exist physically, or two or more units can be integrated into one unit.

[0178] If the function is realized in the form of a software function 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 solutions of the application essentially or the part that contributes to the prior art or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the various embodiment methods of the application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various program code storage media.

[0179] Those skilled in the art can understand that all or part of the steps of the above-mentioned method embodiments can be completed by program instruction related hardware. The aforementioned program can be stored in a computer readable storage medium. The program executes the steps including the above-mentioned method embodiments when executed; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk, and various program code storage media.

[0180] Finally, it should be noted that those skilled in the art, after considering the specification and practicing the application disclosed herein, will easily think of other embodiments of the application. The application is intended to cover any variations, uses, or adaptations of the application that follow the general principles of the application and include common knowledge or conventional techniques in the art that are not disclosed by the application, and is not limited to the precise structure described above and shown in the drawings, and various modifications and changes can be made without departing from the scope thereof. The scope of the application is only limited by the appended claims.

Claims

1. A method for correcting SOH, characterized in that: The method comprises: Obtain SOH value big data samples; Determining, based on the SOH value big data sample, a test SOH value of a battery of a selected vehicle in the SOH value big data sample and a dimensionless value corresponding to the SOH value of a battery of a vehicle to be corrected, wherein the test SOH value of the battery of the selected vehicle is a standard SOH value, and the dimensionless value corresponding to the SOH value of the battery of the vehicle to be corrected is determined based on the test SOH value of the battery of the selected vehicle and the SOH value of the battery of the vehicle to be corrected; The SOH correction value of the battery of the vehicle to be corrected is determined according to the SOH value of the battery of the selected vehicle and the dimensionless value corresponding to the SOH value of the battery of the vehicle to be corrected.

2. The SOH correction method according to claim 1, wherein: Determining, based on the SOH value big data sample, a test SOH value of a battery of a selected vehicle in the SOH value big data sample and a dimensionless value corresponding to the SOH value of a battery of a vehicle to be corrected, includes: Determining the usage status corresponding to the vehicle in the SOH value big data sample; Determining, based on the usage status corresponding to the vehicles in the SOH value big data sample, a first selected vehicle in a new vehicle state and a second selected vehicle in an advanced vehicle state; Taking the test SOH value of the battery of the first selected vehicle as the first standard SOH value, and taking the test SOH value of the battery of the second selected vehicle as the second standard SOH value, determine the dimensionless value corresponding to the SOH value of the battery of the vehicle to be corrected; wherein, the first standard SOH value represents the maximum SOH value in the SOH value big data sample, and the second standard SOH value represents the minimum SOH value in the SOH value big data sample.

3. The SOH correction method according to claim 2, wherein: The vehicle to be corrected is of the same type as the selected vehicle; and / or, The method further comprises: When there are more than two batteries of the second selected vehicle that are in a state of advanced usage, the SOH values ​​of the batteries of the second selected vehicle that are in a state of advanced usage are weightedly averaged to obtain a second standard value representing the minimum SOH value in the SOH value big data sample.

4. The SOH correction method according to claim 2, wherein: The step of determining a dimensionless value corresponding to the SOH value of the battery of the vehicle to be corrected by using the tested SOH value of the battery of the first selected vehicle as a first standard value and the tested SOH value of the battery of the second selected vehicle as a second standard value includes: Determining a difference between the SOH value of the battery of the vehicle to be repaired and the first standard SOH value, and a difference between the SOH value of the battery of the vehicle to be repaired and the second standard SOH value; A dimensionless value corresponding to the SOH value of the battery of the vehicle to be corrected is determined based on the difference between the SOH value of the battery of the vehicle to be corrected and the first standard SOH value, and the difference between the SOH value of the battery of the vehicle to be corrected and the second standard SOH value.

5. The SOH correction method according to claim 1, wherein: The method further comprises: determining the accumulated power of the battery of the vehicle to be corrected during the charging process according to the charging segment data of the battery of the vehicle to be corrected; Determining a dynamic SOH value of the battery of the vehicle to be corrected according to the accumulated electricity of the battery of the vehicle to be corrected during the charging process and the SOC change of the battery of the vehicle to be corrected; The SOH value of the battery of the selected vehicle is determined according to a change trend of the dynamic SOH value of the battery of the vehicle to be corrected within a target time period.

6. The SOH correction method according to claim 5, wherein: When acquiring the charging segment data, the charging segment data satisfies at least one of the following conditions: The time during which the battery of the vehicle to be repaired is in a static state before charging satisfies a preset time condition, wherein the static state indicates that the output current of the battery of the vehicle to be repaired is less than a preset output current; The charging start temperature of the battery of the vehicle to be corrected meets the preset temperature condition; The charging temperature of the battery of the vehicle to be corrected meets the preset temperature range condition; The current standard deviation of the charging segment data of the battery of the vehicle to be corrected meets a preset standard deviation condition.

7. A method for correcting SOH, characterized in that: The method comprises: Sending charging segment data of a battery of a vehicle to be repaired, wherein the charging segment data is used to determine the state of health (SOH) value of the battery of the vehicle to be repaired; receiving a SOH correction value of the battery of the vehicle to be corrected obtained according to the method of claim 1; The current SOH value of the battery of the vehicle to be corrected is corrected according to the SOH correction value of the battery of the vehicle to be corrected.

8. A SOH correction device, characterized in that: include: Acquisition module, used to obtain SOH value big data samples; a first determining module, configured to determine, based on the SOH value big data sample, a test SOH value of a battery of a selected vehicle in the SOH value big data sample and a dimensionless value corresponding to the SOH value of a battery of a vehicle to be corrected, wherein the test SOH value of the battery of the selected vehicle is a standard SOH value, and the dimensionless value corresponding to the SOH value of the battery of the vehicle to be corrected is determined based on the test SOH value of the battery of the selected vehicle and the SOH value of the battery of the vehicle to be corrected; The second determining module is configured to determine a corrected SOH value of the battery of the vehicle to be corrected according to the SOH value of the battery of the selected vehicle and a dimensionless value corresponding to the SOH value of the battery of the vehicle to be corrected.

9. A SOH correction device, characterized in that: include: a sending module, configured to send charging segment data of a battery of a vehicle to be corrected, wherein the charging segment data is used to determine the SOH value of the battery of the vehicle to be corrected; a receiving module, configured to receive the SOH correction value of the battery of the vehicle to be corrected obtained according to the method of claim 1; The correction module is used to correct the current SOH value of the battery of the vehicle to be corrected according to the SOH correction value of the battery of the vehicle to be corrected.

10. An electronic device, characterized in that: include: a processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 8.

11. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 8 when the computer program is executed by a processor.

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