Battery capacity retention estimation methods, devices, storage media, and vehicles

Abstract

The present disclosure relates to a battery capacity retention rate estimation method, device, storage medium and vehicle. The method relates to the field of battery technology, including: collecting charging characteristic data of the battery, and pre-processing the charging characteristic data to obtain data to be processed; determining the first capacity retention rate corresponding to the data to be processed based on the double estimation method, and determining the second capacity retention rate corresponding to the data to be processed based on the two-point method; obtaining the first historical capacity retention rate and the second historical capacity retention rate; determining the weight coefficient of the first capacity retention rate and the second capacity retention rate according to the size relationship between the first historical capacity retention rate and the second historical capacity retention rate, and fusing the first capacity retention rate and the second capacity retention rate based on the weight coefficient to obtain the target capacity retention rate. The present disclosure can obtain a more accurate capacity retention rate by combining the two-point method and the double estimation method when certain conditions are met between the first capacity retention rate and the second capacity retention rate.

Description

Technical Field

[0001] The present disclosure relates to the field of battery technology, and in particular, to a method, device, storage medium and vehicle for estimating a capacity retention rate of a battery. Background Art

[0002] With the widespread popularity of new energy vehicles, power batteries have become an indispensable energy supply device for automobiles, and battery capacity retention rate is one of the core indicators for evaluating battery life. It is closely related to the evaluation of battery health status, that is, the accuracy of battery capacity retention rate evaluation will directly affect the battery life, vehicle range, and battery state of charge (SOC) estimation. Therefore, how to more accurately evaluate the battery capacity retention rate is a technical problem that needs to be solved urgently. Summary of the invention

[0003] The purpose of the present disclosure is to provide a method, device, storage medium and vehicle for estimating the capacity retention rate of a battery to solve the above-mentioned technical problems.

[0004] In order to achieve the above object, the present disclosure provides a method for estimating a capacity retention rate of a battery in a first aspect, the method comprising:

[0005] Collecting charging characteristic data of the battery, and preprocessing the charging characteristic data to obtain data to be processed;

[0006] Determining a first capacity retention rate corresponding to the data to be processed based on a double estimation method, and determining a second capacity retention rate corresponding to the data to be processed based on a two-point method;

[0007] Obtaining a first historical capacity retention rate and a second historical capacity retention rate, wherein the first historical capacity retention rate is determined by a plurality of historical capacity retention rates obtained by the dual estimation method, and the second historical capacity retention rate is determined by a plurality of historical capacity retention rates obtained by the two-point method;

[0008] The weight coefficient of the first capacity retention rate and the second capacity retention rate is determined according to the magnitude relationship between the first historical capacity retention rate and the second historical capacity retention rate, and the first capacity retention rate and the second capacity retention rate are merged based on the weight coefficient to obtain a target capacity retention rate.

[0009] Optionally, determining a weight coefficient of the first capacity retention rate and the second capacity retention rate according to a magnitude relationship between the first historical capacity retention rate and the second historical capacity retention rate, and fusing the first capacity retention rate and the second capacity retention rate based on the weight coefficient to obtain a target capacity retention rate includes:

[0010] The larger historical capacity retention rate between the first historical capacity retention rate and the second historical capacity retention rate is obtained. If it is determined that a first preset percentage of the first current capacity retention rate corresponding to the larger historical capacity retention rate is less than the second current capacity retention rate corresponding to the smaller historical capacity retention rate, the first current capacity retention rate and the second current capacity retention rate are merged based on a preset weight coefficient to obtain the target capacity retention rate.

[0011] Optionally, the fusing the first current capacity retention rate and the second current capacity retention rate based on a preset weight coefficient to obtain the target capacity retention rate includes:

[0012] multiplying the first current capacity retention rate by a first preset percentage to obtain a first result, and multiplying the second current capacity retention rate by a second preset percentage to obtain a second result, wherein the first preset percentage is greater than the second preset percentage, and the sum of the first preset percentage and the second preset percentage is 1;

[0013] The target capacity retention rate is determined based on the first result and the second result.

[0014] Optionally, the method further comprises:

[0015] If it is determined that the first preset percentage of the first current capacity retention rate is greater than or equal to the second current capacity retention rate, the first current capacity retention rate is used as the target capacity retention rate.

[0016] Optionally, the method further comprises:

[0017] Obtaining the current cumulative capacity, and determining a third capacity retention rate corresponding to the cumulative capacity based on a first fitting relationship obtained in advance, wherein the first fitting relationship is a relationship obtained by fitting a plurality of historical target capacity retention rates and a historical cumulative capacity corresponding to each of the historical target capacity retention rates based on a least squares method;

[0018] When it is determined that the target capacity retention rate is less than the third capacity retention rate, the target capacity retention rate is merged with the third capacity retention rate to obtain a final capacity retention rate.

[0019] Optionally, the method further comprises:

[0020] When the battery is not in a charging state, obtaining current time information or mileage information;

[0021] The target capacity retention rate corresponding to the time information is obtained based on a second fitting relationship, or the target capacity retention rate corresponding to the mileage information is obtained based on a third fitting relationship, wherein the second fitting relationship is a relationship obtained by fitting multiple historical target capacity retention rates and the time information corresponding to each of the historical target capacity retention rates based on the least squares method, and the third fitting relationship is a relationship obtained by fitting each of the historical target capacity retention rates and the corresponding mileage information based on the least squares method.

[0022] Optionally, the method further comprises:

[0023] Acquire a plurality of historical first capacity retention rates, and use an average of the plurality of historical first capacity retention rates as the first historical capacity retention rate;

[0024] A plurality of historical second capacity retention rates are obtained, and an average of the plurality of historical second capacity retention rates is used as the second historical capacity retention rate.

[0025] In a second aspect, the present disclosure provides a device for estimating a capacity retention rate of a battery, the device comprising:

[0026] A collection module is configured to collect charging characteristic data of the battery and pre-process the charging characteristic data to obtain data to be processed;

[0027] a determination module configured to determine a first capacity retention rate corresponding to the data to be processed based on a dual estimation method, and to determine a second capacity retention rate corresponding to the data to be processed based on a two-point method;

[0028] an acquisition module configured to acquire a first historical capacity retention rate and a second historical capacity retention rate, wherein the first historical capacity retention rate is determined by a plurality of historical capacity retention rates acquired by the dual estimation method, and the second historical capacity retention rate is determined by a plurality of historical capacity retention rates acquired by the two-point method;

[0029] The fusion module is configured to determine a weight coefficient of the first capacity retention rate and the second capacity retention rate according to a size relationship between the first historical capacity retention rate and the second historical capacity retention rate, and fuse the first capacity retention rate and the second capacity retention rate based on the weight coefficient to obtain a target capacity retention rate.

[0030] In a third aspect, the present disclosure provides a computer-readable storage medium having a computer program stored thereon, which implements the method described in the first aspect when the computer program is executed by a processor.

[0031] In a fourth aspect, the present disclosure provides a vehicle, the vehicle comprising:

[0032] a memory having a computer program stored thereon;

[0033] A processor is used to execute the computer program in the memory to implement the steps of the method described in the first aspect.

[0034] In a fifth aspect, the present disclosure provides a computer program product, including a computer program, which implements the method described in the first aspect when executed by a processor.

[0035] The present disclosure can improve the accuracy of obtaining the capacity retention rate by fusing the capacity retention rates obtained in different ways. Specifically, the charging characteristic data of the battery is collected, and the charging characteristic data is preprocessed to obtain the data to be processed. The first capacity retention rate corresponding to the data to be processed is determined based on the double estimation method, and the second capacity retention rate corresponding to the data to be processed is determined based on the two-point method. On this basis, the weight coefficient of the first capacity retention rate and the second capacity retention rate is determined according to the size relationship between the first historical capacity retention rate and the second historical capacity retention rate, and the first capacity retention rate and the second capacity retention rate are fused based on the weight coefficient to obtain the target capacity retention rate. Since the target capacity retention rate is obtained by fusing the first capacity retention rate and the second capacity retention rate, the obtained capacity retention rate is more accurate, that is, the fusion of the two-point method and the double estimation method can make full use of their respective advantages and improve the accuracy and robustness of the battery capacity retention rate estimation. In addition, by combining the prediction ability of the model with the actual measurement of the data, the target capacity retention rate finally obtained can be closer to the actual state of the battery.

[0036] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The accompanying drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the present disclosure but do not constitute a limitation of the present disclosure. In the accompanying drawings:

[0038] Figure 1 The figure is a flow chart showing a method for estimating a capacity retention rate of a battery according to an exemplary embodiment.

[0039] Figure 2 The figure is a flow chart showing another method for estimating the capacity retention rate of a battery according to an exemplary embodiment.

[0040] Figure 3 is an example diagram of an OCV-SOC curve in another method for estimating a capacity retention rate of a battery according to an exemplary embodiment.

[0041] Figure 4is a specific flow chart of another method for estimating the capacity retention rate of a battery according to an exemplary embodiment.

[0042] Figure 5 The figure is a block diagram of a device for estimating a capacity retention rate of a battery according to an exemplary embodiment.

[0043] Figure 6 is a block diagram of a vehicle according to an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0044] The specific implementation of the present disclosure is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the present disclosure, and is not used to limit the present disclosure.

[0045] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0046] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0047] The term "including" and its variations used herein are open inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0048] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0049] The capacity retention rate of a power battery is an important indicator of its health status. When standard testing is not possible, this indicator is usually estimated based on actual operating data. However, the actual operating data cannot meet standard testing conditions, making the assessment of the capacity retention rate inaccurate.

[0050] Figure 1FIG. 1 is a flow chart showing a method for estimating a capacity retention rate of a battery according to an exemplary embodiment. Figure 1 , the battery capacity retention rate estimation method may include the following steps:

[0051] In step S110, charging characteristic data of the battery is collected, and the charging characteristic data is pre-processed to obtain data to be processed.

[0052] In the embodiment of the present disclosure, the charging characteristic data of the battery may include charging current, charging voltage, charging time, temperature, charging power, polarization voltage, and charging curve, etc. When it is detected that the battery enters the charging state, the embodiment of the present disclosure may collect the charging characteristic data at a regular interval.

[0053] That is, the charging characteristic data may include multiple sub-charging data, each of which may include charging current, charging voltage, charging time, temperature, charging power, polarization voltage, and charging curve, etc. For example, a sub-charging data may be collected every 10 seconds. Here, the charging characteristic data may be an original data set, that is, the collection process of the charging characteristic data may be referred to as the process of constructing the original data set.

[0054] As an optional method, after acquiring the charging characteristic data, the embodiment of the present disclosure may pre-process the charging characteristic data to obtain the data to be processed. Here, pre-processing the charging characteristic data may be performing a data cleaning operation.

[0055] Specifically, the embodiment of the present disclosure can clean the charging status data, that is, retain the charging status data and delete the non-charging status data. For example, the charging status data is identified as 1, and the non-charging status data is identified as 0. By cleaning the charging status, the data with the data identification of 1 can be filtered out.

[0056] Optionally, the disclosed embodiment can perform data cleaning on the total current. When the total current is greater than or equal to 0, it indicates that the battery is in a discharging state, and when the total current is less than 0, it indicates that the battery is in a charging state. Therefore, data greater than or equal to 0 can be deleted during the data cleaning process.

[0057] Optionally, the embodiment of the present disclosure may perform data cleaning on the SOC. Since there may be abnormal data among the multiple SOCs obtained, in order to ensure the accuracy of the estimation, the embodiment of the present disclosure may delete the SOC that exceeds the preset range. Here, the preset range may be 0 to 100. For example, if there is data greater than 100 among the multiple SOC data, then the SOC greater than 100 may be deleted.

[0058] Optionally, the disclosed embodiment can perform data cleaning on time to delete discontinuous data, that is, to ensure that the acquired charging characteristic data is a continuous charging process. For example, if the time interval between a certain data and other data among multiple data is discontinuous, that is, exceeds the preset time interval, then the data can be deleted.

[0059] Optionally, the disclosed embodiment can perform data cleaning on the maximum voltage of the battery to delete the data with incorrect format, that is, detect whether there is voltage data with incorrect format in the charging characteristic data, and if so, delete the voltage data with incorrect format. Here, the data with incorrect format can be an incorrect symbol, that is, the voltage is not a numerical value but an incorrect symbol.

[0060] Optionally, the disclosed embodiment can perform data cleaning on the vehicle mileage to delete the data with incorrect format, that is, detect whether there is mileage data with incorrect format in the charging characteristic data, and if so, delete the mileage data with incorrect format. Here, the data with incorrect format can be an incorrect symbol, that is, the mileage is not a numerical value but an incorrect symbol.

[0061] Optionally, the embodiment of the present disclosure can perform data cleaning on the minimum temperature of the battery to delete the data with incorrect format, that is, detect whether there is temperature data with incorrect format in the charging characteristic data, and if so, delete the temperature data with incorrect format. Here, the data with incorrect format can be an incorrect symbol, that is, the temperature is not a numerical value but an incorrect symbol.

[0062] In the embodiment of the present disclosure, the data to be processed obtained by preprocessing the charging characteristic data may include polarization capacitance value, polarization resistance value, ohmic internal resistance value, etc. In other words, data such as polarization capacitance value, polarization resistance value, and ohmic internal resistance value can be obtained through the data cleaning operation.

[0063] It should be noted that after preprocessing the charging characteristic data, the embodiment of the present disclosure can extract characteristic data from the cleaned data. That is, after performing the data cleaning operation, the charging segment data is extracted. By extracting the characteristics of the cleaned data, the data can be made to conform to the input of the Kalman filter model.

[0064] Exemplarily, the disclosed embodiment can perform battery modeling and state estimation based on a first-order RC equivalent circuit model, and can use an extended Kalman filter (EKF) algorithm for linearization processing. Specifically, the disclosed embodiment can use an extended Kalman filter algorithm to calculate the SOC value, and in this process, the battery data can be combined with the battery's ohmic internal resistance value, polarization internal resistance value (polarization resistance value), polarization capacitance value, SOC, current value, voltage value, etc. to perform multi-time scale parameter adaptation, so that the data to be processed can adapt to the extended Kalman filter.

[0065] In step S120, a first capacity retention rate corresponding to the data to be processed is determined based on a dual estimation method, and a second capacity retention rate corresponding to the data to be processed is determined based on a two-point method.

[0066] As an optional method, after cleaning the charging characteristic data to obtain the data to be processed, the embodiment of the present disclosure can determine the first capacity retention rate corresponding to the data to be processed based on the double estimation method, and determine the second capacity retention rate corresponding to the data to be processed based on the two-point method.

[0067] Among them, the dual estimation method can be a joint estimation algorithm of SOC and SOH based on extended Kalman filtering established according to the battery equivalent circuit model. The disclosed embodiment can use two extended Kalman filters to comprehensively obtain the first capacity retention rate, the first extended Kalman filter can be used to estimate the SOC; the second extended Kalman filter can be used to estimate the equivalent circuit model parameters. In addition, the update time scales between the two extended Kalman filters are different, such as the SOC estimation time scale can be smaller than the estimation time scale of the model parameters.

[0068] The two-point method can determine two accurate SOC values ​​based on the OCV (Open Circuit Voltage)-SOC curve, and calculate the cumulative charging capacity between the two SOCs in ampere-hours. On this basis, the capacity of the battery is determined according to the cumulative charging capacity to obtain the capacity retention rate.

[0069] It should be noted that if the collected voltage value and SOC value are not in the OCV-SOC table, the embodiment of the present disclosure can use linear interpolation to calculate and correct. The corresponding ohmic internal resistance, polarization internal resistance and polarization capacitance can be obtained according to the SOC and temperature values.

[0070] In addition, due to the large data interval of cloud data, there is a lot of loss of working condition information, which will lead to inaccurate ampere-hour integration. Therefore, the embodiment of the present disclosure can use the charging segment with relatively stable current to perform the two-point method calculation. Moreover, if the accuracy of the vehicle-mounted SOC is high enough, the embodiment of the present disclosure can also use the vehicle-mounted SOC to obtain a more accurate SOC value.

[0071] In step S130 , a first historical capacity retention rate and a second historical capacity retention rate are acquired.

[0072] In the embodiment of the present disclosure, the first historical capacity retention rate may be determined by a plurality of historical capacity retention rates obtained by the dual estimation method, and the second historical capacity retention rate may be determined by a plurality of historical capacity retention rates obtained by the two-point method.

[0073] As a specific implementation method, the embodiment of the present disclosure can obtain multiple historical first capacity retention rates, and use the average of the multiple historical first capacity retention rates as the first historical capacity retention rate. In other words, the embodiment of the present disclosure can obtain multiple historical capacity retention rates determined by the dual estimation method, and then take the average of these historical capacity retention rates, and the average value can be used as the first historical capacity retention rate.

[0074] Optionally, the embodiment of the present disclosure may obtain multiple historical second capacity retention rates, and use the average of the multiple historical second capacity retention rates as the second historical capacity retention rate. That is, the embodiment of the present disclosure may obtain multiple historical capacity retention rates determined by the two-point method, and then take the average of these historical capacity retention rates, and the average value may be used as the second historical capacity retention rate.

[0075] In step S140, the weight coefficients of the first capacity retention rate and the second capacity retention rate are determined according to the size relationship between the first historical capacity retention rate and the second historical capacity retention rate, and the first capacity retention rate and the second capacity retention rate are merged based on the weight coefficient to obtain the target capacity retention rate.

[0076] As an optional method, after obtaining the first historical capacity retention rate and the second historical capacity retention rate, the embodiment of the present disclosure can obtain the size relationship between the first historical capacity retention rate and the second historical capacity retention rate, and on this basis, determine the weight coefficient of the first capacity retention rate and the second capacity retention rate according to the size relationship. If the size relationship between the first historical capacity retention rate and the second historical capacity retention rate is different, the corresponding weight coefficients of the first capacity retention rate and the second capacity retention rate are also different.

[0077] On this basis, the embodiment of the present disclosure can fuse the first capacity retention rate and the second capacity retention rate according to the weight coefficient to obtain the target capacity retention rate. The two-point method and the dual estimation method both have their own advantages and disadvantages. By fusing the capacity retention rates obtained by the two methods, not only can complementarity be achieved, but also, since the capacity retention rates obtained by the two methods are both stable, it can ensure that the target capacity retention rate finally obtained has a certain stability to a certain extent.

[0078] By combining the two-point method and the dual estimation method, the disclosed embodiment can obtain a more accurate target capacity retention rate. Specifically, the charging characteristic data of the power battery system is collected, and specific screening conditions are set to clean the charging characteristic data to reduce the impact of abnormal data caused by special circumstances and extract current data. In addition, the disclosed embodiment can combine the means of big data analysis to remove the impact of outliers, so as to calculate the capacity retention rate of the power battery system and evaluate the health status of the power battery system.

[0079] Since the dual estimation method can provide a theoretical basis and long-term prediction capability based on the model, and the two-point rule can provide short-term accuracy based on experimental data, the fusion of the two can simultaneously obtain the depth of theoretical prediction and the accuracy of experimental data. In addition, the fusion of the capacity retention rate obtained by the two-point method and the dual estimation method can reduce the limitations of the dual estimation method and the two-point method, thereby improving the robustness of the capacity retention rate estimation. For example, when the data of the dual estimation method is insufficient, the embodiment of the present disclosure can still provide estimation results. For another example, through the two-point method, the embodiment of the present disclosure can make corrections when the dual estimation prediction is inaccurate.

[0080] Furthermore, by integrating the two-point method with the dual estimation method, the embodiments of the present disclosure can reduce the calculation errors generated by the two-point method or the dual estimation method. For example, the dual estimation method may overestimate or underestimate the battery performance in some cases, while the two-point method can correct these errors through actual measurements.

[0081] Figure 2 FIG. 1 is a flow chart of another method for estimating the capacity retention rate of a battery according to an exemplary embodiment. Figure 2 , the battery capacity retention rate estimation method may include the following steps:

[0082] In step S210, charging characteristic data of the battery is collected and pre-processed to obtain data to be processed.

[0083] The specific implementation of step S210 has been described in detail in the above embodiment and will not be repeated here.

[0084] In step S220, a first capacity retention rate corresponding to the data to be processed is determined based on the dual estimation method, and a second capacity retention rate corresponding to the data to be processed is determined based on the two-point method.

[0085] From the above introduction, it is known that the dual extended Kalman filter can be used to estimate the SOC and battery model parameters. In the process of determining the first capacity retention rate corresponding to the data to be processed based on the dual estimation method, the embodiment of the present disclosure can estimate the state of charge SOC and battery model parameters corresponding to the data to be processed based on the dual extended Kalman filter, wherein the battery model parameters may include ohmic internal resistance, polarization resistance and polarization capacitance, which can be used to correct the SOC. On this basis, the first capacity retention rate is determined based on the SOC.

[0086] In some embodiments, the double extended Kalman filter may include a state filter and a parameter filter. In the process of estimating the state of charge SOC and battery model parameters corresponding to the data to be processed using the double extended Kalman filter, the embodiment of the present disclosure can use the state filter to predict the SOC of the data to be processed, and use the parameter filter to predict the battery model parameters.

[0087] Based on the double extended Kalman filter, the disclosed embodiment can combine the state filter and the parameter filter to obtain SOC, so that a more accurate and robust battery capacity retention rate estimation can be obtained. In addition, the double extended Kalman filter has nonlinear processing capabilities, joint estimation capabilities, real-time performance, high-precision estimation, and adaptability to changes in battery parameters, so obtaining the first capacity retention rate through it can ensure that the target capacity retention rate finally obtained is more accurate.

[0088] In the embodiment of the present disclosure, the sampling time of the state filter may be less than the sampling time of the parameter filter. When the dual extended Kalman filter is used to estimate the battery state and parameters, the embodiment of the present disclosure may use two extended Kalman filters (EKF) as the state filter and the parameter filter respectively. In addition, the battery model parameters change slowly over time, while the SOC changes rapidly over time, that is, the battery model parameters change faster over time, while the SOC changes faster over time. In order to adapt, the embodiment of the present disclosure may use a multi-scale approach to reduce the complexity of capacity calculation. Specifically, a long time scale can be used to estimate the battery model parameters, while a short time scale is used to estimate the SOC.

[0089] Before using the double extended Kalman filter to estimate the battery state and parameters, the embodiment of the present disclosure may first perform an initialization operation, and the corresponding initial equation is as follows:

[0090] , ,

[0091] Optionally, after performing the initialization operation, the embodiment of the present disclosure may use a parameter filter to predict the battery model parameters. The specific prediction formula is as follows:

[0092] ;

[0093] Among them, the sampling time of parameter filtering can be t, and the step size of parameter filtering is .

[0094] On this basis, the embodiment of the present disclosure can use the first update equation to update the battery model parameters. The first update equation can be a parameter filtering prediction update equation, which is shown as follows:

[0095] ;

[0096] In addition, the embodiment of the present disclosure may use a state filter to predict the SOC, wherein the prediction equation of the state filter may be as follows:

[0097] , ;

[0098] Where T can be the sampling time of the state filter, and the step size of the state filter can be . A is the state transfer matrix; It can be the prior state estimate at time k, which is the intermediate calculation result of filtering, that is, the result at time k predicted based on the optimal estimate at the previous time (time k-1), which is the result of the prediction equation; It can be the posterior estimated covariance at time k-1, which is one of the results of filtering. is the prior estimate variance at time k; Q is the morning covariance of the process excitation.

[0099] On this basis, the embodiment of the present disclosure can use the second update equation to update the SOC. The second update equation can be a state filter prediction update equation, which is shown as follows:

[0100] ;

[0101] Among them, H is the transformation matrix from state variables to measurements, that is, the relationship between states and observations; R is the measurement noise covariance; is the measured value, is the filter gain matrix.

[0102] From the above introduction, we know that the state filter and parameter filter have different step sizes t1 and t2 respectively. Usually, the sampling time t1 of the state filter can be smaller than the sampling time t2 of the parameter filter. In other words, the update speed of the parameter filter is slower than that of the state filter, which can reduce the running time of the algorithm.

[0103] Here, the sampling time of the state filter is shorter than that of the parameter filter, which enables the state estimation to be updated more frequently to quickly respond to immediate changes in the battery state. This is especially important for application scenarios that require fast response (such as the battery management system of electric vehicles). That is, the sampling time of the state filter is shorter than that of the parameter filter, which can improve the flexibility and adaptability of the capacity retention rate estimation.

[0104] As an optional method, the disclosed embodiment can perform cumulative ampere-hour calculation on the data to be processed in the stable charging time period to obtain the cumulative charged power (cumulative charged power). On this basis, the first capacity retention rate is comprehensively determined based on the SOC and cumulative charged power obtained by the previous calculation.

[0105] For example, the embodiment of the present disclosure may take the ratio of the accumulated charging power obtained by the accumulated ampere-hour integral to the SOC output by the dual-cal as the first value, and on this basis, calculate the ratio between the first value and the rated capacity of the battery, and take the ratio as the first capacity retention rate. Here, the accumulated charging power may be the ampere-hour integral value.

[0106] The embodiments of the present disclosure can directly measure the change in battery capacity through cumulative ampere-hour calculation, thereby obtaining a more accurate and reliable capacity retention rate. In addition, the cumulative ampere-hour calculation can adapt to different usage conditions and environments, thus providing an accurate assessment of the battery's capacity retention rate in different application scenarios.

[0107] In other embodiments, the embodiment of the present disclosure may determine the second capacity retention rate corresponding to the data to be processed based on the two-point method. Specifically, the embodiment of the present disclosure may obtain a first SOC and a second SOC, wherein the first SOC may be the SOC of the battery when the vehicle is in a stationary condition, and the first SOC may be an initial SOC, which may be obtained based on the first single cell in the battery, and the voltage of the first single cell is the lowest. Specifically, when the vehicle is in a stationary condition, the embodiment of the present disclosure may obtain the voltage of each single cell in the battery, and obtain the single cell with the lowest voltage, which may be used as the first single cell, and based on the voltage of the first single cell, the embodiment of the present disclosure may convert and obtain the first SOC.

[0108] Optionally, the second SOC may be the SOC of the battery after charging is completed, which may be obtained based on the second single cell, where the second single cell may be the cell corresponding to the highest voltage after charging is completed. That is, after obtaining the highest voltage, the embodiment of the present disclosure may obtain the second SOC by converting the highest voltage.

[0109] On this basis, it is determined whether the first SOC and the second SOC meet a preset condition, and the preset condition may be that the difference between the first SOC and the second SOC is greater than the preset difference. If it is determined that the difference between the first SOC and the second SOC is greater than the preset difference, the embodiment of the present disclosure may determine the second capacity retention rate corresponding to the data to be processed according to the two-point method.

[0110] It should be noted that before obtaining the first SOC and the second SOC, the embodiment of the present disclosure may also determine whether the vehicle meets the stationary condition. If it is determined that the vehicle meets the stationary condition, the embodiment of the present disclosure may obtain the first SOC and the second SOC. Conversely, if it is determined that the vehicle does not meet the stationary condition, the SOC acquisition operation is not performed.

[0111] Among them, static can mean that the battery has no significant charging, discharging or current input / output for a certain period of time and is in a stable open voltage state, that is, the internal chemical reaction of the battery and the electrode polarization phenomenon tend to be balanced when the vehicle is in a static state. Static conditions may include: current close to zero, voltage tending to be stable, sufficient static time, stable temperature, and no polarization phenomenon. Exemplarily, when it is detected that the battery current is lower than the set threshold and the voltage change rate is lower than the set threshold, it is determined that the vehicle meets the static condition.

[0112] In the embodiment of the present disclosure, SOC can be based on Figure 3 The OCV-SOC curve (OCV-SOC list) shown in FIG. 1 is obtained. From the above introduction, it is known that the charging characteristic data of the battery may include voltage information. In the embodiment of the present disclosure, the charging characteristic data of the battery may be obtained by searching for the following information: Figure 3 The OCV-SOC curve shown in the figure is used to obtain the SOC corresponding to the voltage information. If the collected voltage value and SOC value are not in the OCV-SOC curve, the embodiment of the present disclosure can use linear interpolation to perform calculation correction.

[0113] Based on the OCV-SOC curve, the embodiment of the present disclosure can determine two accurate SOC values, and can also calculate the cumulative charge between the two SOCs based on the ampere-hour accumulation. On this basis, the capacity retention rate of the battery system is determined based on the cumulative charge and the rated capacity. After obtaining the SOC, the two-point method and the dual estimation method can obtain the capacity retention rate based on the SOC in a similar manner, which will not be described in detail here.

[0114] It should be noted that multiple first thermal capacity retention rates and multiple second capacity retention rates can be obtained by the double estimation method and the two-point method. Among them, there may be abnormal data in the multiple capacity retention rates. In order to ensure the accuracy of the capacity retention rate acquisition, the embodiment of the present disclosure can first delete the abnormal capacity retention rate.

[0115] Specifically, due to the influence of working conditions, it is necessary to optimize and screen the capacity retention rate value, that is, to judge each acquired capacity retention rate value. First, determine whether the current capacity retention rate value is within the first preset range. Among them, the first preset range can be obtained through the historical capacity retention rate, which can be the average of multiple historical capacity retention rates. Exemplarily, the first preset range can be within 5% of the average of all capacity retention rates contained in the most recent quarter. For example, if the average capacity retention rate is A, then if the current capacity retention rate is greater than A-5% and less than A+5%, it is determined that the capacity retention rate is available. Otherwise, it means that the current capacity retention rate is unavailable, and the capacity retention rate can be discarded at this time.

[0116] Optionally, if it is determined that the vehicle has no valid driving in a specified time period, the embodiment of the present disclosure may obtain charging data of a preset number of times, and determine the average of the charging data, and determine a second preset range based on the average. On this basis, it is determined whether the current capacity retention rate is within the second preset range, and if it is within the second preset range, it indicates that the capacity retention rate is valid.

[0117] For example, if it is detected that the vehicle has no valid driving in the last quarter, the last five charging data are determined, and a mean threshold is obtained based on the five charging data. If the current capacity retention rate value is within the range of the mean threshold, it is retained. The range of the mean threshold may be a second preset range, which may be [mean -%5, mean +%5].

[0118] By screening the first capacity retention rate and the second capacity retention rate, the final first capacity retention rate and the second capacity retention rate can be obtained. On this basis, the embodiment of the present disclosure can subsequently merge the screened first capacity retention rate and the second capacity retention rate to obtain the target capacity retention rate.

[0119] In step S230 , a first historical capacity retention rate and a second historical capacity retention rate are acquired.

[0120] The specific implementation of step S230 has been described in detail in the above embodiment, and will not be repeated here.

[0121] In step S240, the weight coefficients of the first capacity retention rate and the second capacity retention rate are determined according to the size relationship between the first historical capacity retention rate and the second historical capacity retention rate, and the first capacity retention rate and the second capacity retention rate are merged based on the weight coefficient to obtain the target capacity retention rate.

[0122] As an optional method, in the process of determining the weight coefficients of the first capacity retention rate and the second capacity retention rate based on the size relationship between the first historical capacity retention rate and the second historical capacity retention rate, the embodiment of the present disclosure can obtain the larger historical capacity retention rate between the first historical capacity retention rate and the second historical capacity retention rate. The capacity retention rate corresponding to the larger historical capacity retention rate can be the first current capacity retention rate, and the capacity retention rate corresponding to the smaller historical capacity retention rate can be the second current capacity retention rate.

[0123] As an example, if the larger historical capacity retention rate between a first historical capacity retention rate and a second historical capacity retention rate is determined to be the first historical capacity retention rate, the first capacity retention rate corresponding to the first historical capacity retention rate can be used as the first current capacity retention rate, and the second capacity retention rate corresponding to the second historical capacity retention rate can be used as the second current capacity retention rate.

[0124] As another example, if the larger of the first historical capacity retention rate and the second historical capacity retention rate is determined to be the second historical capacity retention rate, the second capacity retention rate corresponding to the second historical capacity retention rate can be used as the first current capacity retention rate, and the first capacity retention rate corresponding to the first historical capacity retention rate can be used as the second current capacity retention rate.

[0125] On this basis, if it is determined that the first preset percentage of the first current capacity retention rate corresponding to the larger historical capacity retention rate is less than the second current capacity retention rate corresponding to the smaller historical capacity retention rate, then the embodiment of the present disclosure can fuse the first current capacity retention rate and the second current capacity retention rate based on a preset weight coefficient to obtain the target capacity retention rate.

[0126] Specifically, the embodiment of the present disclosure may multiply the first current capacity retention rate by the first preset percentage to obtain a first result, and multiply the second current capacity retention rate by the second preset percentage to obtain a second result, wherein the first preset percentage may be greater than the second preset percentage, and the sum of the first preset percentage and the second preset percentage is 1. On this basis, the target capacity retention rate is determined based on the first result and the second result. Exemplarily, the first preset percentage may be 80%, the second preset percentage may be 20%, and the target capacity retention rate may be equal to the first current capacity retention rate × 0.8 + the second current capacity retention rate × 0.2.

[0127] Optionally, if it is determined that the first preset percentage of the first current capacity retention rate is greater than or equal to the second current capacity retention rate, the first current capacity retention rate is used as the target capacity retention rate. That is, at this time, the embodiment of the present disclosure can use the larger value of the first capacity retention rate and the second capacity retention rate as the target capacity retention rate. If the first historical capacity retention rate is greater than the second historical capacity retention rate, and the first preset percentage of the first capacity retention rate is greater than or equal to the second capacity retention rate, the first capacity retention rate can be used as the target capacity retention rate.

[0128] As a specific implementation, when the first historical capacity retention rate SOH1 is greater than the second historical capacity retention rate SOH2, if it is determined that the first preset percentage of the first capacity retention rate SOHA is less than the second capacity retention rate SOHB, the disclosed embodiment may merge the first capacity retention rate SOHA and the second capacity retention rate SOHB according to the first weight coefficient. At this time, the first current capacity retention rate is the first capacity retention rate SOHA, and the second current capacity retention rate SOHB.

[0129] For example, the disclosed embodiment may multiply the first capacity retention rate SOHA by the first preset percentage (80%) to obtain a first result, and multiply the second capacity retention rate SOHB by the second preset percentage (20%) to obtain a second result. Then, the first result and the second result are summed to obtain the target capacity retention rate.

[0130] Optionally, when the first historical capacity retention rate SOH1 is greater than the second historical capacity retention rate SOH2, if it is determined that a first preset percentage of the first capacity retention rate SOHA is greater than or equal to the second capacity retention rate SOHB, the first capacity retention rate SOHA may be used as a target capacity retention rate.

[0131] As another specific implementation, when the first historical capacity retention rate SOH1 is less than or equal to the second historical capacity retention rate SOH2, if it is determined that the first preset percentage of the second capacity retention rate SOHB is less than the first capacity retention rate SOHA, the embodiment of the present disclosure may merge the first capacity retention rate SOHA and the second capacity retention rate SOHB according to the second weight coefficient. At this time, the first current capacity retention rate is the second capacity retention rate SOHB, and the second current capacity retention rate is SOHA.

[0132] For example, the disclosed embodiment may multiply the second capacity retention rate SOHB by the first preset percentage (80%) to obtain a third result, and multiply the first capacity retention rate SOHA by the second preset percentage (20%) to obtain a fourth result. Then, the third result and the fourth result are summed to obtain the target capacity retention rate.

[0133] Optionally, when the first historical capacity retention rate SOH1 is less than or equal to the second historical capacity retention rate SOH2, if it is determined that a first preset percentage of the second capacity retention rate SOHB is greater than or equal to the first capacity retention rate SOHA, the second capacity retention rate SOHB may be used as a target capacity retention rate.

[0134] It should be noted that when determining that the first preset percentage of the larger value of the first capacity retention rate and the second capacity retention rate is less than the smaller value, the embodiment of the present disclosure can also determine whether the first capacity retention rate and the second capacity retention rate are both within the threshold range. If both are within the threshold range, the first capacity retention rate and the second capacity retention rate are merged based on the weight coefficient to obtain the target capacity retention rate. Exemplarily, the threshold range can be a range less than 1, that is, determining whether the first capacity retention rate is less than 1, and determining whether the second capacity retention rate is less than 1.

[0135] In step S250, the current cumulative capacity is obtained, and a third capacity retention rate corresponding to the cumulative capacity is determined based on a pre-acquired first fitting relationship.

[0136] As an optional method, after the first capacity retention rate and the second capacity retention rate are merged to obtain the target capacity retention rate, the embodiment of the present disclosure can also obtain the current cumulative capacity. The current cumulative capacity can be a value obtained by calculating the accumulated ampere-hours of the data to be processed within the stable charging time period. In other words, the current cumulative capacity can be a cumulative ampere-hour value.

[0137] On this basis, the embodiment of the present disclosure can determine the third capacity retention rate corresponding to the cumulative capacity based on the first fitting relationship obtained in advance. The first fitting relationship can be a relationship obtained by fitting multiple historical target capacity retention rates and the historical cumulative capacity corresponding to each historical target capacity retention rate based on the least squares method.

[0138] That is to say, the embodiment of the present disclosure can obtain multiple historical target capacity retention rates and the historical cumulative capacity corresponding to each target capacity retention rate, and on this basis, use the least squares method to fit multiple historical target capacity retention rates and historical cumulative capacity to obtain a first fitting relationship formula. After the cumulative capacity is subsequently obtained, the corresponding third capacity retention rate can be obtained based on the first fitting relationship formula.

[0139] In step S260, when it is determined that the target capacity retention rate is less than the third capacity retention rate, the target capacity retention rate is merged with the third capacity retention rate to obtain a final capacity retention rate.

[0140] As an optional method, after obtaining the target capacity retention rate and the third capacity retention rate, the embodiment of the present disclosure can determine whether the target capacity retention rate is less than or equal to the third capacity retention rate. If it is determined that the target capacity retention rate is less than the third capacity retention rate, the target capacity retention rate can be merged with the third capacity retention rate to obtain the final capacity retention rate.

[0141] Exemplarily, the embodiment of the present disclosure may merge the target capacity retention rate and the third capacity retention rate according to a 280 weight, such as the final capacity retention rate=0.8×target capacity retention rate+0.2×third capacity retention rate.

[0142] Optionally, if it is determined that the target capacity retention rate is greater than or equal to the third capacity retention rate, the embodiment of the present disclosure may use the target capacity retention rate as the final capacity retention rate.

[0143] As an example, the first capacity retention rate and the second capacity retention rate calculated using the two-point method and the double estimation method are SOH1 and SOH2 respectively. Based on the battery mechanism, the embodiment of the present disclosure can set a threshold range and obtain the target capacity retention rate through two data fusion processes, that is, the SOH value (target capacity retention rate) of the battery can be obtained according to different influence weights.

[0144] Specifically, in the first data fusion process, the embodiment of the present disclosure can compare the values ​​of SOH1 and SOH2, and determine whether eighty percent of the larger value is less than the smaller value, and can also determine whether the values ​​of SOH1 and SOH2 are within the threshold range. If the above two conditions are met, the embodiment of the present disclosure can perform data fusion processing to obtain the target capacity retention rate SOHA.

[0145] In addition, in the second data fusion process, the embodiment of the present disclosure can use the least squares method to fit the SOH' value (third capacity retention rate) and SOHA (target capacity retention rate) to compare. If the SOHA value is less than the SOH' value, the fusion is performed to generate the final SOH. It can be seen that before the second fusion process, the embodiment of the present disclosure can determine whether the target capacity retention rate value obtained by the first fusion is less than the third capacity retention rate value. If it is less than, the embodiment of the present disclosure can fuse the two to obtain the minimum target capacity retention rate.

[0146] The least squares method can find the best function of the data by minimizing the sum of squares of errors, which can make the fitting result more accurate. Therefore, the third capacity retention rate obtained by the least squares method is secondary fused with the target capacity retention rate obtained by the double estimation method and the two-point method to improve the estimation accuracy of the capacity retention rate. In addition, the least squares method has a certain robustness to noise and outliers, especially after data preprocessing and filtering. Therefore, even in the case of low data quality, the embodiment of the present disclosure can obtain a relatively stable fitting result based on the least squares method, and fusing it with the target capacity retention rate can further enhance the robustness of the capacity retention rate estimation.

[0147] Furthermore, the least squares method adopts the statistical characteristics of parameter estimation, such as variance and covariance, in the process of fitting to obtain the third capacity retention rate. The embodiment of the present disclosure integrates the third capacity retention rate obtained by the method with the target capacity retention rate, which can not only ensure the accuracy of the estimation, but also ensure the credibility of the estimation.

[0148] As another optional method, when it is detected that the battery is not in a charging state, the embodiment of the present disclosure can also obtain the current time information or mileage information. On this basis, the target capacity retention rate corresponding to the time information is obtained according to the second fitting relationship, or the target capacity retention rate corresponding to the mileage information is obtained based on the third fitting relationship.

[0149] In the embodiment of the present disclosure, the second fitting relationship may be a relationship obtained by fitting multiple historical target capacity retention rates and the time information corresponding to each historical target capacity retention rate based on the least squares method; the third fitting relationship may be a relationship obtained by fitting each historical target capacity retention rate and the corresponding mileage information based on the least squares method. Based on this, according to the mileage information or time information, the embodiment of the present disclosure can still obtain an accurate capacity retention rate value when the vehicle is not in a charging state.

[0150] Optionally, after obtaining the target capacity retention rate / final capacity retention rate, the embodiment of the present disclosure can evaluate the health status of the battery based on the target capacity retention rate / final capacity retention rate to obtain an evaluation result. Specifically, the embodiment of the present disclosure can determine whether the target capacity retention rate / final capacity retention rate is less than a preset capacity retention rate. If the target capacity retention rate / final capacity retention rate is less than the preset capacity retention rate, it is determined that the battery is aged and needs to be replaced.

[0151] Exemplarily, when it is determined that the target capacity retention rate / final capacity retention rate is less than the preset capacity retention rate, it is determined that the battery has reached the end of its service life and needs to be replaced, and a replacement prompt can be output at this time.

[0152] It should be noted that, when evaluating the battery health status, in addition to considering the capacity retention rate, the embodiments of the present disclosure may also consider other factors. For example, the change in the battery internal resistance, the self-discharge rate, the cycle life, etc. can be considered. By comprehensively evaluating the capacity retention rate and these parameters, the battery health status can be more comprehensively understood.

[0153] In summary, based on big data statistical analysis, the embodiments of the present disclosure can use the double estimation method and the two-point method to calculate the capacity retention rate respectively, and fuse the calculation results with the data fusion method. Since the capacity retention rate is obtained by adopting the improved dual Kalman filter model, the capacity retention rate can be accurately calculated.

[0154] See also Figure 6 As a specific implementation, after obtaining the raw data, the embodiment of the present disclosure can clean it and extract the cleaned data, that is, extract the charging segment data. On this basis, the double estimation method and the two-point method can be entered respectively. When entering the double estimation method, the embodiment of the present disclosure can traverse the charging segment and perform cumulative ampere-hour calculation.

[0155] In addition, the embodiment of the present disclosure can obtain deltSOC during the charging process, and the deltSOC can be the SOC obtained when charging starts and the SOC obtained when charging ends. The SOC here is the SOC obtained in a simple way, which can be included in the original data. On this basis, if it is determined that deltSOC is greater than a preset value, such as greater than 50, a Kalman filter update can be performed to calculate the first capacity retention rate through a dual estimation method.

[0156] Before performing the two-point method estimation, the disclosed embodiment may first determine whether the vehicle meets the stationary condition. If it is determined that the vehicle meets the stationary condition, the current SOC and the accumulated charging capacity may be stored. Here, the SOC may be determined by looking up the OCV-SOC table. On this basis, according to the stored SOC, determine whether the conditions for calculating the capacity retention rate using the two-point method are met, where the conditions for calculating the capacity retention rate using the two-point method may be whether the difference in SOC from the start of charging to the end of charging is greater than a preset difference. If the stored SOC meets the conditions for calculating the capacity retention rate using the two-point method, the second capacity retention rate may be calculated using the two-point method.

[0157] At this time, the embodiment of the present disclosure can remove abnormal data based on big data means, that is, screen the first capacity retention rate and the second capacity retention rate, and then fuse the screened capacity retention rates to obtain the target capacity retention rate. On this basis, the target capacity retention rates calculated by the two-point method and the double estimation method are evaluated and output.

[0158] It should be noted that if it is determined that the vehicle does not meet the stationary condition, the present solution is terminated, that is, there is no need to store the current SOC and the accumulated charging capacity.

[0159] Based on big data statistical methods and through analysis of the operating rules of commercial vehicles throughout their life cycle, the disclosed embodiments can combine battery mechanisms and improve the dual Kalman filter model to form an accurate calculation method and architecture for the capacity retention rate of a power battery system, thereby being able to accurately calculate the capacity retention rate of the vehicle.

[0160] After collecting the charging characteristic data of the power battery system, the embodiment of the present disclosure can build a dual Kalman filter model based on a nonlinear system, and can also use a combination of a two-point method and a dual estimation method to obtain the capacity retention rate of the battery. In addition, in the process of obtaining the capacity retention rate, the embodiment of the present disclosure can analyze the characteristic data in combination with big data statistical analysis and statistical means, which can reduce interference factors. On this basis, the capacity retention rate of the power battery system is calculated to evaluate the current health status of the battery. It can be seen that the embodiment of the present disclosure has high interpretability.

[0161] Figure 5 is a block diagram of a battery capacity retention rate estimation device 500 according to an exemplary embodiment. Figure 5 As shown, the battery capacity retention rate estimation device 500 may include a collection module 510 , a determination module 520 , an acquisition module 530 and a fusion module 540 .

[0162] The acquisition module 510 is configured to acquire charging characteristic data of the battery and pre-process the charging characteristic data to obtain data to be processed;

[0163] The determination module 520 is configured to determine a first capacity retention rate corresponding to the data to be processed based on a dual estimation method, and to determine a second capacity retention rate corresponding to the data to be processed based on a two-point method;

[0164] The acquisition module 530 is configured to acquire a first historical capacity retention rate and a second historical capacity retention rate, wherein the first historical capacity retention rate is determined by a plurality of historical capacity retention rates acquired by the dual estimation method, and the second historical capacity retention rate is determined by a plurality of historical capacity retention rates acquired by the two-point method;

[0165] The fusion module 540 is configured to determine a weight coefficient of the first capacity retention rate and the second capacity retention rate according to a size relationship between the first historical capacity retention rate and the second historical capacity retention rate, and to fuse the first capacity retention rate and the second capacity retention rate based on the weight coefficient to obtain a target capacity retention rate.

[0166] In some embodiments, the fusion module 540 is also configured to obtain the larger historical capacity retention rate between the first historical capacity retention rate and the second historical capacity retention rate. If it is determined that a first preset percentage of the first current capacity retention rate corresponding to the larger historical capacity retention rate is less than the second current capacity retention rate corresponding to the smaller historical capacity retention rate, the first current capacity retention rate and the second current capacity retention rate are fused based on a preset weight coefficient to obtain the target capacity retention rate.

[0167] In some embodiments, the fusion module 540 is also configured to multiply the first current capacity retention rate by a first preset percentage to obtain a first result, and to multiply the second current capacity retention rate by a second preset percentage to obtain a second result, wherein the first preset percentage is greater than the second preset percentage, and the sum of the first preset percentage and the second preset percentage is 1; and determine the target capacity retention rate based on the first result and the second result.

[0168] In some embodiments, the fusion module 540 is further configured to use the first current capacity retention rate as the target capacity retention rate if it is determined that a first preset percentage of the first current capacity retention rate is greater than or equal to the second current capacity retention rate.

[0169] In some embodiments, the battery capacity retention rate estimation device 500 may further include:

[0170] A third capacity retention rate acquisition module is configured to acquire the current cumulative capacity, and determine a third capacity retention rate corresponding to the cumulative capacity based on a first fitting relationship equation acquired in advance, wherein the first fitting relationship equation is a relationship acquired by fitting a plurality of historical target capacity retention rates and a historical cumulative capacity corresponding to each of the historical target capacity retention rates based on a least squares method;

[0171] The secondary fusion module is configured to fuse the target capacity retention rate with the third capacity retention rate to obtain a final capacity retention rate when it is determined that the target capacity retention rate is less than the third capacity retention rate.

[0172] In some embodiments, the battery capacity retention rate estimation device 500 may further include:

[0173] an information acquisition module, configured to acquire current time information or mileage information when the battery is not in a charging state;

[0174] The real-time retention rate acquisition module is configured to acquire the target capacity retention rate corresponding to the time information based on a second fitting relationship, or to acquire the target capacity retention rate corresponding to the mileage information based on a third fitting relationship, wherein the second fitting relationship is a relationship acquired by fitting a plurality of historical target capacity retention rates with the time information corresponding to each of the historical target capacity retention rates based on the least squares method, and the third fitting relationship is a relationship acquired by fitting each of the historical target capacity retention rates with the corresponding mileage information based on the least squares method.

[0175] In some embodiments, the acquisition module 530 is also configured to acquire multiple historical first capacity retention rates, and use the average of the multiple historical first capacity retention rates as the first historical capacity retention rate; acquire multiple historical second capacity retention rates, and use the average of the multiple historical second capacity retention rates as the second historical capacity retention rate.

[0176] The disclosed embodiment can improve the accuracy of obtaining the capacity retention rate by fusing the capacity retention rates obtained in different ways. Specifically, the charging characteristic data of the battery is collected, and the charging characteristic data is preprocessed to obtain the data to be processed. The first capacity retention rate corresponding to the data to be processed is determined based on the double estimation method, and the second capacity retention rate corresponding to the data to be processed is determined based on the two-point method. On this basis, the weight coefficient of the first capacity retention rate and the second capacity retention rate is determined according to the size relationship between the first historical capacity retention rate and the second historical capacity retention rate, and the first capacity retention rate and the second capacity retention rate are fused based on the weight coefficient to obtain the target capacity retention rate. Since the target capacity retention rate is obtained by fusing the first capacity retention rate and the second capacity retention rate, the obtained capacity retention rate is more accurate, that is, fusing the two-point method with the double estimation method can make full use of their respective advantages and improve the accuracy and robustness of the battery capacity retention rate estimation. In addition, by combining the prediction ability of the model with the actual measurement of the data, the target capacity retention rate finally obtained can be closer to the actual state of the battery.

[0177] Regarding the battery capacity retention rate estimation device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the battery capacity retention rate estimation method, and will not be elaborated here.

[0178] Figure 6 is a block diagram of a vehicle 600 according to an exemplary embodiment. For example, the vehicle 600 may be a hybrid vehicle, a non-hybrid vehicle, an electric vehicle, a fuel cell vehicle, or other types of vehicles. The vehicle 600 may be an autonomous vehicle or a semi-autonomous vehicle.

[0179] Reference Figure 6, the vehicle 600 may include various subsystems, for example, an infotainment system 610, a perception system 620, a decision control system 630, a drive system 640, and a computing platform 650. The vehicle 600 may also include more or fewer subsystems, and each subsystem may include multiple components. In addition, each subsystem and each component of the vehicle 600 may be interconnected by wire or wireless means.

[0180] In some embodiments, the infotainment system 610 may include a communication system, an entertainment system, and a navigation system, among others.

[0181] The perception system 620 may include several sensors for sensing information about the environment around the vehicle 600. For example, the perception system 620 may include a global positioning system (the global positioning system may be a GPS system, or a Beidou system or other positioning systems), an inertial measurement unit (IMU), a laser radar, a millimeter wave radar, an ultrasonic radar, and a camera.

[0182] The decision control system 630 may include a computing system, a vehicle controller, a steering system, a throttle, and a braking system.

[0183] The drive system 640 may include components that provide powered motion for the vehicle 600. In one embodiment, the drive system 640 may include an engine, an energy source, a transmission system, and wheels. The engine may be one or a combination of an internal combustion engine, an electric motor, and an air compression engine. The engine is capable of converting energy provided by the energy source into mechanical energy.

[0184] Some or all functions of the vehicle 600 are controlled by a computing platform 650. The computing platform 650 may include at least one processor 651 and a memory 652, and the processor 651 may execute instructions 653 stored in the memory 652.

[0185] The processor 651 may be any conventional processor, such as a commercially available CPU. The processor may also include a graphics processor (Graphic Process Unit, GPU), a field programmable gate array (Field Programmable Gate Array, FPGA), a system on chip (System on Chip, SOC), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC) or a combination thereof.

[0186] The memory 652 may be implemented by any type of volatile or nonvolatile memory device or a combination thereof, 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 memory, flash memory, magnetic disk or optical disk.

[0187] In addition to the instructions 653 , the memory 652 may also store data, such as road maps, route information, and data such as the location, direction, and speed of the vehicle. The data stored in the memory 652 may be used by the computing platform 650 .

[0188] In the embodiment of the present disclosure, the processor 651 may execute instruction 653 to complete all or part of the steps of the above-mentioned method for estimating the capacity retention rate of the battery.

[0189] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the computer program instructions are executed by a processor, the steps of the above-mentioned method for estimating the capacity retention rate of the battery are implemented. For example, the computer-readable storage medium may be the above-mentioned memory 652 including program instructions, and the above-mentioned program instructions may be executed by the processor 651 of the vehicle 600 to complete the above-mentioned method for estimating the capacity retention rate of the battery.

[0190] In another exemplary embodiment, a computer program product is further provided. The computer program product includes a computer program executable by a processor. When the computer program is executed by the processor, the steps of the above-mentioned method for estimating the capacity retention rate of a battery are implemented.

[0191] The preferred embodiments of the present disclosure are described in detail above in conjunction with the accompanying drawings; however, the present disclosure is not limited to the specific details in the above embodiments. Within the technical concept of the present disclosure, a variety of simple modifications can be made to the technical solution of the present disclosure, and these simple modifications all fall within the protection scope of the present disclosure.

[0192] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present disclosure will not further describe various possible combinations.

[0193] In addition, various embodiments of the present disclosure may be arbitrarily combined, and as long as they do not violate the concept of the present disclosure, they should also be regarded as the contents disclosed by the present disclosure.

Claims

1. A method for estimating the capacity retention rate of a battery, characterized in that: The method comprises: Collecting charging characteristic data of the battery, and preprocessing the charging characteristic data to obtain data to be processed; Determine a first capacity retention rate corresponding to the data to be processed based on a double estimation method, and determine a second capacity retention rate corresponding to the data to be processed based on a two-point method, wherein the double estimation method is used to estimate the state of charge corresponding to the data to be processed according to a state filter of a double extended Kalman filter, and estimate the battery model parameters corresponding to the data to be processed according to a parameter filter of the double extended Kalman filter, and obtain the first capacity retention rate based on the state of charge and the battery model parameters, wherein a sampling time of the state filter is less than a sampling time of the parameter filter; Obtaining a first historical capacity retention rate and a second historical capacity retention rate, wherein the first historical capacity retention rate is determined by a plurality of historical capacity retention rates obtained by the dual estimation method, and the second historical capacity retention rate is determined by a plurality of historical capacity retention rates obtained by the two-point method; Determining a weight coefficient of the first capacity retention rate and the second capacity retention rate according to a magnitude relationship between the first historical capacity retention rate and the second historical capacity retention rate, and fusing the first capacity retention rate and the second capacity retention rate based on the weight coefficient to obtain a target capacity retention rate; Obtaining the current cumulative capacity, and determining a third capacity retention rate corresponding to the cumulative capacity based on a first fitting relationship obtained in advance, wherein the first fitting relationship is a relationship obtained by fitting a plurality of historical target capacity retention rates and a historical cumulative capacity corresponding to each of the historical target capacity retention rates based on a least squares method; When it is determined that the target capacity retention rate is less than the third capacity retention rate, the target capacity retention rate is merged with the third capacity retention rate to obtain a final capacity retention rate.

2. The method for estimating the capacity retention rate of a battery according to claim 1, characterized in that: The step of determining a weight coefficient of the first capacity retention rate and the second capacity retention rate according to a magnitude relationship between the first historical capacity retention rate and the second historical capacity retention rate, and fusing the first capacity retention rate and the second capacity retention rate based on the weight coefficient to obtain a target capacity retention rate includes: The larger historical capacity retention rate between the first historical capacity retention rate and the second historical capacity retention rate is obtained. If it is determined that a first preset percentage of the first current capacity retention rate corresponding to the larger historical capacity retention rate is less than the second current capacity retention rate corresponding to the smaller historical capacity retention rate, the first current capacity retention rate and the second current capacity retention rate are merged based on a preset weight coefficient to obtain the target capacity retention rate.

3. The method for estimating the capacity retention rate of a battery according to claim 2, characterized in that: The step of fusing the first current capacity retention rate and the second current capacity retention rate based on a preset weight coefficient to obtain the target capacity retention rate includes: multiplying the first current capacity retention rate by a first preset percentage to obtain a first result, and multiplying the second current capacity retention rate by a second preset percentage to obtain a second result, wherein the first preset percentage is greater than the second preset percentage, and the sum of the first preset percentage and the second preset percentage is 1; The target capacity retention rate is determined based on the first result and the second result.

4. The method for estimating the capacity retention rate of a battery according to claim 2, characterized in that: The method further comprises: If it is determined that the first preset percentage of the first current capacity retention rate is greater than or equal to the second current capacity retention rate, the first current capacity retention rate is used as the target capacity retention rate.

5. The method for estimating the capacity retention rate of a battery according to claim 1, characterized in that: The method further comprises: When the battery is not in a charging state, obtaining current time information or mileage information; The target capacity retention rate corresponding to the time information is obtained based on a second fitting relationship, or the target capacity retention rate corresponding to the mileage information is obtained based on a third fitting relationship, wherein the second fitting relationship is a relationship obtained by fitting multiple historical target capacity retention rates and the time information corresponding to each of the historical target capacity retention rates based on the least squares method, and the third fitting relationship is a relationship obtained by fitting each of the historical target capacity retention rates and the corresponding mileage information based on the least squares method.

6. The method for estimating the capacity retention rate of a battery according to any one of claims 1 to 5, characterized in that: The method further comprises: Acquire a plurality of historical first capacity retention rates, and use an average of the plurality of historical first capacity retention rates as the first historical capacity retention rate; A plurality of historical second capacity retention rates are obtained, and an average of the plurality of historical second capacity retention rates is used as the second historical capacity retention rate.

7. A battery capacity retention rate estimation device, characterized in that: The device comprises: A collection module is configured to collect charging characteristic data of the battery and pre-process the charging characteristic data to obtain data to be processed; a determination module configured to determine a first capacity retention rate corresponding to the data to be processed based on a double estimation method, and to determine a second capacity retention rate corresponding to the data to be processed based on a two-point method, wherein the double estimation method is used to estimate the state of charge corresponding to the data to be processed according to a state filter of a double extended Kalman filter, and to estimate the battery model parameters corresponding to the data to be processed according to a parameter filter of the double extended Kalman filter, and to obtain the first capacity retention rate based on the state of charge and the battery model parameters, wherein a sampling time of the state filter is less than a sampling time of the parameter filter; an acquisition module configured to acquire a first historical capacity retention rate and a second historical capacity retention rate, wherein the first historical capacity retention rate is determined by a plurality of historical capacity retention rates acquired by the dual estimation method, and the second historical capacity retention rate is determined by a plurality of historical capacity retention rates acquired by the two-point method; a fusion module, configured to determine a weight coefficient of the first capacity retention rate and the second capacity retention rate according to a magnitude relationship between the first historical capacity retention rate and the second historical capacity retention rate, and fuse the first capacity retention rate and the second capacity retention rate based on the weight coefficient to obtain a target capacity retention rate; A third capacity retention rate acquisition module is configured to acquire the current cumulative capacity, and determine a third capacity retention rate corresponding to the cumulative capacity based on a first fitting relationship equation acquired in advance, wherein the first fitting relationship equation is a relationship acquired by fitting a plurality of historical target capacity retention rates and a historical cumulative capacity corresponding to each of the historical target capacity retention rates based on a least squares method; The secondary fusion module is configured to fuse the target capacity retention rate with the third capacity retention rate to obtain a final capacity retention rate when it is determined that the target capacity retention rate is less than the third capacity retention rate.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method described in any one of claims 1 to 6 are implemented.

9. A vehicle, characterized in that: include: a memory having a computer program stored thereon; A processor, configured to execute the computer program in the memory to implement the steps of the method according to any one of claims 1 to 6.

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