Method and device for correcting state of health of power battery, vehicle and storage medium

Abstract

The application relates to the technical field of battery management, in particular to a SOH correction method and device of a power battery, a vehicle and a storage medium, wherein the method comprises the following steps: acquiring a battery type and battery data of the power battery; determining an execution sequence of an estimation strategy of a state of health (SOH) of the power battery according to the battery type, wherein the estimation strategy comprises a vehicle-side estimation strategy and a cloud-side estimation strategy; executing the estimation strategy according to the execution sequence, calculating a target SOH of the power battery according to the estimation strategy and the battery data, and correcting a current SOH of the power battery to the target SOH. Therefore, the problems in the prior art that the SOH correction method has low coverage, poor adaptability, cannot adapt to different types of batteries and has large SOH estimation deviation are solved.

Description

Technical Field

[0001] This invention relates to the field of battery management technology, and more specifically to a method, apparatus, vehicle, and storage medium for correcting the SOH (State of Health) of a power battery. Background Technology

[0002] With the continuous advancement of hybrid and electric vehicle technologies, most automakers are extensively adopting lithium-ion batteries as energy storage and power sources for existing and future vehicles. While lithium-ion batteries are widely used in various electric vehicles, their overall performance, especially cycle life, declines due to low operating temperatures and increased usage years. This affects the output power and charging rate of electric vehicles, and the remaining usable capacity of the battery also decreases accordingly. Therefore, to improve the safety of lithium-ion battery use and further expand the electric vehicle market, State of Hypothesis (SOH) estimation is an extremely important consideration for the performance and cost-effectiveness of electric vehicles. To ensure the high efficiency and safety of electric vehicles, prevent overcharging and over-discharging of batteries, extend the lifespan of lithium-ion battery systems, and predict their ultimate lifespan, estimating the battery's SOH is necessary and crucial.

[0003] An existing patent (application number: 202111137547.2) discloses a method for estimating the State of Charge (SOH) of a power battery based on the fusion of vehicle network operation data and test data. This method mainly obtains the required battery health status by establishing a database and comparing it with vehicle network operation data. The most important aspect is the establishment of the database. By conducting capacity tests on the battery under different temperatures, rates, and operating conditions, a database of SOC (State of Charge) and OCV (Open Circuit Voltage) during charging and discharging is obtained. At the same time, full-charge tests are conducted under different temperatures, rates, and operating conditions to establish a corresponding new vehicle capacity database. Based on keywords such as temperature, rate, and operating conditions from the current vehicle network operation data, the corresponding capacity information is extracted from the two databases. After calculation, the battery capacity information can be estimated. Although this method considers the impact of different temperatures, rates, and operating conditions on the estimation of battery health status, the database method still cannot cover all temperatures and rates. Moreover, the operating conditions are based on theoretical scenarios, while real driving conditions are more complex.

[0004] The existing patent, "A Method for Estimating the State of Health of Lithium-ion Batteries Based on Machine Learning and Combined State of Charge" (application number: 202110152863.0), discloses a machine learning method based on offline data. This method establishes an OCV-SOC fitting relationship, builds an equivalent circuit model of the lithium-ion battery, and then identifies parameters of the voltage rebound characteristic curve within a charge-discharge cycle to obtain a machine learning model and calculate the State of Health (SOH). However, this method imposes a significant computational burden, increasing the computational cost for its application in vehicles. Similar to building empirical models in vehicles, improving the accuracy of battery state estimation often requires a large amount of data samples for training and optimization, which greatly increases the computational difficulty. Since real-time computing conditions are generally not available in vehicles, combining cloud servers with battery modeling is particularly important and urgent. Summary of the Invention

[0005] One objective of this invention is to provide a SOH correction method for power batteries, in order to solve the problems of low coverage, poor adaptability, inability to adapt to different types of batteries, and large deviation in SOH estimation in existing SOH correction methods; a second objective is to provide a SOH correction device for power batteries; a third objective is to provide a vehicle; and a fourth objective is to provide a computer-readable storage medium.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A method for correcting the State of Health (SOH) of a power battery, applied to a vehicle, comprising the following steps: acquiring the battery type and battery data of the power battery; determining the execution order of an estimation strategy for the SOH of the power battery based on the battery type, wherein the estimation strategy includes a vehicle-side estimation strategy and a cloud-based estimation strategy; executing the estimation strategy according to the execution order; calculating the target SOH of the power battery based on the estimation strategy and the battery data; and correcting the current SOH of the power battery to the target SOH.

[0008] Based on the above technical means, the embodiments of this application can determine the execution order of the SOH estimation strategy of the power battery according to different battery types, and can provide different SOH estimation strategies to adapt to different types of vehicles. The target SOH of the power battery is calculated based on the results obtained from the estimation strategy and battery data. The SOH of the power battery is corrected using the target SOH. By making reasonable use of vehicle-side estimation strategies and cloud-side estimation strategies, the applicability of SOH estimation is improved through vehicle-cloud fusion, the computational burden on the vehicle side is reduced, the update frequency of the calculation results is increased, the accuracy of SOH calculation is guaranteed, and the coverage and adaptability of the SOH correction method are improved.

[0009] Furthermore, the vehicle-side estimation strategy includes a dynamic estimation strategy and a static estimation strategy.

[0010] Furthermore, the execution order of the estimation strategy for determining the State of Health (SOH) of the power battery based on the battery type includes: if the battery type is a first battery type, the execution order is the dynamic estimation strategy, the static estimation strategy, and the cloud-based estimation strategy in sequence; or, the execution order is the dynamic estimation strategy, the cloud-based estimation strategy, and the static estimation strategy in sequence, wherein the voltage curvature of the first battery type with respect to the State of Charge (SOC) is greater than a preset curvature; if the battery type is a second battery type, the execution order is the static estimation strategy, the cloud-based estimation strategy, and the static estimation strategy in sequence; or, the execution order is the cloud-based estimation strategy, the static estimation strategy, and the dynamic estimation strategy in sequence, wherein the voltage curvature of the first battery type with respect to the SOC is less than or equal to a preset curvature.

[0011] Based on the above technical means, the embodiments of this application can execute different estimation strategies according to different battery types, taking into account the situation where different battery types need to perform SOH.

[0012] Furthermore, the step of calculating the target SOH of the power battery based on the estimation strategy and the battery data includes: determining whether the SOH estimated by the current estimation strategy meets the preset reasonable conditions; if the SOH estimated by the current estimation strategy meets the preset reasonable conditions, then the SOH estimated by the current estimation strategy is taken as the target SOH; otherwise, the next estimation strategy is executed; if the SOH estimated by the last estimation strategy does not meet the preset reasonable conditions, then the SOH of the power battery is not updated.

[0013] Based on the above technical means, the embodiments of this application can take the estimated SOH as the target SOH of the power battery when it meets the preset reasonable conditions; otherwise, the next strategy will be executed to ensure the accuracy of the calculation.

[0014] Furthermore, if the battery type is the first battery type, and the SOH estimated by the last estimation strategy meets the preset reasonable condition, the method further includes: taking the average of the historical SOH of the estimation strategies whose SOH does not meet the preset reasonable condition as the SOH of the corresponding estimation strategy; performing a weighted average based on the SOH estimated by all estimation strategies and their respective weights to obtain a weighted average value, and using the weighted average value to correct the SOC and / or remaining charging time of the power battery.

[0015] Based on the above technical means, the embodiments of this application can take the average of the historical SOH obtained by the estimation strategy that does not meet the preset reasonable conditions as the SOH of the corresponding estimation strategy, and perform a weighted average of the SOH estimated by all estimation strategies to correct the SOC and / or remaining charging time of the power battery.

[0016] Furthermore, if the battery type is the second battery type, and the SOH estimated by the static estimation strategy does not meet the preset reasonable conditions, the method further includes: correcting the charging rate and / or discharging power based on the SOH estimated by the cloud estimation strategy.

[0017] Based on the above technical means, the embodiments of this application can correct the charging rate and / or discharging power using the SOH estimated by the cloud estimation strategy when the battery type is the second battery type and the SOH of the static estimation strategy does not meet the preset reasonable conditions, so as to ensure the accuracy of the estimation.

[0018] Furthermore, determining whether the SOH estimated by the current estimation strategy meets the preset reasonableness conditions includes: determining whether the SOH estimated by the current estimation strategy is within a preset range; if the SOH estimated by the current estimation strategy is within the preset range, then determining that the SOH estimated by the current estimation strategy meets the preset reasonableness conditions.

[0019] Based on the above technical means, the embodiments of this application can estimate that the SOH estimated by the strategy is within a preset range, and determine that the estimated SOH meets the preset reasonable conditions.

[0020] Furthermore, the battery data includes one or more of the following: resting time before and after charging, charging temperature, open-circuit voltage (OCV) at the start of charging, OCV at the end of charging, charging current, and charging duration. The dynamic estimation strategy includes: determining the state of charge (SOC) before and after charging based on the charging temperature, OCV at the start of charging, and OCV at the end of charging; obtaining the actual charging capacity by integrating the charging current and the charging duration in ampere-hours, and calculating the SOC change based on the SOC before and after charging; calculating the current capacity of the power battery based on the actual charging capacity and the SOC change; and calculating the vehicle-side state of equilibrium (SOH) of the power battery based on the current capacity and the initial capacity of the power battery.

[0021] Based on the above technical means, the embodiments of this application can perform ampere-hour integration calculation on the charging current and charging time to obtain the actual charging capacity, calculate the SOC change based on the obtained SOC before charging and SOC after charging, calculate the current capacity of the power battery based on the actual charging capacity and SOC change, and finally calculate the vehicle-side dynamic SOH based on the current capacity and the initial capacity of the power battery.

[0022] Furthermore, the battery data also includes the resting time before and after charging. Before determining the SOC before and after charging based on the charging temperature, the OCV at the start of charging, and the OCV at the end of charging, the data further includes: determining whether the resting time before and after charging is less than or equal to a time threshold; if the resting time before and after charging is less than or equal to the time threshold, then the resting time before and after charging and the OCV are fitted to obtain the OCV at the start of charging and the OCV at the end of charging.

[0023] Based on the above technical means, the embodiments of this application can determine whether the resting time before and after charging reaches the specified threshold. If it does not reach the threshold, the voltage is obtained according to the fitted resting time and OCV relationship and then subsequent calculations are performed to obtain the dynamically calculated SOH.

[0024] Furthermore, the static estimation strategy includes: obtaining the current vehicle model; determining the correction coefficient of the battery data based on the current vehicle model; correcting the battery data based on the correction coefficient; and calculating the first vehicle-side SOH of the power battery using the corrected battery data.

[0025] Based on the above technical means, the embodiments of this application can correct the battery data according to different vehicle models, and use the corrected data to calculate the vehicle-side SOH of the power battery, thereby realizing the calculation of the vehicle-side SOH of the power battery according to different vehicle models and improving the accuracy of SOH calculation.

[0026] Furthermore, the battery data includes one or more of the following: cumulative charging capacity, cumulative discharging capacity, cumulative mileage, cumulative parking time, pure electric mileage, and gasoline-powered mileage.

[0027] Furthermore, the current vehicle model includes a first vehicle model and a second vehicle model. The step of determining the correction coefficient of the battery data based on the current vehicle model includes: if the current vehicle model is the first vehicle model, then calculating a first correction coefficient of the battery data based on the cumulative discharge capacity and the cumulative charging capacity; if the current vehicle model is the second vehicle model, then calculating a second correction coefficient of the battery data based on the pure electric driving range and the gasoline driving range.

[0028] Based on the above technical means, the embodiments of this application can use different calculation methods to obtain the correction coefficient of battery data according to different vehicle models, so as to correct the battery data of different vehicle models.

[0029] Furthermore, the step of correcting the battery data according to the correction coefficient includes: if the current vehicle model is the first vehicle model, then the cumulative charging capacity and the cumulative parking time are corrected using the first correction coefficient; if the current vehicle model is the second vehicle model, then the cumulative charging capacity and the cumulative parking time are corrected using the second correction coefficient.

[0030] Furthermore, before calculating the first correction factor for the battery data based on the cumulative discharge capacity and the cumulative charge capacity, the method further includes: correcting the cumulative discharge capacity based on the cumulative mileage.

[0031] Furthermore, the cloud estimation strategy is applied to the server, and the cloud estimation strategy includes: acquiring battery data of the power battery uploaded by the vehicle; clustering the battery data to obtain the number of clusters and clustering parameters, calculating the membership matrix of each data point in the battery data based on the number of clusters and the clustering parameters; updating the cluster centers based on the membership matrix, and performing iterative clustering until the cluster centers remain unchanged or a preset number of iterations is reached, determining the clustering result of the battery data based on the cluster centers; and calculating the cloud SOH of the power battery based on the clustering result.

[0032] Based on the above technical means, the embodiments of this application can cluster battery data to obtain the number of clusters and clustering parameters, and calculate the membership matrix of each data point in the battery data according to the number of clusters and clustering parameters, and use it to update the cluster center until the cluster center no longer changes or reaches a predetermined number of iterations. The clustering result of the battery data is determined according to the cluster center, and finally the cloud SOH is calculated according to the clustering result, so as to make reasonable use of cloud computing resources.

[0033] Furthermore, the battery data uploaded by the vehicle includes one or more of voltage, current, and temperature.

[0034] Furthermore, the clustering results include a maximum voltage value and a minimum voltage value. The calculation of the cloud-based SOH of the power battery based on the clustering results includes: calculating a first voltage difference based on the voltage and the minimum voltage value; calculating a second voltage difference based on the voltage and the maximum voltage value; and calculating the cloud-based SOH of the power battery based on the first voltage difference and the second voltage difference.

[0035] Based on the above-mentioned technical means, the embodiments of this application can calculate the cloud-based SOH according to the voltage.

[0036] Furthermore, before clustering the battery data to obtain the number of clusters and clustering parameters, the method further includes: normalizing the battery data to obtain normalized battery data.

[0037] Based on the above technical means, the embodiments of this application can normalize battery data to eliminate differences between attributes and improve the convergence speed and accuracy of subsequent calculations.

[0038] A State of Health (SOH) correction device for a power battery, the device being applied to a vehicle, comprising: an acquisition module for acquiring the battery type and battery data of the power battery; a determination module for determining the execution order of an estimation strategy for the SOH of the power battery based on the battery type, wherein the estimation strategy includes a vehicle-side estimation strategy and a cloud-based estimation strategy; and a correction module for executing the estimation strategy according to the execution order, calculating a target SOH of the power battery based on the estimation strategy and the battery data, and correcting the current SOH of the power battery to the target SOH.

[0039] Furthermore, the vehicle-side estimation strategy includes a dynamic estimation strategy and a static estimation strategy.

[0040] Furthermore, the determining module is further configured to: if the battery type is a first battery type, then the execution order is the dynamic estimation strategy, the static estimation strategy, and the cloud estimation strategy in sequence, or the execution order is the dynamic estimation strategy, the cloud estimation strategy, and the static estimation strategy in sequence, wherein the voltage curvature of the first battery type with respect to the state of charge (SOC) is greater than a preset curvature; if the battery type is a second battery type, then the execution order is the static estimation strategy, the cloud estimation strategy, and the static estimation strategy in sequence, or the execution order is the cloud estimation strategy, the static estimation strategy, and the dynamic estimation strategy in sequence, wherein the voltage curvature of the first battery type with respect to the SOC is less than or equal to a preset curvature.

[0041] Furthermore, the correction module is further configured to: determine whether the SOH estimated by the current estimation strategy meets the preset reasonable conditions; if the SOH estimated by the current estimation strategy meets the preset reasonable conditions, then the SOH estimated by the current estimation strategy is taken as the target SOH, otherwise the next estimation strategy is executed; if the SOH estimated by the last estimation strategy does not meet the preset reasonable conditions, then the SOH of the power battery is not updated.

[0042] Furthermore, the correction module is further configured to: take the average historical SOH of the estimation strategy that does not meet the preset reasonable conditions as the SOH of the corresponding estimation strategy; perform a weighted average by averaging the SOH estimated by all estimation strategies and their respective weights to obtain a weighted average value, and use the weighted average value to correct the SOC and / or remaining charging time of the power battery.

[0043] Furthermore, the correction module is further used to: correct the charging rate and / or discharging power based on the SOH estimated by the cloud estimation strategy.

[0044] Furthermore, the correction module is further configured to: determine whether the SOH estimated by the current estimation strategy is within a preset range; if the SOH estimated by the current estimation strategy is within the preset range, then determine that the SOH estimated by the current estimation strategy meets the preset reasonableness condition.

[0045] Furthermore, the battery data includes one or more of the following: resting time before and after charging, charging temperature, open-circuit voltage (OCV) at the start of charging, OCV at the end of charging, charging current, and charging duration. The dynamic estimation strategy includes: determining the state of charge (SOC) before and after charging based on the charging temperature, OCV at the start of charging, and OCV at the end of charging; obtaining the actual charging capacity by integrating the charging current and the charging duration in ampere-hours, and calculating the SOC change based on the SOC before and after charging; calculating the current capacity of the power battery based on the actual charging capacity and the SOC change; and calculating the vehicle-side state of equilibrium (SOH) of the power battery based on the current capacity and the initial capacity of the power battery.

[0046] Furthermore, the battery data also includes the resting time before and after charging. The SOH correction device for the power battery further includes: a judgment module, used to determine whether the resting time before and after charging is less than or equal to a time threshold before determining the SOC before and after charging based on the charging temperature, the OCV at the start of charging, and the OCV at the end of charging; if the resting time before and after charging is less than or equal to the time threshold, then the resting time before and after charging and the OCV are fitted to obtain the OCV at the start of charging and the OCV at the end of charging.

[0047] Furthermore, the static estimation strategy includes: obtaining the current vehicle model; determining the correction coefficient of the battery data based on the current vehicle model; correcting the battery data based on the correction coefficient; and calculating the vehicle-side SOH of the power battery using the corrected battery data.

[0048] Furthermore, the battery data includes one or more of the following: cumulative charging capacity, cumulative discharging capacity, cumulative mileage, cumulative parking time, pure electric mileage, and gasoline-powered mileage.

[0049] Furthermore, the current vehicle model includes a first vehicle model and a second vehicle model. The step of determining the correction coefficient of the battery data based on the current vehicle model includes: if the current vehicle model is the first vehicle model, then calculating a first correction coefficient of the battery data based on the cumulative discharge capacity and the cumulative charging capacity; if the current vehicle model is the second vehicle model, then calculating a second correction coefficient of the battery data based on the pure electric driving range and the gasoline driving range.

[0050] Furthermore, the step of correcting the battery data according to the correction coefficient includes: if the current vehicle model is the first vehicle model, then the cumulative charging capacity and the cumulative parking time are corrected using the first correction coefficient; if the current vehicle model is the second vehicle model, then the cumulative charging capacity and the cumulative parking time are corrected using the second correction coefficient.

[0051] Furthermore, before calculating the first correction factor for the battery data based on the cumulative discharge capacity and the cumulative charge capacity, the method further includes: correcting the cumulative discharge capacity based on the cumulative mileage.

[0052] Furthermore, the cloud estimation strategy is applied to the server, and the cloud estimation strategy includes: acquiring battery data of the power battery uploaded by the vehicle; clustering the battery data to obtain the number of clusters and clustering parameters, calculating the membership matrix of each data point in the battery data based on the number of clusters and the clustering parameters; updating the cluster centers based on the membership matrix, and performing iterative clustering until the cluster centers remain unchanged or a preset number of iterations is reached, determining the clustering result of the battery data based on the cluster centers; and calculating the cloud SOH of the power battery based on the clustering result.

[0053] Furthermore, the battery data uploaded by the vehicle includes one or more of voltage, current, and temperature.

[0054] Furthermore, the clustering results include a maximum voltage value and a minimum voltage value. The calculation of the cloud-based SOH of the power battery based on the clustering results includes: calculating a first voltage difference based on the voltage and the minimum voltage value; calculating a second voltage difference based on the voltage and the maximum voltage value; and calculating the cloud-based SOH of the power battery based on the first voltage difference and the second voltage difference.

[0055] Furthermore, before clustering the battery data to obtain the number of clusters and clustering parameters, the method further includes: normalizing the battery data to obtain normalized battery data.

[0056] A vehicle includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the SOH correction method for a power battery as described in the above embodiments.

[0057] A computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the SOH correction method for a power battery as described in the above embodiments.

[0058] The beneficial effects of this invention are:

[0059] This application embodiment can provide different SOH estimation strategies and determine the execution order of estimation strategies according to the battery type. It combines cloud-based SOH estimation strategies and vehicle-based estimation strategies, making reasonable use of both vehicle-based and cloud-based estimation strategies. By integrating vehicle and cloud, it improves the applicability of SOH estimation. By using cloud-based SOH estimation results and combining them with vehicle-based estimation strategies, it reduces the computational burden on the vehicle, increases the update frequency of calculation results, and ensures the accuracy of SOH calculation, thereby improving the coverage and adaptability of the SOH estimation method.

[0060] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0061] Figure 1 A schematic flowchart of the SOH correction method for a power battery provided in an embodiment of the present invention;

[0062] Figure 2 A schematic diagram of a battery SOH estimation method provided in a specific embodiment of the present invention;

[0063] Figure 3 This diagram illustrates the update order of the three SOH estimation methods provided in this embodiment of the invention under different application scenarios.

[0064] Figure 4 A block diagram illustrating the SOH correction device for a power battery provided in an embodiment of the present invention;

[0065] Figure 5 This is a structural schematic diagram of a vehicle provided in an embodiment of the present invention. Detailed Implementation

[0066] The embodiments of the present invention will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention and not for limiting the scope of protection of the present invention.

[0067] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0068] Specifically, Figure 1 This is a schematic flowchart of a power battery SOH correction method provided in an embodiment of this application.

[0069] like Figure 1 As shown, the SOH correction method for this power battery, applied to vehicles, includes the following steps:

[0070] In step S101, the battery type and battery data of the power battery are obtained.

[0071] Among them, there are various types of power batteries, including ternary lithium batteries and lithium iron phosphate batteries.

[0072] In this application, the battery data includes one or more of the following: rest time before and after charging, charging temperature, open circuit voltage (OCV) at the start of charging, OCV at the end of charging, charging current, and charging duration.

[0073] In step S102, the execution order of the estimation strategy for the State of Health (SOH) of the power battery is determined according to the battery type. The estimation strategy includes the vehicle-side estimation strategy and the cloud-based estimation strategy.

[0074] Among them, the vehicle-side estimation strategy includes dynamic estimation strategy and static estimation strategy.

[0075] It is understood that, according to the battery type, the execution order of the battery health state (SOH) estimation strategy of the power battery can be determined. The specific estimation strategy for each battery type is as follows.

[0076] In this embodiment of the application, the execution order of the battery health state (SOH) estimation strategy for determining the power battery's state of health (SOH) based on the battery type includes: if the battery type is a first battery type, the execution order is dynamic estimation strategy, static estimation strategy, and cloud estimation strategy in sequence; or, the execution order is dynamic estimation strategy, cloud estimation strategy, and static estimation strategy in sequence, wherein the voltage curvature of the first battery type with the change of SOC is greater than a preset curvature; if the battery type is a second battery type, the execution order is static estimation strategy, cloud estimation strategy, and static estimation strategy in sequence; or, the execution order is cloud estimation strategy, static estimation strategy, and dynamic estimation strategy in sequence, wherein the voltage curvature of the first battery type with the change of SOC is less than or equal to a preset curvature.

[0077] The first battery type can be a ternary lithium battery, and the second battery type can be a lithium iron phosphate battery.

[0078] It is understood that, in the embodiments of this application, if the battery type is a first battery type, the estimation principle is to use a dynamic estimation strategy as the main approach, supplemented by a static estimation strategy and a cloud-based estimation strategy. The specific execution order can be either dynamic estimation strategy, static estimation strategy, and cloud-based estimation strategy, or dynamic estimation strategy, cloud-based estimation strategy, and static estimation strategy. If the battery type is a second battery type, the estimation principle is to use a static estimation strategy and a cloud-based estimation strategy as the main approach, supplemented by a dynamic estimation strategy. The specific execution order can be either cloud-based estimation strategy, static estimation strategy, and dynamic estimation strategy.

[0079] The following examples will illustrate dynamic estimation strategies, static estimation strategies, and cloud-based estimation strategies, respectively.

[0080] I. Dynamic Estimation Strategy

[0081] The battery data used in the dynamic estimation strategy includes one or more of the following: rest time before and after charging, charging temperature, open-circuit voltage (OCV) at the start of charging, OCV at the end of charging, charging current, and charging duration. The dynamic estimation strategy includes: determining the State of Charge (SOC) before and after charging based on the charging temperature, OCV at the start of charging, and OCV at the end of charging; obtaining the actual charging capacity by integrating the charging current and charging duration in ampere-hours, and calculating the SOC change based on the SOC before and after charging; calculating the current capacity of the power battery based on the actual charging capacity and the SOC change; and calculating the vehicle-side State of Harmony (SOH) of the power battery based on the current capacity and the initial capacity of the power battery.

[0082] Understandably, this application can look up tables based on different battery types, charging temperatures, OCV at the start of charging, and OCV at the end of charging to obtain the SOC before and after charging. It can then calculate the actual charging capacity by performing ampere-hour integration based on the charging current and charging duration. Based on the previously obtained SOC before and after charging, it can obtain the SOC change ΔSOC. Based on the actual charging capacity and the SOC change, it can calculate the current capacity. Furthermore, it can calculate the vehicle-side SOH of the power battery based on the current capacity and the initial capacity of the power battery.

[0083] The specific formula for calculating the current capacity is as follows:

[0084]

[0085] The formula for calculating SOH is:

[0086]

[0087] In this embodiment of the application, the battery data also includes the resting time before and after charging. Before determining the SOC before and after charging based on the charging temperature, the OCV at the start of charging and the OCV at the end of charging, the data further includes: determining whether the resting time before and after charging is less than or equal to a time threshold; if the resting time before and after charging is less than or equal to the time threshold, then the resting time before and after charging and the OCV are fitted to obtain the OCV at the start of charging and the OCV at the end of charging.

[0088] It is understood that, in the embodiments of this application, before determining the SOC before and after charging based on the charging temperature, the OCV at the start of charging, and the OCV at the end of charging, it is determined whether the resting time before and after charging reaches the time threshold. If the resting time does not reach the time threshold, the resting time before and after charging and the OCV are fitted to obtain the OCV at the start of charging and the OCV at the end of charging, and then subsequent calculations are performed. Otherwise, the obtained voltage can be used directly for subsequent calculations.

[0089] The specific formula for the OCV fitting function is as follows:

[0090] V(t)=OCV(t)-a1*exp(-b1*t)-a2*exp(-b2*t)-a3*exp(-b3*t),

[0091] Where a1, a2, a3, b1, b2, b3 represent the fitting coefficients.

[0092] It should be noted that the dynamic estimation strategy described in this application is applied to the vehicle side. Since the vehicle side is not suitable for real-time training and updating of large battery models, the capacity increment and SOC before and after charging are calculated using the charging segment. This method has low computational cost and high accuracy, but requires a long resting time to obtain an accurate OCV value. To reduce the impact of this drawback, OCV fitting is performed for different resting times to obtain a more accurate SOH. However, this method is more suitable for ternary lithium battery packs in practical applications, and may be difficult to trigger updates for lithium iron phosphate batteries. Therefore, to address the problems of long plateau periods and limited high- and low-end trigger update capabilities of lithium iron phosphate batteries, the following static estimation strategy is designed.

[0093] II. Static Estimation Strategy

[0094] The battery data used in the static estimation strategy includes one or more of the following: cumulative charging capacity, cumulative discharging capacity, cumulative mileage, cumulative parking time, pure electric mileage, and gasoline-powered mileage. The static estimation strategy includes: obtaining the current vehicle model; determining the correction coefficient for the battery data based on the current vehicle model; correcting the battery data based on the correction coefficient; and calculating the vehicle-side SOH of the power battery using the corrected battery data.

[0095] The current models include the first model and the second model.

[0096] It should be noted that if the above dynamic estimation strategy is applied to lithium iron phosphate batteries where the SOH has not been updated for a long time, a static estimation strategy can be adopted. That is, the correction coefficient of the battery data can be determined according to the vehicle model, the battery data can be corrected according to the correction coefficient, and the vehicle-side SOH of the power battery can be calculated using the corrected battery data (specifically the cumulative charging capacity and cumulative parking time).

[0097] In this embodiment of the application, determining the correction coefficient of battery data based on the current vehicle model includes: if the current vehicle model is a first vehicle model, then calculating a first correction coefficient of battery data based on the cumulative discharge capacity and the cumulative charging capacity; if the current vehicle model is a second vehicle model, then calculating a second correction coefficient of battery data based on the pure electric driving range and the gasoline driving range.

[0098] The first vehicle type can be a mild hybrid vehicle, and the second vehicle type can be a strong hybrid vehicle.

[0099] It is understood that the embodiments of this application can calculate the correction coefficient of battery data according to different vehicle models. When the current vehicle model is the first vehicle model, the first correction coefficient of battery data is calculated based on the cumulative discharge capacity and the cumulative charging capacity. When the current vehicle model is the second vehicle model, the second correction coefficient of battery data is calculated based on the pure electric driving range and the oil driving range.

[0100] The following uses the example of a mild hybrid vehicle (Type 1) and a strong hybrid vehicle (Type 2) to illustrate the calculation method of the correction coefficient.

[0101] (1) When the current vehicle model is a mild hybrid, α = cumulative discharge capacity / cumulative charging capacity, where the cumulative discharge capacity is corrected according to the cumulative mileage, and α represents the first correction coefficient.

[0102] (2) When the current vehicle model is a strong hybrid, β = pure electric driving range / (pure electric driving range + oil driving range), where β represents the second correction coefficient.

[0103] In this embodiment of the application, before calculating the first correction factor of the battery data based on the cumulative discharge capacity and the cumulative charge capacity, the method further includes: correcting the cumulative discharge capacity based on the cumulative driving mileage.

[0104] It is understood that, before calculating the correction coefficient, this application embodiment determines whether the cumulative charging capacity, cumulative discharging capacity, cumulative mileage, and cumulative parking time obtained when power-on are valid. If the cumulative mileage is less than a certain threshold, or the cumulative parking time is less than or equal to a set threshold, the validity of the cumulative charging capacity and cumulative discharging capacity is determined in the same way. Then, the cumulative discharging capacity is corrected based on the cumulative mileage. The set threshold can be specifically calibrated.

[0105] In this embodiment of the application, correcting battery data according to a correction coefficient includes: if the current vehicle model is a first vehicle model, then using a first correction coefficient to correct the cumulative charging capacity and cumulative parking time; if the current vehicle model is a second vehicle model, then using a second correction coefficient to correct the cumulative charging capacity and cumulative parking time.

[0106] Specifically, when the current vehicle model is the first model, the cumulative charging capacity is corrected using the first correction coefficient, and the cumulative charging capacity = α * Q. 纯 Q 纯 To determine the cumulative charging capacity of the pure battery pack's SOH, similarly, the cumulative parking time = α * T 纯 T 纯 This represents the cumulative parking time corresponding to the pure battery pack; when the current model is the second model, the cumulative charging capacity is corrected using a second correction factor, and the cumulative charging capacity = β * Q. 纯 Q 纯 To determine the cumulative charging capacity of the pure battery pack's SOH, similarly, the cumulative parking time = β*T 纯 T 纯 This refers to the cumulative parking time corresponding to the pure battery pack.

[0107] It should be noted that the static estimation strategy described in this application embodiment can estimate based on electric vehicle driving data such as cumulative throughput, parking time, and cumulative mileage, and further correct the above data according to the hybridization level of different vehicles to obtain the battery health status of a vehicle with a certain hybridization level. However, the static estimation strategy described in this application embodiment may have calculation deviations due to extreme cases. Therefore, it can be combined with the cloud estimation strategy described below for auxiliary correction, which can more accurately estimate the SOH while effectively avoiding the difficulty in triggering caused by the long plateau period of lithium iron phosphate batteries.

[0108] III. Cloud-based estimation strategy

[0109] The cloud-based estimation strategy is applied to the server and includes: acquiring battery data from the power battery uploaded by the vehicle; clustering the battery data to obtain the number of clusters and clustering parameters, calculating the membership matrix of each data point in the battery data based on the number of clusters and clustering parameters; updating the cluster centers based on the membership matrix and performing iterative clustering until the cluster centers remain unchanged or a preset number of iterations is reached, determining the clustering result of the battery data based on the cluster centers; and calculating the cloud-based State of Health (SOH) of the power battery based on the clustering result.

[0110] The battery data uploaded by the vehicle includes one or more of voltage, current, and temperature. The preset number of iterations can be set according to specific circumstances and is not specifically limited.

[0111] Specifically, the cloud-based estimation strategy described in the embodiments of this application includes:

[0112] 1. Determine the number of clusters and clustering parameters

[0113] Before using the Fuzzy Clustering (FCM) algorithm for clustering, the number of clusters and parameters can be determined based on the voltage, current, and temperature data uploaded to the cloud. These parameters serve as the initial values ​​for the number of dataset groups, the fuzziness factor, and the cluster centers, respectively. These can be determined using model selection techniques and experimental optimization; and cluster centers can be initialized using a cluster center algorithm (such as the K-Means algorithm).

[0114] 2. Calculate the membership matrix

[0115] The most important step in the FCM algorithm is to calculate the membership matrix, assigning each data point a membership degree to each cluster; the membership degree represents the degree to which a data point belongs to each cluster.

[0116] 3. Update cluster centers

[0117] Based on the membership matrix, the center of each cluster is recalculated, which is the mean vector of each cluster.

[0118] 4. Repeated iterations and result determination

[0119] Repeat steps 1-3 until the cluster centers no longer change or the predetermined number of iterations is reached. Assign each data point to the nearest cluster center according to the membership matrix and clarify the clustering results. Calculate the required SOH value based on the voltage.

[0120] In this embodiment of the application, the clustering result may include the maximum voltage value and the minimum voltage value. Calculating the cloud-based SOH of the power battery based on the clustering result includes: calculating a first voltage difference based on the voltage and the minimum voltage value; calculating a second voltage difference based on the voltage and the maximum voltage value; and calculating the cloud-based SOH of the power battery based on the first voltage difference and the second voltage difference.

[0121] It is understood that the clustering results in this application embodiment include the maximum voltage value and the minimum voltage value. A first voltage difference is calculated based on the voltage value and the minimum voltage value. A second voltage difference is calculated based on the voltage value and the maximum voltage value. Finally, the cloud-based SOH of the power battery is calculated based on the first voltage difference and the second voltage difference.

[0122] In this embodiment of the application, before clustering the battery data to obtain the number of clusters and clustering parameters, the method further includes: normalizing the battery data to obtain normalized battery data.

[0123] It is understandable that, in order to calculate the cloud-based State of Health (SOH), before uploading battery data such as voltage, current, and temperature to the cloud server, the embodiments of this application may perform normalization preprocessing on the collected battery data to eliminate differences between attributes, thereby improving the convergence speed and accuracy of subsequent algorithms.

[0124] In step S103, the estimation strategy is executed in the order of execution. The target SOH of the power battery is calculated based on the estimation strategy and battery data, and the current SOH of the power battery is corrected to the target SOH.

[0125] It is understood that, according to the embodiments of this application, the execution order of the estimation strategy for the SOH of the power battery can be determined based on the battery type. The estimation strategy and battery data are executed according to the execution order to calculate the target SOH of the power battery, and the current SOH of the power battery is corrected to the target SOH.

[0126] In this embodiment of the application, the target SOH of the power battery is calculated based on the estimation strategy and battery data, including: determining whether the SOH estimated by the current estimation strategy meets the preset reasonable conditions; if the SOH estimated by the current estimation strategy meets the preset reasonable conditions, the SOH estimated by the current estimation strategy is taken as the target SOH, otherwise the next estimation strategy is executed; if the SOH estimated by the last estimation strategy does not meet the preset reasonable conditions, the SOH of the power battery is not updated.

[0127] It is understood that the embodiments of this application can determine whether the SOH estimated by the current estimation strategy meets the preset reasonable conditions. If the SOH estimated by the current estimation strategy meets the preset reasonable conditions, the current SOH is taken as the target SOH. Otherwise, the next estimation strategy is executed until the preset reasonable conditions are met. If the SOH estimated by the last estimation strategy still does not meet the preset reasonable conditions, the update of the SOH of the power battery is not executed.

[0128] In this embodiment of the application, determining whether the SOH estimated by the current estimation strategy meets the preset reasonableness conditions includes: determining whether the SOH estimated by the current estimation strategy is within a preset range; if the SOH estimated by the current estimation strategy is within the preset range, then determining that the SOH estimated by the current estimation strategy meets the preset reasonableness conditions.

[0129] It is understood that, in this application embodiment, the SOH estimated by the current estimation strategy is within a preset range, and the SOH is determined to meet the preset reasonableness conditions so that the SOH that meets the conditions can be used as the target SOH of the power battery in the future.

[0130] In this embodiment of the application, if the battery type is a first battery type and the SOH estimated by the last estimation strategy meets the preset reasonable conditions, the method further includes: taking the average of the historical SOH of the estimation strategies whose SOH does not meet the preset reasonable conditions as the SOH of the corresponding estimation strategy; performing a weighted average based on the SOH estimated by all estimation strategies and their respective weights to obtain a weighted average value, and using the weighted average value to correct the SOC and / or remaining charging time of the power battery.

[0131] It is understandable that if the battery type is the first battery type, the historical SOH average of the estimation strategy that does not meet the preset reasonable conditions is used as the SOH of the corresponding estimation strategy. The SOH and their respective weights are weighted and averaged to obtain the weighted average value. The weighted average value is used to correct one or more of the SOC and remaining charging time of the power battery.

[0132] The calculation formula is as follows:

[0133] SOH=W1*SOH1+W2*SOH2+W3*SOH3,

[0134] Among them, SOH1, SOH2, and SOH3 correspond to the estimated SOH of the three estimation strategies, and W1, W2, and W3 correspond to their respective weighted average values.

[0135] Specifically, this can be understood as follows: when the SOH obtained by a certain estimation strategy is an invalid value, the sliding window mean can be used as a substitute value, with the sliding window mean smooth(SOH,n).

[0136] In this application embodiment, if the battery type is a second battery type and the SOH estimated by the static estimation strategy does not meet the preset reasonable conditions, it further includes: correcting the charging rate and / or discharging power according to the SOH estimated by the cloud estimation strategy.

[0137] It is understood that if the battery type is the second battery type and the SOH estimated by the static estimation strategy does not meet the preset reasonable conditions, the embodiments of this application correct one or more of the charging rate and discharging power based on the SOH estimated by the cloud estimation strategy.

[0138] Specifically, if the battery type is the first type, the execution sequence follows the principle of prioritizing dynamic calculation, supplemented by static estimation results and cloud updates. Provided that all preconditions for dynamic algorithm calculation are met, if the calculated State of Health (SOH) is within a reasonable range, it is used to correct the current SOH, and also to correct the calculation of charging rate, discharge power, State of Charge (SOC), and remaining charging time. If the above vehicle-side dynamic calculations are not updated for a long time or the calculated values ​​exceed the valid update range, then the vehicle-side operating data—cumulative charging capacity, cumulative discharging capacity, cumulative parking time, and cumulative mileage—is used. Static estimation is performed, and if the static estimation result is within a reasonable range, it is used to correct the charging rate and discharge power. Otherwise, the result is updated using cloud-based calculations. Simultaneously, a weighted average of the SOH value estimated by the three methods is calculated: SOH = W1*SOH1 + W2*SOH2 + W3*SOH3. If the SOH obtained by a certain estimation method is invalid, a sliding window mean method is used. The final estimation result is given using filtering to correct the SOH used in calculating SOC and remaining charging time. The curvature of the voltage change with SOC for the first battery type is greater than the preset curvature.

[0139] If the battery type is the second battery type, the execution order follows the update principle of prioritizing static calculation and cloud calculation, supplemented by dynamic estimation results. The static SOH can be obtained by interpolating the current cumulative charging capacity, cumulative discharging capacity, cumulative mileage, and cumulative parking time to correct the charging rate and discharging power. The SOH after mean filtering is used for SOC correction and calculation of remaining charging time. If the static estimation result is outside the reasonable range, the cloud estimation result can be used to correct the charging rate and discharging power. The voltage curvature of the first battery type with SOC is less than or equal to the preset curvature.

[0140] In summary, the embodiments of this application provide different SOH estimation strategies based on different application scenarios such as different battery types and different levels of vehicle hybridization, taking ternary lithium batteries and lithium iron phosphate batteries as examples for illustration.

[0141] I. For ternary lithium batteries, the update principle is to primarily use dynamic estimation, supplemented by static estimation and cloud-based estimation, including the following steps:

[0142] Step S11: Before performing dynamic updates at the vehicle end, it is necessary to determine whether the resting time has reached the specified threshold. If it has, the normal calculation can be performed using the obtained voltage. Otherwise, the voltage is obtained based on the fitted resting time and OCV relationship before subsequent calculations are performed to obtain the dynamically calculated SOH.

[0143] Step S12: Based on the above steps, determine whether the SOH dynamically calculated by the vehicle is within a reasonable range. The judgment principle is that within a certain service life, mileage, and cumulative throughput range, the battery health status degradation should not exceed the specified threshold. In addition, if the resting time threshold is not reached, the judgment condition should be more stringent. The reasonable estimated value can be directly used to correct the calculation of charging rate, discharge power, SOC, and remaining charging time.

[0144] Step S13: If the above vehicle-side dynamic calculation is not updated for a long time or the calculated value exceeds the effective update range, a static estimate is performed based on the vehicle-side operating data—cumulative charging capacity, cumulative discharging capacity, cumulative parking time, cumulative mileage, etc. If the static estimate result is within a reasonable range, it is used to correct the charging rate and discharging power; otherwise, the cloud update result is used for updating. The weighted average of the SOH value estimated by the three methods is calculated: SOH = W1*SOH1 + W2*SOH2 + W3*SOH3. If the SOH obtained by a certain estimation method is invalid, the sliding window mean method is used. The final estimation result is given by filtering and is used to correct the SOH calculated for SOC and remaining charging time.

[0145] II. For lithium iron phosphate batteries, especially lithium iron phosphate hybrid vehicle batteries with different degrees of hybridization, the updating principle is to primarily use static and cloud-based estimations, supplemented by dynamic estimation results. This includes the following steps:

[0146] Step S21: For vehicles powered solely by batteries (i.e., pure electric vehicles), the static State of Charge (SOH) can be obtained by interpolating the current cumulative charging capacity, cumulative discharging capacity, cumulative mileage, and cumulative parking time to correct the charging rate and discharging power. The SOH after mean filtering is used for SOC correction and calculation of remaining charging time. If the static estimation result exceeds a reasonable range, the cloud estimation result is directly used to correct the charging rate and discharging power, while the mean-filtered SOH is still used for SOC correction and calculation of remaining charging time.

[0147] Step S22: When using the static calculation method for hybrid vehicles, real-time correction is required. For mild hybrid (range-extended) vehicles, the current driving data (cumulative parking time, cumulative charging capacity, etc.) is calibrated using the cumulative charge-discharge capacity ratio to obtain the corrected SOH. For strong hybrid vehicles, the ratio of electric driving mileage to total mileage needs to be calculated to correct the current driving data and obtain the required SOH value. Other update principles are consistent with S21.

[0148] Using the above method to correct the SOH of the power battery has the following advantages:

[0149] (1) In response to the problems of large deviations in the SOH calculated by the vehicle due to extreme conditions, charging risks and reduced battery life caused by the long plateau period of lithium iron phosphate batteries in hybrid vehicles, this invention provides different SOH update estimation strategies according to different application scenarios such as the type of vehicle battery and the degree of vehicle hybridization.

[0150] (2) The SOH calculation and update of ternary lithium batteries and lithium iron phosphate batteries are combined with cloud-based SOH and static and dynamic SOH calculation. The correction results are reasonably distinguished in different uses and application scenarios, and corresponding strategies are proposed, which not only improves the update frequency of batteries, but also ensures the rationality of the results.

[0151] (3) By making reasonable use of the cloud-based SOH estimation results and combining them with the vehicle-side algorithm, the computational burden on the vehicle-side is reduced, the update frequency of the calculation results is increased, and the accuracy of the results is guaranteed.

[0152] The following is a schematic diagram illustrating the integrated vehicle battery SOH estimation method based on vehicle-cloud fusion of this application, using a specific embodiment. Figure 2 As shown, taking ternary lithium batteries and lithium iron phosphate batteries as examples, and mild hybrid and strong hybrid vehicle models, the steps include:

[0153] Step 1: Based on the HPPC test data, construct the OCV fitting function, as described below:

[0154] V(t)=OCV(t)-a1*exp(-b1*t)-a2*exp(-b2*t)-a3*exp(-b3*t),

[0155] Where a1, a2, a3, b1, b2, b3 represent the fitting coefficients.

[0156] Step 2: Determine whether fitting is needed based on the resting time before and after vehicle charging, and obtain the OCV at the start and end of charging.

[0157] Step 3: Look up the table for different battery types, temperatures, and OCVs to obtain the SOC before and after charging.

[0158] Step 4: Perform ampere-hour integration calculation based on charging current (I) and charging time (t), and obtain the SOC change ΔSOC based on the previously obtained SOC before and after charging. Calculate the current capacity using the following formula:

[0159]

[0160] Finally, the vehicle-side dynamic State of Health (SOH) is calculated using the following formula. If it is not within a reasonable range, static calculation and cloud updates are performed:

[0161]

[0162] Step 5: Steps 1-4 above describe the implementation of the dynamic algorithm. For cases where the State of Health (SOH) of a lithium iron phosphate battery has not been updated for a long time, a static update method can be used. Using the cumulative charging capacity, cumulative discharging capacity, cumulative mileage, and cumulative parking time obtained when the battery is powered on, we first determine whether each signal value is valid. For example, if the cumulative mileage is less than a certain threshold, the cumulative parking time must not be greater than a set threshold. Similarly, we determine the validity of the cumulative charging capacity and cumulative discharging capacity. The specific content includes steps 6-8.

[0163] Step 6: Based on the premise that each cumulative signal is valid, further determine the hybridization level of the current vehicle model. If it is known that the current vehicle is a pure electric vehicle, then perform corresponding interpolation calculation of static SOH based on the currently acquired cumulative charging capacity, cumulative discharging capacity, cumulative driving mileage, and cumulative parking time.

[0164] Step 7: When the current vehicle model is a mild hybrid, obtain the current cumulative charging capacity = α * Q by correction. 纯 Q 纯 To determine the cumulative charging capacity of the pure battery pack's SOH, α = cumulative discharging capacity / cumulative charging capacity, where the cumulative discharging capacity is adjusted based on the cumulative mileage; similarly, the current cumulative parking time = α * T 纯 T 纯 This represents the cumulative parking time corresponding to the pure battery pack. Static estimates are performed using the corrected cumulative charging capacity and cumulative parking time, respectively.

[0165] Step 8: When the current vehicle model is a strong hybrid, the current cumulative charging capacity is calculated as β*Q. 纯 Q 纯 To determine the cumulative charging capacity of the pure battery pack's SOH, β = pure electric driving range / (pure electric driving range + gasoline driving range); similarly, the current cumulative parking time = β * T 纯 T 纯 This represents the cumulative parking time corresponding to the pure battery pack. Static estimates are performed using the corrected cumulative charging capacity and cumulative parking time respectively; if the static algorithm results have large deviations, cloud-based calculation results can be used for correction and updates.

[0166] Step 9: To calculate the cloud-based State of Health (SOH), upload information such as voltage, current, and temperature to the cloud server, and perform normalization preprocessing on the collected signals to eliminate differences between attributes and improve the convergence speed and accuracy of the algorithm.

[0167] Step 10: Determine the number of clusters and parameters: Before using the FCM algorithm for clustering, the number of clusters and parameters need to be determined based on the voltage, current, and temperature data uploaded to the cloud. These parameters represent the number of dataset groups, the fuzziness factor, and the initial values ​​for the cluster centers, respectively. This can be determined using model selection techniques and experimental optimization; and cluster centers should be initialized using a cluster center algorithm (such as the K-Means algorithm).

[0168] Step 11: Calculate the membership matrix: The most important step in the fuzzy clustering (FCM) algorithm is to calculate the membership matrix, which assigns each data point a membership degree to each cluster; the membership degree represents the degree to which a data point belongs to each cluster.

[0169] Step 12: Update cluster centers: Based on the membership matrix, recalculate the center of each cluster, i.e., the mean vector of each cluster.

[0170] Step 13: Repeated Iteration and Result Determination: Repeat steps 11-12 until the cluster centers no longer change or the predetermined number of iterations is reached. Assign each data point to the nearest cluster center according to the membership matrix and determine the clustering result. Calculate the required SOH value based on the voltage.

[0171] Step 14: Steps 9-13 above constitute the basic process of cloud computing. After obtaining the SOH estimation results, the results of the three estimation methods are weighted and averaged according to the battery type, the degree of hybridization of electric vehicles, the update frequency, and the reasonableness of the estimation results to obtain the current SOH value. If necessary, this value is used to correct the calculation of SOC and remaining charging time.

[0172] SOH=W1*SOH1+W2*SOH2+W3*SOH3,

[0173] SOH1, SOH2, and SOH3 correspond to the estimated SOH values ​​of the three algorithms, respectively, while W1, W2, and W3 correspond to their respective weighted average values. If the estimation result of a certain method is a failed value, the sliding window mean is used, with smooth(SOH,n) as the replacement value. The same principle applies to ternary lithium and lithium iron phosphate lithium calculations. A schematic diagram illustrating the update order of the three SOH estimation methods under different application scenarios is shown below. Figure 3 As shown, it can solve the problems of large deviations in the calculation of SOH due to extreme conditions and the long-term difficulty in triggering updates for automotive lithium iron phosphate batteries due to the long plateau period. In particular, it can improve the charging risks and reduced battery life caused by the difficulty in updating lithium iron phosphate batteries in hybrid vehicles, and ensure the accuracy of battery health status calculation.

[0174] The SOH correction method for power batteries proposed in this application provides different SOH estimation strategies based on the battery type and determines the execution order of the estimation strategies. It combines cloud-based SOH estimation strategies and vehicle-based estimation strategies, making reasonable use of both strategies. By integrating vehicle and cloud approaches, the applicability of SOH estimation is improved. By utilizing cloud-based SOH estimation results and combining them with vehicle-based estimation strategies, the computational burden on the vehicle is reduced, the update frequency of calculation results is increased, and the accuracy of SOH calculation is ensured, thereby improving the coverage and adaptability of the SOH estimation method.

[0175] Next, the SOH correction device for a power battery according to an embodiment of this application is described with reference to the accompanying drawings.

[0176] Figure 4 This is a block diagram of the SOH correction device for a power battery according to an embodiment of this application.

[0177] like Figure 4 As shown, the SOH correction device 10 for the power battery is applied to a vehicle and includes: an acquisition module 100, a determination module 200, and a correction module 300.

[0178] The acquisition module 100 is used to acquire the battery type and battery data of the power battery; the determination module 200 is used to determine the execution order of the estimation strategy for the state of health (SOH) of the power battery according to the battery type, wherein the estimation strategy includes the vehicle-side estimation strategy and the cloud-based estimation strategy; the correction module 300 is used to execute the estimation strategy according to the execution order, calculate the target SOH of the power battery according to the estimation strategy and battery data, and correct the current SOH of the power battery to the target SOH.

[0179] In this embodiment, the vehicle-side estimation strategy includes a dynamic estimation strategy and a static estimation strategy.

[0180] In this embodiment of the application, the determining module 200 is further configured to: if the battery type is a first battery type, then the execution order is a dynamic estimation strategy, a static estimation strategy, and a cloud estimation strategy, or the execution order is a dynamic estimation strategy, a cloud estimation strategy, and a static estimation strategy, wherein the voltage curvature of the first battery type with the change of state of charge (SOC) is greater than a preset curvature; if the battery type is a second battery type, then the execution order is a static estimation strategy, a cloud estimation strategy, and a static estimation strategy, or the execution order is a cloud estimation strategy, a static estimation strategy, and a dynamic estimation strategy, wherein the voltage curvature of the first battery type with the change of SOC is less than or equal to a preset curvature.

[0181] In this embodiment of the application, the correction module 300 is further configured to: determine whether the SOH estimated by the current estimation strategy meets the preset reasonable conditions; if the SOH estimated by the current estimation strategy meets the preset reasonable conditions, then the SOH estimated by the current estimation strategy is taken as the target SOH, otherwise the next estimation strategy is executed; if the SOH estimated by the last estimation strategy does not meet the preset reasonable conditions, then the SOH of the power battery is not updated.

[0182] In this embodiment of the application, the correction module 300 is further configured to: take the average of the historical SOH of the estimation strategy that does not meet the preset reasonable conditions as the SOH of the corresponding estimation strategy; perform a weighted average by averaging the SOH estimated by all estimation strategies and their respective weights to obtain a weighted average value, and use the weighted average value to correct the SOC and / or remaining charging time of the power battery.

[0183] In this embodiment, the correction module 300 is further configured to: correct the charging rate and / or discharging power based on the SOH estimated by the cloud-based estimation strategy.

[0184] In this embodiment of the application, the correction module 300 is further used to: determine whether the SOH estimated by the current estimation strategy is within a preset range; if the SOH estimated by the current estimation strategy is within the preset range, then determine that the SOH estimated by the current estimation strategy meets the preset reasonableness condition.

[0185] In this embodiment, battery data includes one or more of the following: rest time before and after charging, charging temperature, open-circuit voltage (OCV) at the start of charging, OCV at the end of charging, charging current, and charging duration. The dynamic estimation strategy includes: determining the state of charge (SOC) before and after charging based on the charging temperature, OCV at the start of charging, and OCV at the end of charging; obtaining the actual charging capacity by integrating the charging current and charging duration in ampere-hours, and calculating the SOC change based on the SOC before and after charging; calculating the current capacity of the power battery based on the actual charging capacity and the SOC change, and calculating the vehicle-side state of charge (SOH) of the power battery based on the current capacity and the initial capacity of the power battery.

[0186] In this embodiment of the application, the battery data also includes the resting time before and after charging, and the device 10 in this embodiment of the application further includes: a judgment module.

[0187] The judgment module is used to determine whether the resting time before and after charging is less than or equal to a time threshold before determining the SOC before and after charging based on the charging temperature, OCV at the start of charging and OCV at the end of charging. If the resting time before and after charging is less than or equal to the time threshold, the resting time before and after charging and OCV are fitted together to obtain the OCV at the start of charging and OCV at the end of charging.

[0188] In this embodiment, the static estimation strategy includes: obtaining the current vehicle model; determining the correction coefficient of the battery data based on the current vehicle model; correcting the battery data based on the correction coefficient; and calculating the vehicle-side SOH of the power battery using the corrected battery data.

[0189] In this embodiment of the application, battery data includes one or more of the following: cumulative charging capacity, cumulative discharging capacity, cumulative driving mileage, cumulative parking time, pure electric driving mileage, and oil-powered driving mileage.

[0190] In this embodiment of the application, the current vehicle model includes a first vehicle model and a second vehicle model. Determining the correction coefficient of the battery data based on the current vehicle model includes: if the current vehicle model is the first vehicle model, then calculating a first correction coefficient of the battery data based on the cumulative discharge capacity and the cumulative charge capacity; if the current vehicle model is the second vehicle model, then calculating a second correction coefficient of the battery data based on the pure electric driving range and the gasoline driving range.

[0191] In this embodiment of the application, correcting battery data according to a correction coefficient includes: if the current vehicle model is a first vehicle model, then using a first correction coefficient to correct the cumulative charging capacity and cumulative parking time; if the current vehicle model is a second vehicle model, then using a second correction coefficient to correct the cumulative charging capacity and cumulative parking time.

[0192] In this embodiment of the application, before calculating the first correction factor of the battery data based on the cumulative discharge capacity and the cumulative charge capacity, the method further includes: correcting the cumulative discharge capacity based on the cumulative driving mileage.

[0193] In this embodiment, the cloud estimation strategy is applied to the server. The cloud estimation strategy includes: acquiring battery data of the power battery uploaded by the vehicle; clustering the battery data to obtain the number of clusters and clustering parameters, calculating the membership matrix of each data point in the battery data based on the number of clusters and clustering parameters; updating the cluster centers based on the membership matrix, and performing iterative clustering until the cluster centers remain unchanged or a preset number of iterations is reached, determining the clustering result of the battery data based on the cluster centers; and calculating the cloud SOH of the power battery based on the clustering result.

[0194] In this embodiment of the application, the battery data uploaded by the vehicle includes one or more of voltage, current, and temperature.

[0195] In this embodiment of the application, the clustering results include the maximum voltage value and the minimum voltage value. Calculating the cloud-based SOH of the power battery based on the clustering results includes: calculating a first voltage difference based on the voltage and the minimum voltage value; calculating a second voltage difference based on the voltage and the maximum voltage value; and calculating the cloud-based SOH of the power battery based on the first voltage difference and the second voltage difference.

[0196] In this embodiment of the application, before clustering the battery data to obtain the number of clusters and clustering parameters, the method further includes: normalizing the battery data to obtain normalized battery data.

[0197] It should be noted that the explanation of the aforementioned embodiment of the SOH correction method for power batteries also applies to the SOH correction device for power batteries in this embodiment, and will not be repeated here.

[0198] The SOH correction device for power batteries proposed in this application provides different SOH estimation strategies and determines the execution order of the estimation strategies according to the battery type. It combines cloud-based SOH estimation strategies and vehicle-based estimation strategies, making reasonable use of both strategies. By integrating vehicle and cloud approaches, it improves the applicability of SOH estimation. By utilizing cloud-based SOH estimation results and combining them with vehicle-based estimation strategies, it reduces the computational burden on the vehicle side, increases the update frequency of calculation results, and ensures the accuracy of SOH calculation, thereby improving the coverage and adaptability of the SOH estimation method.

[0199] Figure 5 A schematic diagram of the structure of a vehicle provided in an embodiment of this application. The vehicle may include:

[0200] The memory 501, the processor 502, and the computer program stored on the memory 501 and capable of running on the processor 502.

[0201] When the processor 502 executes the program, it implements the SOH correction method for the power battery provided in the above embodiments.

[0202] Furthermore, the vehicle also includes:

[0203] Communication interface 503 is used for communication between memory 501 and processor 502.

[0204] The memory 501 is used to store computer programs that can run on the processor 502.

[0205] The memory 501 may include high-speed RAM (Random Access Memory) memory, and may also include non-volatile memory, such as at least one disk storage.

[0206] If the memory 501, processor 502, and communication interface 503 are implemented independently, then the communication interface 503, memory 501, and processor 502 can be interconnected via a bus to complete communication between them. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 5 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0207] Optionally, in a specific implementation, if the memory 501, processor 502, and communication interface 503 are integrated on a single chip, then the memory 501, processor 502, and communication interface 503 can communicate with each other through an internal interface.

[0208] The processor 502 may be a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of this application.

[0209] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described SOH correction method for a power battery.

[0210] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0211] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0212] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0213] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (FPGAs), field-programmable gate arrays (FPGAs), etc.

[0214] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0215] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for correcting the state of harm (SOH) of a power battery, characterized in that, The method is applied to a vehicle, and the method includes the following steps: Obtain the battery type and battery data of the power battery; The execution order of the battery health state (SOH) estimation strategy for the power battery is determined according to the battery type, wherein the estimation strategy includes a vehicle-side estimation strategy and a cloud-based estimation strategy. The estimation strategy is executed according to the execution order. The target SOH of the power battery is calculated based on the estimation strategy and the battery data. The current SOH of the power battery is then corrected to the target SOH. The vehicle-side estimation strategy includes a dynamic estimation strategy and a static estimation strategy; The execution order of the strategy for determining the State of Health (SOH) of the power battery based on the battery type includes: If the battery type is a first battery type, the execution order is the dynamic estimation strategy, the static estimation strategy, and the cloud estimation strategy in sequence; or, the execution order is the dynamic estimation strategy, the cloud estimation strategy, and the static estimation strategy in sequence, wherein the voltage curvature of the first battery type with the change of state of charge (SOC) is greater than a preset curvature. If the battery type is the second battery type, the execution order is the static estimation strategy, the cloud estimation strategy, and the dynamic estimation strategy in sequence, or the execution order is the cloud estimation strategy, the static estimation strategy, and the dynamic estimation strategy in sequence, wherein the voltage curvature of the second battery type with SOC is less than or equal to a preset curvature.

2. The SOH correction method for power batteries according to claim 1, characterized in that, The step of calculating the target SOH of the power battery based on the estimation strategy and the battery data includes: Determine whether the SOH estimated by the current estimation strategy meets the preset reasonable conditions; If the SOH estimated by the current estimation strategy meets the preset reasonable conditions, then the SOH estimated by the current estimation strategy is taken as the target SOH; otherwise, the next estimation strategy is executed. If the SOH estimated by the last estimation strategy does not meet the preset reasonable conditions, the SOH update of the power battery will not be performed.

3. The SOH correction method for a power battery according to claim 2, characterized in that, If the battery type is the first battery type, and the SOH estimated by the last estimation strategy satisfies the preset reasonable condition, it also includes: The average historical SOH of the estimation strategy that does not meet the preset reasonable conditions is taken as the SOH of the corresponding estimation strategy; A weighted average is obtained by taking the SOH estimated by all estimation strategies and their respective weights, and the SOC and / or remaining charging time of the power battery are corrected using the weighted average.

4. The SOH correction method for power batteries according to claim 2, characterized in that, If the battery type is the second battery type, and the SOH estimated by the static estimation strategy does not meet the preset reasonable conditions, the method further includes: The SOH-corrected charge rate and / or discharge power are estimated based on the cloud-based estimation strategy.

5. The SOH correction method for a power battery according to claim 2, characterized in that, The determination of whether the SOH estimated by the current estimation strategy meets the preset reasonable conditions includes: Determine whether the SOH estimated by the current estimation strategy is within a preset range; If the SOH estimated by the current estimation strategy is within the preset range, then the SOH estimated by the current estimation strategy is determined to meet the preset reasonableness condition.

6. The SOH correction method for a power battery according to claim 1, characterized in that, The battery data includes one or more of the following: rest time before and after charging, charging temperature, open-circuit voltage (OCV) at the start of charging, OCV at the end of charging, charging current, and charging duration. The dynamic estimation strategy includes: The SOC before and after charging is determined based on the charging temperature, the OCV at the start of charging, and the OCV at the end of charging. The actual charging capacity is obtained by integrating the charging current and the charging time in ampere-hours, and the change in SOC is calculated based on the SOC before and after charging. The current capacity of the power battery is calculated based on the actual charging capacity and the change in SOC, and the first vehicle-side SOH of the power battery is calculated based on the current capacity and the initial capacity of the power battery.

7. The SOH correction method for a power battery according to claim 6, characterized in that, The battery data also includes the resting time before and after charging, and before determining the SOC before and after charging based on the charging temperature, the OCV at the start of charging, and the OCV at the end of charging, it also includes: Determine whether the resting time before and after charging is less than or equal to the time threshold; If the resting time before and after charging is determined to be less than or equal to the time threshold, then the resting time before and after charging and the OCV are fitted to obtain the OCV at the start of charging and the OCV at the end of charging.

8. The SOH correction method for a power battery according to claim 1, characterized in that, The static estimation strategy includes: Get the current vehicle model; Determine the correction factor for the battery data based on the current vehicle model; The battery data is corrected according to the correction coefficient, and the vehicle-side SOH of the power battery is calculated using the corrected battery data.

9. The SOH correction method for a power battery according to claim 8, characterized in that, The battery data includes one or more of the following: cumulative charging capacity, cumulative discharging capacity, cumulative mileage, cumulative parking time, pure electric mileage, and gasoline-powered mileage.

10. The SOH correction method for a power battery according to claim 9, characterized in that, The current vehicle model includes a first vehicle model and a second vehicle model. Determining the correction coefficient for the battery data based on the current vehicle model includes: If the current vehicle model is the first vehicle model, then a first correction coefficient for the battery data is calculated based on the cumulative discharge capacity and the cumulative charging capacity; If the current vehicle model is the second vehicle model, then a second correction factor for the battery data is calculated based on the pure electric driving range and the gasoline driving range.

11. The SOH correction method for a power battery according to claim 10, characterized in that, The step of correcting the battery data according to the correction coefficient includes: If the current vehicle model is the first vehicle model, then the cumulative charging capacity and the cumulative parking time are corrected using the first correction coefficient; If the current vehicle model is the second vehicle model, then the cumulative charging capacity and the cumulative parking time are corrected using the second correction coefficient.

12. The SOH correction method for a power battery according to claim 10, characterized in that, Before calculating the first correction factor for the battery data based on the cumulative discharge capacity and the cumulative charge capacity, the method further includes: The cumulative discharge capacity is adjusted based on the cumulative mileage.

13. The SOH correction method for a power battery according to claim 1, characterized in that, The cloud-based estimation strategy is applied to the server, and the cloud-based estimation strategy includes: Obtain battery data uploaded by the vehicle's power battery; The battery data is clustered to obtain the number of clusters and clustering parameters. The membership matrix of each data point in the battery data is calculated based on the number of clusters and the clustering parameters. The cluster centers are updated according to the membership matrix, and iterative clustering is performed until the cluster centers remain unchanged or the preset number of iterations is reached. The clustering result of the battery data is determined according to the cluster centers. The cloud-based SOH of the power battery is calculated based on the clustering results.

14. The SOH correction method for a power battery according to claim 13, characterized in that, The battery data uploaded by the vehicle includes one or more of voltage, current, and temperature.

15. The SOH correction method for a power battery according to claim 14, characterized in that, The clustering results include the maximum and minimum voltage values. The calculation of the cloud-based State of Harmony (SOH) of the power battery based on the clustering results includes: Calculate the first voltage difference based on the voltage and the minimum voltage value; Calculate the second voltage difference based on the voltage and the maximum voltage value; The cloud-based SOH of the power battery is calculated based on the first voltage difference and the second voltage difference.

16. The SOH correction method for a power battery according to claim 13, characterized in that, Before clustering the battery data to obtain the number of clusters and clustering parameters, the process also includes: The battery data is normalized to obtain normalized battery data.

17. A state-of-the-art (SOH) correction device for a power battery, characterized in that, The device is applied to a vehicle, wherein the device includes: The acquisition module is used to acquire the battery type and battery data of the power battery; The determination module is used to determine the execution order of the estimation strategy for the State of Health (SOH) of the power battery according to the battery type, wherein the estimation strategy includes a vehicle-side estimation strategy and a cloud-based estimation strategy; The correction module is used to execute the estimation strategy according to the execution order, calculate the target SOH of the power battery based on the estimation strategy and the battery data, and correct the current SOH of the power battery to the target SOH; The vehicle-side estimation strategy includes a dynamic estimation strategy and a static estimation strategy; The determination module is further configured to: if the battery type is a first battery type, execute the dynamic estimation strategy, the static estimation strategy, and the cloud estimation strategy in sequence, or execute the dynamic estimation strategy, the cloud estimation strategy, and the static estimation strategy in sequence, wherein the voltage curvature of the first battery type with the change of state of charge (SOC) is greater than a preset curvature; if the battery type is a second battery type, execute the static estimation strategy, the cloud estimation strategy, and the dynamic estimation strategy in sequence, or execute the cloud estimation strategy, the static estimation strategy, and the dynamic estimation strategy in sequence, wherein the voltage curvature of the second battery type with the change of SOC is less than or equal to a preset curvature.

18. A vehicle, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the SOH correction method for a power battery as described in any one of claims 1-16.

19. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the SOH correction method for the power battery as described in any one of claims 1-16.

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