Method, device and computer storage medium for determining battery usage parameters

Abstract

This application discloses a method, apparatus, and computer storage medium for determining battery usage parameters, belonging to the field of electric vehicle technology. The method includes: acquiring current battery state data of the vehicle during vehicle use, the current battery state data indicating the current state of the battery in the vehicle; determining current weight data based on the current battery state data, the current weight data indicating the degree of influence of the current battery state on the battery usage parameters; and updating the battery usage parameters based on the current weight data. Therefore, in this application embodiment, the battery usage parameters of the vehicle can be adaptively updated based on the vehicle's current battery state data. That is, during the battery's lifespan, the battery usage parameters are adaptively updated according to the battery state to match the battery's usage parameters with the battery's characteristics, thereby improving the battery's lifespan.

Description

Technical Field

[0001] This application relates to the field of electric vehicle technology, and in particular to a method, apparatus, and computer storage medium for determining battery usage parameters. Background Technology

[0002] With the development of new energy technologies, electric vehicles are gaining increasing popularity due to their environmental friendliness. Electric vehicles rely on batteries as their power source, and the battery's operating parameters significantly impact their performance. Therefore, designing the operating parameters of batteries in electric vehicles is currently a hot research topic.

[0003] In related technologies, when electric vehicle batteries leave the factory, manufacturers design a set of battery usage parameters, such as charging voltage. These parameters are then used throughout the battery's lifespan. However, this method of determining battery usage parameters leads to significant battery wear and tear. Summary of the Invention

[0004] This application provides a method, apparatus, and computer storage medium for determining battery usage parameters, which can reduce battery consumption. The technical solution is as follows:

[0005] On the one hand, a method for determining battery usage parameters is provided, the method comprising:

[0006] During vehicle use, the current battery status data of the vehicle is acquired, and the current battery status data indicates the current status of the battery in the vehicle;

[0007] The current weight data is determined based on the current battery state data, and the current weight data indicates the degree of influence of the current state of the battery on the battery's usage parameters;

[0008] The battery's usage parameters are updated based on the current weight data.

[0009] Optionally, updating the battery usage parameters based on the weighted data includes:

[0010] Obtain historical weight data, which is the weight data most recently determined before the current time;

[0011] If the change between the historical weight data and the current weight data meets the target condition, then the battery usage parameters are updated based on the weight data.

[0012] Optionally, the method further includes:

[0013] If the change between the historical weight data and the current weight data does not meet the target condition, then the operation of updating the battery usage parameters based on the weight data will not be performed.

[0014] Optionally, determining the current weight data based on the current battery state data includes:

[0015] Based on the current battery state data and the target learning model, the current weight data is determined.

[0016] Optionally, the battery status data includes multiple status data corresponding one-to-one with multiple status parameters. Each status parameter indicates a parameter that can be collected during the use of the battery, and the status data corresponding to each status parameter indicates the change of the corresponding status parameter during the use of the battery.

[0017] The step of determining the output of the current weight data based on the current battery state data and the target learning model includes:

[0018] Based on the multiple state data that correspond one-to-one with the multiple state parameters, at least one feature of the multiple state parameters is determined;

[0019] At least one feature of the plurality of state parameters is input into the target learning model, and the target learning model outputs the current weight data. The current weight data includes a current weight set, which includes the current weight corresponding to each feature in at least one feature. The current weight corresponding to each feature indicates the degree of influence of the corresponding feature on the usage parameters of the battery.

[0020] Optionally, the plurality of state parameters include one or more of the following: individual cell voltage, total battery voltage, battery temperature, load terminal voltage, bus current, and vehicle operating status.

[0021] On the other hand, an apparatus for determining battery usage parameters is provided, the apparatus comprising:

[0022] The acquisition module is used to acquire the current battery status data of the vehicle during vehicle use, wherein the current battery status data indicates the current status of the battery in the vehicle.

[0023] The determination module is used to determine current weight data based on the current battery state data, wherein the current weight data indicates the degree of influence of the current state of the battery on the battery's usage parameters;

[0024] The update module is used to update the battery's usage parameters based on the current weight data.

[0025] Optionally, the update module is used to:

[0026] Obtain historical weight data, which is the weight data most recently determined before the current time;

[0027] If the change between the historical weight data and the current weight data meets the target condition, then the battery usage parameters are updated based on the weight data.

[0028] On the other hand, an apparatus for determining battery usage parameters is provided, the apparatus comprising:

[0029] processor;

[0030] Memory used to store processor-executable instructions;

[0031] The processor is configured to perform any step in the method described above for determining battery usage parameters.

[0032] On the other hand, a computer-readable storage medium is provided, on which instructions are stored, which, when executed by a processor, implement any step of the method for determining battery usage parameters described above.

[0033] On the other hand, a computer program product containing instructions is provided that, when run on a computer, causes the computer to perform any of the steps in the method for determining battery usage parameters described above.

[0034] The beneficial effects of the technical solutions provided in this application include at least the following:

[0035] During vehicle use, the battery's usage parameters are adaptively updated based on the vehicle's current battery state data. In other words, throughout the battery's lifespan, the battery's usage parameters are adaptively updated according to its state to match its characteristics, thereby extending the battery's lifespan. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a schematic diagram of the architecture of a system for determining battery usage parameters provided in an embodiment of this application.

[0038] Figure 2 This is a flowchart of a method for determining battery usage parameters provided in an embodiment of this application.

[0039] Figure 3 This is a flowchart of another method for determining battery usage parameters provided in an embodiment of this application.

[0040] Figure 4 This is a schematic diagram of a device for determining battery usage parameters provided in an embodiment of this application.

[0041] Figure 5 This is a schematic diagram of the structure of a vehicle-mounted terminal provided in an embodiment of this application.

[0042] Figure 6 This is a schematic diagram of the structure of a server provided in an embodiment of this application. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the implementation methods of this application will be further described in detail below with reference to the accompanying drawings.

[0044] Before providing a detailed explanation of the embodiments of this application, let us first introduce the application scenarios of this application.

[0045] Electric vehicles consist of components such as power batteries, electric drive systems, and electronic control systems. Among these, the power battery is the part consumers are most concerned about, as it directly determines the driving range of an electric vehicle. The battery's operating parameters directly affect its quality, safety, performance, and reliability. These parameters include charging voltage, charging rate, discharging voltage, and discharging power.

[0046] Typically, batteries use the same set of operating parameters configured at the factory throughout their lifespan. However, as the number of battery cycles increases, differences arise between different batteries. In this case, the factory-configured operating parameters may not be entirely applicable to all batteries, potentially leading to battery misuse or underutilization of battery performance.

[0047] Based on this, embodiments of this application provide a method for determining battery usage parameters. This method can reduce battery abuse during use and further maximize battery performance, thereby effectively extending the battery's lifespan.

[0048] Figure 1 This is a schematic diagram of the system architecture for determining battery usage parameters provided in an embodiment of this application. Figure 1 As shown, the system 10 includes a vehicle 11 and a cloud platform 12.

[0049] like Figure 1 As shown, vehicle 11 includes a power battery system 111, a battery management system 112, and a communication system 113.

[0050] The power battery system 111 provides energy to the vehicle. The power battery system 111 typically includes a battery section, high and low voltage wiring harnesses, a thermal management section, and structural components. The battery section can be a single cell or a module composed of multiple cells. The high and low voltage wiring harnesses include various connectors and terminal blocks. The thermal management section includes a fan, heating elements, and cooling elements. The structural components include mounting components, seals, metal parts, and a housing.

[0051] The battery management system 112 provides necessary control and management for the power battery system 111, on the one hand meeting the energy demands of the entire vehicle, and on the other hand realizing the rational use of the batteries in the power battery system 111. For example... Figure 1 As shown, the battery management system 112 generally includes a signal acquisition unit and a function control unit. The signal acquisition unit is used to acquire analog and digital signals required to achieve battery functions and energy control, such as data corresponding to various battery state parameters. Examples of battery state parameters include one or more of the following: individual cell voltage, total battery voltage, battery temperature, load terminal voltage, bus current, and vehicle operating status. The function control unit is used to provide the necessary functions and energy control for the vehicle's power source.

[0052] The communication system 113 may include an internal communication system and an external communication system. The internal communication system provides the necessary communication interaction for the orderly coordination between the various controllers of the vehicle. The external communication system is used to collect necessary real-time data from the vehicle and upload this data to the cloud platform 12, while also receiving various data information pushed by the vehicle-cloud platform 12.

[0053] The cloud platform is used to provide a variety of services, for example, including the service corresponding to the method for determining battery usage parameters provided in the embodiments of this application.

[0054] like Figure 1 As shown, the cloud platform includes a raw database 121, a data preprocessing module library 122, an algorithm learning module library 123, and a result output module library 124.

[0055] The raw database stores real-time data uploaded by vehicles, laying the foundation for subsequent data preprocessing and related calculations and algorithms.

[0056] The data preprocessing module library is used to implement data preprocessing functions such as data cleaning, data format conversion, data description, data integration, and feature extraction. Among them, feature extraction is used to construct more correlation signals using limited battery state data. After construction, a feature set matrix closely related to the battery state parameters is formed, and each element in the feature set matrix corresponds to a feature of the battery state parameter.

[0057] The feature set matrix is ​​then processed by relevant algorithms in the algorithm learning module library to generate corresponding weight set matrices. Each element in the weight set matrix represents the degree of influence of the features in the feature set matrix on the battery usage parameters. The result output module library is used to output the weight set matrix.

[0058] The cloud platform can also normalize the weight set matrix. If the change in any weight in the current normalized weight set matrix compared to the previous normalized weight set matrix exceeds a certain threshold, the cloud platform will record this situation and push the normalized weight set matrix to the vehicle. Specific implementation details will be provided in subsequent embodiments.

[0059] After receiving the weight set matrix from the cloud platform, the vehicle's communication system sends the weight set matrix to the battery management system. Upon receiving the weight set matrix, the battery management system updates the weight set matrix in its storage area and updates its own battery usage parameters based on the updated weight set matrix.

[0060] As the vehicle's battery is continuously cycled, through the collaboration between the vehicle 11 and the cloud platform 12, the cloud platform 12 continuously learns the battery status data of the vehicle 11, so that the vehicle 11 gradually generates battery usage parameters suitable for the vehicle 11.

[0061] In addition, such as Figure 1 As shown, the algorithm learning module library of cloud platform 12 includes, for example, a basic algorithm library, a battery operating condition matching model algorithm library, a battery mechanism model training library, a signal processing algorithm library, and a basic mathematical and statistical algorithm library. With the increase in vehicle data and the growing demand for battery performance research, or the generation of new algorithms, these algorithm libraries will be continuously expanded and updated to meet new development needs.

[0062] The basic algorithm library includes, for example, data fitting algorithms, neural network algorithms, clustering algorithms, and random forest algorithms. The algorithms in the basic algorithm library are used to achieve efficient training and learning of battery state data, determining the aforementioned weight set matrix based on the feature set matrix extracted from the battery state data.

[0063] The battery operating condition matching model algorithm library is used to model batteries based on their actual usage conditions. For example, batteries can be modeled according to vehicle usage conditions, such as: urban discharge, high-speed discharge, fast charging, slow charging, balancing, and starting conditions. Since the characteristics of the battery state differ under different operating conditions, the vehicle's usage conditions can be determined before determining the feature set matrix based on the battery state data. Based on these conditions, the battery modeling type can be determined. The feature set matrix is ​​then obtained from the battery state data based on the determined modeling type.

[0064] The battery mechanism model training library is used to build a model library related to battery mechanisms, starting from the electrochemical and electrical equivalent characteristics of batteries. Examples of models in the library include lithium plating models, thermal field models, impedance models, internal short-circuit models, and self-discharge models. Since different battery mechanisms result in different battery states, the battery mechanism model can serve as a feature of the battery state data to determine the aforementioned feature set matrix.

[0065] The signal processing algorithm library is used to solve problems related to algorithm solving or solution speed. Both the signal processing algorithm library and the basic data statistics algorithm library are used to provide basic applications for other algorithm libraries.

[0066] It should be noted that, Figure 1 The functions of the vehicles and cloud platform in the system shown are for illustrative purposes only. Optionally, Figure 1 Some of the vehicle's functions can be implemented by a cloud platform, such as determining battery usage parameters based on a weight set matrix. Optionally, Figure 1 The functions of the cloud platform shown can also be implemented by vehicles. This application embodiment describes... Figure 1 The deployment location of the data processing functional modules in the system shown is not limited.

[0067] based on Figure 1 The system for determining battery usage parameters shown in this application also provides a method for determining battery usage parameters. Figure 2 This is a flowchart illustrating a method for determining battery usage parameters provided in an embodiment of this application. Figure 2 As shown, the method includes the following steps.

[0068] Step 201: During vehicle use, obtain the vehicle's current battery status data, which indicates the current status of the battery in the vehicle.

[0069] Currently, the battery parameters used in electric vehicles are configured by the manufacturers at the time of manufacture. However, the degradation experienced by batteries in electric vehicles varies under different operating conditions and environments. In this context, it is inappropriate to use only one set of battery parameters at the factory for the entire battery lifecycle. This can lead to problems such as relative abuse or underutilization of battery capacity as the number of cycles increases during use.

[0070] Based on this, embodiments of this application provide a method for adaptively updating battery usage parameters based on the current state data of the battery, so as to achieve personalized adaptation of usage parameters for the battery of an electric vehicle. This improves the effective utilization of the battery and reduces the relative risk of battery abuse.

[0071] In some embodiments, such as Figure 1As shown, the vehicle can periodically collect battery status data and send the collected data to the cloud platform. Each time the cloud platform receives battery status data, it updates the battery usage parameters in the vehicle through steps 202 and 203 below.

[0072] For example, the vehicle can collect battery status data at reference intervals. These reference intervals could be 10 minutes, 30 minutes, or a day, etc.

[0073] The battery status parameters typically include one or more of the following: individual cell voltage, total battery voltage, battery temperature, load terminal voltage, bus current, and vehicle operating status. Individual cell voltage can include the voltage of a single cell collected at multiple time points within the most recent sampling period. Total battery voltage can include the total voltage of all cells collected at multiple time points within the most recent sampling period. Battery temperature can include the temperature collected at multiple time points within the most recent sampling period. Load terminal voltage can include the voltage at the load terminal collected at multiple time points within the most recent sampling period. Bus current can include the bus current collected at multiple time points within the most recent sampling period. Vehicle operating status can include, for example, the distribution ratio of different operating conditions, usage frequency, and the changing trends and patterns of characteristic parameters under each operating condition.

[0074] The most recent data collection period can be understood as the time interval between the last time battery status data was acquired and the current time. For example, in a scenario where battery status data is collected once a day for a vehicle, the most recent data collection period would be the most recent day.

[0075] Step 202: Determine the current weight data based on the current battery state data. The current weight data indicates the degree of influence of the current state of the battery on the battery's usage parameters.

[0076] Battery usage parameters refer to the parameters required when using the battery, such as those needed for charging or discharging. For example, battery usage parameters include charging voltage, charging rate, discharging voltage, and discharging rate.

[0077] In some embodiments, determining the current weight data based on the current battery state data can be achieved by: based on the current battery state data and Figure 1 The target learning model in the algorithm learning model library shown is used to determine the current weight data.

[0078] For example, battery state data can be directly input into the target learning model so that the target learning model outputs the current weight data.

[0079] For example, battery status data includes multiple status data points that correspond one-to-one with multiple status parameters. Each status parameter indicates a parameter that can be collected during battery use, and the status data corresponding to each status parameter indicates the changes in the corresponding status parameter during battery use. For example, multiple status parameters may include one or more of the following: individual cell voltage, total battery voltage, battery temperature, load terminal voltage, bus current, and vehicle operating status.

[0080] In this scenario, multiple state data points, each corresponding to a specific state parameter, can be directly input into the target learning model, causing the model to output current weight data. The current weight data includes a current weight set, which contains the current weight corresponding to each feature in at least one feature of each state parameter. The current weight for each feature indicates the degree of influence that feature currently has on the battery's usage parameters.

[0081] In the example above, the target learning model can be pre-trained using a large amount of sample data. For example, a large amount of sample data can be pre-acquired, with each sample including battery state data and a corresponding label indicating the weight data corresponding to the battery state data. The weight data corresponding to the battery state data can be obtained by experts through analysis. For example, experts can... Figure 1 The battery mechanism model shown analyzes the charging and discharging mechanism of the battery under the state indicated by the sample battery state data, and determines the optimal usage parameters under the current state through relevant theoretical algorithms based on the analyzed charger mechanism. The optimal usage parameters are then used as the label corresponding to the sample battery state data.

[0082] Optionally, in other examples, where the battery state data includes multiple state data corresponding one-to-one with multiple state parameters, at least one feature of multiple state parameters can be determined based on the multiple state data corresponding one-to-one with multiple state parameters; at least one feature of multiple state parameters is input into the target learning model, and the target learning model outputs current weight data, which includes a current weight set, which includes the current weight corresponding to each of the at least one feature, and the current weight corresponding to each feature indicates the degree of influence of the corresponding feature on the battery usage parameters.

[0083] In the example above, the target learning model is also pre-trained using a large amount of sample data. For instance, a large amount of sample data is pre-acquired, with each sample including features extracted from the sample battery state data and a corresponding label. The label indicates the weight of the feature extracted from the sample battery state data. The weights corresponding to the sample battery state data can be obtained by experts through analysis. For example, experts can... Figure 1The battery mechanism model shown analyzes the charging and discharging mechanism of the battery under the features extracted from the sample battery state data. Based on the analyzed charger mechanism, the optimal usage parameters under the current features are determined through relevant theoretical algorithms. The optimal usage parameters are then used as the labels corresponding to the features extracted from the sample battery state data.

[0084] The sample data mentioned above can be data from laboratory tests, data obtained from vehicles undergoing cyclic road tests, or operational data from similar batteries already available on the market.

[0085] Furthermore, based on multiple state data that correspond one-to-one with multiple state parameters, at least one feature of the multiple state parameters can be determined by... Figure 1 The feature extraction function provided by the data preprocessing module library shown is implemented.

[0086] For example, a state parameter might be the voltage of a single battery cell. The state data corresponding to the single battery cell voltage includes the voltage values ​​of the single battery cell collected at various time points within the most recent acquisition period. At least one characteristic of the single battery cell voltage may include the average value of the single battery cell voltage within the most recent acquisition period, the rate of change of the single battery cell voltage, the maximum rate of change of the single battery cell voltage, and the minimum rate of change of the single battery cell voltage. At least one characteristic of other state parameters can be referred to the above explanation, and will not be illustrated further here.

[0087] By extracting features from the state data of state parameters, we can uncover multi-dimensional features of the state parameters, so that we can accurately adjust the battery's operating parameters based on these multi-dimensional features.

[0088] In addition, at least one feature of multiple state parameters can be represented by a feature set matrix S. m×n This indicates that there are currently m state parameters, and each state parameter has n features, which is the feature set matrix S. m×n Each element in the matrix represents a feature corresponding to a certain state parameter. In this scenario, a defined set of weights can be represented by a weight set matrix W. i×j This means that each element in the weight set matrix corresponds to the feature set matrix S. m×n One or more elements in the weight set matrix W i×j Each element in the table represents the degree of influence of the corresponding feature on the battery usage parameters.

[0089] In addition, based on Figure 1 As shown in the system, the characteristics of battery state data may differ under different vehicle operating conditions. Therefore, before extracting features from battery state data, the vehicle's operating conditions can be determined based on the vehicle's operating status. Then, based on the vehicle's operating conditions, the features to be extracted can be determined, and then the features can be extracted from the battery state data.

[0090] In addition, based on Figure 1 As shown in the system, the impact of battery state data on battery usage parameters varies depending on the battery mechanism. Therefore, the extracted features can be further incorporated into a battery mechanism model to achieve a comprehensive analysis of the battery state from various dimensions. For example, extracting the battery mechanism model can be achieved by comprehensively analyzing the state data of each of the multiple state parameters to determine the battery mechanism model.

[0091] In addition, in the embodiments of this application, before feature extraction of the state data of each state parameter among multiple state parameters, the state data can be preprocessed by data cleaning, data format conversion, data integration, etc.

[0092] Data cleaning is used to identify the validity and reliability of data. It removes invalid or unreliable data from the state data. Invalid data can be understood as data that is not very useful for the overall data analysis. For example, data may be interrupted for some reason (i.e., the data continuity is poor), or the data quality may be poor, containing some unlikely data. Data transformation ensures that the data maintains a uniform format, providing a de-differentiation service for subsequent data processing. Data integration enables data redundancy analysis and the identification of abnormal data, thereby improving the reliability of state data for subsequent feature extraction.

[0093] Step 203: Update the battery usage parameters based on the current weight data.

[0094] In some embodiments, battery usage parameters can be determined based on current weight data, current battery state data, and related algorithms. These determined battery usage parameters are then updated to the battery management system, enabling the system to use the battery based on these updated parameters. For example, the battery can be charged based on the charging voltage and charging power specified in the determined battery usage parameters, and / or the battery can be charged and discharged based on the discharging voltage and discharging power specified in the determined battery usage parameters.

[0095] For example, when the current weight data includes the current weight set, which includes the current weight corresponding to each feature in at least one feature of each state parameter, and the current weight corresponding to each feature indicates the degree of influence of the corresponding feature on the battery's usage parameters, the battery's usage parameters can be determined by the following formula.

[0096] T = f(∑wi*Si). Where T represents the usage parameters of a battery, wi represents the weights corresponding to the features, and Si represents the features themselves. f is the specific algorithm for determining the usage parameters based on each feature and its corresponding weight.

[0097] The algorithm used to update the battery usage parameters based on the current weight data can also be obtained by expert analysis, and will not be described in detail in this application embodiment.

[0098] Additionally, in this embodiment, the battery usage parameters can be updated based on the weight data after each determination. Optionally, historical weight data can also be obtained after each determination, where the historical weight data is the most recently determined weight data before the current time; if the change between the historical weight data and the current weight data meets the target condition, the battery usage parameters are updated based on the weight data. Conversely, if the change between the historical weight data and the current weight data does not meet the target condition, the operation of updating the battery usage parameters based on the weight data is not performed.

[0099] When the change between historical weight data and current weight data meets the target conditions, it indicates that the battery's operating state has changed significantly. At this point, it is necessary to update the battery usage parameters to match the actual state of the battery, thereby improving the battery's lifespan.

[0100] When the change between historical weight data and current weight data does not meet the target condition, it indicates that the battery's operating state has not changed significantly. In this case, it is not necessary to update the battery usage parameters to avoid frequent updates to the battery usage parameters, which could lead to unstable battery performance.

[0101] The aforementioned objective condition is used to indicate that the current weight data has changed significantly relative to historical weight data. For example, in a scenario where the current weight data includes the current weight set, the objective condition could be that the change between any element in the current weight set and the element at the corresponding position in the previously determined weight set exceeds a certain threshold.

[0102] Figure 3 This is a flowchart illustrating another method for determining battery usage parameters provided in an embodiment of this application. Figure 3 The illustrated process is for illustrative purposes only and does not constitute a limitation on the embodiments of this application. Figure 3 As shown, determining battery usage parameters involves the following steps.

[0103] Step 301: The battery management system collects real-time battery status data during battery operation. This data includes individual cell voltage, battery temperature, total battery voltage, load voltage, bus current, and vehicle operating status.

[0104] Step 302: The battery management system sends the current battery status data in real time through the communication system, and the communication system receives and stores the current battery status data.

[0105] Step 303: The communication system transmits the received and stored current battery status data to the cloud platform according to a certain upload mechanism. This embodiment of the application does not limit the mechanism for uploading data between the vehicle and the cloud platform. For example, it could be an HTTP POST file upload mechanism, etc.

[0106] Step 304: The cloud platform will perform service routing on the received current status data. The reason for service routing is that the cloud platform provides multiple services. Therefore, when receiving current battery status data, the cloud platform needs to determine whether the current service is one that determines battery usage parameters, so that the data can be processed based on this determined service. After service routing, the current battery status data can be preprocessed. The data preprocessing process includes the following steps.

[0107] ① Data cleaning. The purpose of this step is to identify the validity and reliability of the data.

[0108] ② Data transformation. This step ensures the data maintains a uniform format, providing a de-differentiation service for subsequent data processing.

[0109] ③ Data description. Used to visualize the data in order to generate reports or charts.

[0110] ④ Data integration. Used to perform data redundancy analysis and identify abnormal data.

[0111] ⑤ Feature Extraction. This step involves constructing more features from limited current battery state data to enable multi-dimensional analysis. A feature set matrix is ​​generated in this step.

[0112] Step 305: The aforementioned feature set matrix is ​​trained using an algorithm learning model library to output a weight set matrix. The elements in the weight set matrix represent the degree of influence of the corresponding feature on battery usage parameters. The signal processing algorithm library and the basic algorithm library in the algorithm learning model library primarily provide the computational foundation for other algorithm libraries, while the battery operating condition matching model algorithm library provides more granular application scenarios for the battery mechanism model library.

[0113] Step 306: The cloud platform normalizes the weight set matrix to form a normalized weight set matrix. If the change between any element in the current normalized weight set matrix and the corresponding element in the previous normalized weight set matrix exceeds a certain threshold, the cloud platform will push the normalized weight set matrix to the vehicle's communication system.

[0114] Step 307: After receiving the normalized weight set matrix, the vehicle's communication system promptly sends the weight set matrix to the battery management system.

[0115] Step 308: After receiving the weight set matrix, the battery management system updates the weight set matrix in the storage area and updates the battery usage parameters based on the updated weight set matrix.

[0116] As the number of battery cycles in vehicles increases, Figure 3 Steps 301 to 308 in the process will continuously loop. Thus, the frequency of updating the battery usage parameters depends on the degree of change of each weight in the weight set matrix, that is, on the degree of change of each feature in the feature set matrix. The degree of change of each feature in the feature set matrix is ​​determined by the actual operating conditions of the vehicle and the degree of change in the battery. Therefore, in this embodiment, the purpose of updating the battery usage parameters based on the actual operating conditions of the vehicle and the degree of change in the battery is achieved.

[0117] In summary, the method provided in this application allows for adaptive updates to the vehicle's battery usage parameters based on the vehicle's current battery state data during vehicle use. That is, throughout the battery's lifespan, the battery's usage parameters are adaptively updated according to the battery state to match the battery's characteristics, thereby effectively extending the battery's lifespan.

[0118] All of the above-mentioned optional technical solutions can be combined in any way to form optional embodiments of this application, and the embodiments of this application will not be described in detail one by one.

[0119] Figure 4 This is a schematic diagram of a device for determining battery usage parameters provided in an embodiment of this application. This device can be implemented by software, hardware, or a combination of both. Figure 4 As shown, the device 400 may include the following modules.

[0120] The acquisition module 401 is used to acquire the current battery status data of the vehicle during vehicle use. The current battery status data indicates the current status of the battery in the vehicle.

[0121] The determination module 402 is used to determine the current weight data based on the current battery state data. The current weight data indicates the degree of influence of the current state of the battery on the battery's usage parameters.

[0122] Update module 403 is used to update the battery usage parameters based on the current weight data.

[0123] Optionally, the update module is used for:

[0124] Retrieve historical weight data, which is the weight data most recently determined before the current time;

[0125] If the change between historical weight data and current weight data meets the target conditions, then the battery usage parameters are updated based on the weight data.

[0126] Optionally, the update module is used for:

[0127] If the changes between historical weight data and current weight data do not meet the target conditions, the operation of updating the battery usage parameters based on the weight data will not be performed.

[0128] Optionally, the determination module is used for:

[0129] Based on the current battery state data and the target learning model, determine the current weight data.

[0130] Optionally, the battery status data includes multiple status data corresponding one-to-one with multiple status parameters. Each status parameter indicates a parameter that can be collected during the use of the battery, and the status data corresponding to each status parameter indicates the change of the corresponding status parameter during the use of the battery.

[0131] The determination module is used for:

[0132] Based on multiple state data that correspond one-to-one with multiple state parameters, at least one feature of the multiple state parameters is determined.

[0133] At least one feature of multiple state parameters is input into the target learning model, and the target learning model outputs the current weight data. The current weight data includes the current weight set, which includes the current weight corresponding to each feature in at least one feature of each state parameter. The current weight corresponding to each feature indicates the degree of influence of the corresponding feature on the battery's usage parameters.

[0134] Optionally, multiple state parameters include one or more of the following: individual cell voltage, total battery voltage, battery temperature, load terminal voltage, bus current, and vehicle operating status.

[0135] In summary, during vehicle use, the battery's usage parameters can be adaptively updated based on the vehicle's current battery state data. That is, throughout the battery's lifespan, the battery's usage parameters are adaptively updated according to its state to match the battery's characteristics, thereby extending the battery's lifespan.

[0136] It should be noted that the device for determining battery usage parameters provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the device for determining battery usage parameters and the method embodiment for determining battery usage parameters provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiment, which will not be repeated here.

[0137] Figure 5 This is a structural block diagram of an in-vehicle terminal 500 provided in an embodiment of this application. The functions of the vehicle in the aforementioned embodiments can all be implemented through this in-vehicle terminal. Typically, the in-vehicle terminal 500 includes a processor 501 and a memory 502.

[0138] Processor 501 may include one or more processing cores, such as a quad-core processor, an octa-core processor, etc. Processor 501 may be implemented using at least one hardware form selected from DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). Processor 501 may also include a main processor and a coprocessor. The main processor, also known as a CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, processor 501 may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, processor 501 may also include an AI (Artificial Intelligence) processor, which is used to handle computational operations related to machine learning.

[0139] Memory 502 may include one or more computer-readable storage media, which may be non-transitory. Memory 502 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments, the non-transitory computer-readable storage media in memory 502 is used to store at least one instruction, which is executed by processor 501 to implement the XXXX method provided in the method embodiments of this application.

[0140] In some embodiments, the vehicle terminal 500 may optionally include a peripheral device interface 503 and at least one peripheral device. The processor 501, memory 502, and peripheral device interface 503 can be connected via a bus or signal line. Each peripheral device can be connected to the peripheral device interface 503 via a bus, signal line, or circuit board. Specifically, the peripheral device includes at least one of the following: a radio frequency circuit 504, a touch display screen 505, a camera 506, an audio circuit 507, a positioning component 508, and a power supply 509.

[0141] Peripheral device interface 503 can be used to connect at least one I / O (Input / Output) related peripheral device to processor 501 and memory 502. In some embodiments, processor 501, memory 502 and peripheral device interface 503 are integrated on the same chip or circuit board; in some other embodiments, any one or two of processor 501, memory 502 and peripheral device interface 503 can be implemented on separate chips or circuit boards, which is not limited in this embodiment.

[0142] The radio frequency (RF) circuit 504 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The RF circuit 504 communicates with communication networks and other communication devices via electromagnetic signals. The RF circuit 504 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals back into electrical signals. Optionally, the RF circuit 504 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, etc. The RF circuit 504 can communicate with other terminals through at least one wireless communication protocol. This wireless communication protocol includes, but is not limited to: metropolitan area networks (MANs), various generations of mobile communication networks (2G, 3G, 4G, and 5G), wireless local area networks (WLANs), and / or WiFi (Wireless Fidelity) networks. In some embodiments, the RF circuit 504 may also include circuitry related to NFC (Near Field Communication), which is not limited in this application.

[0143] Display screen 505 is used to display a user interface (UI). This UI may include graphics, text, icons, video, and any combination thereof. When display screen 505 is a touch display screen, it also has the ability to collect touch signals on or above its surface. These touch signals can be input as control signals to processor 501 for processing. In this case, display screen 505 can also be used to provide virtual buttons and / or a virtual keyboard, also known as soft buttons and / or a soft keyboard.

[0144] The camera component 506 is used to capture images or videos.

[0145] The audio circuit 507 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, converting the sound waves into electrical signals that are input to the processor 501 for processing, or input to the radio frequency circuit 504 for voice communication. For stereo sound acquisition or noise reduction purposes, multiple microphones may be used, each positioned at a different location on the vehicle terminal 500. The microphone may also be an array microphone or an omnidirectional microphone. The speaker is used to convert the electrical signals from the processor 501 or the radio frequency circuit 504 into sound waves. The speaker may be a conventional diaphragm speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can convert electrical signals not only into audible sound waves but also into inaudible sound waves for purposes such as distance measurement. In some embodiments, the audio circuit 507 may also include a headphone jack.

[0146] The positioning component 508 is used to locate the current geographical location of the vehicle terminal 500 in order to enable navigation or LBS (Location Based Service). The positioning component 508 can be a positioning component based on the US GPS (Global Positioning System), China's BeiDou system, Russia's Granas system, or the EU's Galileo system.

[0147] Power supply 509 is used to power the various components in vehicle terminal 500. Power supply 509 can be AC ​​power, DC power, a disposable battery, or a rechargeable battery. When power supply 509 includes a rechargeable battery, the rechargeable battery can support wired or wireless charging. The rechargeable battery can also be used to support fast charging technology.

[0148] In some embodiments, the vehicle terminal 500 further includes one or more sensors 510. The one or more sensors 510 include, but are not limited to: an acceleration sensor 511, a gyroscope sensor 512, a pressure sensor 513, a fingerprint sensor 514, an optical sensor 515, and a proximity sensor 516.

[0149] Those skilled in the art will understand that Figure 5 The structure shown does not constitute a limitation on the vehicle terminal 500, and may include more or fewer components than shown, or combine certain components, or use different component arrangements.

[0150] This application also provides a non-transitory computer-readable storage medium, which, when the instructions in the storage medium are executed by the processor of the vehicle terminal, enables the vehicle terminal to execute the method for determining battery usage parameters provided in the above embodiment.

[0151] This application also provides a computer program product containing instructions that, when run on an in-vehicle terminal, causes the in-vehicle terminal to execute the method for determining battery usage parameters provided in the above embodiments.

[0152] Figure 6 This is a schematic diagram of a server structure provided in an embodiment of this application. The aforementioned cloud platform can be implemented using this server. This server can be a server in a backend server cluster. Specifically:

[0153] Server 600 includes a central processing unit (CPU) 601, a system memory 604 including random access memory (RAM) 602 and read-only memory (ROM) 603, and a system bus 605 connecting the system memory 604 and the CPU 601. Server 600 also includes a basic input / output system (I / O system) 606 that facilitates the transfer of information between various devices within the computer, and a mass storage device 607 for storing the operating system 613, application programs 614, and other program modules 615.

[0154] The basic input / output system 606 includes a display 608 for displaying information and an input device 609 for user input, such as a mouse or keyboard. Both the display 608 and the input device 609 are connected to the central processing unit 601 via an input / output controller 610 connected to the system bus 605. The basic input / output system 606 may also include the input / output controller 610 for receiving and processing input from multiple other devices such as a keyboard, mouse, or electronic stylus. Similarly, the input / output controller 610 also provides output to a display screen, printer, or other types of output devices.

[0155] Mass storage device 607 is connected to central processing unit 601 via a mass storage controller (not shown) connected to system bus 605. Mass storage device 607 and its associated computer-readable media provide non-volatile storage for server 600. That is, mass storage device 607 may include computer-readable media (not shown) such as hard disk or CD-ROM drive.

[0156] Without loss of generality, computer-readable media can include computer storage media and communication media. Computer storage media include volatile and non-volatile, removable and non-removable media implemented using any method or technology for storing information such as computer-readable instructions, data structures, program modules, or other data. Computer storage media include RAM, ROM, EPROM, EEPROM, flash memory or other solid-state storage technologies, CD-ROM, DVD or other optical storage, magnetic tape cassettes, magnetic tape, disk storage, or other magnetic storage devices. Of course, those skilled in the art will recognize that computer storage media are not limited to the above-mentioned types. The system memory 604 and mass storage device 607 described above can be collectively referred to as memory.

[0157] According to various embodiments of this application, server 600 can also be connected to a remote computer on a network, such as the Internet. That is, server 600 can be connected to network 612 via network interface unit 611 connected to system bus 605, or it can also use network interface unit 611 to connect to other types of networks or remote computer systems (not shown).

[0158] The aforementioned memory also includes one or more programs, which are stored in the memory and configured to be executed by the CPU. The one or more programs include methods for determining battery usage parameters as provided in the embodiments of this application.

[0159] This application also provides a non-transitory computer-readable storage medium, which, when the instructions in the storage medium are executed by the processor of a server, enables the server to execute the method for determining battery usage parameters provided in the above embodiments.

[0160] This application also provides a computer program product containing instructions that, when run on a server, cause the server to execute the method for determining battery usage parameters provided in the above embodiments.

[0161] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0162] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of this application should be included within the protection scope of the embodiments of this application.

Claims

1. A method for determining battery usage parameters, characterized in that, The method includes: During vehicle use, the current battery status data of the vehicle is acquired. The battery status data includes multiple status data corresponding to multiple status parameters. The status data corresponding to each status parameter indicates the change of the corresponding status parameter during battery use. The multiple status parameters include single cell voltage, total battery voltage, battery temperature, load terminal voltage, bus current, and vehicle operating status. The vehicle's operating conditions are obtained based on the vehicle's operating status. At least one feature of the multiple state parameters is determined from the multiple state data that correspond one-to-one with the multiple state parameters based on the vehicle's operating conditions. At least one feature of the multiple state parameters is input into a target learning model. The target learning model outputs current weight data, which includes a current weight set. The current weight set includes the current weight corresponding to each of the at least one feature. The current weight corresponding to each feature indicates the degree of influence of the corresponding feature on the battery's operating parameters. If the change between the historical weight data and the current weight data meets the target condition, the battery usage parameters are updated based on the current weight data; the historical weight data is the weight data most recently determined before the current time, and the battery usage parameters include charging voltage, charging rate, discharging voltage, and discharging rate; Updating the battery's usage parameters based on the current weight data includes determining the battery's usage parameters using the following formula: T=f(∑wi Si), Where T represents the battery's usage parameters, wi represents the weight corresponding to the feature, Si represents the feature, and f is the algorithm for determining the usage parameters based on each feature and its corresponding weight.

2. The method as described in claim 1, characterized in that, The method further includes: If the change between the historical weight data and the current weight data does not meet the target condition, then the operation of updating the battery usage parameters based on the weight data will not be performed.

3. A device for determining battery usage parameters, characterized in that, The device includes: The acquisition module is used to acquire the current battery status data of the vehicle during vehicle use. The battery status data includes multiple status data corresponding to multiple status parameters. The status data corresponding to each status parameter indicates the change of the corresponding status parameter during battery use. The multiple status parameters include single cell voltage, total battery voltage, battery temperature, load terminal voltage, bus current, and vehicle operating status. The determination module is used to obtain the vehicle's operating conditions based on the vehicle's operating status, determine at least one feature of the multiple state parameters from the multiple state data that correspond one-to-one with the multiple state parameters based on the vehicle's operating conditions, input the at least one feature of the multiple state parameters into a target learning model, and output current weight data, the current weight data including a current weight set, the current weight set including the current weight corresponding to each of the at least one feature, and the current weight corresponding to each feature indicating the degree of influence of the corresponding feature on the battery's operating parameters; An update module is used to update the battery's usage parameters based on the current weight data if the change between the historical weight data and the current weight data meets the target conditions; the historical weight data is the weight data most recently determined before the current time, and the battery's usage parameters include charging voltage, charging rate, discharging voltage, and discharging rate; The update module is used to determine the battery's usage parameters using the following formula: T=f(∑wi Si), Where T represents the battery's usage parameters, wi represents the weight corresponding to the feature, Si represents the feature, and f is the algorithm for determining the usage parameters based on each feature and its corresponding weight.

4. A device for determining battery usage parameters, characterized in that, The device includes: processor; Memory used to store processor-executable instructions; The processor is configured to perform the steps of the method described in any one of claims 1-2.

5. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed by a processor, implement the steps of the method described in any one of claims 1-2.

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