Battery capacity value processing method and device, computer equipment and storage medium

Abstract

The invention discloses a battery capacity value processing method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining a first association relationship, a second association relationship and a third association relationship of a target battery of a target vehicle, and determining a target association relationship according to the association relationships; and determining the target battery capacity value of the target battery according to the current time point, the specified use duration and the target association relationship of the target battery, so that the evaluation of the current health state is not limited to the single reading of the real-time operation parameters of the power battery any more, and the prospective analysis of the battery capacity is realized. Therefore, the user can reasonably plan a travel route according to the battery capacity prediction result of the battery and arrange charging or battery maintenance matters in advance, and the vehicle use experience of the user is remarkably improved.

Description

Technical Field

[0001] This application relates to the field of computer technology, specifically to a method, apparatus, computer device, and storage medium for processing battery capacity values. Background Technology

[0002] To meet people's pursuit of a higher quality of life, vehicles that facilitate user travel have emerged. With the advancement of vehicle intelligence, vehicles are capable of more and more functions, and people's attention to vehicles is no longer limited to their quality but also extends to their maintenance and management. Currently, in the field of battery health testing for new energy vehicles, the mainstream approach is to collect and analyze relevant parameters of the power battery using diagnostic software integrated into the vehicle. By collecting and analyzing real-time operating parameters of the power battery, a health report reflecting the current health status of the battery is generated. This report provides a clear picture of the battery's current health level, offering immediate reference for vehicle maintenance personnel to troubleshoot and for users to understand the vehicle's battery condition.

[0003] However, existing battery health detection solutions based on diagnostic software are limited to reading and processing real-time parameters of the power battery, which has significant limitations. They lack a solution for judging the long-term degradation pattern of the battery, and in particular, they cannot analyze the changes in battery capacity over a specific period of time in the future to provide users with a reference to understand the condition of the vehicle battery, thus affecting the user's vehicle usage experience. Summary of the Invention

[0004] To address the aforementioned technical issues, embodiments of this application provide a method, apparatus, computer device, and storage medium for processing battery capacity values, enabling forward-looking analysis of battery capacity. This allows users to rationally plan travel routes and arrange charging or battery maintenance in advance based on the predicted battery capacity, significantly improving the user's driving experience.

[0005] In a first aspect, embodiments of this application provide a method for processing battery capacity values, including: Based on the battery model of the target battery of the target vehicle, at least one first association relationship is determined for the target battery. The first association relationship is used to indicate the change of the battery capacity value over time under the influence of an internal battery factor of the target battery. Based on at least one target user usage habit feature of the current user for the target battery, at least one preset second association relationship of the target battery is determined. The second association relationship is used to indicate the change of the battery capacity value of the target battery over time under the influence of a user usage habit feature. Based on at least one target external factor of the target battery at present, at least one third association relationship matching the target battery with the target external factor is determined from the third preset association relationship. The third association relationship is used to indicate the change of the battery capacity value of the target battery over time under the influence of a battery external factor. Based on the first association relationship, the second association relationship, and the third association relationship, the target association relationship corresponding to the target battery is determined; Based on the current time point of the target battery, the specified usage time, and the target association, the target battery capacity value is determined. The target battery capacity value is the battery capacity value of the target battery after the specified usage time from the current time point.

[0006] Secondly, embodiments of this application provide a battery capacity value processing apparatus, comprising: The first determining unit is used to determine at least one first association relationship of the target battery based on the battery model of the target battery of the target vehicle. The first association relationship is used to indicate the change of the battery capacity value of the target battery over time under the influence of an internal battery factor. The second determining unit is used to determine at least one preset second association relationship of the target battery based on at least one target user usage habit feature of the current user on the target battery. The second association relationship is used to indicate the change of the battery capacity value of the target battery over time under the influence of a user usage habit feature. The third determining unit is used to determine at least one third association relationship between the target battery and the target external factor from the third preset association relationship based on the current target external factor of the target battery. The third association relationship is used to indicate the change of the battery capacity value of the target battery over time under the influence of a battery external factor. The fourth determining unit is used to determine the target association relationship corresponding to the target battery based on the first association relationship, the second association relationship, and the third association relationship; The fifth determining unit is used to determine the target battery capacity value of the target battery based on the current time point, the specified usage time, and the target association relationship. The target battery capacity value is the battery capacity value of the target battery after the specified usage time from the current time point.

[0007] Thirdly, embodiments of this application also provide a computer device, including a memory storing multiple instructions; a processor loads instructions from the memory to execute the steps of any of the battery capacity value processing methods provided in embodiments of this application.

[0008] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a plurality of instructions adapted for loading by a processor to execute the steps of any of the battery capacity value processing methods provided in embodiments of this application.

[0009] Fifthly, embodiments of this application also provide a computer program product, including a computer program or instructions, which, when executed by a processor, implement the steps in the processing method for any battery capacity value provided in embodiments of this application.

[0010] The solution adopted in this application obtains the first, second, and third association relationships of the target battery of the target vehicle and determines the target association relationship based on the above association relationships. Then, based on the current time point of the target battery, the specified usage time, and the target association relationship, the target battery capacity value of the target battery is determined. This makes it no longer limited to the assessment of the current health status by simply reading the real-time operating parameters of the power battery. It enables forward-looking analysis of battery capacity, allowing users to reasonably plan travel routes and arrange charging or battery maintenance in advance based on the predicted battery capacity results, significantly improving the user's driving experience. Attached Figure Description

[0011] 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.

[0012] Figure 1 This is a schematic diagram of the application environment for the battery capacity value processing method provided in the embodiments of this application; Figure 2 This is a schematic flowchart of an embodiment of the battery capacity value processing method provided in this application. Figure 3 This is a schematic diagram illustrating an application scenario of the battery capacity value processing method provided in the embodiments of this application; Figure 4 This is a schematic diagram of the battery capacity value processing device provided in the embodiments of this application; Figure 5 This is a schematic diagram of the internal structure of the computer device provided in the embodiments of this application. Detailed Implementation

[0013] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. At the same time, in the description of the embodiments of this application, the terms "first," "second," etc., are only used to distinguish descriptions and should not be construed as indicating or implying relative importance. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0014] In one embodiment of this application, the battery capacity value processing method can run on a local terminal device or a server. When the battery capacity value processing method runs on a server, the method can be implemented and executed based on a cloud interaction system, wherein the cloud interaction system includes a server and a client device.

[0015] To better understand the battery capacity value processing method, apparatus, computer equipment, and storage medium provided in the embodiments of this application, the application environment applicable to the embodiments of this application is described below.

[0016] Please see Figure 1 , Figure 1 This illustration shows an application scenario of a battery capacity value processing method provided in an embodiment of this application. As one implementation, the battery capacity value processing method provided in this application embodiment can be applied to terminals (or electronic devices), cloud platforms, and application devices. The terminal can be, for example,... Figure 1 In the mobile phone, tablet, desktop computer, or laptop computer shown, the terminal can connect to the resource library via a network. The terminal can determine the time point at which the battery capacity value needs to be predicted based on information input by the user, and obtain relevant battery parameters and data from the vehicle with which it is communicatively connected. The network serves as the medium for providing a communication link between the terminal (or electronic device) and the resource library. The network can include various connection types, such as wired communication links, wireless communication links, etc., which are not limited in this embodiment. Optionally, in other embodiments, the electronic device can also be a smartphone, laptop computer, etc.

[0017] It should be understood that Figure 1 The terminals (or electronic devices) and resource libraries shown are merely illustrative. Depending on implementation needs, there can be any number of terminals (or electronic devices), cloud platforms, and application devices. It is understood that embodiments of this application can also allow multiple terminals to access the cloud platform simultaneously.

[0018] The following detailed description is provided in conjunction with the accompanying drawings. In this embodiment, the execution subject is a terminal device as an example. It should be noted that the order of description in the following embodiments is not intended to limit the preferred order of the embodiments. Although a logical order is shown in the flowcharts, in some cases, the steps shown or described may be performed in a different order than that shown in the accompanying drawings.

[0019] The following detailed description is provided in conjunction with the accompanying drawings. In this embodiment, the execution subject is a terminal. It should be noted that the order of description in the following embodiments is not intended to limit the preferred order of the embodiments. Although a logical order is shown in the flowcharts, in some cases, the steps shown or described may be performed in a different order than that shown in the accompanying drawings.

[0020] Please see Figure 2 The following describes the method for processing the battery capacity value provided in this application. Please refer to [link / reference needed] for details. Figure 2 The specific process for processing the battery capacity value can be summarized in steps 101 to 105 as follows: Step 101: Based on the battery model of the target battery of the target vehicle, determine at least one first association relationship of the target battery. The first association relationship is used to indicate the change of the battery capacity value of the target battery over time under the influence of an internal battery factor.

[0021] Internal battery factors include electrode material degradation, electrolyte decomposition, separator performance degradation, and electrochemical corrosion. The degradation trend of these internal factors can be determined by analyzing battery testing reports. Since each vehicle model uses different battery types with varying degradation rates, the above data can be objectively analyzed from official online reports or reports published by authoritative third-party organizations for each battery model. This allows for the determination of the primary correlations between different battery internal factors. All of these primary correlations can be reflected by a single formula, the structure of which is as follows:

[0022] Where y1 is the battery capacity value, a0, a1, a2...an and b1, b2...bn are parameters calculated based on the collected data, and x is the time.

[0023] Step 102: Based on at least one target user usage habit feature of the current user for the target battery, determine at least one preset second association relationship of the target battery. The second association relationship is used to indicate the change of the battery capacity value of the target battery over time under the influence of a user usage habit feature.

[0024] The user's charging and discharging habits also affect the rate of battery capacity degradation. These user habits can include the depth of charge / discharge (frequently discharging the battery completely before charging versus charging only when it's 50% discharged, and frequently unplugging the charger before it's fully charged versus unplugging it only when it's fully charged, all of which affect the degradation of the battery's internal materials), charging method (the impact of fast charging versus slow charging), discharge rate (high-power discharge leads to increased temperature and accelerated degradation), and the number of charging cycles.

[0025] By collecting big data such as the current car owners' user habits and battery capacity values ​​at different times, a second correlation is obtained corresponding to different user habits. All the obtained second correlations can be reflected by a formula, the specific formula structure of which is as follows:

[0026] Where y2 is the battery capacity value, c0, c1, c2...an and d1, d2...dn are parameters calculated based on the collected data, and x is the time.

[0027] In one embodiment, the step "determining at least one preset second association relationship of the target battery based on at least one target user usage habit feature of the current user for the target battery" includes: Based on at least one target user usage habit feature of the current user for the target battery, at least one second association relationship is determined from the second preset association relationship to match the user usage habit feature with the target user usage habit feature, wherein one second preset association relationship corresponds to one user usage habit feature.

[0028] Specifically, we can analyze all the data associated with the target battery under various user usage habits to obtain the second preset association relationship of the target battery under various user usage habits. Then, we can find at least one second association relationship that matches the user usage habits of the target user from the second preset association relationship according to the actual analysis needs.

[0029] Optionally, the step "determining at least one preset second association relationship of the target battery based on at least one target user usage habit feature of the current user for the target battery" includes: The target model outputs at least one preset second association relationship for the target battery based on at least one user habit feature of the current user and the battery capacity change information of the target battery. The target model is a model obtained by performing association relationship mapping training based on the user habit features and battery capacity change information of at least one other user and / or the current user corresponding to the target battery.

[0030] In this process, the target model can be obtained by pre-training the model with correlation mapping based on the user habit characteristics and battery capacity change information of at least one other user and / or the current user corresponding to the target battery. In the actual analysis process, the target model determines at least one second correlation relationship of the target battery based on the target user's user habit characteristics and the battery capacity change information of the target battery obtained in real time.

[0031] Step 103: Based on at least one target external factor of the target battery at present, determine at least one third association relationship between the target battery and the target external factor from the third preset association relationship. The third association relationship is used to indicate the change of the battery capacity value of the target battery over time under the influence of a battery external factor.

[0032] External factors of the battery include environmental factors and external disturbance factors. Since battery capacity is affected by geographical location and time, environmental factors can include geographical location (e.g., north and south), season, climate, temperature, etc. Furthermore, different environmental factors lead to different rates of battery capacity degradation; therefore, the temperature effect at specific times must also be considered in specific regions. For example, in coastal and inland areas, the degree of moisture intrusion differs due to varying humidity levels, resulting in different battery degradation rates. Bumpy mountain roads and flat ground cause different vibrations and impacts on the battery; bumpy mountain roads are more likely to loosen internal connections or damage separators, leading to faster battery degradation. External disturbance factors are those that can be controlled by human intervention, such as using insulation shells when temperatures are too low or adding heat sinks when temperatures are too high.

[0033] By collecting large datasets such as battery capacity changes and time data corresponding to different external factors, a third correlation is obtained for different external factors. All the obtained third correlations can be reflected by a formula, the specific formula structure of which is as follows:

[0034] Where y3 is the battery capacity value, e0, e1, e2...en and f1, f2...fn are parameters calculated based on the collected data, and x is the time.

[0035] Step 104: Based on the first association relationship, the second association relationship, and the third association relationship, determine the target association relationship corresponding to the target battery.

[0036] Specifically, all the obtained relationships can be summed up to obtain the target relationship, and the formula structure of the target relationship is as follows: ) ( ) ( )

[0037] Step 105: Based on the current time point of the target battery, the specified usage time, and the target association, determine the target battery capacity value of the target battery. The target battery capacity value is the battery capacity value of the target battery after the specified usage time from the current time point.

[0038] In one embodiment, the step "determining the target battery capacity value based on the current time point of the target battery, the specified usage duration, and the target association relationship" includes: Obtain the start time of use of the target battery; Based on the start time of the target battery, the current time, and the target association, determine the first predicted battery capacity value corresponding to the current time of the target battery; Based on the start time of the target battery, the current time, the specified usage duration, and the target association, a second predicted battery capacity value corresponding to the target battery is determined. The target battery capacity value is determined based on the current actual battery capacity value of the target battery, the first predicted battery capacity value, and the second predicted battery capacity value.

[0039] Furthermore, the step "determining the first predicted battery capacity value corresponding to the current time point of the target battery based on the start time point of the target battery, the current time point, and the target association" includes: The historical usage duration of the target battery is determined based on the start time of use and the current time. The first predicted battery capacity value corresponding to the current time point of the target battery is determined based on the historical usage duration and the target correlation.

[0040] Furthermore, the step "determining the second predicted battery capacity value corresponding to the target battery based on the start time of use of the target battery, the current time, the specified usage duration, and the target correlation" includes: The historical usage duration of the target battery is determined based on the start time of use and the current time. A new historical usage duration is determined based on the historical usage duration and the specified usage duration; Based on the new historical usage duration and the target correlation, a second predicted battery capacity value corresponding to the target battery is determined.

[0041] Furthermore, the step "determining the target battery capacity value based on the current actual battery capacity value of the target battery, the first predicted battery capacity value, and the second predicted battery capacity value" includes: Obtain the first difference between the first predicted battery capacity value and the second predicted battery capacity value; Obtain a second difference between the current actual battery capacity value of the target battery and the first difference, and use the second difference as the target battery capacity value of the target battery.

[0042] Specifically, the start time and current time of the target battery on the vehicle can be obtained. Based on the start time and current time of the target battery, the historical usage duration T1 of the target battery can be determined. For example, T1 can be obtained by subtracting the start time from the current time. In this case, T1 is x. Substituting T1 into the formula corresponding to the target association relationship, the first predicted battery capacity value y corresponding to the current time of the target battery can be obtained. T1 Then, obtain the specified usage duration. Taking a specified usage duration of one week as an example, a new historical usage duration can be determined based on the historical usage duration and the specified usage duration. For example, add the specified usage duration (one week) to the historical usage duration T1 to obtain T2. ​​At this time, T2 is x. Substituting T2 into the formula corresponding to the target correlation, we can obtain the second predicted battery capacity value y of the target battery after one week from the current time point. T2 ; Determine the predicted battery capacity value as y T1 - y T2 At this point, the maximum battery capacity (i.e., the target battery capacity) after one week from the current time is the target battery's current actual battery capacity minus the predicted battery capacity loss. In other words, the target battery capacity = current actual battery capacity - y T1 - y T2 .

[0043] Based on the above description, the following examples will further illustrate the battery capacity value processing method of this application. Specific embodiments are described below.

[0044] In this embodiment of the application, all the above battery factors can be combined to collect information on the degradation rate of the above battery factors, and the battery's SoH time degradation curve can be plotted to obtain the formula corresponding to each battery degradation factor. Then, the degradation situation of one week / one month / one quarter / one year can be calculated by the formula, and each degradation situation can be accumulated to remind the car owner of the battery degradation situation in these periods.

[0045] For example, please see Figure 3 Each factor can be plotted as a scatter plot based on the relationship between time and battery capacity. Then, the calculation formula for each factor can be determined based on the scatter plot. Specifically, the regression equation curve is calculated based on the scatter plot to reflect the correlation between time and battery capacity for each factor.

[0046] In this embodiment of the application, after obtaining the predicted target battery capacity value, the owner can also be notified through vehicle center console reminders, in-vehicle voice broadcasts, mobile phone text messages, APPs, emails, etc., to remind the owner of the future battery degradation.

[0047] In summary, this application embodiment determines at least one preset first association relationship for the target battery based on the battery model of the target vehicle's target battery. This first association relationship indicates how the battery capacity value changes over time under the influence of an internal battery factor. Then, based on at least one target user usage habit characteristic of the target battery, at least one preset second association relationship is determined for the target battery. This second association relationship indicates how the battery capacity value changes over time under the influence of a user usage habit characteristic. Next, based on at least one current target external factor of the target battery, a third preset association relationship is determined. The application establishes at least one third association relationship in the association framework to determine the match between the target battery and the target external factor. This third association relationship indicates how the battery capacity value of the target battery changes over time under the influence of a certain external factor. Then, based on the first, second, and third association relationships, a target association relationship corresponding to the target battery is determined. Finally, based on the current time point of the target battery, the specified usage duration, and the target association relationship, the target battery capacity value is determined. This target battery capacity value is the battery capacity value of the target battery after the specified usage duration from the current time point. This application obtains the first, second, and third association relationships of the target battery of a target vehicle and determines the target association relationship based on these relationships. Then, based on the current time point of the target battery, the specified usage duration, and the target association relationship, the target battery capacity value is determined. This allows for a shift beyond simply reading the real-time operating parameters of the power battery to assess its current health status. It enables forward-looking analysis of battery capacity, allowing users to rationally plan travel routes and arrange charging or battery maintenance in advance based on the predicted battery capacity, significantly improving the user's driving experience.

[0048] It should be understood that although each step in the flowcharts of the above embodiments is shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps.

[0049] Based on the same inventive concept, this application also provides a battery capacity value processing apparatus for implementing the battery capacity value processing method described above, and a battery capacity value processing apparatus for implementing the battery capacity value processing method described above. The solution to the problem provided by this apparatus is similar to the solution described in the above method. Therefore, the specific limitations of the one or more battery capacity value processing apparatuses and battery capacity value processing apparatus embodiments provided below can be found in the limitations of the battery capacity value processing method and battery capacity value processing method described above, and the specific limitations will not be repeated here.

[0050] This embodiment also provides a battery capacity value processing device, which can be specifically integrated into a terminal device. For example, such as Figure 4 As shown, the device for processing the battery capacity value may include: The first determining unit 201 is used to determine at least one first association relationship of the target battery based on the battery model of the target battery of the target vehicle. The first association relationship is used to indicate the change of the battery capacity value of the target battery over time under the influence of a certain internal battery factor. The second determining unit 202 is used to determine at least one preset second association relationship of the target battery based on at least one target user usage habit feature of the current user on the target battery. The second association relationship is used to indicate the change of the battery capacity value of the target battery over time under the influence of a user usage habit feature. The third determining unit 203 is used to determine at least one third association relationship between the target battery and the target external factor from a third preset association relationship based on the current target external factor of the target battery. The third association relationship is used to indicate the change of the battery capacity value of the target battery over time under the influence of a battery external factor. The fourth determining unit 204 is used to determine the target association relationship corresponding to the target battery based on the first association relationship, the second association relationship and the third association relationship; The fifth determining unit 205 is used to determine the target battery capacity value of the target battery based on the current time point of the target battery, the specified usage time, and the target association relationship. The target battery capacity value is the battery capacity value of the target battery after the specified usage time from the current time point.

[0051] In some embodiments, the battery capacity value processing device further includes: The first determining subunit is used to determine at least one second association relationship from the second preset association relationship that matches the user's usage habit feature with the target user's usage habit feature based on at least one target user usage habit feature of the current user for the target battery, wherein one second preset association relationship corresponds to one user usage habit feature.

[0052] In some embodiments, the battery capacity value processing device further includes: The output subunit is used to output at least one preset second association relationship of the target battery based on at least one target user usage habit feature of the current user and the battery capacity change information of the target battery through the target model. The target model is a model obtained by performing association relationship mapping training based on the user usage habit features and battery capacity change information of at least one other user and / or the current user corresponding to the target battery.

[0053] In some embodiments, the battery capacity value processing device further includes: The first acquisition subunit is used to acquire the start time point of the target battery; The second determining subunit is used to determine the first predicted battery capacity value corresponding to the current time point of the target battery based on the start time point of the target battery, the current time point, and the target association relationship. The second determining subunit is used to determine the second predicted battery capacity value corresponding to the target battery based on the start time of the target battery, the current time, the specified usage duration, and the target association relationship. The second determining subunit is used to determine the target battery capacity value of the target battery based on the current actual battery capacity value of the target battery, the first predicted battery capacity value, and the second predicted battery capacity value.

[0054] In some embodiments, the battery capacity value processing device further includes: The third determining subunit is used to determine the historical usage time of the target battery based on the start time of use and the current time. The third determining subunit is used to determine the first predicted battery capacity value corresponding to the current time point of the target battery based on the historical usage duration and the target correlation.

[0055] In some embodiments, the battery capacity value processing device further includes: The fourth determining subunit is used to determine the historical usage time of the target battery based on the start time of use and the current time. The fourth determining subunit is used to determine a new historical usage duration based on the historical usage duration and the specified usage duration; The fourth determining subunit is used to determine the second predicted battery capacity value corresponding to the target battery based on the new historical usage duration and the target correlation.

[0056] In some embodiments, the battery capacity value processing device further includes: The second acquisition subunit is used to acquire a first difference between the first predicted battery capacity value and the second predicted battery capacity value; The second acquisition subunit is used to acquire a second difference between the current actual battery capacity value of the target battery and the first difference, and to use the second difference as the target battery capacity value of the target battery.

[0057] Using the device of this embodiment, a first determining unit 201 determines at least one preset first association relationship of the target battery based on the battery model of the target vehicle's target battery. This first association relationship indicates the change in battery capacity over time under the influence of an internal battery factor. A second determining unit 202 determines at least one preset second association relationship of the target battery based on at least one target user usage habit characteristic of the current user. This second association relationship indicates the change in battery capacity over time under the influence of a user usage habit characteristic. A third determining unit 203 determines the target battery based on at least one current target external factor. The first determining unit 204 determines the target association relationship corresponding to the target battery based on the first association relationship, the second association relationship, and the third association relationship. The second determining unit 205 determines the target battery capacity value of the target battery based on the current time point of the target battery, the specified usage time, and the target association relationship. The target battery capacity value is the battery capacity value of the target battery after the specified usage time from the current time point. This application embodiment obtains the first, second, and third association relationships of the target battery of the target vehicle and determines the target association relationship based on the above association relationships. Then, based on the current time point of the target battery, the specified usage time, and the target association relationship, the target battery capacity value of the target battery is determined. This is no longer limited to the assessment of the current health status by simply reading the real-time operating parameters of the power battery. It enables forward-looking analysis of battery capacity, allowing users to reasonably plan their travel routes and arrange charging or battery maintenance in advance based on the predicted battery capacity results, significantly improving the user's driving experience.

[0058] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0059] Accordingly, this application also provides a computer device, which can be a terminal, such as a smartphone, tablet computer, laptop computer, touch screen, game console, personal computer (PC), personal digital assistant (PDA), or other terminal device. Alternatively, the electronic device can be a server.

[0060] like Figure 5 As shown, Figure 5This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. The computer device 300 includes a processor 301 with one or more processing cores, a memory 302 with one or more computer-readable storage media, and a computer program stored in the memory 302 and executable on the processor. The processor 301 and the memory 302 are electrically connected. Those skilled in the art will understand that the computer device structure shown in the figure does not constitute a limitation on the electronic device, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0061] The processor 301 is the control center of the computer device 300. It connects various parts of the computer device 300 via various interfaces and lines. By running or loading software programs and / or units stored in the memory 302, and by calling data stored in the memory 302, it executes various functions of the computer device 300 and processes data, thereby providing overall monitoring of the computer device 300. The processor 301 can be a central processing unit (CPU), a graphics processing unit (GPU), a network processor (NP), etc., and can implement or execute the methods, steps, and logic diagrams disclosed in the embodiments of this application.

[0062] In this embodiment, the processor 301 in the computer device 300 loads the instructions corresponding to the processes of one or more applications into the memory 302 according to the following steps, and the processor 301 runs the applications stored in the memory 302 to realize various functions, such as: Based on the battery model of the target battery of the target vehicle, at least one first association relationship is determined for the target battery. The first association relationship is used to indicate the change of the battery capacity value over time under the influence of an internal battery factor of the target battery. Based on at least one target user usage habit feature of the current user for the target battery, at least one preset second association relationship of the target battery is determined. The second association relationship is used to indicate the change of the battery capacity value of the target battery over time under the influence of a user usage habit feature. Based on at least one target external factor of the target battery at present, at least one third association relationship matching the target battery with the target external factor is determined from the third preset association relationship. The third association relationship is used to indicate the change of the battery capacity value of the target battery over time under the influence of a battery external factor. Based on the first association relationship, the second association relationship, and the third association relationship, the target association relationship corresponding to the target battery is determined; Based on the current time point of the target battery, the specified usage time, and the target association, the target battery capacity value is determined. The target battery capacity value is the battery capacity value of the target battery after the specified usage time from the current time point.

[0063] The computer device provided in this application embodiment can obtain the first, second, and third association relationships of the target battery of the target vehicle and determine the target association relationship based on the above association relationships. Then, based on the current time point, specified usage time, and target association relationship of the target battery, the target battery capacity value of the target battery is determined. This makes it no longer limited to the assessment of the current health status by simply reading the real-time operating parameters of the power battery. It enables forward-looking analysis of battery capacity, allowing users to reasonably plan travel routes and arrange charging or battery maintenance in advance based on the predicted battery capacity results, significantly improving the user's driving experience.

[0064] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0065] Optional, such as Figure 5 As shown, the computer device 300 also includes: a touch screen display 303, a radio frequency circuit 304, an audio circuit 305, an input unit 306, and a power supply 307. The processor 301 is electrically connected to the touch screen display 303, the radio frequency circuit 304, the audio circuit 305, the input unit 306, and the power supply 307. Those skilled in the art will understand that... Figure 5 The electronic device structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0066] The touch display screen 303 can be used to display a graphical user interface (GUI) and receive operation commands generated by the user interacting with the GUI. The touch display screen 303 may include a display panel and a touch panel. The display panel can be used to display information input by the user or information provided to the user, as well as various graphical user interfaces of the electronic device. These graphical user interfaces can be composed of graphics, text, icons, video, and any combination thereof. Optionally, the display panel can be configured using a liquid crystal display (LCD), organic light-emitting diode (OLED), or other similar technologies. The touch panel can be used to collect touch operations performed by the user on or near it (such as operations performed by the user using a finger, stylus, or any suitable object or accessory on or near the touch panel), generate corresponding operation commands, and execute the corresponding program according to the operation commands. Optionally, the touch panel may include two parts: a touch detection device and a touch controller. The touch detection device detects the user's touch location and the signal generated by the touch operation, transmitting the signal to the touch controller. The touch controller receives touch information from the touch detection device, converts it into touch point coordinates, and sends it to the processor 301. It can also receive and execute commands from the processor 301. The touch panel can cover the display panel. When the touch panel detects a touch operation on or near it, it transmits the information to the processor 301 to determine the type of touch event. Subsequently, the processor 301 provides corresponding visual output on the display panel based on the type of touch event. In this embodiment, the touch panel and the display panel can be integrated into the touch display screen 303 to achieve input and output functions. However, in some embodiments, the touch panel and the touch display screen 303 can be implemented as two independent components to achieve input and output functions. That is, the touch display screen 303 can also be used as part of the input unit 306 to achieve input functions.

[0067] The radio frequency circuit 304 can be used to transmit and receive radio frequency signals to establish wireless communication with network devices or other electronic devices, and to transmit and receive signals with network devices or other electronic devices.

[0068] Audio circuitry 305 can be used to provide an audio interface between a user and an electronic device via a speaker and a microphone. Audio circuitry 305 converts received audio data into electrical signals, transmits them to the speaker, and the speaker converts them into sound signals for output. Conversely, the microphone converts collected sound signals into electrical signals, which are then received by audio circuitry 305, converted back into audio data, and then processed by processor 301 before being transmitted via radio frequency circuitry 304 to, for example, another electronic device, or output to memory 302 for further processing. Audio circuitry 305 may also include an earphone jack to facilitate communication between peripheral headphones and electronic devices.

[0069] The input unit 306 can be used to receive input numbers, characters, or user characteristic information (such as fingerprints, iris, facial information, etc.), and to generate keyboard, mouse, joystick, optical, or trackball signal inputs related to user settings and function control.

[0070] Power supply 307 is used to supply power to various components of computer device 300. Optionally, power supply 307 can be logically connected to processor 301 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. Power supply 307 may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0071] although Figure 5 As not shown in the diagram, computer equipment 300 may also include a camera, sensor, wireless fidelity module, Bluetooth module, etc., which will not be described in detail here.

[0072] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0073] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0074] Therefore, embodiments of this application provide a computer-readable storage medium storing a plurality of computer programs. These computer programs can be loaded by a processor to execute any of the battery capacity value processing methods provided in embodiments of this application. The computer program can execute the steps of the following battery capacity value processing method: Based on the battery model of the target battery of the target vehicle, at least one first association relationship is determined for the target battery. The first association relationship is used to indicate the change of the battery capacity value over time under the influence of an internal battery factor of the target battery. Based on at least one target user usage habit feature of the current user for the target battery, at least one preset second association relationship of the target battery is determined. The second association relationship is used to indicate the change of the battery capacity value of the target battery over time under the influence of a user usage habit feature. Based on at least one target external factor of the target battery at present, at least one third association relationship matching the target battery with the target external factor is determined from the third preset association relationship. The third association relationship is used to indicate the change of the battery capacity value of the target battery with time under the influence of a battery external factor. Based on the first association relationship, the second association relationship, and the third association relationship, the target association relationship corresponding to the target battery is determined; Based on the current time point of the target battery, the specified usage time, and the target association, the target battery capacity value is determined. The target battery capacity value is the battery capacity value of the target battery after the specified usage time from the current time point.

[0075] Because the computer program stored in the storage medium can obtain the first, second, and third associations of the target battery of the target vehicle and determine the target association based on the above associations, and then determine the target battery capacity value of the target battery based on the current time point, specified usage time, and target association, it is no longer limited to the assessment of the current health status by simply reading the real-time operating parameters of the power battery. It enables forward-looking analysis of battery capacity, allowing users to reasonably plan travel routes and arrange charging or battery maintenance in advance based on the predicted battery capacity results, significantly improving the user's driving experience.

[0076] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0077] The computer-readable storage medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0078] Since the computer program stored in the computer-readable storage medium can execute any of the battery capacity value processing methods provided in the embodiments of this application, the beneficial effects that any of the battery capacity value processing methods provided in the embodiments of this application can achieve can be realized, as detailed in the preceding embodiments, and will not be repeated here.

[0079] According to one aspect of this application, a computer program product or computer program is also provided, comprising computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform the methods provided in the various optional implementations of the above embodiments.

[0080] In some embodiments, this application also provides a controller, including a memory and a processor; thereon storing a computer program, and the processor running the computer program in the memory, which, when executed by the processor, implements the steps in the embodiments of this application.

[0081] In some embodiments, this application also provides a vehicle; the vehicle can send relevant parameters or data of the battery in the vehicle to a terminal via a network, so that the terminal can execute the battery capacity value processing method provided in the embodiments of this application; or, the vehicle includes a controller, and the vehicle executes the battery capacity value processing method provided in the embodiments of this application through the controller.

[0082] It should be noted that all data involved in this application are information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above.

[0083] In the above embodiments of the battery capacity value processing apparatus, computer-readable storage medium, computer device, and computer program product, the descriptions of each embodiment have different focuses. Parts not described in detail in a particular embodiment can be referred to in the relevant descriptions of other embodiments. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process and beneficial effects of the battery capacity value processing apparatus, computer-readable storage medium, computer program product, electronic device, and their corresponding units described above can be referred to the description of the battery capacity value processing method in the above embodiments, and will not be repeated here.

[0084] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0085] The foregoing has provided a detailed description of a battery capacity value processing method, apparatus, electronic device, computer-readable storage medium, and computer program product provided in the embodiments of this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for processing battery capacity values, characterized in that, include: Based on the battery model of the target battery of the target vehicle, at least one first association relationship is determined for the target battery. The first association relationship is used to indicate the change of the battery capacity value over time under the influence of an internal battery factor of the target battery. Based on at least one target user usage habit feature of the current user for the target battery, at least one preset second association relationship of the target battery is determined. The second association relationship is used to indicate the change of the battery capacity value of the target battery over time under the influence of a user usage habit feature. Based on at least one target external factor of the target battery at present, at least one third association relationship matching the target battery with the target external factor is determined from the third preset association relationship. The third association relationship is used to indicate the change of the battery capacity value of the target battery with time under the influence of a battery external factor. Based on the first association relationship, the second association relationship, and the third association relationship, the target association relationship corresponding to the target battery is determined; Based on the current time point of the target battery, the specified usage time, and the target association, the target battery capacity value is determined. The target battery capacity value is the battery capacity value of the target battery after the specified usage time from the current time point.

2. The method according to claim 1, characterized in that, The step of determining at least one preset second association relationship of the target battery based on at least one target user usage habit feature of the current user for the target battery includes: Based on at least one target user usage habit feature of the current user for the target battery, at least one second association relationship is determined from the second preset association relationship to match the user usage habit feature with the target user usage habit feature, wherein one second preset association relationship corresponds to one user usage habit feature.

3. The method according to claim 1, characterized in that, The step of determining at least one preset second association relationship of the target battery based on at least one target user usage habit feature of the current user for the target battery includes: The target model outputs at least one preset second association relationship for the target battery based on at least one user habit feature of the current user and the battery capacity change information of the target battery. The target model is a model obtained by performing association relationship mapping training based on the user habit features and battery capacity change information of at least one other user and / or the current user corresponding to the target battery.

4. The method according to claim 1, characterized in that, Determining the target battery capacity value based on the current time point of the target battery, the specified usage duration, and the target association relationship includes: Obtain the start time of use of the target battery; Based on the start time of the target battery, the current time, and the target association, determine the first predicted battery capacity value corresponding to the current time of the target battery; Based on the start time of the target battery, the current time, the specified usage duration, and the target association, a second predicted battery capacity value corresponding to the target battery is determined. The target battery capacity value is determined based on the current actual battery capacity value of the target battery, the first predicted battery capacity value, and the second predicted battery capacity value.

5. The method according to claim 4, characterized in that, The step of determining the first predicted battery capacity value corresponding to the current time point of the target battery based on the start time point of use of the target battery, the current time point, and the target association relationship includes: The historical usage duration of the target battery is determined based on the start time of use and the current time. The first predicted battery capacity value corresponding to the current time point of the target battery is determined based on the historical usage duration and the target correlation.

6. The method according to claim 5, characterized in that, The step of determining the second predicted battery capacity value corresponding to the target battery based on the start time of use, the current time, the specified usage duration, and the target association relationship includes: The historical usage duration of the target battery is determined based on the start time of use and the current time. A new historical usage duration is determined based on the historical usage duration and the specified usage duration; Based on the new historical usage duration and the target correlation, a second predicted battery capacity value corresponding to the target battery is determined.

7. The method according to claim 6, characterized in that, Determining the target battery capacity value based on the current actual battery capacity value, the first predicted battery capacity value, and the second predicted battery capacity value includes: Obtain the first difference between the first predicted battery capacity value and the second predicted battery capacity value; Obtain a second difference between the current actual battery capacity value of the target battery and the first difference, and use the second difference as the target battery capacity value of the target battery.

8. A battery capacity value processing device, characterized in that, include: The first determining unit is used to determine at least one first association relationship of the target battery based on the battery model of the target battery of the target vehicle. The first association relationship is used to indicate the change of the battery capacity value of the target battery over time under the influence of an internal battery factor. The second determining unit is used to determine at least one preset second association relationship of the target battery based on at least one target user usage habit feature of the current user on the target battery. The second association relationship is used to indicate the change of the battery capacity value of the target battery over time under the influence of a user usage habit feature. The third determining unit is used to determine at least one third association relationship between the target battery and the target external factor from the third preset association relationship based on the current target external factor of the target battery. The third association relationship is used to indicate the change of the battery capacity value of the target battery over time under the influence of a battery external factor. The fourth determining unit is used to determine the target association relationship corresponding to the target battery based on the first association relationship, the second association relationship, and the third association relationship; The fifth determining unit is used to determine the target battery capacity value of the target battery based on the current time point, the specified usage time, and the target association relationship. The target battery capacity value is the battery capacity value of the target battery after the specified usage time from the current time point.

9. A computer device, characterized in that, The device includes a processor and a memory, the memory storing multiple instructions; the processor loads instructions from the memory to perform the steps of the method for processing the battery capacity value as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a plurality of instructions adapted for loading by a processor to perform the steps of the method for processing the battery capacity value as described in any one of claims 1 to 7.

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