Battery data processing method, vehicle, electronic equipment and readable storage medium

Abstract

The embodiment of the invention provides a battery data processing method, a vehicle, electronic equipment and a readable storage medium, and relates to the technical field of computers. According to the embodiment of the invention, at least one group of charging data can be acquired, and the electric quantity accumulative error set of each battery under each in-vehicle duration is determined according to each group of charging data. Furthermore, according to the embodiment of the invention, data fitting can be carried out on the electric quantity accumulative error set based on each in-vehicle duration so as to obtain an electric quantity error parameter, and the electric quantity error parameter can represent an error brought by the initial compensation current. Furthermore, according to the embodiment of the invention, the target compensation current is determined after the initial compensation current is updated and corrected through the electric quantity error parameter, so that the compensation current is closer to the real loss, the electric vehicle can be effectively helped to determine the battery capacity loss generated in the dormancy process more accurately, and the power consumption of the electric vehicle is reduced. The estimation precision of the battery power is improved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a battery data processing method, a vehicle, an electronic device, and a readable storage medium. Background Art

[0002] Electric vehicles mainly rely on power batteries to obtain electrical energy. Users can check the remaining power of the power battery through the battery power displayed on the electric vehicle. Since the remaining power of the battery cannot be directly obtained, the relevant technology currently estimates the remaining power of the battery through data such as the current output of the battery in real time to determine the remaining power of the battery.

[0003] However, when the electric vehicle is in a dormant state (i.e., not driving), the battery management system (BMS) will switch to a dormant state, and the power battery will still output part of the power to ensure the normal operation of other components of the electric vehicle. Therefore, the electric vehicle cannot estimate the power loss caused by the above-mentioned part of the power when it is in a dormant state, which also causes errors in the subsequent estimation of the remaining battery power of the electric vehicle. Summary of the invention

[0004] In view of this, embodiments of the present application provide a battery data processing method, a vehicle, an electronic device, and a readable storage medium to improve the estimation accuracy of the battery power.

[0005] In a first aspect, a battery data processing method is provided, the method comprising:

[0006] At least one set of charging data is obtained, each set of charging data corresponds to a different on-vehicle time, the charging data includes a pre-charging capacity of at least one battery behind the vehicle, a post-charging capacity behind the vehicle, and a charged power, the charged power is determined based on a charging device, and the pre-charging capacity is determined based on an initial compensation current.

[0007] According to each group of charging data, a set of accumulated errors of electric quantity of each battery under each vehicle driving time is determined.

[0008] The power accumulation error set is subjected to data fitting based on each of the vehicle in-vehicle time periods to obtain a power error parameter.

[0009] The initial compensation current is updated and corrected based on the electrical quantity error parameter to determine a target compensation current.

[0010] In some embodiments, determining the accumulated error set of the power of each battery under the vehicle duration according to each group of charging data includes:

[0011] A charging capacity difference of the corresponding battery behind the vehicle is determined according to the pre-charging capacity and the post-charging capacity.

[0012] According to the difference between the charging capacity difference and the charging power, the power accumulation error of the corresponding battery behind the vehicle is determined.

[0013] According to each of the power accumulation errors, a set of power accumulation errors of each battery under each vehicle usage time is determined.

[0014] In some embodiments, determining the set of accumulated power errors of each battery under the vehicle duration according to each accumulated power error includes:

[0015] Clustering processing is performed on the accumulated errors of the electric quantity under the same vehicle-in-vehicle time length to determine the designated percentile value corresponding to each vehicle-in-vehicle time length.

[0016] The power accumulation error set is generated according to the specified percentile values ​​corresponding to each of the vehicle in-vehicle time periods.

[0017] In some embodiments, performing data fitting on the power accumulation error set based on each of the vehicle in-vehicle time to obtain the power error parameter includes:

[0018] Based on the vehicle in-vehicle time, a linear fit is performed on the power accumulation error set to determine a linear distribution of the power accumulation error set.

[0019] The electric quantity error parameter is determined according to the slope of the linear distribution.

[0020] In some embodiments, the unit of the vehicle time is day, and the power error parameter is the daily average power error.

[0021] In some embodiments, the method further comprises:

[0022] In response to the dormant state of the target vehicle ending, a dormant duration of the target vehicle is determined.

[0023] The battery capacity of the target vehicle is compensated according to the sleep time and the target compensation current, and the real-time battery capacity corresponding to the target vehicle when the sleep state ends is determined.

[0024] In some embodiments, the target compensation current is determined based on a predetermined cycle, and the target compensation current determined in each predetermined cycle is an initial compensation current of a next cycle.

[0025] In some embodiments, obtaining at least one set of charging data includes:

[0026] An original data set is obtained, where the original data set includes charging information corresponding to multiple batteries under different vehicle durations.

[0027] According to a preset duration threshold, the batteries in the original data set whose vehicle duration is greater than or equal to the duration threshold are determined.

[0028] At least one set of the charging data is generated according to the charging information corresponding to the batteries whose in-vehicle time is greater than or equal to the time threshold and the in-vehicle time.

[0029] In a second aspect, a vehicle is provided, the vehicle comprising at least a power battery and a vehicle control terminal;

[0030] The vehicle control terminal is configured to determine the sleep duration in response to the end of the sleep state, compensate the battery capacity of the power battery according to the sleep duration and the target compensation current, and determine the real-time battery capacity of the power battery corresponding to the end of the sleep state;

[0031] Wherein, the target compensation current is determined by the electronic device according to the battery data processing method as described in the first aspect.

[0032] In a third aspect, a battery data processing device is provided, the device comprising:

[0033] The charging data acquisition module is configured to execute acquisition of at least one set of charging data, each set of charging data corresponds to a different on-vehicle time, and the charging data includes the pre-charging capacity of at least one battery behind the vehicle, the post-charging capacity behind the vehicle, and the charged power, the charged power is determined based on the charging equipment, and the pre-charging capacity is determined based on the initial compensation current.

[0034] The power accumulation error set determination module is configured to determine the power accumulation error set of each battery under the vehicle duration according to each group of charging data.

[0035] The fitting module is configured to perform data fitting on the power accumulation error set based on each of the vehicle in-vehicle time periods to obtain a power error parameter.

[0036] The correction module is configured to update and correct the initial compensation current based on the electrical quantity error parameter to determine a target compensation current.

[0037] In some embodiments, the power accumulation error set determination module is specifically configured to execute:

[0038] A charging capacity difference of the corresponding battery behind the vehicle is determined according to the pre-charging capacity and the post-charging capacity.

[0039] According to the difference between the charging capacity difference and the charging power, the power accumulation error of the corresponding battery behind the vehicle is determined.

[0040] According to each of the power accumulation errors, a set of power accumulation errors of each battery under each vehicle usage time is determined.

[0041] In some embodiments, the power accumulation error set determination module is specifically configured to execute:

[0042] Clustering processing is performed on the accumulated errors of the electric quantity under the same vehicle-in-vehicle time length to determine the designated percentile value corresponding to each vehicle-in-vehicle time length.

[0043] The power accumulation error set is generated according to the specified percentile values ​​corresponding to each of the vehicle in-vehicle time periods.

[0044] In some embodiments, the fitting module is specifically configured to perform:

[0045] Based on the vehicle in-vehicle time, a linear fit is performed on the power accumulation error set to determine a linear distribution of the power accumulation error set.

[0046] The electric quantity error parameter is determined according to the slope of the linear distribution.

[0047] In some embodiments, the unit of the vehicle time is day, and the power error parameter is the daily average power error.

[0048] In some embodiments, the apparatus further comprises:

[0049] The sleep duration determination module is configured to determine the sleep duration of the target vehicle in response to the end of the sleep state of the target vehicle.

[0050] The real-time battery capacity determination module is configured to compensate the battery capacity of the target vehicle according to the sleep duration and the target compensation current, and determine the real-time battery capacity corresponding to the target vehicle when the sleep state ends.

[0051] In some embodiments, the target compensation current is determined based on a predetermined cycle, and the target compensation current determined in each predetermined cycle is an initial compensation current of a next cycle.

[0052] In some embodiments, the charging data acquisition module is specifically configured to execute:

[0053] An original data set is obtained, where the original data set includes charging information corresponding to multiple batteries under different vehicle durations.

[0054] According to a preset duration threshold, the batteries in the original data set whose vehicle duration is greater than or equal to the duration threshold are determined.

[0055] At least one set of the charging data is generated according to the charging information corresponding to the batteries whose in-vehicle time is greater than or equal to the time threshold and the in-vehicle time.

[0056] In a fourth aspect, an embodiment of the present application provides an electronic device, comprising a memory and a processor, wherein the memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method described in the first aspect.

[0057] In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method described in the first aspect is implemented.

[0058] In a sixth aspect, an embodiment of the present application provides a computer program product, including a computer program / instructions, which, when executed by a processor, implement the method described in the first aspect.

[0059] The embodiment of the present application can obtain at least one set of charging data, and determine the cumulative error set of the charge of each battery at each vehicle time based on each set of charging data. Furthermore, the embodiment of the present application can perform data fitting on the cumulative error set of charge based on each vehicle time to obtain a charge error parameter, which can characterize the error caused by the initial compensation current. Furthermore, the embodiment of the present application determines the target compensation current after updating and correcting the initial compensation current through the charge error parameter, which can make the compensation current closer to the actual loss, thereby effectively helping the electric vehicle to more accurately determine the battery capacity loss generated during the sleep process and improve the estimation accuracy of the battery charge. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] The above and other purposes, features and advantages of the embodiments of the present application will become more apparent through the following description of the embodiments of the present application with reference to the accompanying drawings, in which:

[0061] Figure 1 A flowchart of a battery data processing method according to an embodiment of the present application;

[0062] Figure 2 This is a flow chart of determining a power accumulation error set by difference method in an embodiment of the present application;

[0063] Figure 3 A flowchart of an embodiment of the present application further determining a power accumulation error set by determining a specified quantile value;

[0064] Figure 4 This is a diagram of the analysis results of the data statistical analysis performed on the battery samples in the embodiment of the present application;

[0065] Figure 5 This is a flow chart of determining the power error parameter by linear fitting in an embodiment of the present application;

[0066] Figure 6 This is a schematic diagram of a fitting result chart after linear fitting is performed on multiple sets of accumulated power errors in an embodiment of the present application;

[0067] Figure 7 A flowchart for determining at least one set of charging data for an embodiment of the present application;

[0068] Figure 8 A flow chart for determining real-time battery capacity for an embodiment of the present application;

[0069] Fig. 9 A schematic diagram of the structure of a vehicle provided in an embodiment of the present application;

[0070] Fig.10 A schematic diagram of the structure of a battery data processing device according to an embodiment of the present application;

[0071] Fig.11 A schematic diagram of the structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0072] The present application is described below based on embodiments, but the present application is not limited to these embodiments. In the detailed description of the present application below, some specific details are described in detail. It is possible for those skilled in the art to fully understand the present application without the description of these details. In order to avoid confusing the essence of the present application, known methods, processes, flows, components and circuits are not described in detail.

[0073] In addition, persons of ordinary skill in the art will appreciate that the drawings provided herein are for illustration purposes and are not necessarily drawn to scale.

[0074] Unless the context clearly requires otherwise, the words "include", "comprising" and the like throughout this application should be interpreted as including rather than exclusive or exhaustive; that is, as meaning "including but not limited to".

[0075] In the description of this application, it should be understood that the terms "first", "second", etc. are used for descriptive purposes only and cannot be understood as indicating or implying relative importance. In addition, in the description of this application, unless otherwise specified, "multiple" means two or more. In addition, the schemes described in this specification and the embodiments, if involving the processing of personal information, will be processed on the premise of having a legal basis (for example, with the consent of the subject of personal information, or necessary for the performance of a contract, etc.), and will only be processed within the prescribed or agreed scope. The user's refusal to process personal information other than the necessary information required for basic functions will not affect the user's use of basic functions.

[0076] Electric vehicles mainly rely on power batteries (hereinafter referred to as batteries) to obtain electrical energy. Users can check the remaining battery power through the battery power displayed on the electric vehicle. Since the remaining battery power cannot be directly obtained, the relevant technology currently estimates the remaining battery power through the current, voltage and other data output by the battery in real time.

[0077] Specifically, the remaining power of the battery can be represented by the state of charge (SOC). SOC can be used to characterize the ratio of the remaining capacity of the battery to its capacity in a fully charged state. When SOC=0, it indicates that the battery is fully discharged, and when SOC=1, it indicates that the battery is fully charged.

[0078] Since SOC cannot be obtained directly, the relevant technologies currently estimate the battery SOC mainly through the ampere-hour integration method, open circuit voltage method, etc. Specifically, the analog front end chip (Analog Front End, AFE) in the battery management system (Battery Management System, BMS) of electric vehicles can be used to collect battery open circuit voltage, temperature, current and other information, and then estimate the battery SOC through the above information. Taking the ampere-hour integration method as an example, the ampere-hour integration method can integrate the current information with time, and according to the SOC value of the battery at the initial moment, the remaining power in the battery at a certain moment can be obtained.

[0079] However, when the electric vehicle is in a dormant state (i.e., not driving), the battery management system will switch to a dormant state, and the battery will still output part of the power to ensure the normal operation of other components of the electric vehicle. Therefore, when the electric vehicle is in a dormant state, it is impossible to estimate the power loss caused by the above-mentioned part of the power, which also causes errors in the subsequent estimation of the remaining battery power of the electric vehicle.

[0080] Taking shared electric motorcycles as an example, when shared electric motorcycles are in operation, there are two usage scenarios: borrowing and returning. In the borrowing scenario, users can ride shared electric motorcycles. At this time, the battery management system is in an activated state, and can collect information related to the battery open-circuit voltage, temperature, current, etc. in real time to estimate the SOC. In the return scenario, the shared electric motorcycle is in a locked state (that is, in a dormant state). At this time, the central control and other components on the shared electric motorcycle still need to communicate with the Internet. Therefore, in order to power other components on the shared electric motorcycle, the battery still needs to maintain the output state. However, in order to reduce the power consumption of the battery, in the return scenario, the battery management system of the shared electric motorcycle will switch to a dormant state, that is, the battery management system at this time cannot collect relevant information such as current in real time to estimate the ampere-hour integral of the SOC.

[0081] In the case that electric vehicles still lose power when in dormant state, according to the relationship curve between SOC and open circuit voltage, there is a relatively flat area in the middle section of the relationship curve between SOC and open circuit voltage (generally the SOC95%-20% interval, also known as the platform area), and the sampling accuracy of the analog front-end chip is usually around 10mV (millivolts), so it is impossible to calibrate SOC through open circuit voltage in the above platform area. In other words, in the case of the above power loss, the battery SOC is generally calibrated by the ampere-hour integration method.

[0082] Furthermore, the relevant technology currently improves the accuracy of battery SOC by controlling the battery management system to remain activated, or adding a fuel meter chip to the hardware. However, if the battery management system is controlled to remain activated when the electric vehicle is in a dormant state, the power consumption of the battery management system will be greatly increased (the power consumption increase is about 10 times to 50 times), resulting in failure to meet product specifications. On the other hand, if a fuel meter chip is added to the hardware, in addition to increasing the hardware cost, due to the limited measurement accuracy of the fuel meter (generally, currents below 20 mA cannot be collected by the fuel meter), and the power consumption current is often less than 20 mA when the electric vehicle is in a dormant state, therefore, this method will also have certain errors.

[0083] Therefore, the related technology still has the problem of low SOC estimation accuracy. That is to say, how to simply and effectively improve the SOC estimation accuracy is a problem that needs to be solved urgently.

[0084] In order to solve the above problems, the embodiment of the present application provides a battery data processing method, which can effectively improve the estimation accuracy of SOC by updating and correcting the initial compensation current without adding additional hardware equipment. Among them, the method can be applied to electronic devices, the electronic device can be a terminal device or a server, the terminal device can be a smart phone, a tablet computer, a vehicle control terminal (i.e., the vehicle computer or central control of an electric vehicle) or a personal computer (Personal Computer, PC), etc. The server can be a single server, a server cluster configured in a distributed manner, or a cloud server.

[0085] like Figure 1 As shown, the battery data processing method of the embodiment of the present application may include the following steps:

[0086] In step S110 , at least one set of charging data is acquired.

[0087] Among them, each group of charging data corresponds to a different in-vehicle duration. Specifically, "in-vehicle" is used to represent the stage from when the battery is self-charged and installed in the electric vehicle or charged in the electric vehicle until it is removed from the electric vehicle next time or until the next electric vehicle starts charging. Furthermore, the in-vehicle duration is the duration corresponding to the battery's "in-vehicle" stage.

[0088] For example, for electric vehicles with removable batteries (such as motorcycles and electric tricycles with removable batteries), electric vehicles with removable batteries often use the method of directly replacing the battery or charging the battery after disassembling it. Therefore, "on the vehicle" in this case means that the battery has been self-charged and installed in the electric vehicle until the battery is removed from the electric vehicle next time, which includes both the driving stage and the dormant stage of the electric vehicle. For electric vehicles with non-removable batteries (such as motorcycles, electric tricycles, electric cars, etc. with non-removable batteries), electric vehicles with non-removable batteries often use the method of directly charging the electric vehicle to replenish power. Therefore, "on the vehicle" in this case means that the battery has been self-charged in the electric vehicle until the next time the electric vehicle starts charging, which includes both the driving stage and the dormant stage of the electric vehicle.

[0089] The charging data may include at least one of the pre-charging capacity of the battery after the vehicle, the post-charging capacity after the vehicle, and the charged power. That is, the pre-charging capacity is used to characterize the battery capacity before charging during the charging process after the "in the vehicle" stage. The post-charging capacity is used to characterize the battery capacity after charging during the charging process after the "in the vehicle" stage. The charged power is used to characterize the power charged into the battery by the charging device during the charging process after the "in the vehicle" stage.

[0090] In the embodiment of the present application, the charging power is determined based on the charging device (such as a charging pile or other charging device), wherein the charging pile or other charging device can determine the exact power charged to the battery by the charging device based on the current, time and other information during the charging process. In other words, the charging power of the embodiment of the present application can be used to characterize the actual total power loss of the battery in the "in-vehicle" stage.

[0091] In addition, the capacity after charging is usually 1 (that is, the battery power is 100%). In other cases (such as charging interruption, or early termination of charging, etc.), the capacity after charging may also be less than 1. The capacity before charging is determined based on the initial compensation current, wherein the "compensation current" is the current value used to compensate for the battery SOC, which can make up for the capacity loss of the battery SOC during the sleep process of the electric vehicle. Specifically, when the electric vehicle is in a dormant state, the battery management system of the electric vehicle can record the duration of its own dormancy (that is, the duration of the electric vehicle's dormancy). When the electric vehicle starts (that is, when the battery management system is awakened), the battery management system can determine the capacity loss of the battery SOC during the sleep process of the electric vehicle through the duration of the sleep and the "compensation current", and the capacity loss can be expressed as: ΔQ = Ia*Ts. Among them, ΔQ is used to characterize the capacity loss of the battery SOC during the sleep process of the electric vehicle, Ia is used to characterize the "compensation current", and Ts is used to characterize the duration of the sleep of the electric vehicle (that is, the battery management system). That is to say, the capacity before charging is determined based on the sum of the driving loss power and the dormant loss power (the dormant loss power is determined based on the above-mentioned "compensation current") of the electric vehicle in the "in-vehicle" stage, relative to the initial power in the "in-vehicle" stage.

[0092] In practical applications, due to the influence of various objective factors (for example, the current output by the power battery of an electric vehicle may differ by 5-10 times when the small central control battery is fully charged and when it is powered off), if a fixed "compensation current" is used to compensate for the battery SOC, there may still be battery SOC errors caused by the above objective factors.

[0093] Further, in view of this situation, the embodiment of the present application can update and correct the "compensation current" itself, so that the updated and corrected "compensation current" is more accurate. Specifically, the embodiment of the present application can update and correct the initial compensation current to determine the target compensation current, wherein the initial compensation current can be the original fixed "compensation current" or the target compensation current determined some time ago.

[0094] In step S120, based on each set of charging data, a set of accumulated errors of the electric quantity of each battery at each vehicle-in-use time is determined.

[0095] Among them, since the pre-charging capacity and post-charging capacity of the battery behind the vehicle are determined by the electric vehicle itself based on the initial compensation current, and the charging power is the accurate power determined by the charging device side, therefore, based on the pre-charging capacity, the post-charging capacity and the charging power, the embodiment of the present application can determine the error caused by the initial compensation current relative to the actual situation, that is, the power accumulation error set. Specifically, the embodiment of the present application can determine the power accumulation error set of each battery under each vehicle time based on a machine learning model, determining a difference, etc.

[0096] In an optional implementation, the embodiment of the present application can determine the cumulative error set of the power of each battery under each vehicle duration by determining the difference, such as Figure 2 As shown, the above step S120 may include the following steps:

[0097] In step S121, the charging capacity difference of the corresponding battery behind the vehicle is determined according to the capacity before charging and the capacity after charging.

[0098] The difference between the pre-charging capacity and the post-charging capacity can be used to characterize the amount of electricity consumed by the battery during the "on-vehicle" stage. Since the pre-charging capacity is determined by the electric vehicle itself based on the initial compensation current, the difference in charging capacity includes the error caused by the initial compensation current.

[0099] In step S122, the accumulated error of the charge of the corresponding battery behind the vehicle is determined according to the difference between the charge capacity difference and the charge power.

[0100] Among them, since the power loss determined by the electric vehicle during driving and the charging power determined by the charging equipment are relatively accurate power, and the charging power can be used to characterize the actual total power loss of the battery in the "on-vehicle" stage, the difference between the charging capacity difference and the charging power can characterize the error caused by the initial compensation current in the "on-vehicle" stage. In other words, the power accumulation error can be used to characterize the error caused by the initial compensation current in the "on-vehicle" stage.

[0101] In step S123, based on the accumulated errors of each power level, a set of accumulated errors of each battery level at each vehicle usage time is determined.

[0102] That is to say, since each set of charging data corresponds to a different in-vehicle time, the set of accumulated errors of the charge of each battery under each in-vehicle time is the set of accumulated errors of the charge of each battery under each set of charging data. Among them, the set of accumulated errors of charge may include the accumulated errors of charge of all batteries in each set of charging data, and may also include the accumulated errors of charge of some batteries. By determining the set of accumulated errors of charge by determining the difference, the embodiment of the present application can quickly determine the accumulated errors of charge of each battery, thereby improving the efficiency of battery data processing.

[0103] In an optional embodiment, if Figure 3 As shown, the above step S123 may include the following steps:

[0104] In step S1231, clustering processing is performed on the accumulated errors of each power under the same vehicle on-board time to determine the designated percentile value corresponding to each vehicle on-board time.

[0105] For example, Figure 4 As shown, Figure 4 The analysis result chart 41 of the data statistical analysis of the battery samples in the embodiment of the present application is shown in FIG41, wherein the horizontal axis of the chart 41 is the percentage of the daily average SOC error (the value decreases from left to right). The embodiment of the present application can divide the accumulated error of the power of each battery by the corresponding vehicle time ( Figure 4 The number of days in the graph is used to determine the average daily SOC error of each battery. The bar graph in Figure 41 is the proportion of the number of battery samples corresponding to each daily average SOC error, that is, the bar graph in Figure 41 corresponds to the ordinate on the left (the ordinate on the left is the sample proportion, and its value increases from bottom to top). The curve in Figure 41 is the cumulative proportion of the number of battery samples that are lower than the corresponding value (average daily SOC error), that is, the curve in Figure 41 corresponds to the ordinate on the right (the ordinate on the right is the cumulative proportion, and its value increases from bottom to top).

[0106] As shown in Figure 41, the average daily SOC errors of the battery samples are relatively concentrated. Therefore, the embodiment of the present application can set a more reasonable designated quantile value to determine the error value of each vehicle duration (i.e., each group of charging data) that is closer to the actual situation. For example, the designated quantile value can be a value selected from the range of 50-99.7. Further, in order to make the error value closer to the actual situation, the designated quantile value can be selected from the range of 75-97.

[0107] In step S1232, a power accumulation error set is generated according to the specified percentile values ​​corresponding to each vehicle on-board time.

[0108] Among them, since the embodiment of the present application performs clustering processing on each power accumulation error under the same vehicle duration (i.e., the same set of charging data) and determines a specified quantile value, therefore, in the power accumulation error set in this case, each vehicle duration corresponds to a power accumulation error (i.e., the above-mentioned specified quantile value). By clustering processing and determining the specified quantile value, the embodiment of the present application can effectively simplify the amount of data in the power accumulation error set and improve the efficiency of battery data processing.

[0109] In step S130, data fitting is performed on the power accumulation error set based on each vehicle in-vehicle time to obtain a power error parameter.

[0110] In combination with the above embodiments, the power accumulation error set may include the specified percentile values ​​corresponding to each vehicle time, that is, each vehicle time corresponds to an error value. It may also include the power accumulation errors corresponding to each battery under each vehicle time, that is, each vehicle time corresponds to multiple error values.

[0111] In addition, the power error parameter obtained after data fitting of the power cumulative error set in the embodiment of the present application can be the mean power error per unit time. For example, the power error parameter can be the average daily power error with days as the unit time, or the average hourly power error with hours as the unit time, or the average power error with other time (such as 12 hours, 2 days or 3 days, etc.) as the unit time.

[0112] In an optional embodiment, if Figure 5 As shown, the above step S130 may include the following steps:

[0113] In step S131, based on the vehicle on-board time, a linear fitting is performed on the power accumulation error set to determine the linear distribution of the power accumulation error set.

[0114] In an optional implementation, the unit of the vehicle time in the embodiment of the present application is day, and the power error parameter is the daily average power error (ie, daily average SOC error).

[0115] For example, Figure 6 As shown, Figure 6 This is a fitting result chart 61 of the embodiment of the present application after linear fitting for multiple sets of accumulated power errors, wherein the horizontal axis of chart 61 is the vehicle time (in days), and the vertical axis is the cumulative SOC error (whose values ​​increase from bottom to top). Results 1-Result 4 are the fitting results obtained by linear fitting for different sets of accumulated power errors, and the corrected results are the error fitting results after the initial compensation current is updated and corrected by the battery data processing method of the embodiment of the present application. It should be noted that the unit of the vehicle time in the embodiment of the present application is not limited to days, but can also be other applicable units, such as 1 hour, 12 hours, 2 days or 3 days, etc.

[0116] In the process of determining the fitting results, it was found that the fitting degree of the linear distribution corresponding to each result was high. Therefore, it can be seen that the cumulative SOC error is linearly related to the vehicle time.

[0117] In step S132, the power error parameter is determined according to the slope of the linear distribution.

[0118] According to the above Figure 6It can be seen that the cumulative SOC error is linearly related to the vehicle time, so the daily average power error corresponding to each linear distribution can be represented by the slope of the linear distribution. In other words, if the vehicle time in the embodiment of the present application is in days, the power error parameter can be the daily average power error.

[0119] By performing linear fitting on the set of accumulated power errors, the embodiment of the present application can determine the relationship between the accumulated power error and the vehicle time, thereby determining the accurate power error parameter.

[0120] In step S140 , the initial compensation current is updated and corrected based on the electrical quantity error parameter to determine a target compensation current.

[0121] Taking the daily average power error as an example, the process of updating and correcting the initial compensation current and determining the target compensation current can be characterized as follows: I a1 =I a0 -SOC t *Cap / 24. Among them, I a1 Used to characterize the target compensation current (in milliamperes), I a0 Used to characterize the initial compensation current (in mA), SOC t It is used to characterize the power error parameter, and Cap is used to characterize the battery capacity of the corresponding battery (in milliampere-hours).

[0122] Through the above formula, the embodiment of the present application can update and correct the initial compensation current based on the power error parameter to determine the accurate target compensation current. Among them, the target compensation current can be used to determine the capacity loss of the battery of the electric vehicle during the dormant process. In other words, improving the accuracy of determining the target compensation current can effectively improve the accuracy of determining the capacity loss, thereby effectively improving the estimation accuracy of SOC.

[0123] like Figure 6 As shown, the cumulative SOC error of the corrected result is significantly smaller than result 1-result 4 before correction. Therefore, after the update and correction of the embodiment of the present application, the cumulative SOC error can be close to 0. In other words, the power error parameter obtained by data fitting the cumulative power error set based on each vehicle time in the embodiment of the present application can characterize the error caused by the initial compensation current. Furthermore, the embodiment of the present application determines the target compensation current after updating and correcting the initial compensation current through the power error parameter, which can make the compensation current closer to the actual loss, thereby effectively helping the electric vehicle to more accurately determine the battery capacity loss generated during the sleep process and improve the estimation accuracy of the battery power.

[0124] In an optional implementation, the target compensation current of the embodiment of the present application may be determined based on a predetermined cycle, and the target compensation current determined in each predetermined cycle is the initial compensation current of the next cycle.

[0125] The predetermined period may be a reasonable duration set according to actual conditions, for example, the predetermined period may be 6 months, 3 months, 1 month or 15 days, etc. By setting a predetermined period and iteratively updating the target compensation current according to the predetermined period, the embodiment of the present application can continuously correct the target compensation current to further improve the compensation current performance, thereby further improving the estimation accuracy of the battery power.

[0126] In an optional implementation manner, the embodiment of the present application may further filter the original data set before acquiring the charging data, specifically, Figure 7 As shown, the above step S110 may include the following steps:

[0127] In step S111, an original data set is obtained.

[0128] The original data set includes charging information corresponding to multiple batteries under vehicle duration, and the charging information at least includes the pre-charging capacity behind the vehicle, the post-charging capacity behind the vehicle, and the charging power.

[0129] In step S112, based on a preset duration threshold, batteries in the original data set whose vehicle duration is greater than or equal to the duration threshold are determined.

[0130] The unit time corresponding to the duration threshold is the same as the unit time corresponding to the vehicle time. For example, if the minimum unit corresponding to the vehicle time is 12 hours, the minimum unit corresponding to the duration threshold is also 12 hours. If the minimum unit corresponding to the vehicle time is day, the minimum unit corresponding to the duration threshold is also day. Furthermore, the embodiment of the present application can filter batteries with too short vehicle time (i.e., vehicle time less than the duration threshold) to retain batteries with vehicle time greater than or equal to the duration threshold.

[0131] In step S113, at least one set of charging data is generated according to the charging information corresponding to the batteries whose in-vehicle time is greater than or equal to the time threshold and the in-vehicle time.

[0132] In one case of actual application, if the single driving time of an electric vehicle is too long, the battery power may be exhausted during one driving process, resulting in the situation that the battery is in the car for too short a time. In another case, if the electric vehicle is an electric vehicle with a detachable battery, and the user removes the battery after a single driving, the battery may be in the car for too short a time. For the above-mentioned battery with too short a time in the car, since most of the time in the "in the car" stage is in the driving state of the electric vehicle, the capacity loss of the battery can be ignored, that is, the charging information of the battery with too short a time in the car is invalid for updating the target compensation current. Furthermore, the embodiment of the present application can filter batteries with too short a time in the car (that is, the time in the car is less than the time threshold) to reduce invalid data in the charging data and improve the efficiency of battery data processing.

[0133] In an optional implementation, the embodiment of the present application can determine the real-time battery capacity corresponding to the target vehicle at the end of the sleep state based on the target compensation current. Specifically, Figure 8 As shown, the embodiment of the present application may also include the following steps:

[0134] In step S210 , in response to the end of the sleep state of the target vehicle, the sleep duration of the target vehicle is determined.

[0135] Among them, after the user authorizes and turns on the compensation current correction function, the embodiment of the present application can receive the sleep time reported by the target vehicle based on the wireless communication connection with the target vehicle.

[0136] In step S220, the battery capacity of the target vehicle is compensated according to the sleep duration and the target compensation current, and the real-time battery capacity corresponding to the target vehicle at the end of the sleep state is determined.

[0137] Among them, since the battery management system will also switch to the sleep state when the target vehicle is in the sleep state, the battery management system cannot determine the real-time battery capacity when it is in the sleep state. Furthermore, the embodiment of the present application can determine the capacity loss generated during the sleep process according to the sleep duration and the target compensation current (for example, determined by the above formula ΔQ=Ia*Ts), and compensate the battery capacity before the sleep state by the capacity loss to determine the real-time battery capacity corresponding to the target vehicle at the end of the sleep state.

[0138] Further, if the electronic device used to execute the battery data processing method of the embodiment of the present application is not the vehicle control terminal of the target vehicle, then after determining the real-time battery capacity corresponding to the target vehicle at the end of the sleep state, the embodiment of the present application can send the real-time battery capacity to the vehicle control terminal of the target vehicle, so that the target vehicle can obtain its own real-time battery capacity in time. If the electronic device used to execute the battery data processing method of the embodiment of the present application is the vehicle control terminal of the target vehicle, then after determining the real-time battery capacity corresponding to the target vehicle at the end of the sleep state, the target vehicle can directly determine its own real-time battery capacity.

[0139] Therefore, the embodiment of the present application determines the target compensation current by updating and correcting the initial compensation current through the power error parameter, which can make the compensation current closer to the actual loss, thereby effectively helping the electric vehicle to more accurately determine the battery capacity loss generated during the sleep process and improve the estimation accuracy of the battery power.

[0140] Based on the same technical concept, the embodiment of the present application also provides a vehicle, which may be an electric motorcycle, an electric tricycle or an electric car. Fig. 9 As shown, the vehicle 91 may include at least a power battery 911 and a vehicle control terminal 912 , wherein the power battery 911 is used to provide electrical energy for the vehicle 91 .

[0141] The vehicle control terminal 912 may be configured to determine the sleep duration in response to the end of the sleep state, compensate the battery capacity of the power battery 911 according to the sleep duration and the target compensation current 92, and then determine the real-time battery capacity of the power battery 911 corresponding to the end of the sleep state.

[0142] Among them, the target compensation current 92 can be determined by an electronic device 93 for executing the battery data processing method. Specifically, the electronic device 93 can obtain at least one set of charging data, and update and correct the initial supplementary current based on the various embodiments corresponding to the above-mentioned battery data processing method to determine the target compensation current 92. Further, after determining the target compensation current 92, the electronic device 93 can send the target compensation current 92 to the vehicle control terminal 912 of the vehicle 91 through an applicable method such as wireless connection, so that the vehicle control terminal 912 compensates the battery capacity of the power battery 911 according to the sleep time and the target compensation current 92, and then determines the real-time battery capacity.

[0143] Therefore, the embodiment of the present application determines the target compensation current 92 by updating and correcting the initial compensation current through the power error parameter, which can make the compensation current closer to the actual loss, thereby effectively helping the vehicle 91 to more accurately determine the battery capacity loss generated during the sleep process and improve the estimation accuracy of the battery power.

[0144] Based on the same technical concept, the embodiment of the present application also provides a battery data processing device, such as Fig.10 As shown, the device includes: a charging data acquisition module 1001, a power accumulation error set determination module 1002, a fitting module 1003 and a correction module 1004.

[0145] The charging data acquisition module 1001 is configured to execute acquisition of at least one set of charging data, each set of charging data corresponds to a different on-vehicle time, and the charging data includes the pre-charging capacity of at least one battery behind the vehicle, the post-charging capacity behind the vehicle, and the charged power, the charged power is determined based on the charging equipment, and the pre-charging capacity is determined based on the initial compensation current.

[0146] The power accumulation error set determination module 1002 is configured to determine the power accumulation error set of each battery under each vehicle duration according to each group of charging data.

[0147] The fitting module 1003 is configured to perform data fitting on the power accumulation error set based on each of the vehicle in-vehicle time periods to obtain a power error parameter.

[0148] The correction module 1004 is configured to update and correct the initial compensation current based on the power error parameter to determine a target compensation current.

[0149] In some embodiments, the power accumulation error set determination module 1002 is specifically configured to execute:

[0150] A charging capacity difference of the corresponding battery behind the vehicle is determined according to the pre-charging capacity and the post-charging capacity.

[0151] According to the difference between the charging capacity difference and the charging power, the power accumulation error of the corresponding battery behind the vehicle is determined.

[0152] According to each of the power accumulation errors, a set of power accumulation errors of each battery under each vehicle usage time is determined.

[0153] In some embodiments, the power accumulation error set determination module 1002 is specifically configured to execute:

[0154] Clustering processing is performed on the accumulated errors of the electric quantity under the same vehicle-in-vehicle time length to determine the designated percentile value corresponding to each vehicle-in-vehicle time length.

[0155] The power accumulation error set is generated according to the specified percentile values ​​corresponding to each of the vehicle in-vehicle time periods.

[0156] In some embodiments, the fitting module 1003 is specifically configured to perform:

[0157] Based on the vehicle in-vehicle time, a linear fit is performed on the power accumulation error set to determine a linear distribution of the power accumulation error set.

[0158] The electric quantity error parameter is determined according to the slope of the linear distribution.

[0159] In some embodiments, the unit of the vehicle time is day, and the power error parameter is the daily average power error.

[0160] In some embodiments, the apparatus further comprises:

[0161] The sleep duration determination module is configured to determine the sleep duration of the target vehicle in response to the end of the sleep state of the target vehicle.

[0162] The real-time battery capacity determination module is configured to compensate the battery capacity of the target vehicle according to the sleep duration and the target compensation current, and determine the real-time battery capacity corresponding to the target vehicle when the sleep state ends.

[0163] In some embodiments, the target compensation current is determined based on a predetermined cycle, and the target compensation current determined in each predetermined cycle is an initial compensation current of a next cycle.

[0164] In some embodiments, the charging data acquisition module 1001 is specifically configured to execute:

[0165] An original data set is obtained, where the original data set includes charging information corresponding to multiple batteries under different vehicle durations.

[0166] According to a preset duration threshold, the batteries in the original data set whose vehicle duration is greater than or equal to the duration threshold are determined.

[0167] At least one set of the charging data is generated according to the charging information corresponding to the batteries whose in-vehicle time is greater than or equal to the time threshold and the in-vehicle time.

[0168] The embodiment of the present application can obtain at least one set of charging data, and determine the cumulative error set of the charge of each battery at each vehicle time based on each set of charging data. Furthermore, the embodiment of the present application can perform data fitting on the cumulative error set of charge based on each vehicle time to obtain a charge error parameter, which can characterize the error caused by the initial compensation current. Furthermore, the embodiment of the present application determines the target compensation current after updating and correcting the initial compensation current through the charge error parameter, which can make the compensation current closer to the actual loss, thereby effectively helping the electric vehicle to more accurately determine the battery capacity loss generated during the sleep process and improve the estimation accuracy of the battery charge.

[0169] Fig.11 Schematic diagram of an electronic device according to an embodiment of the present application. Fig.11 As shown, Fig.11 The electronic device shown is a general address query device, which includes a general computer hardware structure, which at least includes a processor 1101 and a memory 1102. The processor 1101 and the memory 1102 are connected via a bus 1103. The memory 1102 is suitable for storing instructions or programs executable by the processor 1101. The processor 1101 can be an independent microprocessor or a collection of one or more microprocessors. Thus, the processor 1101 executes the instructions stored in the memory 1102, thereby executing the method flow of the embodiment of the present application as described above to realize the processing of data and the control of other devices. The bus 1103 connects the above-mentioned multiple components together, and at the same time connects the above-mentioned components to the display controller 1104 and the display device and the input / output (I / O) device 1105. The input / output (I / O) device 1105 can be a mouse, a keyboard, a modem, a network interface, a touch input device, a somatosensory input device, a printer, and other devices known in the art. Typically, the input / output device 1105 is connected to the system via an input / output (I / O) controller 1106.

[0170] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, devices (equipment) or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present application may adopt a computer program product implemented on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0171] The present application is described with reference to flowcharts of methods, apparatuses (devices) and computer program products according to embodiments of the present application. It should be understood that each process in the flowchart can be implemented by computer program instructions.

[0172] These computer program instructions may be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device that implements the process Figure 1 A function specified in a process or multiple processes.

[0173] These computer program instructions may also be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce the instructions for implementing the process Figure 1A device that specifies functions in a process or multiple processes.

[0174] Another embodiment of the present application relates to a non-volatile storage medium for storing a computer-readable program, wherein the computer-readable program is used for a computer to execute part or all of the above method embodiments.

[0175] That is, those skilled in the art can understand that all or part of the steps in the above-mentioned embodiment method can be completed by specifying relevant hardware through a program, and the program is stored in a storage medium, including several instructions for a device (which can be a single-chip microcomputer, chip, etc.) or a processor (processor) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0176] Another embodiment of the present application relates to a computer program product, including a computer program / instruction, which can implement part or all of the above method embodiments when the computer program / instruction is executed by a processor.

[0177] That is, those skilled in the art can understand that the embodiments of the present application can specify relevant hardware (including the processor itself) through the processor executing a computer program product (computer program / instructions), thereby implementing all or part of the steps in the above-mentioned embodiment method.

[0178] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A battery data processing method, characterized in that: The method comprises: Acquire at least one set of charging data, each set of charging data corresponding to a different on-vehicle time, the charging data including a pre-charging capacity of at least one battery behind the vehicle, a post-charging capacity behind the vehicle, and a charged power, the charged power being determined based on a charging device, and the pre-charging capacity being determined based on an initial compensation current; Determine, according to each group of charging data, a set of accumulated errors of power of each battery under each vehicle driving time; Performing data fitting on the power accumulation error set based on each of the vehicle in-vehicle time periods to obtain a power error parameter; The initial compensation current is updated and corrected based on the electrical quantity error parameter to determine a target compensation current.

2. The method according to claim 1, characterized in that Determining the accumulated error set of the power of each battery under the vehicle duration according to each group of charging data includes: Determining a charging capacity difference of a corresponding battery behind the vehicle according to the pre-charging capacity and the post-charging capacity; Determine the accumulated error of the power of the corresponding battery behind the vehicle according to the difference between the charging capacity difference and the charging power; According to each of the power accumulation errors, a set of power accumulation errors of each battery under each vehicle usage time is determined.

3. The method according to claim 2, characterized in that Determining the set of accumulated power errors of each battery under the vehicle duration according to each accumulated power error includes: Performing clustering processing on the accumulated errors of the power under the same vehicle time length, and determining the designated quantile value corresponding to each vehicle time length; The power accumulation error set is generated according to the specified percentile values ​​corresponding to each of the vehicle in-vehicle time periods.

4. The method according to claim 1, characterized in that: The performing data fitting on the power accumulation error set based on each of the vehicle in-vehicle time to obtain the power error parameter comprises: Based on each of the vehicle in-vehicle time, linear fitting is performed on the power accumulation error set to determine a linear distribution of the power accumulation error set; The electric quantity error parameter is determined according to the slope of the linear distribution.

5. The method according to claim 4, characterized in that The unit of the vehicle time is day, and the power error parameter is the daily average power error.

6. The method according to claim 1, characterized in that The method further comprises: In response to the end of the dormant state of the target vehicle, determining a dormant duration of the target vehicle; The battery capacity of the target vehicle is compensated according to the sleep time and the target compensation current, and the real-time battery capacity corresponding to the target vehicle when the sleep state ends is determined.

7. The method according to claim 1, characterized in that The target compensation current is determined based on a predetermined cycle, and the target compensation current determined in each predetermined cycle is an initial compensation current of a next cycle.

8. The method according to claim 1, characterized in that The acquiring at least one set of charging data comprises: Acquire an original data set, wherein the original data set includes charging information corresponding to multiple batteries under vehicle duration; According to a preset duration threshold, determine the batteries in the raw data set whose vehicle duration is greater than or equal to the duration threshold; At least one set of the charging data is generated according to the charging information corresponding to the batteries whose in-vehicle time is greater than or equal to the time threshold and the in-vehicle time.

9. A vehicle, characterized in that: The vehicle at least includes a power battery and a vehicle control terminal; The vehicle control terminal is configured to determine the sleep duration in response to the end of the sleep state, compensate the battery capacity of the power battery according to the sleep duration and the target compensation current, and determine the real-time battery capacity of the power battery corresponding to the end of the sleep state; The target compensation current is determined by the electronic device according to the battery data processing method as described in any one of claims 1-8.

10. A battery data processing device, characterized in that: The device comprises: a charging data acquisition module, configured to acquire at least one set of charging data, each set of charging data corresponding to a different on-vehicle time, the charging data including a pre-charging capacity of at least one battery behind the vehicle, a post-charging capacity behind the vehicle, and a charged power, the charged power being determined based on a charging device, and the pre-charging capacity being determined based on an initial compensation current; A power accumulation error set determination module is configured to determine a power accumulation error set of each battery under each vehicle duration according to each group of charging data; A fitting module is configured to perform data fitting on the power accumulation error set based on each of the vehicle in-vehicle time periods to obtain a power error parameter; The correction module is configured to update and correct the initial compensation current based on the electrical quantity error parameter to determine a target compensation current.

11. An electronic device comprising a memory and a processor, characterized in that: The memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method according to any one of claims 1 to 8.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

13. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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