A vehicle battery management method, system and readable storage medium under cold conditions

Abstract

A vehicle battery management method, system and readable storage medium under cold conditions, relating to the field of battery technology. In the method, the predicted start time corresponding to the current date is determined in a preset comparison table according to the current date; the nearest short-term behavior pattern group of a motor vehicle matching the type of the current date is obtained; the predicted start time is adjusted according to the short-term behavior pattern group; the preheating time length is determined according to the current weather information and the parameters of the motor vehicle; when the start time is reached, a preheating prompt is sent to the user terminal, and the start time is calculated by the adjusted predicted start time and the preheating time length; when the preheating operation is received from the user terminal, the vehicle battery is preheated using a capacitor battery. By preheating the vehicle in advance, the method greatly reduces the user's waiting time and increases the user experience.

Description

Technical Field

[0001] The present application relates to the field of battery technology, and in particular to a vehicle-mounted battery management method, system, and readable storage medium under cold conditions. Background Art

[0002] Cold environments pose a challenge to the performance of vehicle batteries, especially under extremely low temperature conditions. The electrochemical reaction rate of the battery slows down, resulting in a decrease in energy output, which in turn affects the starting and operation of the vehicle. Low temperature environments are prevalent in many parts of the world, especially in high latitudes.

[0003] The on-board battery management system provided by the related technology can keep the battery temperature within the optimal working range by heating the battery before starting.

[0004] However, the vehicle battery management system provided by the related technology has problems in practical application, that is, the vehicle needs to be preheated in extremely cold environments to ensure smooth start, but the traditional preheating process is time-consuming and relies on the driver to start in advance, which is often inconsistent with modern fast-paced life. Especially for time-sensitive users, such as office workers, waiting for the vehicle to preheat on a cold morning may be unacceptable. Therefore, the vehicle battery management system provided by the related technology prolongs the preparation time before the vehicle starts, greatly affecting the user experience. Summary of the invention

[0005] The present application provides a vehicle battery management method, system and readable storage medium under cold conditions, which are used to preheat the vehicle in advance, thereby greatly reducing the user's waiting time and improving the user experience.

[0006] In a first aspect, the present application provides a method for managing a vehicle battery in cold conditions, comprising:

[0007] Determine the predicted start time corresponding to the current date in the preset comparison table according to the current date;

[0008] Obtaining a recent short-term behavior pattern group of motor vehicles that matches the type of the current date, where the short-term behavior pattern group is the recent motor vehicle start-up time recorded in historical data and having the same type as the current date and an upper limit of a preset number;

[0009] Adjust the predicted start time based on the short-term behavior pattern group;

[0010] Determine the length of preheating time according to current weather information and parameters of the motor vehicle;

[0011] When the startup time is reached, a preheating prompt is sent to the user terminal. The startup time is calculated by adjusting the predicted startup time and the preheating time length. The preheating prompt is used to give the user the choice of whether to perform the preheating operation;

[0012] When receiving the preheating operation instruction sent by the user terminal, the capacitor battery is used to preheat the vehicle battery.

[0013] In the above embodiment, the startup time of the vehicle is intelligently predicted based on the current date while taking into account the unique behavior patterns of different users. A preheating plan is tailored for each user through the most recent short-term behavior pattern group that matches the current date. Combined with the current weather information and vehicle parameters, the most suitable preheating time length can be calculated to ensure that the battery works in the best condition. In short, by preheating the vehicle in advance, the user's waiting time is greatly reduced, which is in line with the fast-paced lifestyle and can provide a more convenient travel preparation experience, especially for time-sensitive users.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, before the step of determining the predicted start time corresponding to the current date in the preset comparison table according to the current date, the method further includes:

[0015] Classify the historical data according to the type of date to obtain a number of sub-historical data, the historical data including a plurality of start times and the dates corresponding to the start times;

[0016] Remove the discrete data in each sub-historical data to obtain several optimized sub-historical data;

[0017] The predicted startup time is determined according to the median of all startup times in the optimized sub-historical data, and the predicted startup time is combined with the type corresponding to the optimized sub-historical data to construct a preset comparison table.

[0018] In the above embodiment, by classifying historical data by date type and optimizing the processing to remove discrete data, the median is calculated using the obtained optimized sub-historical data as the predicted startup time, which reduces the random fluctuations of the data and the interference of outliers, not only improves the accuracy of the prediction, but also provides customized startup time predictions for different types of dates.

[0019] In conjunction with some embodiments of the first aspect, in some embodiments, the step of removing discrete data in each sub-historical data to obtain a plurality of optimized sub-historical data specifically includes:

[0020] Determine the mean and standard deviation of each sub-history data;

[0021] Identify the start-up time in the sub-historical data whose difference from the mean value exceeds the standard deviation as discrete data;

[0022] Remove the discrete data in each sub-historical data to obtain several optimized sub-historical data.

[0023] In the above embodiment, by calculating the mean and standard deviation of the sub-historical data, identifying and removing outlier start times, the optimized sub-historical data can more accurately reflect the real start time trend, thereby reducing the noise caused by abnormal data, thereby improving the accuracy and credibility of the vehicle start time prediction.

[0024] In conjunction with some embodiments of the first aspect, in some embodiments, the step of adjusting the predicted start time according to the short-term behavior pattern group specifically includes:

[0025] Assigning weights to a preset number of recent motor vehicle start times, wherein the weights increase according to how close the start times are to the predicted start time, and the sum of all weights is equal to 1;

[0026] Calculate the difference between each startup time and the predicted startup time;

[0027] Each difference is multiplied by the corresponding weight, and the weighted differences are accumulated to get the total difference;

[0028] Add the total difference to the predicted start time to get the adjusted predicted start time.

[0029] In the above embodiment, by assigning increasing weights to the most recent motor vehicle start-up time, the predicted start-up time is adjusted according to the user's recent behavior pattern, ensuring that the prediction result fully reflects the user's current usage habits. Such a dynamic weighting method makes the prediction more timely and personalized, improving user satisfaction. At the same time, since the weight allocation is dynamically determined based on the nearness and distance of time, the adjusted predicted start-up time can more flexibly adapt to changes in user behavior, so that the start-up time prediction can quickly respond to the user's latest behavior pattern, improving practicality and accuracy.

[0030] In conjunction with some embodiments of the first aspect, in some embodiments, before the step of determining the predicted start time corresponding to the current date in the preset comparison table according to the current date, the method further includes:

[0031] Determine whether the current battery state is greater than a health threshold when the motor vehicle is turned off;

[0032] If the current battery status is not greater than the health threshold, the capacitor battery is used to periodically micro-charge the vehicle battery to maintain the status of the vehicle battery.

[0033] In the above embodiment, by judging the battery status when the motor vehicle is turned off and starting the micro-charging procedure when the status is not greater than the health threshold, the battery can be effectively prevented from aging rapidly due to a long period of low power, thereby extending the battery life. At the same time, periodic micro-charging maintains the status of the on-board battery, ensuring that the battery is always in a healthy state, so that the vehicle can be started immediately even when the vehicle is not used frequently, improving the convenience and reliability of vehicle use.

[0034] In conjunction with some embodiments of the first aspect, in some embodiments,

[0035] The steps of periodically micro-charging the vehicle battery using a capacitor battery to maintain the state of the vehicle battery specifically include:

[0036] The vehicle battery is periodically micro-charged using a capacitor battery with reference to the current temperature information to maintain the state of the vehicle battery. The lower the current temperature information, the slower the micro-charging rate.

[0037] In the above embodiments, the change of battery charging characteristics under different temperature conditions is optimized. Slowing down the charging rate under low temperature environment can reduce the damage that may occur to the battery due to fast charging under low temperature, thereby extending the service life of the battery.

[0038] In conjunction with some embodiments of the first aspect, in some embodiments, before the step of determining the predicted start time corresponding to the current date in the preset comparison table according to the current date, the method further includes:

[0039] It is determined that when the motor vehicle is started, the capacitor battery is charged by the vehicle battery so that the capacitor battery reaches a preset power threshold.

[0040] In the above embodiment, by ensuring that the capacitor battery is charged to a preset power threshold when the motor vehicle is started, this strategy can ensure that the capacitor battery always maintains a certain amount of stored power before the vehicle is started next time.

[0041] In a second aspect, an embodiment of the present application provides a vehicle-mounted battery management system in cold conditions, including:

[0042] A time determination module is used to determine the predicted start time corresponding to the current date in a preset comparison table according to the current date;

[0043] A short-term behavior acquisition module is used to acquire the most recent short-term behavior pattern group of a motor vehicle that matches the type of the current date, where the short-term behavior pattern group is the most recent motor vehicle start-up time recorded in the historical data and having the same type as the current date and an upper limit of a preset number;

[0044] an adjustment module for adjusting the predicted start time according to the short-term behavior pattern group;

[0045] A preheating time determination module, used to determine the length of the preheating time according to current weather information and parameters of the motor vehicle;

[0046] A sending module, used for sending a preheating prompt to the user terminal when the start time is reached, the start time is calculated by the adjusted predicted start time and the preheating time length, and the preheating prompt is used to give the user the choice of whether to perform the preheating operation;

[0047] The preheating module is used to preheat the vehicle-mounted battery using a capacitor battery when receiving a preheating operation instruction sent by a user terminal.

[0048] In conjunction with some embodiments of the second aspect, in some embodiments, the system further includes:

[0049] A category classification module is used to classify the historical data according to the type of date to obtain a number of sub-historical data, the historical data including a plurality of start times and the dates corresponding to the start times;

[0050] A discrete data removal module is used to remove discrete data in each sub-historical data to obtain a number of optimized sub-historical data;

[0051] The comparison table construction module is used to determine the predicted startup time according to the median of all startup times in the optimized sub-historical data, and to combine the predicted startup time with the type corresponding to the optimized sub-historical data to construct a preset comparison table.

[0052] In conjunction with some embodiments of the second aspect, in some embodiments, the discrete data removal module specifically includes:

[0053] A first calculation submodule is used to determine the average value and standard deviation of each sub-historical data;

[0054] An identification submodule, used for identifying the start time in the sub-historical data whose difference from the average value exceeds the standard deviation as discrete data;

[0055] The sub-module is used to remove discrete data in each sub-historical data to obtain a number of optimized sub-historical data.

[0056] In conjunction with some embodiments of the second aspect, in some embodiments, the adjustment module specifically includes:

[0057] An allocation submodule, for allocating weights to a preset number of nearest motor vehicle start times, wherein the weights increase according to the proximity of the start time to the predicted start time, and the sum of all weights is equal to 1;

[0058] A second calculation submodule is used to calculate the difference between each startup time and the predicted startup time;

[0059] A third calculation submodule is used for multiplying each difference by a corresponding weight and accumulating the weighted differences to obtain a total difference;

[0060] The fourth calculation submodule is used to add the total difference to the predicted startup time to obtain an adjusted predicted startup time.

[0061] In conjunction with some embodiments of the second aspect, in some embodiments, the system further includes:

[0062] A health judgment module is used to determine whether the current battery state is greater than a health threshold when the motor vehicle is turned off;

[0063] The micro-charging module is used to periodically micro-charge the vehicle battery using a capacitor battery if the current battery status is not greater than a health threshold, so as to maintain the status of the vehicle battery.

[0064] In conjunction with some embodiments of the second aspect, in some embodiments, the micro-charging module specifically includes:

[0065] The micro-charging submodule is used to refer to the current temperature information and use the capacitor battery to periodically micro-charge the vehicle battery to maintain the state of the vehicle battery. The lower the current temperature information, the slower the micro-charging rate.

[0066] In conjunction with some embodiments of the second aspect, in some embodiments, the system further includes:

[0067] The charging submodule is used to determine that when the motor vehicle is started, the capacitor battery is charged by the vehicle battery so that the capacitor battery reaches a preset power threshold.

[0068] In a third aspect, an embodiment of the present application provides a vehicle battery management system under cold conditions, the system comprising: one or more processors and a memory;

[0069] The memory is coupled to the one or more processors, and is used to store computer program code, which includes computer instructions. The one or more processors call the computer instructions to enable the on-board battery management system in the cold conditions to execute the method described in the first aspect and any possible implementation of the first aspect.

[0070] In a fourth aspect, an embodiment of the present application provides a computer program product comprising instructions, which, when the computer program product is run on a server, enables the server to execute the method described in the first aspect and any possible implementation of the first aspect.

[0071] In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium, comprising instructions. When the above instructions are executed on an on-board battery management system under cold conditions, the above-mentioned on-board battery management system under cold conditions executes the method described in the first aspect and any possible implementation method of the first aspect.

[0072] It can be understood that the vehicle-mounted battery management system under cold conditions provided in the second aspect, the vehicle-mounted battery management system under cold conditions provided in the third aspect, the computer program product provided in the fourth aspect, and the computer storage medium provided in the fifth aspect are all used to execute the vehicle-mounted battery management method under cold conditions provided in the embodiment of the present application. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, and will not be repeated here.

[0073] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0074] 1. The vehicle battery management method in cold conditions provided by the present application intelligently predicts the vehicle startup time based on the current date while taking into account the unique behavior patterns of different users. A preheating plan is tailored for each user through the most recent short-term behavior pattern group that matches the current date. Combined with current weather information and vehicle parameters, the most suitable preheating time length can be calculated to ensure that the battery works in the best condition. In short, by preheating the vehicle in advance, the user's waiting time is greatly reduced, which is in line with the fast-paced lifestyle and can provide a more convenient travel preparation experience, especially for time-sensitive users.

[0075] 2. The vehicle battery management method under cold conditions provided in the present application classifies historical data by date type and optimizes the processing to remove discrete data. The median of the obtained optimized sub-historical data is calculated as the predicted start time, thereby reducing the random fluctuations of the data and the interference of outliers. This not only improves the accuracy of the prediction, but also provides customized start time predictions for different types of dates.

[0076] 3. The vehicle battery management method in cold conditions provided by this application assigns increasing weights to the most recent motor vehicle startup time, adjusts the predicted startup time according to the user's recent behavior patterns, and ensures that the prediction results fully reflect the user's current usage habits. Such a dynamic weighting method makes the prediction more timely and personalized, improving user satisfaction. At the same time, since the weight allocation is dynamically determined based on the nearness of time, the adjusted predicted startup time can more flexibly adapt to changes in user behavior, so that the startup time prediction can quickly respond to the user's latest behavior patterns, improving practicality and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1A flow chart of a method for managing a vehicle battery under cold conditions provided in this application.

[0078] Figure 2 Another flowchart of the vehicle battery management method under cold conditions provided by this application.

[0079] Figure 3 Schematic diagram of a modular virtual device for a vehicle battery management system under cold conditions provided by this application.

[0080] Figure 4 This is a schematic diagram of a physical device of an on-board battery management system under cold conditions provided by this application. DETAILED DESCRIPTION

[0081] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more listed items.

[0082] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0083] The following describes the vehicle battery management method under cold conditions in this embodiment:

[0084] like Figure 1 As shown, Figure 1 A flow chart of a method for managing a vehicle battery under cold conditions provided in this application.

[0085] S101. Determine a predicted start time corresponding to the current date in a preset comparison table according to the current date.

[0086] It should be noted that in order to ensure the effectiveness of the technical solution, the implementation of this step should strictly abide by the schedule. Specifically, this step should be executed at the earliest time point on the current date, which should be at least the same as or earlier than the earliest vehicle start time recorded in the user's historical data. This is to avoid implementation delays. If the start time is too late, the user's actual start time may be missed, resulting in the method being unable to serve the user as intended. Therefore, a timely start is the key to ensuring that users can receive preheating prompts when needed and prepare for the smooth start of the vehicle.

[0087] In some embodiments, it is first necessary to determine the current date and look up the predicted start time corresponding to the current date in a preset comparison table. The comparison table is derived from historical data analysis and contains typical start times associated with specific dates for different date types (such as weekdays, weekends, holidays, etc.). The current date is provided by a built-in calendar module, which can identify the date of the day and automatically match it with the date type in the comparison table. The purpose of this step is to quickly derive a rough predicted start time based on historical habits and provide an initial value for subsequent steps.

[0088] In some embodiments, time series analysis or machine learning algorithms (such as regression analysis, clustering algorithms, or neural networks) are used to process these data to establish a prediction model. This model will predict future startup times based on historical usage patterns, and then aggregate the results of different types of dates to obtain a preset comparison table. Alternatively, other methods may be used in other embodiments, which are not limited here.

[0089] In the following embodiment, a different method for creating a preset comparison table will be introduced, which will not be described in detail here.

[0090] S102, obtaining a recent short-term behavior pattern group of motor vehicles that matches the type of the current date, where the short-term behavior pattern group is the recent motor vehicle start-up time recorded in historical data and having the same type as the current date and an upper limit of a preset number.

[0091] The most recent short-term behavior pattern group of motor vehicles that matches the type of the current date will be obtained. The short-term behavior pattern group is defined as the most recent motor vehicle startup time recorded in the historical data, of the same type as the current date and with an upper limit of a preset number. For example, if today is Monday, the vehicle startup data for all Mondays in history will be found, and then the most recent data (such as Mondays in the last four weeks) will be selected as the short-term behavior pattern group. This data is recorded by the vehicle's built-in sensors and stored in the vehicle's data storage unit. This step takes into account the user's changing habits, making the startup time prediction more accurate.

[0092] S103: Adjust the predicted start time according to the short-term behavior pattern group.

[0093] In some embodiments, step S103 specifically includes:

[0094] S1031, assigning weights to a preset number of recent motor vehicle start times, wherein the weights increase according to the proximity of the start time to the predicted start time, and the sum of all weights is equal to 1;

[0095] In order to reflect the user's latest travel habits in the prediction of the start time, it is necessary to assign weights to the preset number of recent motor vehicle start times. The weight is determined based on the time interval between each start time and the predicted start time. The shorter the time interval, the more likely the start time is to be close to the actual start time in the future, so the assigned weight is greater. This weight allocation mechanism ensures that the most recent behavior pattern has a greater influence on the prediction results.

[0096] In some embodiments, a weight increment function is set, which can be linear or nonlinear. The weight assigned to each start time is calculated by the function, and the sum of all weights is guaranteed to be equal to 1, which is achieved by normalizing all the initially calculated weights.

[0097] For example, if the selected preset number is 5, the last five start times are 7:00, 7:05, 7:10, 7:15, and 7:20. Here, if linear weight increment is used, 7:20 has the largest weight and 7:00 has the smallest weight. After normalization, each weight will account for a certain proportion of the total to ensure that the total is 1.

[0098] S1032, calculating the difference between each startup time and the predicted startup time;

[0099] The difference between each actual startup time and the predicted startup time needs to be calculated. This step is to quantify the degree to which each actual startup time deviates from the predicted time, providing a basis for the next step of weighted adjustment.

[0100] Specifically, a predicted start-up time point is first determined, that is, the predicted start-up time provided in step S101. Subsequently, the recorded actual start-up time is compared with the predicted time, and the difference at each time point is calculated.

[0101] S1033, multiply each difference by a corresponding weight, and accumulate the weighted differences to obtain a total difference;

[0102] Each time difference is weighted and then summed. This step multiplies each time difference by the corresponding weight assigned in S1031, and then all weighted differences are accumulated to obtain a total difference. In this way, the most recent startup time contributes more to the total difference, while the earlier startup time contributes less.

[0103] S1034. Add the total difference to the predicted startup time to obtain an adjusted predicted startup time.

[0104] The total difference obtained in S1033 is added to the original predicted start time. The implementation of this step is very simple and only requires a mathematical addition operation.

[0105] Assuming that the original predicted startup time is 7:10, and the total difference calculated by S1033 is 6 minutes, these 6 minutes are added to 7:10, resulting in an adjusted predicted startup time of 7:16. In this way, the predicted time is updated based on the most recent actual usage data, thereby being closer to the user's actual usage pattern.

[0106] It can be seen that by assigning increasing weights to the most recent motor vehicle start-up time, the predicted start-up time is adjusted according to the user's recent behavior pattern, ensuring that the prediction results fully reflect the user's current usage habits. This dynamic weighting method makes the prediction more timely and personalized, improving user satisfaction. At the same time, since the weight allocation is dynamically determined based on the nearness of time, the adjusted predicted start-up time can more flexibly adapt to changes in user behavior, allowing the start-up time prediction to quickly respond to the user's latest behavior pattern, improving practicality and accuracy.

[0107] S104: Determine the length of preheating time according to current weather information and parameters of the motor vehicle.

[0108] In some embodiments, different weights are assigned to different weather parameters and vehicle parameters because they have different degrees of influence on the preheating time. As for the specific method of assigning weights, it is not limited here.

[0109] Based on the collected data and defined weights, design an algorithm to calculate the warm-up time. This algorithm may be a mathematical model that combines all influencing factors and outputs a recommended value for the warm-up time. The model may consider linear or nonlinear relationships and may be optimized through machine learning methods. For example: T = a*Temp_factor+b*Humidity_factor+c*Wind_speed_factor+d*Engine_factor+e*Vehicle_age_factor

[0110] Where, is the preheating time adjustment value based on the current temperature. Humidity_factor and Wind_speed_factor are the preheating time adjustment values ​​based on the current humidity and wind speed. Engine_factor and Vehicle_age_factor are the preheating time adjustment values ​​based on the vehicle engine type and vehicle age. a, b, c, d, e are the weight coefficients corresponding to each factor.

[0111] S105. When the startup time is reached, a preheating prompt is sent to the user terminal. The startup time is calculated by the adjusted predicted startup time and the preheating time length. The preheating prompt is used to give the user the choice of whether to perform the preheating operation.

[0112] Before the calculated start time arrives, a preheating reminder will be sent to the user's terminal device (such as a smartphone or car). This reminder will be sent based on the final start time calculated based on the adjusted predicted start time and the length of the preheating time. After receiving this preheating reminder, the user can choose whether to perform the preheating operation. This interactive design allows users to maintain the flexibility of daily activities while being able to make choices based on their needs, allowing users to better control the vehicle's start and preheating process.

[0113] S106: When receiving a preheating operation instruction from the user terminal, preheat the vehicle-mounted battery using a capacitor battery.

[0114] The on-board battery is preheated using a capacitor battery. This preheating operation is not the traditional engine preheating, but the preheating of the on-board battery to ensure that the battery performance is not affected in a low temperature environment, while also providing sufficient power for the upcoming engine start. Capacitor batteries have the characteristics of rapid discharge and charging, providing high current in a short time, which is very suitable for this rapid preheating process. The current and voltage during the preheating process will be controlled to ensure safety, and the preheating process will be optimized according to the specific conditions of the vehicle and the ambient temperature to prevent the battery from overheating or undercharging.

[0115] It can be seen that the vehicle startup time is intelligently predicted according to the current date while taking into account the unique behavior patterns of different users. A preheating plan is tailored for each user through the most recent short-term behavior pattern group that matches the current date. Combined with current weather information and vehicle parameters, the most suitable preheating time length can be calculated to ensure that the battery works in the best condition. In short, by preheating the vehicle in advance, the user's waiting time is greatly reduced, which is in line with the fast-paced lifestyle. Especially for time-sensitive users, it can provide a more convenient travel preparation experience.

[0116] In some embodiments, before step S101, the method further includes:

[0117] Determine whether the current battery state is greater than a health threshold when the motor vehicle is turned off;

[0118] If the current battery status is not greater than the health threshold, the capacitor battery is used to periodically micro-charge the vehicle battery to maintain the status of the vehicle battery.

[0119] It can be seen that by judging the battery status when the motor vehicle is turned off and starting the micro-charging program when the status is not greater than the health threshold, the battery can be effectively prevented from accelerating aging due to a long period of low power, thereby extending the battery life. At the same time, periodic micro-charging maintains the status of the on-board battery, ensuring that the battery is always in a healthy state, so that the vehicle can be started immediately even when the vehicle is not used frequently, improving the convenience and reliability of vehicle use.

[0120] In other embodiments, the step of periodically micro-charging the vehicle battery using a capacitor battery to maintain the state of the vehicle battery specifically includes:

[0121] The vehicle battery is periodically micro-charged using a capacitor battery with reference to the current temperature information to maintain the state of the vehicle battery. The lower the current temperature information, the slower the micro-charging rate.

[0122] The charging rate of the micro-charger is dynamically adjusted through a preset temperature-charging rate mapping table. The mapping table is set based on experimental data on the relationship between battery chemical properties and temperature. For example, for every 1°C drop in temperature, the charging rate is reduced by a certain percentage to avoid battery damage caused by over-fast charging at low temperatures.

[0123] It can be seen that the changes in battery charging characteristics under different temperature conditions have been optimized. Slowing down the charging rate in a low temperature environment can reduce the damage that may occur to the battery due to fast charging at low temperatures, thereby extending the battery life.

[0124] In some embodiments, before step S101, the method further includes:

[0125] It is determined that when the motor vehicle is started, the capacitor battery is charged by the vehicle battery so that the capacitor battery reaches a preset power threshold.

[0126] It can be seen that by ensuring that the capacitor battery is charged to a preset power threshold when the motor vehicle is started, this strategy can ensure that the capacitor battery always maintains a certain amount of stored power before the vehicle is started next time.

[0127] like Figure 2 As shown, Figure 2 Another flowchart of the vehicle battery management method under cold conditions provided by this application.

[0128] Before step S101, the following steps are also included:

[0129] S201. Classify historical data according to date types to obtain a plurality of sub-historical data, wherein the historical data includes a plurality of start-up times and dates corresponding to the start-up times.

[0130] Historical data needs to be classified. Historical data usually includes multiple start times and their corresponding dates. The classification of dates can be based on multiple dimensions, such as working days and non-working days, seasons, holidays, etc. This classification can help understand the potential impact of different date types on the start time.

[0131] In some embodiments, data is processed by a pre-set classification rule. For example, all historical data can be classified into two categories: "working days" and "non-working days". For these two categories, all start-up times in each category will be recorded separately, laying the foundation for subsequent data analysis.

[0132] S202: Determine the mean value and standard deviation of each sub-historical data.

[0133] For each sub-historical data set, its average startup time and standard deviation need to be calculated. The average reflects the center point of the startup time under this category, while the standard deviation measures the dispersion of the startup time, that is, the variability of the data.

[0134] In some embodiments, the start times for each subset are summed and then divided by the number of data points for that subset to obtain the average. The calculation of the standard deviation is more complex and requires taking the sum of the squares of the difference between each data point and the average, dividing it by the number of data points minus one, and then taking the square root.

[0135] S203: Identify the start-up time in the sub-historical data whose difference from the average value exceeds the standard deviation as discrete data.

[0136] The discrete data points in each sub-historical data set will be identified. The discrete data points are those startup times that differ from the mean by more than one standard deviation. These data points may represent abnormal situations, such as atypical startup times caused by special events.

[0137] In some embodiments, all the start times in each subset are traversed, the difference between each time and the average is calculated, and the difference is compared with the standard deviation. Data points that exceed the standard deviation will be marked as discrete data.

[0138] S204: Remove discrete data from each sub-historical data to obtain a plurality of optimized sub-historical data.

[0139] In this step, the identified discrete data will be removed to optimize each sub-historical data set. Removing discrete data can reduce the adverse effects of outliers on future forecasts, making the forecast results more stable and reliable.

[0140] In some embodiments, the start-up times marked as discrete in step S203 are removed from each subset, and the remaining data constitutes an optimized sub-historical data set.

[0141] S205: Determine the predicted startup time according to the median of all startup times in the optimized sub-historical data, and combine the predicted startup time with the type corresponding to the optimized sub-historical data to construct a preset comparison table.

[0142] The predicted startup time is determined based on the median of all startup times in each optimized sub-historical data set. The median is the value in the middle of all startup times after sorting. It is insensitive to extreme values ​​and is therefore a robust measure of central tendency.

[0143] After determining the predicted start time, combine this time with the corresponding date type to build a preset comparison table. This table includes each date type and its corresponding start time forecast for future forecasting.

[0144] In some embodiments, each optimized sub-historical data set is sorted to find the start time value in the middle. If there are an even number of data points in the data set, the average of the two middle data points is taken as the median. Subsequently, these medians are combined with the corresponding date types to generate a preset comparison table. This table can intuitively show what the expected start time should be under different date types.

[0145] For example, if the median of the optimized sub-historical data set is determined to be 8:00 AM for weekdays and 10:00 AM for non-weekdays, the preset comparison table will show the predicted start times corresponding to these two different types of dates.

[0146] It can be seen that by classifying historical data by date type and optimizing it to remove discrete data, the median is calculated using the obtained optimized sub-historical data as the predicted startup time, which reduces the random fluctuations of the data and the interference of outliers. This not only improves the accuracy of the prediction, but also provides customized startup time predictions for different types of dates.

[0147] It can be seen that by calculating the mean and standard deviation of the sub-historical data, identifying and removing outlier start times, the optimized sub-historical data can more accurately reflect the real start time trend. Therefore, the noise caused by abnormal data is reduced, thereby improving the accuracy and credibility of the vehicle start time prediction.

[0148] The following are device embodiments of the present application, which can be used to execute the method embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the method embodiments of the present application.

[0149] refer to Figure 3 , the embodiment of the present application provides a vehicle battery management system under cold conditions, the vehicle battery management system under cold conditions includes:

[0150] A time determination module 301 is used to determine the predicted start time corresponding to the current date in a preset comparison table according to the current date;

[0151] The short-term behavior acquisition module 302 is used to acquire the most recent short-term behavior pattern group of the motor vehicle that matches the type of the current date, where the short-term behavior pattern group is the most recent motor vehicle start-up time recorded in the historical data and having the same type as the current date and an upper limit of a preset number;

[0152] An adjustment module 303, used for adjusting the predicted start time according to the short-term behavior pattern group;

[0153] A preheating time determination module 304 is used to determine the length of the preheating time according to current weather information and parameters of the motor vehicle;

[0154] A sending module 305 is used to send a preheating prompt to the user terminal when the start time is reached. The start time is calculated by the adjusted predicted start time and the preheating time length. The preheating prompt is used to give the user the choice of whether to perform the preheating operation;

[0155] The preheating module 306 is used to preheat the vehicle-mounted battery using the capacitor battery when receiving a preheating operation instruction sent by the user terminal.

[0156] In some embodiments, the system further comprises:

[0157] A category classification module is used to classify the historical data according to the type of date to obtain a number of sub-historical data, the historical data including a plurality of start times and the dates corresponding to the start times;

[0158] A discrete data removal module is used to remove discrete data in each sub-historical data to obtain a number of optimized sub-historical data;

[0159] The comparison table construction module is used to determine the predicted startup time according to the median of all startup times in the optimized sub-historical data, and to combine the predicted startup time with the type corresponding to the optimized sub-historical data to construct a preset comparison table.

[0160] In some embodiments, the discrete data removal module specifically includes:

[0161] A first calculation submodule is used to determine the average value and standard deviation of each sub-historical data;

[0162] An identification submodule, used for identifying the start time in the sub-historical data whose difference from the average value exceeds the standard deviation as discrete data;

[0163] The sub-module is used to remove discrete data in each sub-historical data to obtain a number of optimized sub-historical data.

[0164] In some embodiments, the adjustment module specifically includes:

[0165] An allocation submodule, for allocating weights to a preset number of nearest motor vehicle start times, wherein the weights increase according to the proximity of the start time to the predicted start time, and the sum of all weights is equal to 1;

[0166] A second calculation submodule is used to calculate the difference between each startup time and the predicted startup time;

[0167] A third calculation submodule is used for multiplying each difference by a corresponding weight and accumulating the weighted differences to obtain a total difference;

[0168] The fourth calculation submodule is used to add the total difference to the predicted startup time to obtain an adjusted predicted startup time.

[0169] In some embodiments, the system further comprises:

[0170] A health judgment module is used to determine whether the current battery state is greater than a health threshold when the motor vehicle is turned off;

[0171] The micro-charging module is used to periodically micro-charge the vehicle battery using a capacitor battery if the current battery status is not greater than a health threshold, so as to maintain the status of the vehicle battery.

[0172] In some embodiments, the micro-charging module specifically includes:

[0173] The micro-charging submodule is used to refer to the current temperature information and use the capacitor battery to periodically micro-charge the vehicle battery to maintain the state of the vehicle battery. The lower the current temperature information, the slower the micro-charging rate.

[0174] In some embodiments, the system further comprises:

[0175] The charging submodule is used to determine that when the motor vehicle is started, the capacitor battery is charged by the vehicle battery so that the capacitor battery reaches a preset power threshold.

[0176] The present application also discloses a vehicle-mounted battery management system in cold conditions. Figure 4, is a schematic diagram of a physical device of a vehicle battery management system under cold conditions provided by the present application. The computer 400 may include: at least one processor 401 , at least one network interface 404 , a user interface 403 , a memory 405 , and at least one communication bus 402 .

[0177] The communication bus 402 is used to realize the connection and communication between these components.

[0178] The user interface 403 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 403 may also include a standard wired interface and a wireless interface.

[0179] The network interface 404 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0180] Among them, the processor 401 may include one or more processing cores. The processor 401 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 405, and calling data stored in the memory 405. Optionally, the processor 401 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 401 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processor (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 401, and it can be implemented separately through a chip.

[0181] Among them, the memory 405 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 405 includes a non-transitory computer-readable storage medium. The memory 405 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 405 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area may store data involved in the above-mentioned method embodiments, etc. The memory 405 may optionally be at least one storage device located away from the aforementioned processor 401. Refer to Figure 4 , the memory 405 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application for vehicle battery management in cold conditions.

[0182] exist Figure 4 In the computer 400 shown, the user interface 403 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 401 can be used to call the application for vehicle battery management in cold conditions stored in the memory 405. When executed by one or more processors 401, the computer 400 executes one or more of the methods described in the above embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited to the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0183] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0184] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0185] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0186] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0187] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0188] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0189] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not described in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for managing a vehicle battery in cold conditions, characterized in that: include: Classifying the historical data according to the types of dates to obtain a plurality of sub-historical data, wherein the historical data includes a plurality of start times and dates corresponding to the start times one by one; Removing discrete data from each of the sub-historical data to obtain a plurality of optimized sub-historical data; Determine the predicted startup time according to the median of all startup times in the optimized sub-historical data, and combine the predicted startup time with the type corresponding to the optimized sub-historical data to construct a preset comparison table; Determine the predicted start time corresponding to the current date in a preset comparison table according to the current date; Acquire a recent short-term behavior pattern group of motor vehicles that matches the type of the current date, wherein the short-term behavior pattern group is the recent start-up time of motor vehicles that are of the same type as the current date and whose upper limit is a preset number and recorded in historical data; The predicted start time is adjusted according to the short-term behavior pattern group; wherein: weights are assigned to a preset number of recent motor vehicle start times, wherein the weights increase according to the proximity of the start time to the predicted start time, and the sum of all weights is equal to 1; Calculating the difference between each of the startup times and the predicted startup time; Each difference is multiplied by the corresponding weight, and the weighted differences are accumulated to obtain a total difference; Adding the total difference to the predicted start time to obtain an adjusted predicted start time; Determine the length of preheating time according to current weather information and parameters of the motor vehicle; When the startup time is reached, a preheating prompt is sent to the user terminal, where the startup time is calculated based on the adjusted predicted startup time and the preheating time length, and the preheating prompt is used to give the user the option of whether to perform the preheating operation; When receiving the preheating operation instruction sent by the user terminal, the vehicle-mounted battery is preheated by using the capacitor battery.

2. The vehicle battery management method in cold conditions according to claim 1, characterized in that: The step of removing the discrete data in each of the sub-historical data to obtain a plurality of optimized sub-historical data specifically includes: Determine the mean and standard deviation of each of the sub-historical data; identifying the start-up time in the sub-historical data whose difference from the average value exceeds the standard deviation as discrete data; The discrete data in each of the sub-historical data is removed to obtain a plurality of optimized sub-historical data.

3. The vehicle battery management method in cold conditions according to claim 1, characterized in that: Before the step of determining the predicted start time corresponding to the current date in a preset comparison table according to the current date, the method further includes: Determine whether the current battery state is greater than a health threshold when the motor vehicle is turned off; If the current battery state is not greater than the health threshold, the on-board battery is periodically micro-charged using a capacitor battery to maintain the state of the on-board battery.

4. The vehicle battery management method in cold conditions according to claim 3, characterized in that: The step of periodically micro-charging the vehicle battery using the capacitor battery to maintain the state of the vehicle battery specifically includes: The vehicle-mounted battery is periodically micro-charged using a capacitor battery with reference to the current temperature information to maintain the state of the vehicle-mounted battery. The lower the current temperature information is, the slower the micro-charging rate is.

5. The vehicle battery management method in cold conditions according to claim 1, characterized in that: Before the step of determining the predicted start time corresponding to the current date in a preset comparison table according to the current date, the method further includes: It is determined that when the motor vehicle is started, the capacitor battery is charged by the vehicle battery so that the capacitor battery reaches a preset power threshold.

6. A vehicle-mounted battery management system in cold conditions, used to execute the method according to any one of claims 1 to 5, characterized in that: include: A time determination module is used to determine the predicted start time corresponding to the current date in a preset comparison table according to the current date; A short-term behavior acquisition module, used to acquire a recent short-term behavior pattern group of a motor vehicle that matches the type of the current date, wherein the short-term behavior pattern group is the recent start-up time of a motor vehicle that is recorded in historical data and has the same type as the current date and has an upper limit of a preset number; An adjustment module, used for adjusting the predicted start time according to the short-term behavior pattern group; A preheating time determination module, used to determine the length of the preheating time according to current weather information and parameters of the motor vehicle; A sending module, configured to send a preheating prompt to a user terminal when a start-up time is reached, wherein the start-up time is calculated by the adjusted predicted start-up time and the preheating time length, and the preheating prompt is used to allow the user to choose whether to perform a preheating operation; The preheating module is used to preheat the vehicle-mounted battery using a capacitor battery when receiving a preheating operation instruction sent by the user terminal.

7. A vehicle-mounted battery management system for cold conditions, characterized in that: include: one or more processors and memory; The memory is coupled to the one or more processors, and the memory is used to store computer program codes, wherein the computer program codes include computer instructions, and the one or more processors call the computer instructions to enable the on-board battery management system to execute the method as described in any one of claims 1-5 under cold conditions.

8. A computer-readable storage medium comprising instructions, characterized in that: When the instruction is executed on a vehicle battery management system under cold conditions, the vehicle battery management system under cold conditions executes the method according to any one of claims 1 to 5.

Images