Vehicle warning method, device, equipment and storage medium

Abstract

The present application relates to the field of vehicle control technology, and discloses a vehicle early warning method, device, equipment and storage medium, the method comprising: obtaining the current battery monitoring data of the vehicle; determining the abnormal self-discharging battery cell based on the current battery monitoring data; and performing abnormal self-discharging early warning of the vehicle battery according to the abnormal self-discharging battery cell. The present application can timely detect the abnormal self-discharging phenomenon of the battery and improve driving safety.

Description

Technical Field

[0001] The present application relates to the field of vehicle control technology, and in particular to a vehicle early warning method, device, equipment and storage medium. Background Art

[0002] With the rapid development of electric vehicles and other fields, batteries, as core components of energy storage and supply, are crucial to the operation of the entire system in terms of performance and safety. However, battery self-discharge is a common phenomenon, which may lead to a decline in battery performance and even cause safety problems. In current technology, abnormal battery self-discharge can only be detected by disassembling the battery pack and testing it in the laboratory. The abnormal self-discharge of some batteries can only be discovered by disassembling the battery. This method not only consumes a lot of manpower and material resources, but also with the continuous expansion of the scale of new energy electric vehicles, it is impossible to test every electric vehicle on the market. There are very large limitations, which leads to the inability to detect abnormal battery self-discharge in time, and driving safety cannot be guaranteed.

[0003] Therefore, how to detect abnormal battery self-discharge in time and improve driving safety is a problem that needs to be solved urgently.

[0004] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The main purpose of this application is to provide a vehicle warning method, device, equipment and storage medium, aiming to solve the technical problem of how to timely detect abnormal battery self-discharge and improve driving safety.

[0006] To achieve the above purpose, the present application proposes a vehicle early warning method, the method comprising:

[0007] Get the vehicle's current battery monitoring data;

[0008] Determine an abnormal self-discharging battery cell based on the current battery monitoring data;

[0009] An abnormal self-discharge warning of the vehicle battery is performed based on the abnormal self-discharge battery cell.

[0010] In one embodiment, determining the abnormal self-discharging battery cell based on the current battery monitoring data includes:

[0011] Acquire historical battery monitoring data of a plurality of vehicles, and construct an abnormal self-discharge monitoring model based on the historical battery monitoring data of the plurality of vehicles;

[0012] An abnormal self-discharging cell is determined based on the current battery monitoring data and the abnormal self-discharging monitoring model.

[0013] In one embodiment, the acquiring of historical battery monitoring data of a plurality of vehicles and constructing an abnormal self-discharge monitoring model based on the historical battery monitoring data of the plurality of vehicles includes:

[0014] Acquire historical battery monitoring data of multiple vehicles, and perform data cleaning on the historical battery monitoring data of the multiple vehicles to obtain the historical battery monitoring data of the multiple vehicles after data cleaning;

[0015] Determine a target vehicle based on the historical battery monitoring data of the plurality of vehicles after the data cleaning;

[0016] An abnormal self-discharge monitoring model is constructed based on the historical battery monitoring data of the target vehicle.

[0017] In one embodiment, determining a target vehicle based on the historical battery monitoring data of the plurality of vehicles after the data cleaning includes:

[0018] Determine the number of cells in the battery of each vehicle based on the historical battery monitoring data of the plurality of vehicles after the data cleaning;

[0019] Determine a voltage list corresponding to each vehicle battery according to the number of cells in each vehicle battery;

[0020] determining the state of charge of each vehicle battery based on the voltage list corresponding to each vehicle battery;

[0021] A target vehicle is determined based on the states of charge of the respective vehicle batteries.

[0022] In one embodiment, the determining the abnormal self-discharging battery cell based on the current battery monitoring data and the abnormal self-discharging monitoring model includes:

[0023] Determine the self-discharge rate of each battery cell according to the current battery monitoring data and the abnormal self-discharge monitoring model;

[0024] The abnormal self-discharging cells are determined according to the self-discharging rates of the cells.

[0025] In one embodiment, the determining the self-discharge rate of each battery cell according to the current battery monitoring data and the abnormal self-discharge monitoring model includes:

[0026] Determine the voltage difference of each battery cell according to the current battery monitoring data and the abnormal self-discharge monitoring model;

[0027] Performing noise reduction processing on the pressure difference of each battery cell to obtain a fitted pressure difference of each battery cell;

[0028] The self-discharge rate of each battery cell is obtained by calculating based on the fitted pressure difference of each battery cell.

[0029] In one embodiment, the abnormal self-discharging warning of the vehicle battery according to the abnormal self-discharging battery cell includes:

[0030] Obtaining the cell number and self-discharge rate of the abnormal self-discharging cell;

[0031] The cell number and self-discharge rate of the abnormally self-discharging cell are sent to the vehicle screen for display to provide an early warning of abnormal self-discharge of the vehicle battery.

[0032] In addition, to achieve the above purpose, the present application also proposes a vehicle warning device, the vehicle warning device comprising:

[0033] An acquisition module, used to acquire the current battery monitoring data of the vehicle;

[0034] A determination module, configured to determine an abnormal self-discharging battery cell based on the current battery monitoring data;

[0035] The early warning module is used to provide an early warning of abnormal self-discharge of the vehicle battery according to the abnormal self-discharge battery cell.

[0036] In addition, to achieve the above-mentioned purpose, the present application also proposes a vehicle warning device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the vehicle warning method described above.

[0037] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the vehicle warning method described above are implemented.

[0038] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the vehicle warning method described above are implemented.

[0039] The present application provides a vehicle early warning method, which first obtains the current battery monitoring data of the vehicle to promptly discover whether the battery is abnormally self-discharging; based on the current battery monitoring data, the abnormal self-discharging battery cell is determined, and the abnormal self-discharging battery cell can be located in a timely and accurate manner; based on the abnormal self-discharging battery cell, an abnormal self-discharging early warning of the vehicle battery is performed, and the abnormal self-discharging phenomenon of the battery can be discovered in time, thereby improving driving safety.

[0040] In summary, the present application uses the current battery monitoring data to timely and accurately locate abnormal self-discharging cells and issue an abnormal self-discharge warning for the vehicle battery. By monitoring the current battery monitoring data, an abnormal self-discharge warning for the vehicle battery can be achieved, overcoming the technical defects of being unable to timely detect abnormal battery self-discharge and the lack of guarantee for driving safety. The abnormal self-discharge of the battery can be detected in time, thereby improving driving safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0042] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0043] Figure 1 A flow chart of the first embodiment of the vehicle warning method of the present application;

[0044] Figure 2 A flow chart of the second embodiment of the vehicle warning method of the present application;

[0045] Figure 3 This is a schematic diagram of the module structure of the vehicle warning device according to an embodiment of the present application;

[0046] Figure 4 This is a schematic diagram of the device structure of the hardware operating environment involved in the vehicle warning method in the embodiment of the present application.

[0047] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0048] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0049] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0050] The main solution of the embodiment of the present application is: obtaining the current battery monitoring data of the vehicle; determining the abnormal self-discharging battery cell based on the current battery monitoring data; and issuing an abnormal self-discharging warning for the vehicle battery according to the abnormal self-discharging battery cell.

[0051] In current technology, abnormal self-discharge of batteries can only be detected by disassembling the battery pack and testing in the laboratory. Some abnormal self-discharge of batteries can only be detected by disassembling the battery. Using this method not only consumes a lot of manpower and material resources, but also with the continuous expansion of the scale of new energy electric vehicles, it is impossible to test every electric vehicle on the market. There are very large limitations, resulting in the inability to detect abnormal self-discharge of batteries in time, and driving safety cannot be guaranteed. Therefore, how to detect abnormal self-discharge of batteries in time and improve driving safety is a problem that needs to be solved urgently.

[0052] The present application uses the current battery monitoring data to timely and accurately locate abnormal self-discharging cells and issue an abnormal self-discharge warning for the vehicle battery. By monitoring the current battery monitoring data, an abnormal self-discharge warning for the vehicle battery can be achieved, overcoming the technical defects of being unable to timely detect abnormal battery self-discharge and the lack of guarantee for driving safety. The application can timely detect abnormal battery self-discharge and improve driving safety.

[0053] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, a vehicle warning device, etc. The following takes the vehicle warning device as an example to illustrate this embodiment and the following embodiments.

[0054] Based on this, the embodiment of the present application provides a vehicle warning method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the vehicle warning method of the present application.

[0055] In this embodiment, the vehicle early warning method includes steps S10 to S30:

[0056] Step S10, obtaining current battery monitoring data of the vehicle.

[0057] It should be noted that the current battery monitoring data refers to the current voltage value of each battery cell in the vehicle battery pack, and this embodiment does not impose specific restrictions on this. The current battery monitoring data can be monitored and recorded in real time by a battery management system (BMS) installed on the vehicle.

[0058] It is understandable that the vehicle battery management system (BMS) is a device used to manage each battery cell in the battery pack, which can monitor and record the voltage, current, temperature and other parameters of each battery cell in real time to ensure the safe and stable operation of the battery. By obtaining the current battery monitoring data of the vehicle, the real-time status of each battery cell can be understood, which facilitates the timely detection of abnormal self-discharging batteries.

[0059] Step S20: determining abnormal self-discharging cells based on the current battery monitoring data.

[0060] It should be noted that by analyzing the current battery monitoring data and comparing the voltage values ​​of each battery cell, the battery cell with abnormal voltage value, i.e., abnormal self-discharging battery cell, is found. The voltage value of the abnormal self-discharging battery cell is usually lower than the voltage value of other normal battery cells. Therefore, a voltage threshold can be set to determine the battery cell with a voltage value lower than the threshold as an abnormal self-discharging battery cell, and this embodiment does not impose specific restrictions on this.

[0061] It is understandable that the process of determining abnormal self-discharging cells can also be combined with other parameters for comprehensive analysis, such as current value, temperature value, etc., to improve the accuracy and reliability of the judgment. In addition, in order to avoid misjudgment and missed judgment, a variety of algorithms and models can be used for data processing and analysis, such as machine learning, deep learning and other artificial intelligence technologies, to improve the accuracy and efficiency of identifying abnormal self-discharging cells.

[0062] Step S30, providing a warning of abnormal self-discharge of the vehicle battery according to the abnormal self-discharge battery cell.

[0063] It should be noted that when an abnormal self-discharging battery cell is determined, a warning signal can be issued through the vehicle warning device to remind the driver to pay attention to the abnormal self-discharge of the battery, or some measures can be taken automatically, such as reducing the speed of the vehicle, shutting down some electrical equipment, etc., to prevent further deterioration of the battery and ensure driving safety. This embodiment does not impose specific restrictions on this.

[0064] In a feasible implementation, step S30 may include: obtaining the cell number and self-discharge rate of the abnormal self-discharging cell; sending the cell number and self-discharge rate of the abnormal self-discharging cell to the vehicle screen for display, so as to realize abnormal self-discharge warning of the vehicle battery.

[0065] It should be noted that in order to ensure that the driver can understand the battery status in time, the system will send the acquired abnormal battery cell information to the vehicle screen for display in real time. The displayed content includes not only the battery cell number and self-discharge rate, but also warning text information, such as "Battery self-discharge abnormally, please check in time", so that the driver can understand the battery health status at the first time and take corresponding countermeasures, such as stopping for inspection, replacing the battery, etc., to avoid possible vehicle failures and safety hazards.

[0066] It is understandable that a dynamic adjustment mechanism for the self-discharge rate threshold can be set. According to factors such as the vehicle usage environment and battery aging, the system can automatically or manually adjust the self-discharge rate threshold to more accurately identify abnormal batteries. The dynamic adjustment mechanism can improve the accuracy and adaptability of the early warning system, so that it can perform its best under different conditions.

[0067] It is worth mentioning that after an abnormally self-discharging battery cell is identified, other warning information can be sent to the vehicle driver, such as sound alarm, light alarm, etc., to remind the driver to pay attention to the abnormal self-discharge of the battery and take timely measures to deal with it, so as to ensure driving safety.

[0068] The present embodiment provides a vehicle early warning method, which first obtains the current battery monitoring data of the vehicle to promptly discover whether the battery is abnormally self-discharging; based on the current battery monitoring data, the abnormal self-discharging battery cell is determined, and the abnormal self-discharging battery cell can be located in a timely and accurate manner; based on the abnormal self-discharging battery cell, an abnormal self-discharging early warning of the vehicle battery is performed, and the abnormal self-discharging phenomenon of the battery can be discovered in a timely manner, thereby improving driving safety.

[0069] In summary, this embodiment uses the current battery monitoring data to timely and accurately locate abnormal self-discharging cells and perform abnormal self-discharge warnings for the vehicle battery. By monitoring the current battery monitoring data, abnormal self-discharge warnings for the vehicle battery can be achieved, overcoming the technical defects of being unable to timely detect abnormal battery self-discharge and the inability to ensure driving safety. Abnormal battery self-discharge can be detected in time, thereby improving driving safety.

[0070] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can refer to the above introduction, and will not be repeated later. Figure 2 , the step S20 further includes steps S201-S202:

[0071] Step S201 : acquiring historical battery monitoring data of a plurality of vehicles, and constructing an abnormal self-discharge monitoring model based on the historical battery monitoring data of the plurality of vehicles.

[0072] It should be noted that the abnormal self-discharge monitoring model is constructed based on the abnormal self-discharge intermediate table, which refers to a table that records the historical monitoring data of the vehicle battery. By analyzing these historical data, the laws and characteristics of abnormal battery self-discharge can be found, and thus the abnormal self-discharge monitoring model can be constructed, so that abnormal self-discharge cells can be identified more accurately, and the accuracy and reliability of early warning can be improved.

[0073] In a feasible implementation, step S201 may include: acquiring historical battery monitoring data of multiple vehicles, and performing data cleaning on the historical battery monitoring data of the multiple vehicles to obtain the historical battery monitoring data of the multiple vehicles after data cleaning; determining a target vehicle based on the historical battery monitoring data of the multiple vehicles after data cleaning; and constructing an abnormal self-discharge monitoring model based on the historical battery monitoring data of the target vehicle.

[0074] It should be noted that data cleaning is an important preprocessing step to remove or correct errors, anomalies or duplicate data in historical battery monitoring data to ensure data quality and accuracy. The cleaned data can better reflect the actual state of the battery, thereby improving the accuracy and reliability of the abnormal self-discharge monitoring model.

[0075] It is understandable that when determining the target vehicle, factors such as the vehicle's usage environment, battery type, and battery aging degree may be considered. This embodiment does not impose specific restrictions on this, and a representative vehicle is selected as the target vehicle to make the constructed abnormal self-discharge monitoring model more universal and applicable.

[0076] It is worth noting that when constructing an abnormal self-discharge monitoring model, machine learning, deep learning and other algorithms can be used to learn and train the historical battery monitoring data of the target vehicle to obtain a model that can identify abnormal self-discharge cells. When constructing the model, other factors such as vehicle driving status and external environment can also be considered to improve the accuracy and reliability of the model.

[0077] In the specific implementation, data segments with SOC ≥ 60% are obtained, and the interval between each frame is less than 120S. A frame of data is taken every 120S, and the first frame and the last frame of each segment are retained. According to the number of frames obtained, the VIN, time, maximum voltage, minimum voltage, SOC, current, voltage list and other values ​​in the data are obtained as the historical battery monitoring data of the target vehicle, and the historical battery monitoring data of the target vehicle is stored in the abnormal self-discharge intermediate table, and an abnormal self-discharge monitoring model is constructed according to the abnormal self-discharge intermediate table.

[0078] In a feasible implementation manner, the target vehicle is determined based on the historical battery monitoring data of multiple vehicles after the data is cleaned, including: determining the number of cells in each vehicle battery based on the historical battery monitoring data of multiple vehicles after the data is cleaned; determining a voltage list corresponding to each vehicle battery according to the number of cells in each vehicle battery; determining the charge state of each vehicle battery based on the voltage list corresponding to each vehicle battery; and determining the target vehicle based on the charge state of each vehicle battery.

[0079] It should be noted that the historical battery monitoring data of multiple vehicles after data cleaning is used to query the static information table to obtain the actual number of battery cells for each vehicle, and the voltage list is sorted by the number of battery cells to obtain the voltage list of the correct number of battery cells, and the maximum voltage and minimum voltage are obtained through the voltage list, where the maximum voltage and minimum voltage values ​​are limited to the range of (0, 5V].

[0080] It can be understood that the SOC of the data segments of each vehicle is calculated based on the highest voltage and the lowest voltage, and the vehicle with SOC ≥ 60% is taken as the target vehicle, where SOC refers to the state of charge of the battery, which indicates the ratio of the current remaining power of the battery to its rated capacity, usually expressed as a percentage. When SOC ≥ 60%, it means that the current remaining power of the battery is at least 60% of its rated capacity.

[0081] Step S202: determining abnormal self-discharging cells based on the current battery monitoring data and the abnormal self-discharging monitoring model.

[0082] It should be noted that by inputting the current battery monitoring data into the abnormal self-discharge monitoring model, the model can be compared and calculated to identify which cells have abnormal self-discharge behavior, thereby determining the abnormal self-discharging cells, improving the accuracy of the early warning and enhancing the intelligence level of the system.

[0083] It is understandable that after the current battery monitoring data is input into the abnormal self-discharge monitoring model, the model will process and analyze the current battery monitoring data based on historical data and learned rules to obtain the prediction results of abnormal self-discharge cells. Based on the prediction results, the system can determine the abnormal self-discharge cells and carry out subsequent warnings and response measures.

[0084] In a feasible implementation, step S202 may include: determining the self-discharge rate of each battery cell according to current battery monitoring data and the abnormal self-discharge monitoring model; and determining the abnormal self-discharging battery cell according to the self-discharge rate of each battery cell.

[0085] It should be noted that the self-discharge rate describes the rate at which the battery's own power is lost when it is stationary, and is usually expressed as a percentage of power reduction per unit time. Based on the current battery monitoring data, the voltage information of each battery cell can be obtained, and then the self-discharge rate of each battery cell can be calculated. By comparing the self-discharge rate of each battery cell with the preset threshold, it can be determined which battery cells have abnormal self-discharge behavior, that is, abnormal self-discharging batteries, and the abnormal self-discharging batteries can be accurately identified, providing a reliable basis for subsequent processing.

[0086] It can be understood that the self-discharge rate of the voltage difference of each battery cell is traversed, and if the self-discharge rate exceeds the self-discharge rate (k value) threshold specified by the factory, the battery cell is marked as an abnormal self-discharge battery cell.

[0087] In a feasible implementation manner, the self-discharge rate of each battery cell is determined based on the current battery monitoring data and the abnormal self-discharge monitoring model, including: determining the voltage difference of each battery cell based on the current battery monitoring data and the abnormal self-discharge monitoring model; performing noise reduction processing on the voltage difference of each battery cell to obtain the fitted pressure difference of each battery cell; and performing calculation based on the fitted pressure difference of each battery cell to obtain the self-discharge rate of each battery cell.

[0088] It should be noted that the current voltage of each cell in the battery pack is obtained according to the current battery monitoring data, and the current voltage of each cell is input into the abnormal self-discharge monitoring model. The voltage difference of each cell is obtained according to the highest voltage in the abnormal self-discharge intermediate table and the current voltage value of each cell, as shown in Formula 1:

[0089] Δcell_volti=max_volt-cell_volti (Formula 1)

[0090] In equation 1, Δcell_volt i Indicates the voltage difference of cell i, max_volt indicates the maximum voltage, cell_volt i Indicates the current voltage value of battery cell i.

[0091] It can be understood that the savgol_filter algorithm is used to reduce the noise of the voltage difference of each battery cell to obtain the voltage difference after fitting of each battery cell. The savgol_filter algorithm refers to the Savitzky-Golay filter, which is a sliding window filter based on the least squares method, which is used to smooth data and remove noise. By applying the Savitzky-Golay filter to the voltage difference of each battery cell, more stable and reliable voltage difference data can be obtained, thereby more accurately calculating the self-discharge rate.

[0092] It is worth noting that the voltage difference of the last frame of data is taken, and the voltage difference value corresponding to SOC±2 is obtained to calculate the self-discharge rate of each battery cell, as shown in the following formula 2:

[0093]

[0094] In equation 1, volt_self_discharge i Indicates the self-discharge rate of cell i, volt_last is the voltage difference at the end, volt_first is the voltage difference at the start, and ΔT is the time difference.

[0095] In this embodiment, by constructing an abnormal self-discharge monitoring model, the abnormal self-discharge battery cell is determined according to the current battery monitoring data through the abnormal self-discharge monitoring model, so that the abnormal self-discharge battery cell can be located timely and accurately, and the abnormal self-discharge phenomenon of the battery can be discovered in time.

[0096] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the vehicle warning method of the present application. More simple transformations based on this technical concept are all within the protection scope of the present application.

[0097] This application also provides a vehicle warning device, please refer to Figure 3 , the vehicle warning device comprises:

[0098] The acquisition module 10 is used to acquire the current battery monitoring data of the vehicle.

[0099] The determination module 20 is used to determine the abnormal self-discharging battery cell based on the current battery monitoring data.

[0100] The early warning module 30 is used to provide an early warning of abnormal self-discharge of the vehicle battery according to the abnormal self-discharge battery cell.

[0101] The present embodiment provides a vehicle warning device, which first obtains the current battery monitoring data of the vehicle to promptly discover whether the battery is abnormally self-discharging; based on the current battery monitoring data, the abnormal self-discharging battery cell is determined, and the abnormal self-discharging battery cell can be located in a timely and accurate manner; based on the abnormal self-discharging battery cell, an abnormal self-discharging warning of the vehicle battery is performed, and the abnormal self-discharging phenomenon of the battery can be discovered in time, thereby improving driving safety.

[0102] In summary, this embodiment uses the current battery monitoring data to timely and accurately locate abnormal self-discharging cells and perform abnormal self-discharge warnings for the vehicle battery. By monitoring the current battery monitoring data, abnormal self-discharge warnings for the vehicle battery can be achieved, overcoming the technical defects of being unable to timely detect abnormal battery self-discharge and the inability to ensure driving safety. Abnormal battery self-discharge can be detected in time, thereby improving driving safety.

[0103] Optionally, the determination module 20 is further used to obtain historical battery monitoring data of multiple vehicles, and construct an abnormal self-discharge monitoring model based on the historical battery monitoring data of the multiple vehicles; and determine the abnormal self-discharge battery cell based on the current battery monitoring data and the abnormal self-discharge monitoring model.

[0104] Optionally, the determination module 20 is also used to obtain historical battery monitoring data of multiple vehicles, and perform data cleaning on the historical battery monitoring data of the multiple vehicles to obtain the historical battery monitoring data of the multiple vehicles after data cleaning; determine the target vehicle based on the historical battery monitoring data of the multiple vehicles after data cleaning; and construct an abnormal self-discharge monitoring model based on the historical battery monitoring data of the target vehicle.

[0105] Optionally, the determination module 20 is also used to determine the number of battery cells in each vehicle battery based on the historical battery monitoring data of multiple vehicles after the data cleaning; determine the voltage list corresponding to each vehicle battery according to the number of battery cells in each vehicle battery; determine the charge state of each vehicle battery based on the voltage list corresponding to each vehicle battery; and determine the target vehicle based on the charge state of each vehicle battery.

[0106] Optionally, the determination module 20 is further configured to determine the self-discharge rate of each battery cell according to current battery monitoring data and the abnormal self-discharge monitoring model; and determine the abnormal self-discharging battery cell according to the self-discharge rate of each battery cell.

[0107] Optionally, the determination module 20 is also used to determine the voltage difference of each battery cell according to the current battery monitoring data and the abnormal self-discharge monitoring model; perform noise reduction processing on the pressure difference of each battery cell to obtain the fitted pressure difference of each battery cell; and perform calculation based on the fitted pressure difference of each battery cell to obtain the self-discharge rate of each battery cell.

[0108] Optionally, the early warning module 30 is also used to obtain the cell number and self-discharge rate of the abnormal self-discharging cell; and send the cell number and self-discharge rate of the abnormal self-discharging cell to the vehicle screen for display, so as to realize abnormal self-discharge early warning of the vehicle battery.

[0109] The vehicle warning device provided by the present application adopts the vehicle warning method in the above embodiment, which can solve the technical problem of vehicle warning. Compared with the prior art, the beneficial effects of the vehicle warning device provided by the present application are the same as the beneficial effects of the vehicle warning method provided by the above embodiment, and the other technical features in the vehicle warning device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0110] The present application provides a vehicle warning device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the vehicle warning method in the above-mentioned embodiment one.

[0111] Reference below Figure 4, which shows a schematic diagram of the structure of a vehicle warning device suitable for implementing an embodiment of the present application. The vehicle warning device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 4 The vehicle warning device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0112] like Figure 4 As shown, the vehicle warning device may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the vehicle warning device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the vehicle warning device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a vehicle warning device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0113] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0114] The vehicle warning device provided by the present application adopts the vehicle warning method in the above embodiment to solve the technical problem of vehicle warning. Compared with the prior art, the beneficial effects of the vehicle warning device provided by the present application are the same as the beneficial effects of the vehicle warning method provided by the above embodiment, and the other technical features in the vehicle warning device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0115] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0116] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0117] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the vehicle warning method in the above-mentioned embodiment.

[0118] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0119] The computer-readable storage medium may be included in the vehicle warning device; or may exist independently without being assembled into the vehicle warning device.

[0120] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the vehicle warning device, the vehicle warning device: obtains the current battery monitoring data of the vehicle; determines the abnormal self-discharging battery cell based on the current battery monitoring data; and issues an abnormal self-discharging warning for the vehicle battery based on the abnormal self-discharging battery cell.

[0121] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0122] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0123] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0124] The readable storage medium provided in this application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned vehicle early warning method, and can solve the technical problem of vehicle early warning. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the vehicle early warning method provided in the above-mentioned embodiment, and will not be repeated here.

[0125] The present application also provides a computer program product, including a computer program, which implements the steps of the vehicle warning method as described above when executed by a processor.

[0126] The computer program product provided by this application can solve the technical problem of vehicle early warning. Compared with the prior art, the beneficial effects of the computer program product provided by this application are the same as the beneficial effects of the vehicle early warning method provided by the above embodiment, which will not be repeated here.

[0127] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A vehicle early warning method, characterized in that: The method comprises: Acquire current battery monitoring data of the vehicle, wherein the current battery monitoring data includes current voltage values ​​of each battery cell in the vehicle battery pack; Acquiring historical battery monitoring data of multiple vehicles, and building an abnormal self-discharge monitoring model based on the historical battery monitoring data of the multiple vehicles, including: acquiring historical battery monitoring data of multiple vehicles, and performing data cleaning on the historical battery monitoring data of the multiple vehicles to obtain the historical battery monitoring data of the multiple vehicles after data cleaning; determining a target vehicle based on the historical battery monitoring data of the multiple vehicles after data cleaning; Determine an abnormal self-discharging cell based on the current battery monitoring data and the abnormal self-discharging monitoring model; Providing an abnormal self-discharge warning for the vehicle battery according to the abnormal self-discharge battery cell; The determining of the target vehicle based on the historical battery monitoring data of the plurality of vehicles after the data cleaning comprises: determining the number of cells in each vehicle battery based on the historical battery monitoring data of the plurality of vehicles after the data cleaning; determining a voltage list corresponding to each vehicle battery according to the number of cells in each vehicle battery; determining the state of charge of each vehicle battery based on the voltage list corresponding to each vehicle battery; and determining the target vehicle based on the state of charge of each vehicle battery; The determining of abnormal self-discharging cells based on the current battery monitoring data and the abnormal self-discharging monitoring model includes: determining the voltage difference of each cell according to the current battery monitoring data and the abnormal self-discharging monitoring model; performing noise reduction processing on the voltage difference of each cell to obtain the fitted voltage difference of each cell; performing calculation based on the fitted voltage difference of each cell to obtain the self-discharge rate of each cell; and determining the abnormal self-discharging cell according to the self-discharge rate of each cell; The current voltage of each cell in the battery pack is obtained according to the current battery monitoring data, and the current voltage of each cell is input into the abnormal self-discharge monitoring model. The voltage difference of each cell is obtained according to the highest voltage in the abnormal self-discharge intermediate table and the current voltage value of each cell, as shown in the following formula: In the formula, Represents the voltage difference of cell i, Indicates the highest voltage, Indicates the current voltage value of battery cell i; Take the voltage difference of the last frame of data, get the voltage difference value corresponding to SOC±2, and calculate the self-discharge rate of each battery cell as follows: In the formula, volt_self_dischargei represents the self-discharge rate of cell i, volt_last is the voltage difference at the end, and volt_first is the voltage difference at the start. T is the time difference.

2. The method according to claim 1, characterized in that The acquiring of historical battery monitoring data of a plurality of vehicles and constructing an abnormal self-discharge monitoring model based on the historical battery monitoring data of the plurality of vehicles includes: Acquire historical battery monitoring data of multiple vehicles, and clean the historical battery monitoring data of the multiple vehicles to obtain the historical battery monitoring data of the multiple vehicles after data cleaning; Determine a target vehicle based on the historical battery monitoring data of the plurality of vehicles after the data cleaning; An abnormal self-discharge monitoring model is constructed based on the historical battery monitoring data of the target vehicle.

3. The method according to claim 1, characterized in that The abnormal self-discharge warning of the vehicle battery according to the abnormal self-discharge battery cell includes: Obtaining the cell number and self-discharge rate of the abnormal self-discharging cell; The cell number and self-discharge rate of the abnormally self-discharging cell are sent to the vehicle screen for display to provide an early warning of abnormal self-discharge of the vehicle battery.

4. A vehicle warning device, characterized in that: The vehicle early warning device comprises: An acquisition module, used to acquire current battery monitoring data of the vehicle, wherein the current battery monitoring data includes a current voltage value of each battery cell in the vehicle battery pack; A determination module is used to obtain historical battery monitoring data of multiple vehicles and build an abnormal self-discharge monitoring model based on the historical battery monitoring data of the multiple vehicles, including: obtaining historical battery monitoring data of multiple vehicles, and performing data cleaning on the historical battery monitoring data of the multiple vehicles to obtain the historical battery monitoring data of the multiple vehicles after data cleaning; determining a target vehicle based on the historical battery monitoring data of the multiple vehicles after data cleaning; determining an abnormal self-discharging battery cell based on the current battery monitoring data and the abnormal self-discharge monitoring model; An early warning module, used for providing an early warning of abnormal self-discharge of a vehicle battery according to the abnormal self-discharge battery cell; The determination module is further used to obtain historical battery monitoring data of multiple vehicles, and perform data cleaning on the historical battery monitoring data of the multiple vehicles to obtain the historical battery monitoring data of the multiple vehicles after data cleaning; determine the target vehicle based on the historical battery monitoring data of the multiple vehicles after data cleaning; determine the number of cells in each vehicle battery based on the historical battery monitoring data of the multiple vehicles after data cleaning; determine the voltage list corresponding to each vehicle battery according to the number of cells in each vehicle battery; determine the charge state of each vehicle battery based on the voltage list corresponding to each vehicle battery; determine the target vehicle based on the charge state of each vehicle battery; The determination module is further used to determine the voltage difference of each battery cell according to the current battery monitoring data and the abnormal self-discharge monitoring model; perform noise reduction processing on the voltage difference of each battery cell to obtain the fitted voltage difference of each battery cell; perform calculation based on the fitted voltage difference of each battery cell to obtain the self-discharge rate of each battery cell; determine the abnormal self-discharging battery cell according to the self-discharge rate of each battery cell; The current voltage of each cell in the battery pack is obtained according to the current battery monitoring data, and the current voltage of each cell is input into the abnormal self-discharge monitoring model. The voltage difference of each cell is obtained according to the highest voltage in the abnormal self-discharge intermediate table and the current voltage value of each cell, as shown in the following formula: In the formula, Represents the voltage difference of cell i, Indicates the highest voltage, Indicates the current voltage value of battery cell i; Take the voltage difference of the last frame of data, get the voltage difference value corresponding to SOC±2, and calculate the self-discharge rate of each battery cell as follows: In the formula, volt_self_dischargei represents the self-discharge rate of cell i, volt_last is the voltage difference at the end, and volt_first is the voltage difference at the start. T is the time difference.

5. A vehicle warning device, characterized in that: The vehicle warning device comprises: a memory, a processor, and a vehicle warning program stored in the memory and executable on the processor, wherein the vehicle warning program is configured to implement the vehicle warning method according to any one of claims 1 to 3.

6. A storage medium, characterized in that: The storage medium stores a vehicle warning program, and when the vehicle warning program is executed by the processor, the vehicle warning method according to any one of claims 1 to 3 is implemented.

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