Lithium battery health state assessment method and system based on data analysis

Abstract

The invention relates to the technical field of lithium batteries, in particular to a lithium battery health state evaluation method and system based on data analysis. And performing lithium battery danger analysis based on the lithium battery safety analysis result, the driving habit data of the driver of the corresponding vehicle and the driving data of the driving road section, and performing early warning of the health state according to the lithium battery danger analysis result. The method not only considers the electric signal operation characteristics of the lithium battery and the temperature change condition, but also considers the influence of the driving habit data of a driver and the driving data of a driving road section on the health state of the lithium battery, so as to comprehensively analyze and judge the health state of the lithium battery in the vehicle driving process. And the accuracy of analysis and judgment is improved.

Description

Technical Field

[0001] This application relates to the technical field of lithium batteries, and particularly to a method and system for evaluating the health state of lithium batteries based on data analysis. Background Art

[0002] Due to characteristics such as high energy density, environmental friendliness, and long service life, lithium batteries are currently widely used in energy storage devices such as energy storage systems, electric vehicles, and power tools. In the field of energy storage systems, lithium batteries generally consist of multiple batteries with a complex series-parallel structure to form a high-voltage lithium battery system to meet the high-voltage and high-capacity usage requirements of energy storage systems. The performance of lithium battery energy storage systems will decline due to increased usage times, changing usage environments, and changing charge-discharge power. Generally, the health of the battery is used as a measurement basis. When a new energy vehicle is driving, the prior art usually only comprehensively analyzes the performance of the battery through changes in the electrical signals of the battery. However, due to different impacts on the battery during different driving processes, the prior art does not consider the driving habit data of the driver and the driving data of the driving section on the health state of the lithium battery, and also does not consider the impact of different positions of the battery on the health state of the lithium battery, resulting in low accuracy of analysis and judgment. How to dynamically evaluate the operation of lithium batteries during vehicle driving is an urgent problem to be solved;

[0003] For example, in a Chinese patent with the application publication number CN114397577A, a method for evaluating the health state of lithium batteries in new energy vehicles based on an ASTUKF-GRA-LSTM model is disclosed. First, a smooth IC curve is obtained, and then a partial region of the IC curve is selected, and the battery degradation characteristics are extracted using the grey relational analysis method. Secondly, based on the input feature data, the long short-term memory network LSTM is used to online estimate the SOH of the lithium battery. Finally, through actual engineering experimental data, it is verified that this method has strong SOH evaluation accuracy. The method of this application has a high correct rate for evaluating the power SOH of new energy vehicles, with a mean absolute percentage error of 0.96% and a root mean square error of 0.57%. The average evaluation time is 2.1s, which is much less than the 8.9s evaluation time of the traditional LSTM model and the 4.6s evaluation time of the GAN-CNN-LSTM model. It can effectively meet the safety analysis requirements for accurate and rapid dynamic evaluation of the health state of lithium batteries in new energy vehicles by DC charging piles. This method has a high accuracy rate, which to a certain extent solves the problem of low accuracy rate in estimating the SOH of lithium batteries and has certain engineering application value. However, it does not consider the driving habit data of the driver and the driving data of the driving section on the health state of the lithium battery, and also does not consider the impact of different positions of the battery on the health state of the lithium battery, resulting in low accuracy of analysis and judgment. Obviously, it cannot solve the technical problems proposed in this application.

[0004] To solve these problems, the present application designs a method and system for evaluating the health state of lithium batteries based on data analysis. Summary of the Invention

[0005] To overcome the defects and deficiencies of the prior art, the present application provides a method and system for evaluating the health state of lithium batteries based on data analysis, obtains the change curve of the operating data of the lithium battery, and at the same time obtains the driving habit data of the driver of the corresponding vehicle and the driving data of the driving section. Based on the change curve of the operating data of the lithium battery during operation, the safety analysis of the lithium battery operation is carried out. Based on the lithium battery safety analysis result, the driving habit data of the driver of the corresponding vehicle and the driving data of the driving section, the lithium battery hazard analysis is carried out, and the early warning of the health state is carried out according to the lithium battery hazard analysis result. When evaluating the health state of the lithium battery, the present application not only considers the operating characteristics of the electrical signal of the lithium battery and the change of temperature, but also takes into account the influence of the driving habit data of the driver and the driving data of the driving section on the health state of the lithium battery, and then comprehensively analyzes and judges the health state of the lithium battery during the vehicle driving process, improving the accuracy of the analysis and judgment.

[0006] To achieve the above object, the present application adopts the following technical solutions:

[0007] In the first aspect, the present application provides a method for evaluating the health state of lithium batteries based on data analysis, including the following steps:

[0008] S1. Obtain the change curve of the operating data of the lithium battery, and at the same time obtain the driving habit data of the driver of the corresponding vehicle and the driving data of the driving section;

[0009] S2. Carry out the safety analysis of the lithium battery operation based on the change curve of the operating data of the lithium battery during operation;

[0010] S3. Carry out the lithium battery hazard analysis based on the lithium battery safety analysis result, the driving habit data of the driver of the corresponding vehicle and the driving data of the driving section;

[0011] S4. Carry out the early warning of the health state according to the lithium battery hazard analysis result.

[0012] In an implementation manner of the present application, the change curve of the operating data of the lithium battery includes the change curves of the current, voltage, temperature, etc. of the lithium battery during driving on the corresponding driving road, which reflect the operating quality of the lithium battery; the driving habit data includes the braking force data of the driver when driving on the corresponding specification road and the driving speed data of the driver, and the driving data of the driving section includes the road surface flatness data of the section. It should be specifically noted that the road surface flatness data of the section is obtained through the road surface acquisition terminal.

[0013] In an implementation manner of the present application, in step S2, lithium battery operation safety analysis is performed based on the operation data change curve during the operation of the lithium battery, including the following specific steps:

[0014] S21. Obtain the operation data of the lithium battery during operation, and perform operation anomalies in the previous operation cycle based on the fluctuation situation and dangerous situation of the operation data of the lithium battery in the previous operation cycle. Among them, the specific steps for dangerous situation analysis are: compare the electrical signal output by the lithium battery operation with the electrical signal that needs to be output, and obtain the ratio of the absolute value of the difference value of the electrical signal at the corresponding moment to the electrical signal that needs to be output at the corresponding moment to obtain the electrical signal anomaly at the corresponding moment. Integrate the electrical signal anomalies at each moment during the operation process over the time length and then divide by the time length to obtain the dangerous situation analysis result during the operation process; at the same time, the specific steps for fluctuation situation analysis are: obtain the absolute value of the difference between the electrical signal anomalies at the separated time intervals divided by the average value of the electrical signal anomalies to obtain the fluctuation situation at the corresponding time, integrate over the time length and then divide by the time length to obtain the fluctuation situation analysis result during the operation process. Among them, the operation anomaly in the previous operation cycle is the weighted sum result of the dangerous situation analysis result and the fluctuation situation analysis result. In this step, the electrical signal operation anomaly in the operation cycle is evaluated through the electrical signal output anomaly and the fluctuation of the electrical signal output during the operation cycle of the lithium battery;

[0015] S22. Obtain the temperature change situation of each position of the lithium battery during operation, obtain the difference between the temperature at the beginning and end of each position during the operation cycle and the median of the temperature safety range, and perform battery temperature change anomaly analysis based on the importance of each position and the difference between the temperature at the beginning and end of each position during the operation cycle and the median of the temperature safety range;

[0016] S23. Obtain the operation anomaly result and the battery temperature change anomaly analysis result corresponding to the operation cycle, perform standardization and then weighted summation, and take the reciprocal to obtain the lithium battery safety analysis result;

[0017] In an implementation manner of the present application, in step S3, lithium battery danger analysis includes the following specific steps:

[0018] S31. Obtain the driving habit data of the driver of the corresponding vehicle and the driving data of the driving section for driving anomaly analysis;

[0019] Among them, the driving anomaly analysis includes the following specific contents:

[0020] S311. Obtain the driving habit data of the driver of the corresponding vehicle, and perform driving habit danger analysis based on the driving habit data of the driver of the corresponding vehicle. Among them, the driving habit danger analysis calculation formula is: , where S is the average braking force of the driver, Sz is the safe braking force of the driver, and vc is the weighted sum value of the speed change fluctuation within a cycle and the integral of the difference between the vehicle speed and the safe vehicle speed. Among them, the speed change fluctuation is the integral of the vehicle speed change over the time interval during driving, and the integral of the difference between the vehicle speed and the safe vehicle speed is also the integral of the difference between the vehicle speed and the safe vehicle speed over the driving time. In this step, the battery anomalies caused by the driver's habits are analyzed by integrating the braking force data of the driver and the driving speed data of the driver when driving on the corresponding standard roads;

[0021] S312. Obtain the road surface flatness data, and perform road anomaly analysis based on the road surface flatness data. The road anomaly analysis is preferably the ratio of the volume of the unevenness on the road to the volume of the road. Among them, the unevenness on the road includes bumps or pits on the road;

[0022] S313. Perform weighted summation on the obtained driving habit risk analysis result and the road anomaly analysis result to obtain the driving anomaly analysis result;

[0023] S32. Obtain the driving anomaly analysis result and the lithium battery safety analysis result for the corresponding operation cycle, and divide the reciprocal of the lithium battery safety analysis result by the driving anomaly analysis result to obtain the lithium battery risk analysis result. In this way, the error in the lithium battery risk analysis caused by the combined influence of the driving habit data of the driver of the corresponding vehicle and the driving data of the driving section is removed during the lithium battery risk analysis process, thereby improving the accuracy of the lithium battery risk analysis.

[0024] In one implementation manner of the present application, in step S4, early warning of the health status is performed according to the lithium battery risk analysis result, including the following specific contents:

[0025] Obtain the obtained lithium battery risk analysis result, compare the obtained lithium battery risk analysis result with the set lithium battery risk analysis threshold. If the lithium battery risk analysis result is greater than or equal to the set lithium battery risk analysis threshold, it indicates that the operation is unhealthy and early warning of the health status is performed. If the lithium battery risk analysis result is less than the set lithium battery risk analysis threshold, it indicates that the operation is healthy and no early warning of the health status is performed.

[0026] In a second aspect, the present application also provides a lithium battery health status evaluation system based on data analysis, including:

[0027] A data acquisition module for acquiring the operation data change curve of the lithium battery, and simultaneously acquiring the driving habit data of the driver of the corresponding vehicle and the driving data of the driving section;

[0028] The operation safety analysis module performs the operation safety analysis of the lithium battery based on the operation data change curve during the operation of the lithium battery;

[0029] The lithium battery hazard analysis module performs the lithium battery hazard analysis based on the lithium battery safety analysis result, the driving habit data of the driver of the corresponding vehicle, and the driving data of the driving section;

[0030] The health status warning module gives a warning of the health status according to the lithium battery hazard analysis result.

[0031] Thirdly, an electronic device provided by the present application includes: a processor and a memory. Among them, a computer program that can be called by the processor is stored in the memory, and the processor executes the lithium battery health status evaluation method based on data analysis by calling the computer program stored in the memory.

[0032] Fourthly, a computer-readable storage medium provided by the present application stores instructions. When the instructions run on a computer, the computer executes the lithium battery health status evaluation method based on data analysis.

[0033] Compared with the prior art, the present application has the following advantages and beneficial effects:

[0034] The present application obtains the operation data change curve of the lithium battery, and at the same time obtains the driving habit data of the driver of the corresponding vehicle and the driving data of the driving section. It performs the operation safety analysis of the lithium battery based on the operation data change curve during the operation of the lithium battery, performs the lithium battery hazard analysis based on the lithium battery safety analysis result, the driving habit data of the driver of the corresponding vehicle, and the driving data of the driving section, and gives a warning of the health status according to the lithium battery hazard analysis result. When evaluating the health status of the lithium battery, the present application not only considers the operation characteristics of the electrical signal of the lithium battery and the change of temperature, but also takes into account the influence of the driving habit data of the driver and the driving data of the driving section on the health status of the lithium battery, and then comprehensively analyzes and judges the health status of the lithium battery during the vehicle driving process, improving the accuracy of the analysis and judgment;

[0035] When performing the operation safety analysis of the lithium battery, the present application comprehensively evaluates the abnormal operation of the electrical signal during the operation cycle by combining the abnormal output of the electrical signal and the fluctuation of the electrical signal output. At the same time, the present application analyzes the abnormal change of the battery temperature by the importance of each position of the corresponding lithium battery and the abnormal change of the temperature at the beginning and end of the operation process cycle, further improving the accuracy of the analysis and judgment. Description of the Drawings

[0036] By reading the detailed description of the non-restrictive embodiments with reference to the following drawings, other features, purposes, and advantages of the present application will become more obvious:

[0037] Figure 1 It is a schematic diagram of the overall process of the method of this application;

[0038] Figure 2 It is a working flowchart of S2 in the method of this application;

[0039] Figure 3 It is a working flowchart of S31 in the method of this application;

[0040] Figure 4 It is a schematic diagram of the structure of the system of this application;

[0041] Figure 5 It is a flowchart of the analysis of abnormal driving in the method of this application. Specific embodiments

[0042] The technical solution of this application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments of this application and the specific features therein are detailed descriptions of the technical solution of this application, rather than limitations on the technical solution of this application. Without conflict, the technical features in the embodiments of this application and the embodiments can be combined with each other.

[0043] Embodiment 1

[0044] As Figures 1 to 3 shown, this embodiment provides a method for evaluating the health state of a lithium battery based on data analysis, which specifically includes the following steps:

[0045] S1. Obtain the change curve of the operation data of the lithium battery, and at the same time obtain the driving habit data of the driver of the corresponding vehicle and the driving data of the driving section;

[0046] In this embodiment, the change curve of the operation data of the lithium battery includes the change curves of the current, voltage, and temperature of the lithium battery during the driving process on the corresponding driving road, which reflect the operation quality of the lithium battery. Exemplarily, for example, the current is obtained through a current sensor, and the voltage is obtained through a voltage sensor; the driving habit data includes the braking force data of the driver and the driving speed data of the driver when driving on the corresponding standard road, and the driving data of the driving section includes the road surface flatness data of the section. It should be specifically noted that the road surface flatness data of the section is obtained through a road surface acquisition terminal, and at the same time, the braking force data of the driver and the driving speed data of the driver when driving on the corresponding standard road are obtained through corresponding braking force sensors. If the obtained content involves the privacy of the user, it has been clearly informed before obtaining, and at the same time, the consent of the user has also been obtained;

[0047] S2. Perform lithium battery operation safety analysis based on the change curve of the operation data of the lithium battery during operation;

[0048] In this embodiment, in step S2, the operation safety analysis of the lithium battery is performed based on the operation data change curve during the operation of the lithium battery, including the following specific steps:

[0049] S21. Obtain the operation data of the lithium battery during operation, and perform operation anomalies in the previous operation cycle based on the fluctuation and dangerous conditions of the operation data of the lithium battery in the previous operation cycle. Among them, the specific steps for analyzing dangerous conditions are as follows: Compare the electrical signal output by the lithium battery operation with the electrical signal to be output, and obtain the ratio of the absolute value of the difference in electrical signals at the corresponding moment to the electrical signal to be output at the corresponding moment to obtain the electrical signal anomaly at the corresponding moment. Integrate the electrical signal anomalies at each moment during the operation process over the time length and then divide by the time length to obtain the analysis result of dangerous conditions during the operation process; At the same time, the specific steps for analyzing the fluctuation conditions are as follows: Obtain the absolute value of the difference in electrical signal anomalies at different times divided by the average value of the electrical signal anomalies to obtain the fluctuation conditions at the corresponding time, integrate over the time length and then divide by the time length to obtain the analysis result of the fluctuation conditions during the operation process. Among them, the operation anomaly in the previous operation cycle is the weighted sum result of the analysis result of dangerous conditions and the analysis result of fluctuation conditions. In this step, the electrical signal operation anomaly in the operation cycle is evaluated through the electrical signal output anomaly and the fluctuation of the electrical signal output during the operation cycle of the lithium battery. Here, it should be noted that the operation cycle is evenly divided according to the road section and can be 1 km or 10 km;

[0050] S22. Obtain the temperature change conditions of each position of the lithium battery during operation, obtain the difference between the temperature at the beginning and end of each position during the operation cycle and the median value of the temperature safety range, and perform battery temperature change anomaly analysis based on the importance of each position and the difference between the temperature at the beginning and end of each position during the operation cycle and the median value of the temperature safety range. Among them, the battery temperature change anomaly analysis formula is: , where n is the number of monitoring positions, ci is the importance of the i-th position, exp() is the power of the natural constant, Tic is the temperature at the end of the operation cycle of the i-th position, Tia is the temperature at the start of the operation cycle of the i-th position, and Tim is the median value of the temperature safety range. Among them, the importance is obtained by dividing the electrical energy output of the corresponding position during the operation cycle by the electrical energy storage capacity of the corresponding position. Because the larger the electrical energy output, the greater the operation output of the corresponding battery module in the entire lithium battery. For this step, the battery temperature change anomaly analysis is performed through the importance of each position of the corresponding lithium battery and the temperature change anomaly at the beginning and end of the operation cycle;

[0051] S23. Obtain the operation anomaly result and the battery temperature change anomaly analysis result of the corresponding operation cycle, perform weighted summation after standardization, and take the reciprocal to obtain the lithium battery safety analysis result;

[0052] Exemplarily, the standardization method is as follows: dividing the corresponding abnormal analysis result by the corresponding abnormal analysis standard value to obtain the standardized result, so as to avoid one weight being too large and the other being too small when performing weighted summation due to a large difference between the two parameters;

[0053] S3. Perform lithium battery hazard analysis based on the lithium battery safety analysis result, the driving habit data of the driver of the corresponding vehicle, and the driving data of the driving section;

[0054] In this embodiment, as Figure 5 shown, the lithium battery hazard analysis in step S3 includes the following specific steps:

[0055] S31. Obtain the driving habit data of the driver of the corresponding vehicle and the driving data of the driving section to perform driving anomaly analysis;

[0056] Among them, the driving anomaly analysis includes the following specific contents:

[0057] S311. Obtain the driving habit data of the driver of the corresponding vehicle, and perform driving habit hazard analysis based on the driving habit data of the driver of the corresponding vehicle. Among them, the driving habit hazard analysis calculation formula is: , where S is the average braking force of the driver, Sz is the safe braking force of the driver, vc is the weighted summation value of the integral of the speed change fluctuation within the period and the integral of the difference between the vehicle speed and the safe vehicle speed. Among them, the speed change fluctuation is the integral of the vehicle speed change at the time intervals during the driving process over the time length, and the integral of the difference between the vehicle speed and the safe vehicle speed is also the integral of the difference between the vehicle speed and the safe vehicle speed over the time length during the driving time. In this step, analyze the battery anomalies caused by the driver's habits by integrating the braking force data of the driver when driving on the corresponding specified road and the driving speed data of the driver;

[0058] S312. Obtain the road surface flatness data, and perform road anomaly analysis based on the road surface flatness data. The road anomaly analysis is preferably the ratio of the volume of the unevenness on the road to the volume of the road. Among them, the unevenness on the road includes the protrusions or pits on the road;

[0059] S313. Perform weighted summation on the obtained driving habit hazard analysis result and the road anomaly analysis result to obtain the driving anomaly analysis result;

[0060] S32. Obtain the driving anomaly analysis result and the lithium battery safety analysis result corresponding to the operating cycle. Divide the reciprocal of the lithium battery safety analysis result by the driving anomaly analysis result to obtain the lithium battery hazard analysis result, so as to remove the error in the lithium battery hazard analysis caused by the combined influence of the driving habits data of the driver of the corresponding vehicle and the driving data of the driving section during the lithium battery hazard analysis process, thereby improving the accuracy of the lithium battery hazard analysis;

[0061] S4. Perform early warning of the health status according to the lithium battery hazard analysis result;

[0062] In this embodiment, the early warning of the health status according to the lithium battery hazard analysis result in step S4 includes the following specific contents:

[0063] Obtain the obtained lithium battery hazard analysis result, compare the obtained lithium battery hazard analysis result with the set lithium battery hazard analysis threshold. If the lithium battery hazard analysis result is greater than or equal to the set lithium battery hazard analysis threshold, it indicates that the operation is unhealthy and an early warning of the health status is performed. If the lithium battery hazard analysis result is less than the set lithium battery hazard analysis threshold, it indicates that the operation is healthy and no early warning of the health status is performed. Send the warning signal to the client and the vehicle management terminal by means of text message or reminder to remind the client and the vehicle management terminal to make a quick response.

[0064] It should be noted that in this embodiment, the acquisition method of the set parameters (such as the weighted weights of each parameter and the set lithium battery hazard analysis threshold, etc.) in this embodiment is obtained through experiments by those skilled in the art. The specific experimental method is as follows: Obtain the change curve of the historical operating data of the lithium battery, and at the same time obtain the driving habits data of the driver of the corresponding vehicle and the driving data of the driving section, substitute them into each step of this embodiment for the analysis and calculation of the lithium battery hazard analysis result, and at the same time obtain the experimental result of whether the lithium battery fails when the vehicle travels a specified distance on a specified road by a specified person. Import the obtained result of the analysis and calculation value of the lithium battery hazard analysis result and the experimental result of whether the lithium battery fails into the fitting software for iterative fitting of the data, and output the set parameter value that meets the maximum judgment accuracy.

[0065] It should be noted that in this embodiment, the following advantages exist. The change curve of the operating data of the lithium battery is obtained, and at the same time, the driving habit data of the driver of the corresponding vehicle and the driving data of the driving section are obtained. Based on the change curve of the operating data of the lithium battery during operation, the operating safety analysis of the lithium battery is carried out. Based on the lithium battery safety analysis result, the driving habit data of the driver of the corresponding vehicle and the driving data of the driving section, the lithium battery hazard analysis is carried out. According to the lithium battery hazard analysis result, the early warning of the health state is carried out. When evaluating the health state of the lithium battery in this application, not only the electrical signal operation characteristics of the lithium battery and the change of temperature are considered, but also the influence of the driving habit data of the driver and the driving data of the driving section on the health state of the lithium battery is considered. Furthermore, the health state of the lithium battery during vehicle driving is comprehensively analyzed and judged, improving the accuracy of the analysis and judgment. When carrying out the operating safety analysis of the lithium battery in this application, the abnormal evaluation of the electrical signal operation in the operation cycle is carried out by comprehensively considering the abnormal output of the electrical signal and the fluctuation of the electrical signal output. At the same time, in this application, the abnormal analysis of the battery temperature change is carried out by considering the importance of each position of the corresponding lithium battery and the abnormal temperature change at the beginning and end of the operation process cycle, further improving the accuracy of the analysis and judgment.

[0066] Embodiment 2

[0067] As Figure 4 shown, this embodiment provides a lithium battery health state evaluation system based on data analysis, including:

[0068] A data acquisition module for acquiring the change curve of the operating data of the lithium battery and simultaneously acquiring the driving habit data of the driver of the corresponding vehicle and the driving data of the driving section;

[0069] An operating safety analysis module for performing an operating safety analysis of the lithium battery based on the change curve of the operating data of the lithium battery during operation;

[0070] A lithium battery hazard analysis module for performing a lithium battery hazard analysis based on the lithium battery safety analysis result, the driving habit data of the driver of the corresponding vehicle, and the driving data of the driving section;

[0071] A health state early warning module for performing an early warning of the health state according to the lithium battery hazard analysis result.

[0072] For the parameters and the steps for each unit module in the above-mentioned lithium battery health state evaluation system based on data analysis of this application to achieve the corresponding functions and the corresponding effects, reference can be made to the parameters and steps in the embodiment of the lithium battery health state evaluation method based on data analysis in the method embodiment, which will not be elaborated here.

[0073] Embodiment 3

[0074] An electronic device according to an embodiment of the present application includes: a processor and a memory. Among them, a computer program that can be called by the processor is stored in the memory, and the processor executes a method for evaluating the health state of a lithium battery based on data analysis by calling the computer program stored in the memory. It should be noted that: all computer programs of the method for evaluating the health state of a lithium battery based on data analysis are implemented using the C language.

[0075] Embodiment 4

[0076] This embodiment provides a computer-readable storage medium, on which a rewritable computer program is stored;

[0077] When the computer program runs on a computer device, the computer device is caused to execute the above-mentioned method for evaluating the health state of a lithium battery based on data analysis.

[0078] Each embodiment in the present application is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and the key points of each embodiment are the differences from other embodiments. In particular, for the embodiments of the Internet of Things devices and media, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiments.

[0079] The systems and media provided by the embodiments of the present application correspond one-to-one with the methods. Therefore, the systems and media also have beneficial technical effects similar to the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the systems and media will not be elaborated here.

[0080] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) that contain computer-usable program code.

[0081] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to generate a machine, so that the instructions executed by the processors of the computer or other programmable data processing devices generate for implementation in the process Figure 1one or more processes and / or blocks Figure 1 a device for the functions specified in one or more blocks

[0082] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction device that implements the functions specified in one Figure 1 one or more processes and / or blocks Figure 1 one or more blocks

[0083] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0084] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM) and / or non-volatile memory such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0085] Computer-readable media includes permanent and non-permanent, removable and non-removable media and can store information by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0086] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0087] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A method for evaluating the state of health of a lithium battery based on data analysis, characterized in that, Including the following steps: S1. Obtain the change curve of the operating data of the lithium battery, and at the same time obtain the driving habit data of the driver of the corresponding vehicle and the driving data of the driving section; S2. Conduct a safety analysis of the lithium battery operation based on the change curve of the operating data of the lithium battery during operation; S3. Conduct a lithium battery hazard analysis based on the lithium battery safety analysis result, the driving habit data of the driver of the corresponding vehicle, and the driving data of the driving section; S4. Issue a warning for the health status according to the lithium battery hazard analysis result.

2. The method for evaluating the health state of a lithium battery based on data analysis according to claim 1, wherein The safety analysis of the lithium battery operation based on the change curve of the operating data of the lithium battery during operation includes the following specific steps: S21. Obtain the operating data of the lithium battery during operation, and conduct an operating anomaly of the last operating cycle based on the fluctuation and hazard conditions of the operating data of the lithium battery in the last operating cycle; S22. Obtain the temperature change of each position of the lithium battery during operation, obtain the difference between the temperature at the beginning and end of the operation process cycle of each position and the median of the temperature safety range, and perform abnormal analysis of battery temperature change based on the importance of each position and the difference between the temperature at the beginning and end of the operation process cycle of each position and the median of the temperature safety range. Among them, the formula for abnormal analysis of battery temperature change is: , where n is the number of monitoring positions, ci is the importance of the i-th position, exp() is the power of the natural constant, Tic is the temperature at the end of the operation process cycle of the i-th position, Tia is the temperature at the start of the operation process cycle of the i-th position, Tim is the median of the temperature safety range. Among them, the importance is obtained by dividing the electric energy output of the corresponding position during the operation process cycle by the electric energy storage capacity of the corresponding position; S23. Obtain the operating anomaly result of the corresponding operating cycle and the abnormal analysis result of the battery temperature change, perform weighted summation after standardization, and take the reciprocal to obtain the lithium battery safety analysis result.

3. The method for evaluating the state of health of a lithium battery based on data analysis according to claim 2, wherein, The lithium battery hazard analysis includes the following specific steps: S31. Obtain the driving habit data of the driver of the corresponding vehicle and the driving data of the driving section to conduct a driving anomaly analysis; S32. Obtain the driving anomaly analysis result of the corresponding operating cycle and the lithium battery safety analysis result, and obtain the lithium battery hazard analysis result by dividing the reciprocal of the lithium battery safety analysis result by the driving anomaly analysis result.

4. The method for evaluating the health state of a lithium battery based on data analysis according to claim 3, wherein The warning for the health status according to the lithium battery hazard analysis result includes the following specific content: Obtain the lithium battery hazard analysis result, compare the obtained lithium battery hazard analysis result with the set lithium battery hazard analysis threshold. If the lithium battery hazard analysis result is greater than or equal to the set lithium battery hazard analysis threshold, it indicates that the operation is unhealthy and a warning for the health status is issued. If the lithium battery hazard analysis result is less than the set lithium battery hazard analysis threshold, it indicates that the operation is healthy and no warning for the health status is issued.

5. The method for evaluating the health state of a lithium battery based on data analysis according to claim 4, characterized in that, The driving anomaly analysis includes the following specific content: S311. Obtain the driving habit data of the driver of the corresponding vehicle, and conduct a driving habit hazard analysis based on the driving habit data of the driver of the corresponding vehicle; S312. Obtain the road surface flatness data, and conduct a road anomaly analysis based on the road surface flatness data. The road anomaly analysis is the ratio of the volume of the uneven objects on the road to the volume of the road; S313. Obtain the driving habit hazard analysis result and the road anomaly analysis result, and perform weighted summation to obtain the driving anomaly analysis result.

6. The method for evaluating the health state of a lithium battery based on data analysis according to claim 5, wherein, The change curve of the operating data of the lithium battery includes the change curve of the data reflecting the operating quality of the lithium battery during driving on the corresponding driving road; the driving habit data includes the braking force data of the driver when driving on the corresponding standard road and the driving speed data of the driver, and the driving data of the driving section includes the road surface flatness data of the section.

7. The method for evaluating the health state of a lithium battery based on data analysis according to claim 6, wherein, The specific steps of the above-mentioned dangerous situation analysis are as follows: Compare the electrical signal output during the operation of the lithium battery with the electrical signal that needs to be output, obtain the ratio of the absolute value of the difference in electrical signals at the corresponding moment to the electrical signal that needs to be output at the corresponding moment to obtain the abnormality of the electrical signal at the corresponding moment, integrate the electrical signal abnormalities at each moment during the operation over the time length and then divide by the time length to obtain the dangerous situation analysis result during the operation.

8. The method for evaluating the health state of a lithium battery based on data analysis according to claim 7, characterized in that, The specific steps of the above-mentioned fluctuation situation analysis are as follows: Obtain the absolute value of the difference in electrical signal abnormalities at an interval of time, divide it by the average value of the electrical signal abnormalities to obtain the fluctuation situation at the corresponding time, integrate it over the time length and then divide by the time length to obtain the fluctuation situation analysis result during the operation. Among them, the operation abnormality in the previous operation cycle is the weighted sum result of the dangerous situation analysis result and the fluctuation situation analysis result.

9. A lithium battery state of health assessment system based on data analysis, which is implemented based on the lithium battery state of health assessment method based on data analysis described in any one of claims 1-8, and is characterized in that, The system includes: A data acquisition module, configured to acquire the operation data change curve of the lithium battery, and at the same time acquire the driving habit data of the driver of the corresponding vehicle and the driving data of the driving section. An operation safety analysis module, which performs lithium battery operation safety analysis based on the operation data change curve of the lithium battery during operation. A lithium battery danger analysis module, which performs lithium battery danger analysis based on the lithium battery safety analysis result, the driving habit data of the driver of the corresponding vehicle, and the driving data of the driving section. A health status warning module, which gives a warning of the health status according to the lithium battery danger analysis result.

10. An electronic device, comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes the lithium battery health status evaluation method based on data analysis according to any one of claims 1-8 by calling the computer program stored in the memory.

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