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

Abstract

The present application relates to the field of lithium battery technology, and in particular to a lithium battery health status assessment method and system based on data analysis. The present application performs a lithium battery operation safety analysis based on an operating data change curve of the lithium battery during operation, performs a lithium battery hazard analysis based on the lithium battery safety analysis results, the driving habit data of the driver of the corresponding vehicle, and the driving data of the driving section. A health status warning is issued based on the lithium battery hazard analysis results. When performing a lithium battery health status assessment, the present application not only considers the electrical signal operation characteristics of the lithium battery and the temperature changes, but also takes into account the impact of the driver's driving habit data and the driving data of the driving section on the lithium battery health status, and then comprehensively analyzes and judges the lithium battery health status during vehicle driving, thereby improving the accuracy of the analysis and judgment.

Description

Technical Field

[0001] The present application relates to the field of lithium battery technology, and in particular to a method and system for evaluating the health status of lithium batteries based on data analysis. Background Art

[0002] Due to their high energy density, environmental friendliness, and long service life, lithium batteries are currently widely used in energy storage systems, electric vehicles, power tools, and other energy storage devices. In the field of energy storage systems, lithium batteries are generally composed of multiple cells in a complex series-parallel structure, forming a high-voltage lithium battery system to meet the high voltage and high capacity requirements of the energy storage system. Lithium battery energy storage systems can experience performance degradation due to increased usage, changes in the operating environment, and changes in charge and discharge power. Battery health is generally used as a measure of performance. When new energy vehicles are driving, existing technologies generally only conduct a comprehensive analysis of battery performance based on changes in the battery's electrical signals. However, due to the different impacts on the battery during different driving processes, existing technologies do not consider the impact of the driver's driving habits and driving data on the driving section on the health of the lithium battery. At the same time, they do not consider the impact of different battery positions on the health of the lithium battery, resulting in low accuracy of analysis and judgment. How to dynamically evaluate the operation of lithium batteries during the vehicle's driving process is an urgent problem to be solved.

[0003] For example, a Chinese patent application with publication number CN114397577A discloses a health status assessment method for lithium batteries in new energy vehicles based on the ASTUKF-GRA-LSTM model. First, a smooth IC curve is obtained, and then a partial area of the IC curve is selected to extract battery degradation characteristics using grey correlation analysis. Secondly, based on the input feature data, the long short-term memory network LSTM is used to perform online estimation of the SOH of the lithium battery. Finally, based on actual engineering experimental data, the method is verified to have strong SOH assessment accuracy. The method of this application has a high accuracy rate for the SOH assessment of new energy vehicle power, with an average absolute percentage error of 0.96%, a root mean square error of 0.57%, and an average assessment time of 2.1s, which is much shorter than the traditional LSTM model evaluation time of 8.9s and the GAN-CNN-LSTM model evaluation time of 4.6s. This method can effectively meet the safety analysis requirements of DC charging piles for accurate and rapid dynamic evaluation of the health status of lithium batteries in new energy vehicles. It has a high accuracy rate, which to a certain extent solves the problem of low accuracy of SOH estimation of lithium batteries and has certain engineering application value. However, it does not consider the impact of the driver's driving habit data and the driving data of the driving section on the health status of the lithium battery, nor does it consider the impact of different battery positions on the health status of the lithium battery, resulting in low accuracy of analysis and judgment, which obviously cannot solve the technical problems raised in this application.

[0004] In order to solve these problems, this application designs a lithium battery health status assessment method and system based on data analysis. Summary of the Invention

[0005] In order to overcome the defects and shortcomings of the existing technology, the present application provides a lithium battery health status assessment method and system based on data analysis, which obtains the operating 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, performs lithium battery operation safety analysis based on the operating data change curve of the lithium battery during operation, performs lithium battery hazard analysis based on the lithium battery safety analysis results, the driving habit data of the driver of the corresponding vehicle and the driving data of the driving section, and provides a health status warning based on the lithium battery hazard analysis results. When performing lithium battery health status assessment, the present application not only considers the electrical signal operating characteristics of the lithium battery and the temperature changes, but also takes into account the impact of the driver's driving habit data and the driving data of the driving section on the lithium battery health status, and then comprehensively analyzes and judges the health status of the lithium battery during vehicle driving, thereby improving the accuracy of the analysis and judgment.

[0006] In order to achieve the above objectives, this application adopts the following technical solutions:

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

[0008] S1. Obtaining a lithium battery operating data change curve, and simultaneously obtaining the driving habit data and driving data of the corresponding vehicle driver;

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

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

[0011] S4. Provide health status warning based on the results of lithium battery hazard analysis.

[0012] In one implementation of the present application, the operating data change curve of the lithium battery includes a change curve of data reflecting the operating quality of the lithium battery, such as the current, voltage and temperature of the lithium battery during driving on the corresponding driving road; the driving habit data includes the driver's braking force data and the driver's driving speed data when driving on the corresponding specification 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 here is obtained through a road surface acquisition terminal.

[0013] In one implementation of the present application, step S2 performs a lithium battery operation safety analysis based on an operating data change curve of the lithium battery during operation, including the following specific steps:

[0014] S21. Obtaining the operating data of the lithium battery during operation, and performing an operation abnormality of the previous operation cycle based on the fluctuation of the operating data of the lithium battery in the previous operation cycle and the dangerous situation, wherein the specific steps of the dangerous situation analysis are: comparing the electrical signal output by the lithium battery during operation with the electrical signal to be output, obtaining the electrical signal abnormality at the corresponding moment by the ratio of the absolute value of the phase difference value of the electrical signal to the corresponding electrical signal to be output, integrating the electrical signal abnormality at each moment in the operation process over the time length and then dividing it by the time length to obtain the dangerous situation analysis result during the operation process; at the same time, the specific steps of the fluctuation situation analysis are: obtaining the absolute value of the difference between the electrical signal abnormalities at intervals and dividing it by the average value of the electrical signal abnormalities to obtain the fluctuation situation at the corresponding time, integrating over the time length and then dividing it by the time length to obtain the fluctuation situation analysis result during the operation process, wherein the operation abnormality of the previous operation cycle is the weighted sum of the dangerous situation analysis result and the fluctuation situation analysis result. In this step, the electrical signal operation abnormality of the operation cycle is evaluated by the electrical signal output abnormality of the lithium battery during the operation cycle and the fluctuation of the electrical signal output;

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

[0016] S23, obtaining the operation abnormality results of the corresponding operation cycle and the battery temperature change abnormality analysis results, performing normalization and weighted summation, and calculating the reciprocal to obtain the lithium battery safety analysis results;

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

[0018] S31, obtaining driving habit data of the driver of the corresponding vehicle and driving data of the driving section to perform driving abnormality analysis;

[0019] The driving abnormality analysis includes the following specific contents:

[0020] S311. Acquire driving habit data of the driver of the corresponding vehicle, and perform a driving habit risk analysis based on the driving habit data of the driver of the corresponding vehicle. The driving habit risk analysis calculation formula is: , where S is the driver's average braking force, Sz is the driver's safe braking force, and vc is the weighted sum of the vehicle speed fluctuation within a cycle and the integral of the difference between the vehicle speed and the safe speed. The vehicle speed fluctuation is the integral of the vehicle speed change at intervals during driving, and the integral of the difference between the vehicle speed and the safe speed is the integral of the difference between the vehicle speed and the safe speed during driving. In this step, the driver's braking force data and the driver's driving speed data when driving on a road of corresponding specifications are combined to analyze battery abnormalities caused by the driver's habits.

[0021] S312: Obtain road surface smoothness data, and perform road anomaly analysis based on the road surface smoothness data. The road anomaly analysis is preferably performed based on the ratio of the volume of uneven objects on the road to the road volume, wherein the uneven objects on the road include bumps or potholes on the road.

[0022] S313: performing weighted summation on the obtained driving habit risk analysis results and road anomaly analysis results to obtain a driving anomaly analysis result;

[0023] S32. Obtain the driving abnormality analysis results and the lithium battery safety analysis results of the corresponding operating cycle, and obtain the lithium battery hazard analysis results by dividing the inverse of the lithium battery safety analysis results by the driving abnormality analysis results. In this way, during the lithium battery hazard analysis process, the lithium battery hazard analysis error 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 eliminated, thereby improving the accuracy of the lithium battery hazard analysis.

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

[0025] Obtain the obtained lithium battery hazard analysis result, and 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 health status warning 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 health status warning is issued.

[0026] Secondly, this application also provides a lithium battery health status assessment system based on data analysis, including:

[0027] The data acquisition module is used to obtain the operating data change curve 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;

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

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

[0030] The health status warning module provides health status warning based on the lithium battery hazard analysis results.

[0031] In a third aspect, the present application provides an electronic device comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes a lithium battery health status assessment method based on data analysis by calling the computer program stored in the memory.

[0032] In a fourth aspect, the present application provides a computer-readable storage medium storing instructions, which, when executed on a computer, enables the computer to execute a lithium battery health status assessment method based on data analysis.

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

[0034] The present application obtains a lithium battery operating data change curve, and simultaneously obtains the driving habit data of the corresponding vehicle driver and the driving data of the driving section. Based on the operating data change curve of the lithium battery during operation, a lithium battery operation safety analysis is performed. Based on the lithium battery safety analysis results, the driving habit data of the corresponding vehicle driver and the driving data of the driving section, a lithium battery hazard analysis is performed. A health status warning is issued based on the lithium battery hazard analysis results. When performing a lithium battery health status assessment, the present application not only considers the electrical signal operating characteristics and temperature changes of the lithium battery, but also takes into account the impact of the driver's driving habit data and the driving data of the driving section on the lithium battery health status. The health status of the lithium battery during vehicle driving is then comprehensively analyzed and judged, thereby improving the accuracy of the analysis and judgment.

[0035] When performing a safety analysis on the operation of a lithium battery, the present application comprehensively evaluates the abnormality of the output of the electrical signal and the fluctuation of the electrical signal output to evaluate the abnormality of the electrical signal operation during the operation cycle. At the same time, the present application further improves the accuracy of the analysis and judgment by analyzing the abnormal temperature changes of the battery according to the importance of each position of the lithium battery and the abnormal temperature changes at the beginning and end of the operation cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0037] Figure 1 Schematic diagram of the overall process of this application method;

[0038] Figure 2 This is the workflow diagram of S2 in the present application method;

[0039] Figure 3 This is a workflow diagram of S31 in the present application method;

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

[0041] Figure 5 This is a flow chart of the driving abnormality analysis method in this application. DETAILED DESCRIPTION

[0042] The technical solution of the present application is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations on the technical solution of the present application. Unless there is a conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0043] Example 1

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

[0045] S1. Obtaining a lithium battery operating data change curve, and simultaneously obtaining the driving habit data and driving data of the corresponding vehicle driver;

[0046] In this embodiment, the operating data change curve of the lithium battery includes a change curve of data reflecting the operating quality of the lithium battery, such as the current, voltage, and temperature of the lithium battery during driving on the corresponding driving road. For example, the current is obtained by a current sensor, and the voltage is obtained by a voltage sensor. The driving habit data includes the braking force data and the driving speed data of the driver when driving on the corresponding specification road. 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. At the same time, the braking force data and the driving speed data of the driver when driving on the corresponding specification road are obtained through a corresponding braking force sensor. If the obtained content involves user privacy, it has been clearly informed before the acquisition, and the user's consent has also been obtained.

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

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

[0049] S21. Obtain the operating data of the lithium battery during operation, and analyze the operating anomaly of the previous operating cycle based on the fluctuation of the operating data of the lithium battery in the previous operating cycle and the dangerous situation. The specific steps of the dangerous situation analysis are: comparing the electrical signal output by the lithium battery during operation with the electrical signal to be output, obtaining the electrical signal anomaly at the corresponding moment by the ratio of the absolute value of the phase difference value of the electrical signal and the electrical signal to be output, integrating the electrical signal anomaly at each moment in the operation process over the time length and then dividing it by the time length to obtain the dangerous situation analysis result during the operation process; at the same time, the specific steps of the fluctuation situation analysis are as follows: The main steps are: obtaining the absolute value of the difference between the electrical signal anomalies at intervals of time and dividing it by the average value of the electrical signal anomalies to obtain the fluctuation situation of the corresponding time, integrating it over the time length and then dividing it by the time length to obtain the fluctuation situation analysis result during the operation process, wherein the operation anomaly of the previous operation cycle is the weighted sum of the danger situation analysis result and the fluctuation situation analysis result. In this step, the electrical signal operation anomaly of the operation cycle is evaluated by the electrical signal output anomaly of the lithium battery operation in the operation cycle and the fluctuation of the electrical signal output. It should be noted that the operation cycle here is evenly divided according to the road section, which can be 1km or 10km;

[0050] S22. Obtain temperature changes at various locations of the lithium battery during operation, obtain the difference between the temperature at the beginning and end of the operation cycle at each location and the median of the temperature safety range, and perform battery temperature change anomaly analysis based on the importance of each location and the difference between the temperature at the beginning and end of the operation cycle at each location and the median of the temperature safety range. The battery temperature change anomaly analysis formula is: , where n is the number of monitoring locations, ci is the importance of the i-th location, exp() is the power of a natural constant, Tic is the temperature at the end of the i-th location's operating cycle, Tia is the temperature at the beginning of the i-th location's operating cycle, and Tim is the median of the temperature safety range. The importance is obtained by dividing the power output of the corresponding location during the operating cycle by the power storage capacity of the corresponding location. The greater the power output, the greater the operating output of the corresponding battery module over the entire lithium battery. For this step, the battery temperature change anomaly analysis is performed based on the importance of each location of the corresponding lithium battery and the temperature anomaly at the beginning and end of the operating cycle.

[0051] S23, obtaining the operation abnormality results of the corresponding operation cycle and the battery temperature change abnormality analysis results, performing normalization and weighted summation, and calculating the reciprocal to obtain the lithium battery safety analysis results;

[0052] In this exemplary embodiment, the normalization method is: dividing the corresponding abnormality analysis result by the corresponding abnormality analysis standard value to obtain the normalized result. This is to avoid the situation where one of the weights is too large and the other is too small when the weighted sum is calculated due to a large difference between the two parameters.

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

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

[0055] S31, obtaining driving habit data of the driver of the corresponding vehicle and driving data of the driving section to perform driving abnormality analysis;

[0056] The driving abnormality analysis includes the following specific contents:

[0057] S311. Acquire driving habit data of the driver of the corresponding vehicle, and perform a driving habit risk analysis based on the driving habit data of the driver of the corresponding vehicle. The driving habit risk analysis calculation formula is: , where S is the driver's average braking force, Sz is the driver's safe braking force, and vc is the weighted sum of the vehicle speed fluctuation within a cycle and the integral of the difference between the vehicle speed and the safe speed. The vehicle speed fluctuation is the integral of the vehicle speed change at intervals during driving, and the integral of the difference between the vehicle speed and the safe speed is the integral of the difference between the vehicle speed and the safe speed during driving. In this step, the driver's braking force data and the driver's driving speed data when driving on a road of corresponding specifications are combined to analyze battery abnormalities caused by the driver's habits.

[0058] S312: Obtain road surface smoothness data, and perform road anomaly analysis based on the road surface smoothness data. The road anomaly analysis is preferably performed based on the ratio of the volume of uneven objects on the road to the road volume, wherein the uneven objects on the road include bumps or potholes on the road.

[0059] S313: performing weighted summation on the obtained driving habit risk analysis results and road anomaly analysis results to obtain a driving anomaly analysis result;

[0060] S32. Obtaining a driving abnormality analysis result and a lithium battery safety analysis result for a corresponding operating cycle, and obtaining a lithium battery hazard analysis result by dividing the reciprocal of the lithium battery safety analysis result by the driving abnormality analysis result. This eliminates the lithium battery hazard analysis error caused by the combined influence of the driving habit data of the corresponding vehicle driver and the driving data of the driving section, thereby improving the accuracy of the lithium battery hazard analysis.

[0061] S4. Provide health status warning based on the results of lithium battery hazard analysis;

[0062] In this embodiment, the health status warning is performed based on the lithium battery risk analysis results in step S4, including the following specific contents:

[0063] Obtain the obtained lithium battery hazard analysis result, and 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 health status warning 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 health status warning is issued. The warning signal is sent to the client and the vehicle management end via SMS or reminder to remind the client and the vehicle management end to respond quickly.

[0064] It should be noted in this embodiment that the setting parameters in this embodiment (such as the weighted weights of each parameter and the set lithium battery hazard analysis threshold, etc.) are obtained by experiments by those skilled in the art. The specific experimental method is: obtaining a historical operating data change curve of the lithium battery, and at the same time obtaining the driving habit data of the driver of the corresponding vehicle and the driving data of the driving section, substituting them into the various steps of this embodiment to perform analysis and calculation of the lithium battery hazard analysis results, and at the same time obtaining experimental results of whether a lithium battery fails when a designated person drives a vehicle for a specified distance on a designated road. Based on the obtained results of the analytical calculation value of the lithium battery hazard analysis result and the experimental results of whether the lithium battery fails, the fitting software is imported to perform iterative fitting of the data, and the setting parameter values that meet the maximum judgment accuracy are output.

[0065] It should be noted that this embodiment has the following advantages: obtaining a lithium battery operating data change curve, simultaneously obtaining the driving habit data of the corresponding vehicle driver and the driving data of the driving section, performing a lithium battery operating safety analysis based on the lithium battery operating data change curve during operation, performing a lithium battery hazard analysis based on the lithium battery safety analysis results, the driving habit data of the corresponding vehicle driver, and the driving data of the driving section, and providing a health status warning based on the lithium battery hazard analysis results. When evaluating the lithium battery health status, this application not only considers the lithium battery's electrical signal operating characteristics and temperature changes, but also takes into account the impact of the driver's driving habit data and the driving data of the driving section on the lithium battery health status, thereby comprehensively analyzing and judging the lithium battery health status during vehicle driving, thereby improving the accuracy of the analysis and judgment. When performing the lithium battery operating safety analysis, this application comprehensively evaluates the electrical signal output anomalies and electrical signal output fluctuations to evaluate the electrical signal operating anomalies during the operating cycle. At the same time, this application analyzes the battery temperature change anomalies by considering the importance of each lithium battery position and the temperature change anomalies at the beginning and end of the operating cycle, further improving the accuracy of the analysis and judgment.

[0066] Example 2

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

[0068] The data acquisition module is used to obtain the operating data change curve 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;

[0069] Operation safety analysis module, which performs lithium battery operation safety analysis based on the operation data change curve of the lithium battery during operation;

[0070] The lithium battery hazard analysis module performs lithium battery hazard analysis based on the lithium battery safety analysis results, the driving habits data of the corresponding vehicle driver, and the driving data of the driving section;

[0071] The health status warning module provides health status warning based on the lithium battery hazard analysis results.

[0072] The above-mentioned parameters and steps for each unit module to realize corresponding functions and corresponding roles in the lithium battery health status assessment system based on data analysis of the present application can be referred to the parameters and steps in the embodiment of the lithium battery health status assessment method based on data analysis in the method embodiment, and will not be repeated here.

[0073] Example 3

[0074] An electronic device according to an embodiment of the present application includes a processor and a memory. The memory stores a computer program that can be called by the processor. The processor executes a data analysis-based lithium battery health status assessment method by calling the computer program stored in the memory. It should be noted that all computer programs of the data analysis-based lithium battery health status assessment method are implemented in C language.

[0075] Example 4

[0076] This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon;

[0077] When the computer program is executed on a computer device, the computer device is enabled to execute the above-mentioned lithium battery health status assessment method based on data analysis.

[0078] The various embodiments in this application are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other. Each embodiment focuses on the differences between the other embodiments. In particular, the IoT device and media embodiments are described briefly because they are generally similar to the method embodiments. For relevant portions, refer to the description of the method embodiments.

[0079] The system and medium provided in the embodiments of the present application correspond one-to-one to the method. Therefore, the system and medium also have similar beneficial technical effects to their corresponding methods. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the system and medium will not be repeated here.

[0080] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0081] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram and the combination of processes and / or blocks in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0082] These computer program instructions may 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, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

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

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

[0085] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can be implemented using any method or technology for information storage. 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 RAM (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 disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media, such as modulated data signals and carrier waves.

[0086] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0087] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A lithium battery health status assessment method based on data analysis, characterized in that: The steps include: S1. Obtaining a lithium battery operating data change curve, and simultaneously obtaining the driving habit data and driving data of the corresponding vehicle driver; S2. Conducting lithium battery operation safety analysis based on the operating data change curve of the lithium battery during operation; The specific steps include: S21. Obtaining operating data of the lithium battery during operation, and performing operation abnormality analysis of the lithium battery in the previous operation cycle based on fluctuations and dangerous conditions in the operating data of the lithium battery in the previous operation cycle; S22. Obtain temperature changes at various locations of the lithium battery during operation, obtain the difference between the temperature at the beginning and end of the operation cycle at each location and the median of the temperature safety range, and perform battery temperature change anomaly analysis based on the importance of each location and the difference between the temperature at the beginning and end of the operation cycle at each location and the median of the temperature safety range. The battery temperature change anomaly analysis formula is: Where n is the number of monitoring locations, ci is the importance of the i-th location, exp() is the power of a natural constant, Tic is the temperature at the end of the i-th location's operating cycle, Tia is the temperature at the beginning of the i-th location's operating cycle, and Tim is the median of the temperature safety range. The importance is calculated by dividing the power output of the corresponding location during the operating cycle by the power storage capacity of the corresponding location. S23, obtaining the operation abnormality results of the corresponding operation cycle and the battery temperature change abnormality analysis results, performing normalization and weighted summation, and calculating the reciprocal to obtain the lithium battery safety analysis results; S3. Performing a lithium battery hazard analysis based on the lithium battery safety analysis results, the driving habit data of the corresponding vehicle driver, and the driving data of the driving section; S4. Provide health status warning based on the results of lithium battery hazard analysis.

2. The lithium battery health status assessment method based on data analysis according to claim 1, characterized in that: The lithium battery hazard analysis includes the following specific steps: S31, obtaining driving habit data of the driver of the corresponding vehicle and driving data of the driving section to perform driving abnormality analysis; S32: Obtain the driving abnormality analysis result and the lithium battery safety analysis result of the corresponding operation cycle, and obtain the lithium battery hazard analysis result by dividing the reciprocal of the lithium battery safety analysis result by the driving abnormality analysis result.

3. The lithium battery health status assessment method based on data analysis according to claim 2, characterized in that: The health status warning based on the lithium battery hazard analysis results includes the following specific contents: Obtain the obtained lithium battery hazard analysis result, and 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 health status warning 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 health status warning is issued.

4. The lithium battery health status assessment method based on data analysis according to claim 3, characterized in that: The driving abnormality analysis includes the following specific contents: S311, obtaining driving habit data of the driver of the corresponding vehicle, and performing a driving habit risk analysis based on the driving habit data of the driver of the corresponding vehicle; S312: Obtain road surface smoothness data, and perform road anomaly analysis based on the road surface smoothness data, where the road anomaly analysis is performed based on the ratio of the volume of the road bumps to the road volume. S313: Perform weighted summation on the obtained driving habit risk analysis results and road anomaly analysis results to obtain a driving anomaly analysis result.

5. The method for evaluating the health status of a lithium battery based on data analysis according to claim 4, wherein: The lithium battery operation data change curve includes a data change curve reflecting the lithium battery operation quality during driving on the corresponding driving road; the driving habit data includes the driver's braking force data and the driver's driving speed data when driving on the corresponding specification road, and the driving data of the driving section includes the road surface flatness data of the section.

6. The method for evaluating the health status of a lithium battery based on data analysis according to claim 5, wherein: The specific steps of the dangerous situation analysis are: comparing the electrical signal output by the lithium battery during operation with the electrical signal that needs to be output, obtaining the electrical signal abnormality at the corresponding moment by the ratio of the absolute value of the phase difference value of the electrical signal at the corresponding moment and the corresponding electrical signal that needs to be output, integrating the electrical signal abnormality at each moment during the operation process over the time length and then dividing it by the time length to obtain the dangerous situation analysis result during the operation process.

7. The lithium battery health status assessment method based on data analysis according to claim 6, characterized in that: The specific steps of the fluctuation analysis are: obtaining the absolute value of the difference between the electrical signal anomalies at different time intervals and dividing it by the average value of the electrical signal anomalies to obtain the fluctuation situation of the corresponding time, integrating it over the time length and then dividing it by the time length to obtain the fluctuation analysis result during the operation process, wherein the operation anomaly of the previous operation cycle is the weighted sum of the danger situation analysis result and the fluctuation situation analysis result.

8. A lithium battery health status assessment system based on data analysis, which is implemented based on the lithium battery health status assessment method based on data analysis according to any one of claims 1 to 7, characterized in that: The system comprises: The data acquisition module is used to obtain the operating data change curve 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; Operation safety analysis module, which performs lithium battery operation safety analysis based on the operation data change curve of the lithium battery during operation; The lithium battery hazard analysis module performs lithium battery hazard analysis based on the lithium battery safety analysis results, the driving habits data of the corresponding vehicle driver, and the driving data of the driving section; The health status warning module provides health status warning based on the lithium battery hazard analysis results.

9. 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 assessment method based on data analysis as described in any one of claims 1 to 7 by calling the computer program stored in the memory.

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