CN-116315175-B - New energy automobile battery safety performance evaluation method based on big data

CN116315175BCN 116315175 BCN116315175 BCN 116315175BCN-116315175-B

Abstract

The invention discloses a new energy automobile battery safety performance evaluation method based on big data, which comprises the following steps of acquiring new energy automobile battery operation data through an Internet of vehicles data platform; the method comprises the steps of analyzing and processing battery operation data, obtaining an analysis result, performing association degree matching on the analysis result and data of a preset battery database, obtaining the safety degree of a new energy automobile battery, and sending the obtained safety degree of the new energy automobile battery to a mobile terminal in an Internet of things mode. The method and the system can acquire real-time parameters of the new energy automobile battery in real time, can fully utilize the historical safety performance test data of the new energy automobile battery to analyze the safety performance of the current new energy automobile battery under the action of the same influencing factors, further realize real-time evaluation and feedback of the new energy automobile battery, ensure the safety and reliability of the new energy automobile battery, and reduce related property loss.

Inventors

SHEN YUJIE
WU JUNXIAN
YANG XIAOFENG
LIU YANLING

Assignees

江苏大学

Dates

Publication Date: 20260505
Application Date: 20230320

Claims (3)

1. A new energy automobile battery safety performance evaluation method based on big data is characterized by comprising the steps of S1, acquiring new energy automobile battery operation data through an Internet of vehicles data platform, S2, analyzing the battery operation data and obtaining an analysis result, S3, performing association degree matching on the analysis result and data of a preset battery database to obtain the safety degree of the new energy automobile battery, S4, sending the obtained safety degree of the new energy automobile battery to a mobile terminal in an Internet of things mode; the method comprises the steps of S31, performing word segmentation processing on characteristic parameters in an analysis result and historical characteristic parameters in a battery database respectively to obtain word segmentation sets, S32, removing punctuation and stopping words to obtain a dictionary data set, calculating a weight value of each word in the dictionary data set, S33, taking the characteristic parameters in the analysis result and the historical characteristic parameters in the battery database as vector space models, calculating vector cosine values between the vector space model A of the characteristic parameters in the analysis result and the vector space model B of the historical characteristic parameters in the battery database to obtain matching association degrees, S34, selecting the historical characteristic parameters with the largest matching association degrees, taking the new energy automobile battery safety degree in the battery database as the current new energy battery safety degree, constructing a method of the battery database by referring to a literature, performing characteristic extraction on the historical safety performance test data to obtain the characteristic parameters, calculating the safety degree of the new energy automobile battery based on the historical characteristic parameters, performing qualitative factor analysis on the obtained historical characteristic parameters and the new energy automobile battery safety degree, performing qualitative factor elimination on the new energy automobile battery safety system based on the characteristic index system, and performing a characteristic index-influence analysis system based on the new energy system, the safety degree of the new energy automobile battery is calculated based on the influence factor set; The method comprises the steps of constructing a new energy automobile battery safety performance influence factor set U, defining n kinds of influence factor sets U encountered by the new energy automobile battery in the service life process of the new energy automobile battery, defining critical levels of all influence factors of the influence factor sets U to obtain a new energy automobile battery critical safety level set U A , defining a highest safety level set of the new energy automobile battery under the action of all influence factors in the influence factor set U represented by U B , converting safety values among different influence factors and among different characteristic parameters of the same influence factor into uniform safety values p ij through a relation conversion formula, obtaining a safety set A i of the new energy automobile battery relative to the safety level of the influence factor, and converting the safety values among different influence factors and among different characteristic parameters of the same influence factor into uniform safety values p ij through the relation conversion formula, wherein the calculation formula is as follows: p ij represents a safety value representation form of a jth characteristic parameter in an ith influence factor, x ij represents a value of a jth characteristic parameter in the ith influence factor in a critical safety level set U B , x c-ij represents a value of a jth characteristic parameter in the ith influence factor in a highest safety level set U B , k ij represents a conversion factor, and when the value of the characteristic parameter x ij of the safety performance of the new energy automobile battery under the influence of the influence factor increases along with the improvement of the safety performance of the new energy automobile battery, the relation conversion formula is positive, and otherwise the relation conversion formula is negative; Defining a variable R as the safety performance of the new energy automobile battery, wherein the value of R is (0, 1), and the critical safety level of the new energy automobile battery is represented by R=0.5, the higher the value of R is, the better the relative safety performance of the new energy automobile battery is, the lower the value of R is, and the lower the safety performance of the new energy automobile battery is; a) When p ij ∈A i exists and p ij is more than or equal to 0.5, the new energy automobile battery is relatively safe under the action of influence factors, the safety degree R of the new energy automobile battery is calculated through a weighted average method, and the calculation formula for calculating the safety degree R of the new energy automobile battery through the weighted average method is as follows: r represents the safety degree of the new energy automobile battery calculated by a weighted average method; m represents the number of factors affecting the safety performance of the new energy automobile battery; p ij represents the safe value representation form of the j-th characteristic parameter in the i-th influencing factor; b) When p ij ∈A i exists and p ij is smaller than 0.5, the new energy automobile battery is low or unsafe under the action of influence factors, the safety degree R of the new energy automobile battery is not calculated through a weighted average method, and the calculation formula of the safety degree R of the new energy automobile battery is not calculated through the weighted average method is as follows: R represents the safety degree of the new energy automobile battery calculated by a weighted average method.
2. The method for evaluating the safety performance of the new energy automobile battery based on big data according to claim 1, wherein the steps of analyzing and processing the battery operation data and obtaining the analysis result comprise the following steps: s21, denoising, filtering and smoothing the battery operation data, and obtaining accurate data; s22, extracting characteristics of the accurate data, and obtaining characteristic parameters of the new energy automobile battery; S23, carrying out statistical analysis on characteristic parameters of the new energy automobile battery to obtain an analysis result, and pre-storing the analysis result in a text form.
3. The method for evaluating the safety performance of the new energy automobile battery based on big data according to claim 1, wherein the step of sending the obtained safety degree of the new energy automobile battery to the mobile terminal through the internet of things comprises the following steps: S41, when the safety performance of the new energy automobile battery is in a relatively safe condition, not carrying out warning reminding; s42, when the safety performance of the new energy automobile battery is in low safety or unsafe, reminding is carried out in a warning mode.

Description

New energy automobile battery safety performance evaluation method based on big data Technical Field The invention relates to the technical field of power battery safety supervision, in particular to a new energy automobile battery safety performance evaluation method based on big data. Background Along with popularization of electric automobiles in China and application of the technology of internet of vehicles, more and more new energy automobiles enter the consumer market, the system of the new energy automobiles mainly comprises an electric driving system, a power supply system, a related auxiliary system and the like, wherein the electric driving system comprises a driving controller, a motor, a mechanical rotating device and wheels, the driving motor is similar to a fuel oil type automobile starting device, electric energy in a power battery is converted into forward power to drive the automobiles, meanwhile, under the condition of braking, kinetic energy on the wheels can be converted into electric energy, and the electric energy can be recovered into the battery to finish storage of braking energy of the automobiles. The safety of the new energy electric vehicle is worth paying attention to, the battery is used as a high-voltage source of the electric vehicle, the safety problem is that personal safety of a driver is endangered, related property loss is caused, brand image of the electric vehicle is affected, although the existing new energy electric vehicle battery safety performance evaluation method has certain scientificity and reliability, the existing new energy electric vehicle battery safety performance evaluation method still has a certain deficiency, most of the existing new energy electric vehicle battery safety performance evaluation methods adopt laboratory test means, such as electrochemical performance test, thermal runaway test and the like, test results have certain reliability, the test process is complicated and the cost is high, and most of the test results adopt offline test, and the evaluation results are obtained only through certain data processing and analysis, so that real-time evaluation and feedback of the safety performance of the battery are difficult to realize. In the prior art, the historical data and the new recorded data of the battery parameters must be stored in a unified data format to be identified and utilized, and because a large amount of battery historical data are stored in various forms, the battery parameter data newly recorded by various platforms cannot be completely unified, and the most common mode is to store in a text form. Therefore, a large amount of data of the existing new energy automobile battery cannot be effectively used. For the problems in the related art, no effective solution has been proposed at present. Disclosure of Invention Aiming at the problems in the related art, the invention provides a new energy automobile battery safety performance evaluation method based on big data, so as to overcome the technical problems existing in the related art. For this purpose, the invention adopts the following specific technical scheme: A new energy automobile battery safety performance evaluation method based on big data comprises the following steps: s1, acquiring new energy automobile battery operation data through an Internet of vehicles data platform; S2, analyzing and processing the battery operation data, and obtaining an analysis result; S3, performing association degree matching on the analysis result and data of a preset battery database to obtain the safety degree of the new energy automobile battery; and S4, transmitting the obtained safety degree of the new energy automobile battery to the mobile terminal in an Internet of things mode. Further, the analyzing the battery operation data and obtaining the analysis result includes the following steps: S21, denoising, filtering and smoothing the battery operation data, and obtaining accurate data; s22, extracting the characteristics of the accurate data, and obtaining characteristic parameters of the new energy automobile battery; S23, carrying out statistical analysis on the characteristic parameters of the new energy automobile battery to obtain an analysis result, and pre-storing the analysis result in a text form. Further, the construction of the battery database comprises the following steps: collecting historical safety performance test data of the new energy automobile battery in a reference mode; extracting the characteristics of the historical safety performance test data to obtain historical characteristic parameters; Calculating the safety degree of the new energy automobile battery based on the historical characteristic parameters; And storing the obtained historical characteristic parameters and the safety degree of the new energy automobile battery in a text form to obtain a battery database. Further, the calculating the safety degree of the new energy automobile battery bas