{"title":"基于决策树算法的新能源汽车故障检测和故障率分析","authors":"Ping Tan, Lanlan Gong","doi":"10.2478/amns-2024-0803","DOIUrl":null,"url":null,"abstract":"\n New energy vehicles are vital in promoting environmental protection and technological innovation. Fault detection still faces challenges during its operation, and efficient and accurate methods for fault diagnosis are urgently needed. This paper proposes a fault detection and analysis model based on a decision tree algorithm for the fault detection problem of new energy vehicles. The dataset applicable to the model is prepared by preprocessing in-vehicle network data, including data cleaning, integration, and other steps. Fault prediction can be realized after using C4.5 algorithms to construct a decision tree. With a precision of 82.26% on the test set, this model is highly accurate in fault detection, which is 1.23 percentage points higher than the traditional decision tree algorithm. The model’s effectiveness and efficiency in handling large-scale data were demonstrated by its training and testing on training sets of different sizes. Using the traditional algorithm, a training set of 80,000 data was used to reduce the model’s running time from 274,432 seconds to 249,269 seconds. This study provides a practical methodology for fault diagnosis of new energy vehicles, improving fault detection accuracy while optimizing computational efficiency. Real-time monitoring and timely maintenance of new energy vehicles require this.","PeriodicalId":3,"journal":{"name":"ACS Applied Electronic Materials","volume":"121 5-6","pages":""},"PeriodicalIF":4.3000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Fault Detection and Failure Rate Analysis of New Energy Vehicles Based on Decision Tree Algorithm\",\"authors\":\"Ping Tan, Lanlan Gong\",\"doi\":\"10.2478/amns-2024-0803\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n New energy vehicles are vital in promoting environmental protection and technological innovation. Fault detection still faces challenges during its operation, and efficient and accurate methods for fault diagnosis are urgently needed. This paper proposes a fault detection and analysis model based on a decision tree algorithm for the fault detection problem of new energy vehicles. The dataset applicable to the model is prepared by preprocessing in-vehicle network data, including data cleaning, integration, and other steps. Fault prediction can be realized after using C4.5 algorithms to construct a decision tree. With a precision of 82.26% on the test set, this model is highly accurate in fault detection, which is 1.23 percentage points higher than the traditional decision tree algorithm. The model’s effectiveness and efficiency in handling large-scale data were demonstrated by its training and testing on training sets of different sizes. Using the traditional algorithm, a training set of 80,000 data was used to reduce the model’s running time from 274,432 seconds to 249,269 seconds. This study provides a practical methodology for fault diagnosis of new energy vehicles, improving fault detection accuracy while optimizing computational efficiency. Real-time monitoring and timely maintenance of new energy vehicles require this.\",\"PeriodicalId\":3,\"journal\":{\"name\":\"ACS Applied Electronic Materials\",\"volume\":\"121 5-6\",\"pages\":\"\"},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2024-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Applied Electronic Materials\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2478/amns-2024-0803\",\"RegionNum\":3,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Electronic Materials","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2478/amns-2024-0803","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Fault Detection and Failure Rate Analysis of New Energy Vehicles Based on Decision Tree Algorithm
New energy vehicles are vital in promoting environmental protection and technological innovation. Fault detection still faces challenges during its operation, and efficient and accurate methods for fault diagnosis are urgently needed. This paper proposes a fault detection and analysis model based on a decision tree algorithm for the fault detection problem of new energy vehicles. The dataset applicable to the model is prepared by preprocessing in-vehicle network data, including data cleaning, integration, and other steps. Fault prediction can be realized after using C4.5 algorithms to construct a decision tree. With a precision of 82.26% on the test set, this model is highly accurate in fault detection, which is 1.23 percentage points higher than the traditional decision tree algorithm. The model’s effectiveness and efficiency in handling large-scale data were demonstrated by its training and testing on training sets of different sizes. Using the traditional algorithm, a training set of 80,000 data was used to reduce the model’s running time from 274,432 seconds to 249,269 seconds. This study provides a practical methodology for fault diagnosis of new energy vehicles, improving fault detection accuracy while optimizing computational efficiency. Real-time monitoring and timely maintenance of new energy vehicles require this.
期刊介绍:
ACS Applied Electronic Materials is an interdisciplinary journal publishing original research covering all aspects of electronic materials. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials science, engineering, optics, physics, and chemistry into important applications of electronic materials. Sample research topics that span the journal's scope are inorganic, organic, ionic and polymeric materials with properties that include conducting, semiconducting, superconducting, insulating, dielectric, magnetic, optoelectronic, piezoelectric, ferroelectric and thermoelectric.
Indexed/Abstracted:
Web of Science SCIE
Scopus
CAS
INSPEC
Portico