Rudy Tjandra, Hao Jiang, Eryan Bin Zainudin, Muhammad Isa Bin Yasmin, C. B. Soh, Elsa Feng, D. Soh, S. Cao, Kuan Tak Tan
{"title":"利用机器学习技术早期识别电池寿命","authors":"Rudy Tjandra, Hao Jiang, Eryan Bin Zainudin, Muhammad Isa Bin Yasmin, C. B. Soh, Elsa Feng, D. Soh, S. Cao, Kuan Tak Tan","doi":"10.1109/CPEEE56777.2023.10217699","DOIUrl":null,"url":null,"abstract":"This paper presents a methodology to predict near End-of-Life (EOL) of lithium-ion batteries. By predicting if a battery is expected to fail (unable to be discharged to 70% of its nominal capacity) within 30 cycles, timely replacement of battery can be carried out to minimize downtime. This methodology is validated using data from NASA Prognostics Center of Excellence. The battery charge cycling data is extracted and preprocessed as time series input features. The input features are based on mean and range of charge voltage, charge Ah capacity, charge time, and temperature. These features are used to train various machine learning techniques to perform multiclass classification on battery status. Battery status is categorized into three status, namely nonfailure status, near failure status (battery EOL is reached within 30 cycles), and failure status (battery EOL has already been reached). From various machine learning techniques studied, Fine kNN, ensemble bagged trees and ensemble boosted trees are three best techniques with more than 90% accuracy.","PeriodicalId":364883,"journal":{"name":"2023 13th International Conference on Power, Energy and Electrical Engineering (CPEEE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Early Identification of Battery End-of-Life Using Machine Learning\",\"authors\":\"Rudy Tjandra, Hao Jiang, Eryan Bin Zainudin, Muhammad Isa Bin Yasmin, C. B. Soh, Elsa Feng, D. Soh, S. Cao, Kuan Tak Tan\",\"doi\":\"10.1109/CPEEE56777.2023.10217699\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a methodology to predict near End-of-Life (EOL) of lithium-ion batteries. By predicting if a battery is expected to fail (unable to be discharged to 70% of its nominal capacity) within 30 cycles, timely replacement of battery can be carried out to minimize downtime. This methodology is validated using data from NASA Prognostics Center of Excellence. The battery charge cycling data is extracted and preprocessed as time series input features. The input features are based on mean and range of charge voltage, charge Ah capacity, charge time, and temperature. These features are used to train various machine learning techniques to perform multiclass classification on battery status. Battery status is categorized into three status, namely nonfailure status, near failure status (battery EOL is reached within 30 cycles), and failure status (battery EOL has already been reached). From various machine learning techniques studied, Fine kNN, ensemble bagged trees and ensemble boosted trees are three best techniques with more than 90% accuracy.\",\"PeriodicalId\":364883,\"journal\":{\"name\":\"2023 13th International Conference on Power, Energy and Electrical Engineering (CPEEE)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-02-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 13th International Conference on Power, Energy and Electrical Engineering (CPEEE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CPEEE56777.2023.10217699\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 13th International Conference on Power, Energy and Electrical Engineering (CPEEE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CPEEE56777.2023.10217699","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Early Identification of Battery End-of-Life Using Machine Learning
This paper presents a methodology to predict near End-of-Life (EOL) of lithium-ion batteries. By predicting if a battery is expected to fail (unable to be discharged to 70% of its nominal capacity) within 30 cycles, timely replacement of battery can be carried out to minimize downtime. This methodology is validated using data from NASA Prognostics Center of Excellence. The battery charge cycling data is extracted and preprocessed as time series input features. The input features are based on mean and range of charge voltage, charge Ah capacity, charge time, and temperature. These features are used to train various machine learning techniques to perform multiclass classification on battery status. Battery status is categorized into three status, namely nonfailure status, near failure status (battery EOL is reached within 30 cycles), and failure status (battery EOL has already been reached). From various machine learning techniques studied, Fine kNN, ensemble bagged trees and ensemble boosted trees are three best techniques with more than 90% accuracy.