Linxing Xie, Anan Zhang, Wei Yang, Liang Zhang, Qian Li
{"title":"基于CNN-GRU模型的锂电池剩余使用寿命预测","authors":"Linxing Xie, Anan Zhang, Wei Yang, Liang Zhang, Qian Li","doi":"10.1109/EI256261.2022.10117233","DOIUrl":null,"url":null,"abstract":"Aiming at the difficulty in obtaining direct performance parameters such as capacity and internal resistance of lithium batteries, which leads to the low accuracy of predicting the remaining useful life (RUL) of lithium batteries, a prediction model based on Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) is proposed. Firstly, four indirect health factors including constant current charging time interval, constant voltage charging time interval, discharge temperature peak time and cycle times are extracted from lithium battery charging and discharging experiments, and the Pearson and Spearman correlation coefficients are established. Secondly, build a lithium battery RUL prediction model based on CNN-GRU combined neural network. Finally, the rationality of extracting health factors is verified by actual data, and the prediction results are compared with SVR model, long short-term memory (LSTM) model, GRU model, and CNN-LSTM model. The mean square of the proposed model is analyzed. Thus, the superiority and effectiveness of the proposed model are verified.","PeriodicalId":413409,"journal":{"name":"2022 IEEE 6th Conference on Energy Internet and Energy System Integration (EI2)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Remaining Useful Life Prediction of Lithium Batteries Based on CNN-GRU Model\",\"authors\":\"Linxing Xie, Anan Zhang, Wei Yang, Liang Zhang, Qian Li\",\"doi\":\"10.1109/EI256261.2022.10117233\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Aiming at the difficulty in obtaining direct performance parameters such as capacity and internal resistance of lithium batteries, which leads to the low accuracy of predicting the remaining useful life (RUL) of lithium batteries, a prediction model based on Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) is proposed. Firstly, four indirect health factors including constant current charging time interval, constant voltage charging time interval, discharge temperature peak time and cycle times are extracted from lithium battery charging and discharging experiments, and the Pearson and Spearman correlation coefficients are established. Secondly, build a lithium battery RUL prediction model based on CNN-GRU combined neural network. Finally, the rationality of extracting health factors is verified by actual data, and the prediction results are compared with SVR model, long short-term memory (LSTM) model, GRU model, and CNN-LSTM model. The mean square of the proposed model is analyzed. Thus, the superiority and effectiveness of the proposed model are verified.\",\"PeriodicalId\":413409,\"journal\":{\"name\":\"2022 IEEE 6th Conference on Energy Internet and Energy System Integration (EI2)\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE 6th Conference on Energy Internet and Energy System Integration (EI2)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EI256261.2022.10117233\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 6th Conference on Energy Internet and Energy System Integration (EI2)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EI256261.2022.10117233","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Remaining Useful Life Prediction of Lithium Batteries Based on CNN-GRU Model
Aiming at the difficulty in obtaining direct performance parameters such as capacity and internal resistance of lithium batteries, which leads to the low accuracy of predicting the remaining useful life (RUL) of lithium batteries, a prediction model based on Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) is proposed. Firstly, four indirect health factors including constant current charging time interval, constant voltage charging time interval, discharge temperature peak time and cycle times are extracted from lithium battery charging and discharging experiments, and the Pearson and Spearman correlation coefficients are established. Secondly, build a lithium battery RUL prediction model based on CNN-GRU combined neural network. Finally, the rationality of extracting health factors is verified by actual data, and the prediction results are compared with SVR model, long short-term memory (LSTM) model, GRU model, and CNN-LSTM model. The mean square of the proposed model is analyzed. Thus, the superiority and effectiveness of the proposed model are verified.