Research on Lithium battery life prediction model based on CNN-GRU combined neural network
It is difficult to obtain direct performance parameters such as lithium battery capacity and internal resistance, which leads to the problem of low accuracy of lithium battery life prediction. A lithium battery life prediction model based on a combined neural network of convolutional neural network...
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| Format: | Article |
| Language: | zho |
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Harbin Jinhe Electrical Measurement & Instrumentation Magazine Publishing Co., Ltd.
2025-07-01
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| Series: | Diance yu yibiao |
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| Online Access: | http://www.emijournal.net/dcyyb/ch/reader/create_pdf.aspx?file_no=20221231002&flag=1&journal_id=dcyyb&year_id=2025 |
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| author | ZHANG An′an XIE Linxing YANG Wei |
| author_facet | ZHANG An′an XIE Linxing YANG Wei |
| author_sort | ZHANG An′an |
| collection | DOAJ |
| description | It is difficult to obtain direct performance parameters such as lithium battery capacity and internal resistance, which leads to the problem of low accuracy of lithium battery life prediction. A lithium battery life prediction model based on a combined neural network of convolutional neural network (CNN) and gated recurrent unit (GRU) is proposed. Four indirect health factors including constant current charging time interval, constant voltage charging time interval, discharging temperature peak time and cycle times are extracted from lithium battery charging and discharging experiments, and the Pearson and Spearman correlation coefficients are established. And then, a lithium battery life prediction model is built 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 to verify the superiority and effectiveness of the proposed model. |
| format | Article |
| id | doaj-art-9700c5db7a3b49cea773a1fd10e4d145 |
| institution | DOAJ |
| issn | 1001-1390 |
| language | zho |
| publishDate | 2025-07-01 |
| publisher | Harbin Jinhe Electrical Measurement & Instrumentation Magazine Publishing Co., Ltd. |
| record_format | Article |
| series | Diance yu yibiao |
| spelling | doaj-art-9700c5db7a3b49cea773a1fd10e4d1452025-08-20T03:03:16ZzhoHarbin Jinhe Electrical Measurement & Instrumentation Magazine Publishing Co., Ltd.Diance yu yibiao1001-13902025-07-01627778410.19753/j.issn1001-1390.2025.07.0091001-1390(2025)07-0077-08Research on Lithium battery life prediction model based on CNN-GRU combined neural networkZHANG An′an0XIE Linxing1YANG Wei2School of Electrical Engineering and Information, Southwest Petroleum University, Chengdu 610500, ChinaSchool of Electrical Engineering and Information, Southwest Petroleum University, Chengdu 610500, ChinaSchool of Electrical Engineering and Information, Southwest Petroleum University, Chengdu 610500, ChinaIt is difficult to obtain direct performance parameters such as lithium battery capacity and internal resistance, which leads to the problem of low accuracy of lithium battery life prediction. A lithium battery life prediction model based on a combined neural network of convolutional neural network (CNN) and gated recurrent unit (GRU) is proposed. Four indirect health factors including constant current charging time interval, constant voltage charging time interval, discharging temperature peak time and cycle times are extracted from lithium battery charging and discharging experiments, and the Pearson and Spearman correlation coefficients are established. And then, a lithium battery life prediction model is built 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 to verify the superiority and effectiveness of the proposed model.http://www.emijournal.net/dcyyb/ch/reader/create_pdf.aspx?file_no=20221231002&flag=1&journal_id=dcyyb&year_id=2025lithium batteryhealth factorcorrelation coefficientconvolutional neural networkgated recurrent unit |
| spellingShingle | ZHANG An′an XIE Linxing YANG Wei Research on Lithium battery life prediction model based on CNN-GRU combined neural network Diance yu yibiao lithium battery health factor correlation coefficient convolutional neural network gated recurrent unit |
| title | Research on Lithium battery life prediction model based on CNN-GRU combined neural network |
| title_full | Research on Lithium battery life prediction model based on CNN-GRU combined neural network |
| title_fullStr | Research on Lithium battery life prediction model based on CNN-GRU combined neural network |
| title_full_unstemmed | Research on Lithium battery life prediction model based on CNN-GRU combined neural network |
| title_short | Research on Lithium battery life prediction model based on CNN-GRU combined neural network |
| title_sort | research on lithium battery life prediction model based on cnn gru combined neural network |
| topic | lithium battery health factor correlation coefficient convolutional neural network gated recurrent unit |
| url | http://www.emijournal.net/dcyyb/ch/reader/create_pdf.aspx?file_no=20221231002&flag=1&journal_id=dcyyb&year_id=2025 |
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