State-of-Health Prediction of Lithium-Ion Batteries Based on CNN-BiLSTM-AM
State-of-Health (SOH) prediction of lithium-ion batteries is crucial in battery management systems. In order to guarantee the safe operation of lithium-ion batteries, a hybrid model based on convolutional neural network (CNN)-bidirectional long short-term memory (BiLSTM) and attention mechanism (AM)...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Batteries |
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| Online Access: | https://www.mdpi.com/2313-0105/8/10/155 |
| _version_ | 1797475309522518016 |
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| author | Yukai Tian Jie Wen Yanru Yang Yuanhao Shi Jianchao Zeng |
| author_facet | Yukai Tian Jie Wen Yanru Yang Yuanhao Shi Jianchao Zeng |
| author_sort | Yukai Tian |
| collection | DOAJ |
| description | State-of-Health (SOH) prediction of lithium-ion batteries is crucial in battery management systems. In order to guarantee the safe operation of lithium-ion batteries, a hybrid model based on convolutional neural network (CNN)-bidirectional long short-term memory (BiLSTM) and attention mechanism (AM) is developed to predict the SOH of lithium-ion batteries. By analyzing the charging and discharging process of batteries, the indirect health indicator (HI), which is highly correlated with capacity, is extracted in this paper. HI is taken as the input of CNN, and the convolution and pooling operations of CNN layers are used to extract the features of battery time series data. On this basis, a BiLSTM depth model is built in this paper to collect the data coming from CNN forward and reverse dependencies and further emphasize the correlation between the serial data by AM to obtain an accurate SOH estimate. Experimental results based on NASA PCoE lithium-ion battery data demonstrate that the proposed hybrid model outperforms other single models, with the root mean square error (RMSE) of SOH prediction results all less than 0.01, and can accurately predict the SOH of lithium-ion batteries. |
| first_indexed | 2024-03-09T20:42:19Z |
| format | Article |
| id | doaj.art-f1e85a441b3d4139b770c00559a794dd |
| institution | Directory Open Access Journal |
| issn | 2313-0105 |
| language | English |
| last_indexed | 2024-03-09T20:42:19Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Batteries |
| spelling | doaj.art-f1e85a441b3d4139b770c00559a794dd2023-11-23T22:55:03ZengMDPI AGBatteries2313-01052022-10-0181015510.3390/batteries8100155State-of-Health Prediction of Lithium-Ion Batteries Based on CNN-BiLSTM-AMYukai Tian0Jie Wen1Yanru Yang2Yuanhao Shi3Jianchao Zeng4School of Electrical and Control Engineering, North University of China, Taiyuan 030051, ChinaSchool of Electrical and Control Engineering, North University of China, Taiyuan 030051, ChinaSchool of Electrical and Control Engineering, North University of China, Taiyuan 030051, ChinaSchool of Electrical and Control Engineering, North University of China, Taiyuan 030051, ChinaSchool of Data Science and Technology, North University of China, Taiyuan 030051, ChinaState-of-Health (SOH) prediction of lithium-ion batteries is crucial in battery management systems. In order to guarantee the safe operation of lithium-ion batteries, a hybrid model based on convolutional neural network (CNN)-bidirectional long short-term memory (BiLSTM) and attention mechanism (AM) is developed to predict the SOH of lithium-ion batteries. By analyzing the charging and discharging process of batteries, the indirect health indicator (HI), which is highly correlated with capacity, is extracted in this paper. HI is taken as the input of CNN, and the convolution and pooling operations of CNN layers are used to extract the features of battery time series data. On this basis, a BiLSTM depth model is built in this paper to collect the data coming from CNN forward and reverse dependencies and further emphasize the correlation between the serial data by AM to obtain an accurate SOH estimate. Experimental results based on NASA PCoE lithium-ion battery data demonstrate that the proposed hybrid model outperforms other single models, with the root mean square error (RMSE) of SOH prediction results all less than 0.01, and can accurately predict the SOH of lithium-ion batteries.https://www.mdpi.com/2313-0105/8/10/155lithium-ion batterystate of healthconvolutional neural networkbidirectional long- and short-term memoryattention mechanism |
| spellingShingle | Yukai Tian Jie Wen Yanru Yang Yuanhao Shi Jianchao Zeng State-of-Health Prediction of Lithium-Ion Batteries Based on CNN-BiLSTM-AM Batteries lithium-ion battery state of health convolutional neural network bidirectional long- and short-term memory attention mechanism |
| title | State-of-Health Prediction of Lithium-Ion Batteries Based on CNN-BiLSTM-AM |
| title_full | State-of-Health Prediction of Lithium-Ion Batteries Based on CNN-BiLSTM-AM |
| title_fullStr | State-of-Health Prediction of Lithium-Ion Batteries Based on CNN-BiLSTM-AM |
| title_full_unstemmed | State-of-Health Prediction of Lithium-Ion Batteries Based on CNN-BiLSTM-AM |
| title_short | State-of-Health Prediction of Lithium-Ion Batteries Based on CNN-BiLSTM-AM |
| title_sort | state of health prediction of lithium ion batteries based on cnn bilstm am |
| topic | lithium-ion battery state of health convolutional neural network bidirectional long- and short-term memory attention mechanism |
| url | https://www.mdpi.com/2313-0105/8/10/155 |
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