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A Data-Driven Remaining Useful Life Prediction Approach for Lithium-Ion Batteries Based on Charging Health Feature Optimization
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Huiyun Duan1, Wei Xia2, 3, Jie Shao3, Yangqing Wang1, bin Li3
Automobile Technology | 2024, (1) : 20 - 26
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Automobile Technology | 2024, (1): 20-26
A Data-Driven Remaining Useful Life Prediction Approach for Lithium-Ion Batteries Based on Charging Health Feature Optimization
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Huiyun Duan1, Wei Xia2, 3, Jie Shao3, Yangqing Wang1, bin Li3
Affiliations
  • 1 Jiujiang Vocational and Technical College, Jiujiang 332007
  • 2 Wuhan University of Technology, Wuhan 430070
  • 3 SAIC-GM-Wuling Automobile Co., Ltd., Liuzhou 545005
Published: 2024-01-24 doi: 10.19620/j.cnki.1000-3703.20230429
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The Remaining Useful Life (RUL) prediction accuracy of lithium battery is not high because the selected health factors are not ideal. To solve this problem, this paper proposed a data-driven remaining useful life estimation approach for lithium-ion batteries based on charging health feature optimization. Firstly different health factors were selected in the battery charging process, then, a two-step feature selection method based on maximum information coefficient was used to obtain optimal health factors. Finally, the Attention Temporal Convolutional Network (ATCN) mechanism was used to predict the remaining useful life of the battery. The proposed lithium battery RUL prediction framework was validated by a study of NASA’s lithium battery aging data and compared with other modeling methods including Simple Recurrent Neutral Network (SimpleRNN), Long Short Term Memory (LSTM) neutral network and Gate Recurrent Unit (GRU) neutral network. The experimental results indicate the proposed method has achieved optimal prediction results in all the datasets.

Lithium-ion battery  /  Remaining Useful Life (RUL)  /  Two step maximal information coefficient  /  Temporal Convolutional Network (TCN)  /  Attention mechanism
Huiyun Duan, Wei Xia, Jie Shao, Yangqing Wang, bin Li. A Data-Driven Remaining Useful Life Prediction Approach for Lithium-Ion Batteries Based on Charging Health Feature Optimization[J]. Automobile Technology, 2024 , (1) : 20 -26 . DOI: 10.19620/j.cnki.1000-3703.20230429
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doi: 10.19620/j.cnki.1000-3703.20230429
  • Online Date：2025-12-25
  • Published：2024-01-24
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    1 Jiujiang Vocational and Technical College, Jiujiang 332007
    2 Wuhan University of Technology, Wuhan 430070
    3 SAIC-GM-Wuling Automobile Co., Ltd., Liuzhou 545005
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表12种不同金属材料的力学参数
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Family		 属数
Number of
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Number of
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Percentage of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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