Lithium-Ion Battery SOH Prediction Method Based on ICEEMDAN+FC-BiLSTM

Xiangdong Meng, Haifeng Zhang, Haitao Lan, Sheng Cui (), Yiyi Huang, Gang Li, Yunchang Dong and Shuyu Zhou
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Xiangdong Meng: Electric Power Research Institute of Jilin Electric Power Co., Ltd. (State Grid), Changchun 130021, China
Haifeng Zhang: Electric Power Research Institute of Jilin Electric Power Co., Ltd. (State Grid), Changchun 130021, China
Haitao Lan: Electric Power Research Institute of Jilin Electric Power Co., Ltd. (State Grid), Changchun 130021, China
Sheng Cui: College of Instrumentation and Electrical Engineering, Jilin University, Changchun 130061, China
Yiyi Huang: College of Instrumentation and Electrical Engineering, Jilin University, Changchun 130061, China
Gang Li: College of Instrumentation and Electrical Engineering, Jilin University, Changchun 130061, China
Yunchang Dong: Electric Power Research Institute of Jilin Electric Power Co., Ltd. (State Grid), Changchun 130021, China
Shuyu Zhou: Electric Power Research Institute of Jilin Electric Power Co., Ltd. (State Grid), Changchun 130021, China

Energies, 2025, vol. 18, issue 21, 1-14

Abstract: Driven by the rapid promotion of new energy technologies, lithium-ion batteries have found broad applications. Accurate prediction of their state of health (SOH) plays a critical role in ensuring safe and reliable battery management. This study presents a hybrid SOH prediction method for lithium-ion batteries by combining improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and a fully connected bidirectional long short-term memory network (FC-BiLSTM). ICEEMDAN is applied to extract multi-scale features and suppress noise, while the FC-BiLSTM integrates feature mapping with temporal modeling for accurate prediction. Using end-of-discharge time, charging capacity, and historical capacity averages as inputs, the method is validated on the NASA dataset and laboratory aging data. Results show RMSE values below 0.012 and over 15% improvement compared with BiLSTM-based benchmarks, highlighting the proposed method’s accuracy, robustness, and potential for online SOH prediction in electric vehicle battery management systems.

Keywords: SOH prediction; ICEEMDAN; FC-BiLSTM; lithium-ion battery (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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