Deep Learning Based Remaining Useful Life Prediction of Lithium-Ion Batteries Using Early Cycle Degradation Features
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Abstract
The failure of a lithium-ion battery (LiB), which is used as an energy storage system (ESS) in the mobility industry, such as electric vehicles and aircraft, can lead to substantial loss of life and property, thereby causing significant problems. Therefore, it is essential to monitor the capacity degradation of the mobility battery and accurately predict the remaining useful life (RUL) from the early cycle stage. Particularly, RUL prediction is the main objective of the Battery Management System (BMS) and is important for guaranteeing the safety of the mobility system (Wu et al., 2016). This research introduces a hybrid deep learning model for RUL prediction, using LSTM-attention and Multi-Layer Perceptron (MLP) methodologies. The proposed model uses statistical degradation features and domain knowledge-based features as input data acquired from the early 100 cycles of charge/discharge data of a lithium-ion battery. The model's performance evaluation was divided into two phases: primary and secondary, providing root mean square errors of 158.4 and 168.67, respectively. This study's results aim to contribute to the advancement of Prognostic and Health Management (PHM) technology, Condition-Based Maintenance (CBM) strategies, and BMS-based life prediction technology for mobility battery systems.
How to Cite
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Battery PHM
Wu, J., Zhang, C. and Chen, Z. (2016) ‘An online method for lithium-ion battery remaining useful life estimation using importance sampling and neural networks’, Applied Energy, 173, pp. 134–140.
Yu, Q. et al. (2025) ‘Multi-time scale feature extraction for early prediction of battery RUL and knee point using a hybrid deep learning approach’, Journal of Energy Storage, 117, pp. 116024.
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