Lithium-ion Battery Remaining Useful Life Prediction with Long Short-term Memory Recurrent Neural Network

Yuefeng Liu, Guangquan Zhao, Xiyuan Peng, and Cong Hu
Submission Type: 
Full Paper
phmc_17_037.pdf748.99 KBOctober 10, 2017 - 2:17pm

Lithium-ion batteries play critical roles in many electronic devices. It is necessary to develop a reliable and accurate remaining useful life (RUL) prediction approach to provide timely maintenance or replacement of battery systems. A novel RUL prediction approach based on Long Short-term Memory (LSTM) Recurrent Neural Network (RNN) is proposed in this paper. LSTM is able to capture long-term dependencies and model sequential data among the capacity degradation of lithium-ion batteries. The advantages of our proposed method include: 1) obtaining high prediction accuracy without accurate physics-based model or expertise and 2) decreasing the cumulation errors by multi-step ahead prediction each time, while traditional RUL method predicts one-step ahead once and then uses the current estimated value to predict next one, which causes cumulation errors increased. The Center for Advanced Life Cycle Engineering (CALCE) battery datasets are used to demonstrate the effectiveness of the proposed method. The results show that, compared with echo state networks (ESN), the proposed method has higher accuracy, more stable and reliable performance for lithium-ion batteries RUL prediction.

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Submission Keywords: 
Lithium-ion battery
remaining useful life prediction
Long short-term memory recurrent neural network
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
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