Auto-encoder LSTM for Li-ion SOH prediction: a comparative study on various benchmark datasets

Abstract

Lithium-ion batteries are used in most battery powered devices. Today’s research on Lithium-ion batteries mainly focuses on better energy management strategies and predictive maintenance. In this paper, a new approach based on auto-encoders and long short-term memory neural networks applied to usage data (voltage, current, temperature) is used to make a State of Health prediction. Encouraging results are obtained when conducting tests on various battery ageing datasets published by Sandia National Laboratories, the Massachusetts Institute of Technology and NASA’s Prognostics Center of Excellence.

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