Preprint Article Version 1 This version is not peer-reviewed

Prediction of Remaining Useful Life for Lithium-Ionbattery with Multiple Health Indicators

Version 1 : Received: 6 January 2020 / Approved: 7 January 2020 / Online: 7 January 2020 (09:17:28 CET)

How to cite: Zheng, Y.; Chen, H.J.; Su, C. Prediction of Remaining Useful Life for Lithium-Ionbattery with Multiple Health Indicators. Preprints 2020, 2020010054 (doi: 10.20944/preprints202001.0054.v1). Zheng, Y.; Chen, H.J.; Su, C. Prediction of Remaining Useful Life for Lithium-Ionbattery with Multiple Health Indicators. Preprints 2020, 2020010054 (doi: 10.20944/preprints202001.0054.v1).

Abstract

The remaining capacity can only be measured with offline method. This brings great challenge for the online prediction of Li-ion battery’s RUL. A novel online prediction method for Li-ion battery’s RUL was proposed, which is based on multiple health indicators (HIs) and can be derived from the batteries’ historical operation data. Firstly, four indirect HIs were built according to the battery’s operation current, voltage and temperature data respectively. On that basis, a generalized regression neural network (GRNN) was developed to estimate the battery’s remaining capacity, and the non-linear autoregressive approach (NAR) was utilized to predict the battery’s RUL based on the estimated capacity value. Furthermore, to reduce the interference, twice wavelet denoising were performed with different thresholds. A case study is conducted with a NASA battery dataset to demonstrate the effectiveness of the method. The result shows that the proposed method can obtain Li-ion batteries’ RUL effectively.

Subject Areas

lithium-ion (Li-ion) battery; remaining useful life (RUL); health indicator (HI); generalized regression neural network (GRNN); non-linear autoregressive (NAR)

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