RESEARCH PAPER
Prediction of remaining useful life for lithium-ion battery with multiple health indicators
1
School of Mechanical Engineering, Southeast University, Nanjing 211189, China
2
Hunan Provincial Key Lab of Health Maintenance for Mechanical Equipment, Hunan University of Science and Technology, Xiangtan 411201, Hunan, China
Publication date: 2021-03-31
Eksploatacja i Niezawodność – Maintenance and Reliability 2021;23(1):176-183
HIGHLIGHTS
- Four types of health indicators(HIs) are built with the battery operating data.
- GRNN is applied to estimate the battery’s remaining capacity with the HIs.
- Based on the predicted capacity value, the battery’s RUL is estimated with NAR.
KEYWORDS
lithium-ion (Li-ion) batteryremaining useful life (RUL)health indicator (HI)generalizedregression neural network (GRNN)non-linear autoregressive (NAR)
ABSTRACT
Lithium-ion (Li-ion) battery has become a primary energy form for a variety of engineering equipments. To ensure the equipments’ reliability, it is crucial to accurately predict Liion battery’s remaining capacity as well as its remaining useful life (RUL). In this study,
we propose a novel method for Li-ion battery’s online RUL prediction, which is based on
multiple health indicators (HIs) and can be derived from the battery’s historical operation
data. Firstly, four types of indirect HIs are built according to the battery’s operation current,
voltage and temperature data respectively. On this basis, a generalized regression neural
network (GRNN) is presented to estimate the battery’s remaining capacity, and the nonlinear autoregressive approach (NAR) is applied to predict the battery’s RUL based on the
estimated capacity value. Furthermore, to reduce the interference, twice wavelet denoising
are 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.
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| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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