Abstract
The degradation of lithium-ion battery often leads to electrical system failure. Battery remaining useful life (RUL) prediction can effectively prevent this failure. Battery capacity is usually utilized as health indicator (HI) for RUL prediction. However, battery capacity is often estimated on-line and it is difficult to be obtained by monitoring on-line parameters. Therefore, there is a great need to find a simple and on-line prediction method to solve this issue. In this paper, as a novel HI, permutation entropy (PE) is extracted from the discharge voltage curve for analyzing battery degradation. Then the similarity between PE and battery capacity are judged by Pearson and Spearman correlation analyses. Experiment results illustrate the effectiveness and excellent similar performance of the novel HI for battery fading indication. Furthermore, we propose a hybrid approach combining Variational mode decomposition (VMD) denoising technique, autoregressive integrated moving average (ARIMA), and GM(1,1) models for RUL prediction. Experiment results illustrate the accuracy of the proposed approach for lithium-ion battery on-line RUL prediction.
Highlights
Driven by energy crisis and environmental pollution, new energy technologies such as supercapacitors and lithium-ion batteries have attained global attention in the application of electric vehicles [1,2]
The discharge experiments were stopped when the battery capacity dropped by 30%
We use Variational mode decomposition (VMD)–autoregressive integrated moving average (ARIMA)–GM(1,1) and empirical mode decomposition (EMD)–ARIMA approaches to make remaining useful life (RUL) prediction based on permutation entropy (PE) at five different starting points for No 5 and 18 batteries, respectively
Summary
Driven by energy crisis and environmental pollution, new energy technologies such as supercapacitors and lithium-ion batteries have attained global attention in the application of electric vehicles [1,2]. Hu et al [5] combined the Bayesian linear regression model and Artificial Neural Network (ANN) model to improve lithium-ion battery RUL prediction accuracy. To overcome the problem of on-line monitoring, Liu et al [11] proposed that the time interval of equal discharging voltage difference (TIEDVD) can be used as health indicator (HI). In order to give a simple life prediction approach by monitoring the on-line battery parameter, we propose a novel approach for lithium-ion battery on-line RUL prediction based on permutation entropy (PE). To solve the two above problems, we propose a hybrid model that can effectively improve the on-line battery RUL prediction accuracy. We combine the VMD denoising, ARIMA, and GM(1,1) models to predict the battery RUL.
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