A Hybrid Method for Remaining Useful Life Estimation of Lithium-Ion Battery with Regeneration Phenomena

Lin Zhao,Yipeng Wang,Jianhua Cheng

doi:10.3390/app9091890

Abstract

The lithium-ion battery has become the primary energy source of many electronic devices. Accurately forecasting the remaining useful life (RUL) of a battery plays an essential role in ensuring reliable operatioin of an electronic system. This paper investigates the lithium-ion battery RUL prediction problem with capacity regeneration phenomena. We aim to reduce the accumulation of the prediction error by integrating different capacity degradation models and thereby improve the prediction accuracy of the long-term RUL. To describe the degradation process more accurately, we decoupled the degradation process into two types: capacity regeneration and normal degradation. Then, we modelled two kinds of degradation processes separately. In the prediction phase, we predicted the battery state of health (SOH) by using the relevance vector machine (RVM) and the gray model (GM) alternately, updated the training dataset according to the prediction results, and then updated the RVM and GM. The RVM and GM correct each other’s prediction results constantly, which reduces the cumulative error of prediction and improves the prediction accuracy of the battery SOH. Experimental results with the National Aeronautics and Space Administration (NASA) battery dataset demonstrated that the proposed method can accurately establish the degradation model and achieve better performance for the RUL estimation as compared with the single RVM or GM methods.

Highlights

With the advantages of high energy/power density, low self-discharge rate, and longevity, lithium-ion batteries have been widely used in consumer electronics, electric vehicles, and even in space systems [1,2]
Experimental results with the National Aeronautics and Space Administration (NASA) battery dataset demonstrated that the proposed method can accurately establish the degradation model and achieve better performance for the remaining useful life (RUL) estimation as compared with the single relevance vector machine (RVM) or gray model (GM) methods
To predict the normal degradation account the effect of the regenerative phenomena,we extract the state of health (SOH) values of the regeneration cycle, trends more accurately, we propose a hybrid model for fusing the RVM and GM

Summary

Introduction

With the advantages of high energy/power density, low self-discharge rate, and longevity, lithium-ion batteries have been widely used in consumer electronics, electric vehicles, and even in space systems [1,2]. Battery performance will degrade with the charging and discharging cycles. The prognostics of the state of health (SOH) of a lithium-ion battery is meaningful for reducing system risks and maintenance costs [4,5]. The prognostic of a lithium-ion battery is a process of predicting its SOH and remaining useful life (RUL) [6]. The lithium-ion battery RUL is defined as the remaining number of charge-discharge cycles of the battery with a specific output capacity [7]. Methods for battery prognostics have mainly included a model-based approach, a data-driven approach, and a hybrid approach

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Conclusion