Abstract
Remaining useful life (RUL) prediction has great importance in prognostics and health management (PHM). Relaxation effect refers to the capacity regeneration phenomenon of lithium-ion batteries during a long rest time, which can lead to a regenerated useful time (RUT). This paper mainly studies the influence of the relaxation effect on the degradation law of lithium-ion batteries, and proposes a novel RUL prediction method based on Wiener processes. This method can simplify the modeling complexity by using the RUT to model the recovery process. First, the life cycle of a lithium-ion battery is divided into the degradation processes that eliminate the relaxation effect and the recovery processes caused by relaxation effect. Next, the degradation model, after eliminating the relaxation effect, is established based on linear Wiener processes, and the model for RUT is established by using normal distribution. Then, the prior parameters estimation method based on maximum likelihood estimation and online updating method under the Bayesian framework are proposed. Finally, the experiments are carried out according to the degradation data of lithium-ion batteries published by NASA. The results show that the method proposed in this paper can effectively improve the accuracy of RUL prediction and has a strong engineering application value.
Highlights
The above works show that the remaining useful life (RUL) prediction with considering the relaxation effect is of great importance in prognostics and health management (PHM) of lithium-ion batteries
A novel online RUL prediction method is proposed by studying the relaxation effect on the degradation law of lithium-ion batteries, and the experiments are carried out based on the degradation data of lithium-ion batteries published by National Aeronautics and Space Administration (NASA)
The results show the effectiveness and better accuracy of the proposed method by comparing with the method of that not considering the relaxation effect
Summary
Due to the long life, high energy density, fast charging speed, and high voltage, lithium-ion batteries have become an important research and development direction for energy storage batteries in a new energy field [1,2,3]. In PHM, remaining useful life (RUL) prediction can estimate the failure time and mitigate the risk for lithium-ion batteries by assessing the battery health [8], which is a key issue in order to make appropriate maintenance strategies and reduce accident risk [9]. Energies 2019, 12, 1685 the time-varying external environment and the complexity of internal electrochemical performance in practical application, the lithium-ion batteries’ health degrades irregularly. This increases the difficulty for RUL prediction and leads to no universally accepted RUL prediction method [10]. To improve the safety and reliability for lithium-ion batteries, the RUL prediction method with considering the relaxation effect needs to be further studied
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