Abstract
Lithium-ion batteries are the main form of energy providers for electric vehicles. To ensure the reliability and the safety of electric vehicles, it is necessary to estimate the remaining useful life of lithium-ion batteries. Aiming at the strong nonlinear characteristics prevalent in the battery degradation process, this paper proposes a new method for predicting the remaining useful life of lithium-ion batteries based on stochastic model. A new nonlinear degradation model is established based on the diffusion process to characterize the degradation process in the lithium-ion batteries. The battery lifetime and the remaining useful life at any inspection cycle are defined based on the concept of the first hitting time, and the probability density functions of battery lifetime and remaining useful life are derived. Finally, the unknown parameters of the model are estimated by using the maximum likelihood estimation method and the historical data of battery degradation. Remaining useful life prediction experiments are performed based on two published data sets. The experimental results verify with the reliability and accuracy of the proposed method.
Highlights
Pollution and energy deficiency are two major problems that automotive industry is facing
Wang et al [9] proposed a remaining useful life (RUL) prediction method for lithium-ion battery consisting of relevance vector machines (RVM) algorithm and capacity degradation model
The NASA data set is from the National Aeronautics and Space Administration (NASA) Ames Prognostics Center of Excellence, which includes three sets of battery capacity degradation data obtained after three different lithium-ion batteries (#5, #6, #7) were charged and discharged
Summary
Pollution and energy deficiency are two major problems that automotive industry is facing. Wang et al [9] proposed a RUL prediction method for lithium-ion battery consisting of relevance vector machines (RVM) algorithm and capacity degradation model. Aiming at the strong nonlinear characteristics of lithium-ion battery degradation process in dynamic environment, this paper proposes a new stochastic model based battery RUL prediction method. In order to accurately predict lithium-ion battery life and RUL, this paper uses the observed battery capacity degradation data to estimate the unknown model parameters by maximum likelihood estimation. Since the capacity degradation processes of different batteries are independent of each other, the log likelihood function of the parameter vector = (α1, α2, β1, β2, σB) based on all observed battery capacity degradation data X can be represented by The maximum likelihood estimates of parameters (α1, α2, β1, β2, σB) can be obtained by maximizing (13), which will be obtained by multidimensional search in Matlab
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