Prediction of lithium-ion battery's remaining useful life based on Wiener process

Lithium-ion battery has a complex internal structure and is easily affected by the external environment, which makes its capacity state degrade with uncertainties and randomness. State space model is used to describe the degradation process of battery capacity which obeys nonlinear Wiener process, and the parameters of state space model are subject to conjugate distributed random variables, which adds the uncertainties of the model and makes it more consistent with the degradation process of the lithium-ion batteries. Bootstrap method is used to obtain the initial parameters of the prior distribution. Besides, due to the property of conjugate distribution, the posterior distribution type is the same as the type of prior distribution, therefore, a simple parameter estimation method can be obtained. Particle filter (PF) contributes to estimate and update the parameters and degradation state at each time. According to the state threshold set in advance, remaining useful life (RUL) of the battery can be predicted. The accuracy of this method is verified by an example. It is shown that the proposed method can provide reference for remaining useful life prediction of batteries with high reliability and small sample applications.