An Improved Unscented Particle Filter Method for Remaining Useful Life Prognostic of Lithium-ion Batteries with Li(NiMnCo)O2 Cathode with Capacity Diving

Abstract

An improved method for the remaining useful life (RUL) prognostic of Lithium-ion batteries with Li(NiMnCo)O2 cathode using improved unscented particle filter (UPF) is proposed with respect to capacity diving in later capacity degradation curve. Key points of this paper are: (1) An appropriate empirical model for the situation as the most contributive work, is put forward as an alternative to the widely used UPF models, and the prediction performance is respectively verified by least square fitting and the improved UPF; (2) Systematic noise in Gamma distribution is attempted in state space equations of the proposed method, so as to avoid potential shape shifting of the prediction curve after sampling the particles with Gaussian noise, for model parameters could get zero-crossed; (3) With training data preprocessed considering the capacity recovery phenomenon concisely, the residual error and root mean square error of fitting could get further reduced, as a supplement to traditional treatments like smoothing, thus relieving the sensitivity of data-driven methods to data by enhancing quality. Validations are implemented by applying the proposed method to the battery data by conducting cycle aging tests under different working conditions, where improved approximation and prediction performance can be obtained.