An equivalent time-varying circuit model of lithium-ion batteries with its applications

In order to address the challenging issue where model parameters of Thevenin equivalent circuit models of Li(thium)-ion batteries cannot be identified online with constant current, voltage and/or power charge/discharge, a new equivalent time-varying model is reported in this paper. Under various operating conditions, it is demonstrated that the open-circuit voltage (OCV) and internal resistance of Li-ion batteries can be respectively viewed as the time-varying ones with evolutions described by coulomb counting and the random walk model. Although the time-varying self-discharge transient state evolution of batteries cannot be represented by coulomb counting and the random walk model, it can be illustrated by observation noise with a long tail probability density function (pdf) of unknown parameters. In this way, the novel equivalent time-varying model described in terms of a state space equation is given. By a presented adaptive Bayesian learning method, both the parameters of the state space equation and model can be inferred online in sense of maximum a posterior probability. It also implies that Li-ion batteries can be robustly characterized/estimated by our model online. Both the datasets available in internet and our experiments with the Li-ion ternary and Li(thium)-FePO4 batteries verify the effectiveness of our model, analyses, and algorithm.