Stochastic dynamic modeling of lithium battery via expectation maximization algorithm

Lithium battery is a reliable source for mobile, computers and electric vehicles. However, the internal chemical reaction of lithium battery is complex and susceptible to external influences, such that the traditional model-driven approach cannot model it accurately. In this paper, based on the data-driven approach, an expectation maximization algorithm is proposed to model a class of lithium battery. By using the expectation maximization algorithm, the model parameters and actual values of test, as well as the noise intensity can be identified simultaneously. The NASA battery data sets are employed to demonstrate the effectiveness of the proposed algorithm. Several indices are presented to evaluate the inferred lithium battery models.