A Novel Estimation Method for the State of Health of Lithium-Ion Battery Using Prior Knowledge-Based Neural Network and Markov Chain

The state of health (SOH) of lithium-ion batteries (LIBs) is a critical parameter of the battery management system. Because of the complex internal electrochemical properties of LIBs and uncertain external working environment, it is difficult to achieve an accurate SOH determination. In this paper, we have proposed a novel SOH estimation method by using a prior knowledge-based neural network (PKNN) and the Markov chain for a single LIB. First, we extract multiple features to capture the battery aging process. Due to its effective fitting ability for complex nonlinear problems, the neural network with a prior knowledge-based optimization strategy is adopted for the battery SOH prediction. The Markov chain, with the advantageous prediction performance for the long-term system, is established to modify the PKNN estimation results based on the prediction error. Experimental results show that the maximum estimation error of the SOH is reduced to less than 1.7% by adopting the proposed method. By comparing with the group method of data handling and the back-propagation neural network in conjunction with the Levenberg-Marquardt algorithm, the proposed estimation method obtains the highest SOH accuracy.