Elman neural network using ant colony optimization algorithm for estimating of state of charge of lithium-ion battery

Abstract

• The ELMAN neural network (ENN) with memory function is used to estimate the state of charge (SOC) of lithium-ion batteries. • The ant-colony optimization (ACO) algorithm is applied to optimize ENN. • The optimized ENN has higher accuracy in SOC estimation than other neural network methods. The state of charge (SOC) is a parameter to describe the remaining charge of lithium-ion batteries in electric vehicles. It is a key problem to be solved in the field of electric vehicles. In this paper, ant colony optimization (ACO) algorithm is creatively applied to improve Elman neural network to form ACO-Elman neural network model, and it is applied to lithium-ion battery SOC prediction for the first time. The ACO-Elman model is trained and tested under Dynamic Stress Test and Federal Urban Driving Schedule drive profiles. The SOC estimation results of ACO-Elman model are evaluated from three aspects: mean absolute error, root mean square error, and SOC error. The results show that the ACO-Elman model has high accuracy and robustness. It has a good application prospect.