State of charge neural computational models for high energy density batteries in electric vehicles

Abstract

Environmental concerns, increasing gasoline demand together with unpopularity of alternative energy sources to propel vehicles, have pushed on hybrid electric vehicles (HEVs) solutions. The main problem in battery management of HEVs is how to determine the battery state of charge (SOC). Estimation of SOC is an active area of research, and several approaches have been presented in the literature to monitor the SOC of a cell. At the first step, we use an optimized structure multi-layer perceptron (MLP) and an RBF neural network to determine the SOC changes of a high energy density battery in this paper. Then, a hybrid optimized structure neural model is used for SOC prediction considering the aging effect through the state of health (SOH) and discharge efficiency (DE) parameters. In this way, particle swarm optimization (PSO) algorithm is used for determining the optimum number of nodes in hidden layer(s) of MLPs. Experimental results show that the SOC estimation error by the proposed hybrid optimized structure neural model is 1.9% when compared with the real SOC obtained from a discharge test. In addition, monolithic optimized structure MLP and RBF neural models offer a good estimation of differentiated SOC.