Particle swarm optimization of Elman neural network applied to battery state of charge and state of health estimation

Abstract

Lithium-ion batteries have emerged as an energy storage solution for electrified vehicles. A Battery Management System (BMS) is critical for efficient and reliable system operation, in which State of Charge (SoC) estimation and State of Health (SoH) monitoring are of major importance to ensure optimal energy management in battery vehicles for increased autonomy and battery life. This paper presents a neural network with Elman architecture trained for a lithium-ion cell, aiming at SoC and SoH estimation. The multi-objective optimization approach based on the particle swarm algorithm is used for the training in order to lower the root mean square error in calculating the SoC and SoH. For such purposes, the neural network characteristics are optimized, such as the number of hidden layers, the number of neurons in each hidden layer, the activation functions, the bias value, and the weights of the inputs and outputs. The best trade-off solution has an error of 2.56% in the average SoC estimate and 0.003% in the average SoH estimate.