Modeling and state of health estimation of nickel–metal hydride battery using an EPSO-based fuzzy c-regression model

Abstract

The prognostic and health management of the batteries continued to attract interest from automobile manufacturers as the key for lowering life-cycle costs, reducing unexpected power outages, and one of the most important and efficient ways for energy storage for electric vehicle applications. Indeed, an effective battery health monitoring depends on accurate estimation of state of health (SOH). However, the SOH cannot be directly measured by sensors in the battery management system. Moreover, the SOH estimation based on a standard resistor–capacitor (RC) battery model is not so accurate because a RC model is obtained with some approximations and without taking into account more detailed knowledge about the chemical reactions happening inside the battery. In this paper, a combined battery modeling and SOH estimation method over the lifespan of a nickel–metal hydride (Ni–MH) battery is proposed. First, a fuzzy c-regression model based on Euclidean particle swarm optimization is applied to modeling a Ni–MH battery. Second, the SOH monitoring is determined according to the discharge rate of the battery model. The performance of the proposed method has been analyzed through the modeling and the estimation of the SOH using a real data set of the Ni–MH battery.