Estimation of battery parameters of the equivalent circuit model using Grey Wolf Optimization

Abstract

For dynamic simulation of battery electric vehicles, it is vital to estimate accurately battery parameters, to use battery effectively. The estimation of parameters deploys experimental methods that are expensive, require high computational power and are time-consuming. Hence to overcome this problem, a methodology based on meta-heuristic techniques (Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and recently proposed Grey Wolf Optimization (GWO)) has used. These techniques are simple to use and require less computational power. Estimation has done by how close the model estimated voltage curve is to the known catalogue voltage curve and feasibility of techniques evaluated by accuracy (minimizing error) and it's the rate of convergence. Investigation showed that GWO has the best accuracy among meta-heuristic techniques for estimation of the battery parameters.