Equivalent circuit model parameters estimation of Li-ion battery: C-rate, SOC and Temperature effects

The paper describes the estimation of parameters of battery model at various temperatures for the Li-ion battery. The estimation of parameters employs experimental methods that are time-consuming, expensive and require high computational power. Hence, second-order RC network equivalent circuit model parameters estimation is done using GA, PSO, and DE optimization techniques. For proper evaluation, the mathematical model was build to incorporate the effect of the state of charge, crate and temperature variations in the battery. Estimation has been done in terms of the predicted voltage curve’s closeness to the known true voltage curve. Feasibility of various optimization techniques is examined by the accuracy of predicted model and the rate of convergence in the estimation of the model parameters. Investigation showed that the DE algorithm has the best accuracy among the meta-heuristic optimizers for battery parameter estimation at various temperatures of both charging and discharging scenario. Further analysis showed DE algorithm was reliable as well as computationally less expensive compared to other optimization techniques.