Estimation of optimal li-ion battery parameters considering c-rate, SOC and temperature

To improve both the operating performance and cycle life of batteries in battery electric vehicles requires a battery model that can influence fidelity behavior of the battery under different conditions. Hence, two commonly used equivalent circuit battery models (first-order and second-order RC) are tested using GA, PSO, and ASMO optimization techniques. The paper describes the estimation of parameters of battery model at various temperatures for the Li-ion battery. To diagnostic estimation, battery terminal voltage characteristic curve estimation accuracy during charging and discharging scenarios used for comparing. Feasibility of various optimization techniques evaluated by the accuracy of predicted model and the rate of convergence in predicting the model parameters. For performance investigation, optimization techniques are compared in terms of performances index such as mean, standard deviations, worst and best fitness functions values. The lower mean fitness value and standard deviation of the ASMO prove its durability as well as reliability in the battery parameter estimation scenario. Further, the low computational requirements signify the robustness of the algorithm, thus making ASMO as prime choice for the task of battery parameter estimation. Also the second order RC model proved out to be more robust as for all scenarios and the performance of optimization techniques improved by use of this model.