LAUSR

Abstract This paper is concerned with the investigation of accurate parameter identification method and state of charge (SoC) estimation for Lion Lithium battery. The proposed identification method is implemented using an accurate state space model obtained from electric equivalent circuit. The process of parameter identification is expressed as nonlinear optimization problem. An Enhanced sunflower optimization algorithm (ESFOA) is employed to solve such problem. The search space is managed by applying the reduction strategy. This strategy is accomplished with the sunflower optimization algorithm to enhance the solution quality. Three cases studied are considered as single and multi-objective frameworks. In these cases, battery voltage or SoC or combined between them as objective functions are optimized for the three cases studied. Numerical simulations as well as experimental implementation are executed on 40 Ah Kokam Li-Ion Battery to prove the capability of the proposed parameter identification method. The ability of the proposed ESFOA is accomplished with high accuracy is proven compared with Water-Cycle and Whale optimization algorithms for two driving cycle profiles. Added to that, high closeness is achieved compared with the experimental measurements for battery parameters and SoC. The solution quality improvement of the proposed ESFOA is noticed as it achieves the lowest the fitness function levels (in the range 60–90%) of the cases studied compared with the competitive optimization algorithms.