Evaluation of nature-inspired algorithms for battery parameter estimation in renewable energy generation systems

Batteries in renewable energy systems suffer problems that affect their service life and quality of performance. There fore, battery ma nagement systems (BMS) are employed in battery-integrated systems to maintain optimal operating conditions by various dynamic i mpedance based battery models. These models require an accurate model parameter for BMS to work effectively. In this paper, two recently developed metaheuristic optimization, namely bird mating optimizer (BMO) and lightning search algorithm (LSA) is used effectively to determine the required parameters of well -known Randle's battery model. Initially, electrochemical impedance spectroscopy (EIS) test for an EnerSys Cyclon lead-acid cell with a rated capacity of 2.5Ah is conducted using EZSTAT-pro Galvanostat/Potentiostat device from Nuvant systems Inc. Next, the Randle's battery model parameters are obtained by BMO and LSA and its performances are evaluated. The results show that BMO and LSA can accurately find the model parameters. LSA obtains slightly more accurate results than BMO and converges much faster. However, for the same number of iterations, BMO takes less computation time than LSA. The optimized model can be used in BMS for fault finding and condition monitoring