State of health estimation of lead acid battery bank in a renewable energy system by parameter identification with genetic algorithms

Accurate prediction of battery energy storage system state of health is very important in renewable energy systems. This paper presents a methodology for state of health estimation of lead acid battery bank by parametric identification. A particle swarm optimization algorithm is used for parameter fitting of a real battery bank. A periodic perturbation is introduced in the population to prevent the algorithm from falling into local minimums. The perturbation will consist of a new population $PS_j^k$ based on the best global solution allowing the reactivation of the PSO algorithm. The proposed method is validated using experimental data that is obtained from a renewable energy system located at Chocó - Colombia. The capacity, state of health, and internal resistance of the battery bank is estimated and the evolution of the parameters associated with the battery capacity are shown.