Capacity Estimation of Lithium-ion Battery by GA-KF Method

In this study, the capacity estimation of the lithium-ion battery was successfully proposed using the genetic algorithm-Kalman filter method. During the design phase of electrical devices, lithium-based batteries have begun to be preferred instead of wired electricity transmission due to flexibility, freedom of movement, and portability problems. In addition, from an environmental perspective, the use of electric vehicles has become more important than the use of internal combustion engine vehicles. In this study, the capacity estimation of the 18650 lithium-ion battery, which is the most preferred in electric vehicles, was made quickly and accurately. The performances of the Standard Kalman Filter and the Kalman Filter, whose parameters are determined by the Genetic Algorithm, were compared by estimating the battery capacity. By giving the results obtained by the Genetic Algorithm during the parameter search process, the most appropriate values and the important parameters of the Kalman Filter have been determined. The success of the proposed method is given by the experimental results. In the performance comparison, the success of the proposed method is given using RMSE, MSE, and R2 metrics. When the average of all experiments was calculated using the R2 metric, the Genetic Algorithm-Kalman Filter approach achieved the best results in estimating the capacity of the 18650 lithium-ion battery, with a value of 0.999874.