Battery management systems are essential in electric vehicles and renewable energy applications, especially in terms of ensuring optimal battery health and performance and regarding the state of charge (SOC) in batteries consisting of many cells. The lifetime and efficiency of the battery depend on the accuracy of the SOC parameter estimation. Moreover, systems that apply active balancing technology are able to move cells with high SOC data to cells with low SOC. Many methods have been developed, but their long execution time makes them less optimal when applied. High-speed SOC estimation is required in active balancing technology, in addition to high accuracy. Therefore, this study proposes the estimation of SOC parameters using a statistical and metaheuristic approach from voltage and current input data in each battery cell. The experimental results showed that the metaheuristic-based method (ANFIS) had better RSME and R² values compared with the polynomial and linear regression or even the machine learning-based method (recurrent neural network) for training data.

Battery management systems are essential in electric vehicles and renewable energy applications, especially in ensuring optimal battery health and performance. Especially regarding the state of charge (SOC) in batteries consisting of many cells. The lifetime and efficiency of battery depend on the accuracy of the SOC parameter estimation. Moreover, the system that applies active balancing technology will move cells that have high SOC data to cells that have low SOC. Many methods have been developed, but the long execution time makes it less than optimal when applied. The speed of SOC estimation is also required in active balancing technology in addition to the accuracy factor. Therefore, this study proposes to estimate SOC parameters using a statistical and metaheuristic approach method from voltage and current input data in each battery cell. The experimental results showed that the metaheuristic-based method (ANFIS) had better RSME and R2 values compared to the polynomial and linear regression method.