Ant Colony Optimized Extended Kalman Filter for State of Charge Estimation of Lithium-Ion Batteries

This work reports the practical implementation of ant colony optimized (ACO) extended Kalman filter (EKF) for state of charge (SoC) estimation of lithium-ion batteries (LiBs). The scheme is realized by employing a low-priced microcontroller. Two prominent parameters of EKF, namely, process (Q) and measurement (R) error covariances, are fine-tuned using the ACO algorithm. The problem of finding optimum values of Q and R together with SoC estimation is reformulated as an optimization problem. The proposed method has several advantages, such as computational simplicity, derivative-free operation, and near-optimal convergence with random initial guess. The performance, effectiveness, and robustness of the new SoC estimation are demonstrated for four different operating points of the tested battery. Measured mean square, mean absolute, and mean absolute percentage errors of 0.0165%, 1.2%, and 2%, respectively, with 99.34% R-squared value indicates that the proposed method is a promising candidate for the estimation of SoC of LiBs. Furthermore, the effectiveness of the ACO-tuned EKF approach is experimentally validated in comparison to the conventional EKF method.