Battery State-of-Charge Estimator Design based on The Least-Squares Support Vector Machine

The design and development of a data-driven algorithm for battery State-of-Charge estimation is presented. The estimation of battery SoC is important in the development of Battery Management Systems. The proposed approach exploits the Least-Squares Support Vector Machine data-driven estimation paradigm and statistical methods. The algorithm’s computational complexity is reduced by using a data pruning procedure. The optimization of the SVM-based estimator is performed by using a Particle Swarm Optimization method. The design approach proposed to develop to estimator is validated using a simulation model of the battery and an Estimator Design Tool in MATLAB software which provides a user-friendly interface for the different algorithms that may be used in the estimator design. The approach is applicable to a wide range of applications including automotive systems.