Design of Battery State-of-Health Estimator Based on Least-Squares Support Vector Machine

The design and development of a data-driven algorithm to estimate the State-of-Health of a battery is presented. The approach is based on a data-driven Least-Squares Support Vector Machine approach. By combining the data-driven method with a dataset pruning procedure and nonlinear optimization technique, the computational complexity of the estimator is reduced whilst maintaining the performance of the estimator. The design approach was validated in simulation testing by considering the simulated model of a battery. An Estimator Design Tool was developed within the MATLAB environment. It provides a user-friendly interface for the different algorithms that may be used in the estimator design. The approach and tool is quite general and is suitable for a wide range of other estimation applications.