Model Based Battery Management System for Condition Based Maintenance

A generalized approach for combining state of charge (SOC) and state of health (SOH) techniques together to create a self-adaptive battery monitoring system is discussed. First, previously published techniques and their feasibility for on-line SOC and SOH estimation are reviewed. Then, a method of utilizing SOH predictions to update the SOC estimator in order to minimize drift due to capacity loss and cell degradation is given. This method is demonstrated by combing an equivalent circuit model to estimate SOC with an empirical/data driven model of capacity fade to estimate SOH. The method is validated with data obtained through cycle life testing of a lithiumion battery. Parameters for the equivalent circuit model are initially extracted from electrochemical impedance spectroscopy data and are updated by least squares fitting and future SOH predictions. Lastly the results of SOC and SOH estimations are translated into terms that can be easily interpreted by an electric vehicle user so that the presented method can be implemented into an onboard fuel gauge and condition based maintenance system.