Implementation of Online Battery State-of-Power and State-of-Function Estimation in Electric Vehicle Applications

A method for estimating battery state-of-function (SOF) is presented with a mathematical probabilistic statement within the context of Kalman filter estimation. The traditional state-of-power (SOP) metric is replaced with an equivalent statistic that delivers the desired SOF estimate with defined variance characteristics. To reduce error in the recursive estimator, a model based on an offline test relating the open-circuit voltage (OCV) to its rate of change with battery charge is introduced that provides better temperature insensitivity than the SOC vs. OCV model typically used in literature. Experimental test results for a LiFePO4 battery with a vehicle drive cycle are used to build confidence in the estimator results. Additionally, results from the proposed estimator are compared with results from the hybrid pulse power characterization (HPPC) test and the important model assumptions are discussed.