Kalman-Filter SOC Estimation for LiPB HEV Cells

Kalman filtering methods have been reported for SOC estimation [1–3]. What sets this new method apart from previous results is that the SOC must explicitly be a state in the system state vector. The advantage of this approach is that not only is SOC estimated, but also dynamic error bounds on the estimate are automatically given—a by-product of the Kalman approach. That is, instead of reporting the SOC to the vehicle controller (at some point in time) to be “about” 55%, the algorithm is able to report that the SOC is 55%±7%, for example.