Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs

摘要: Abstract Battery management systems in hybrid electric vehicle battery packs must estimate values descriptive of the pack’s present operating condition. These include: state charge, power fade, capacity and instantaneous available power. The estimation mechanism adapt to changing cell characteristics as cells age therefore provide accurate estimates over lifetime pack. In a series three papers, we propose method, based on extended Kalman filtering (EKF), that is able accomplish these goals lithium ion polymer We expect it will also work well other chemistries. papers cover required mathematical background, modeling system identification requirements, final solution, together with results. order use EKF desired quantities, first require model can accurately capture dynamics cell. this paper “evolve” suitable from one very primitive more advanced works practice. includes terms describe dynamic contributions due open-circuit voltage, ohmic loss, polarization time constants, electro-chemical hysteresis, effects temperature. give means, EKF, whereby constant parameters may be determined test data. Results are presented demonstrate possible achieve root-mean-squared error smaller than level quantization expected an implementation.