Battery management systems in hybrid-electric-vehicle battery packs must estimate values descriptive of the pack's present operating condition. These include: battery state-of-charge, power fade, capacity fade, and instantaneous available power. The estimation mechanism must adapt to changing cell characteristics as cells age and therefore provide accurate estimates over the lifetime of the pack. In a series of three papers, we propose methods, based on extended Kalman filtering (EKF), that are able to accomplish these goals for a lithium ion polymer battery pack. We expect that they will also work well on other battery chemistries. These papers cover the required mathematical background, cell modeling and system identification requirements, and the final solution, together with results. This third paper concludes the series by presenting five additional applications where either an EKF or results from EKF may be used in typical BMS algorithms: initializing state estimates after the vehicle has been idle for some time; estimating state-of-charge with dynamic error bounds on the estimate; estimating pack available dis/charge power; tracking changing pack parameters (including power fade and capacity fade) as the pack ages, and therefore providing a quantitative estimate of state-of-health; and determining which cells must be equalized. Results from pack tests are presented. (C) 2004 Elsevier B.V. All rights reserved.