Conditional Extended Kalman Filter for Battery Model Parameter Identification

Abstract

In this paper a conditional Extended Kalman Filter is applied to battery model parameter and state estimations. A decision logic, based on battery input and output data, is designed such that parameter update is stopped when persistent excitation conditions are not met. Persistent excitation conditions are represented by a simpler, easier to implement set of calibrations. Examples, both from desktop simulation, and real world vehicle testing, have been provided to support the validity of this algorithm. The proposed strategy has been successfully deployed in production FHEV/PHEV vehicles.