An improved battery on-line parameter identification and state-of-charge determining method

To improve the estimation accuracy of battery's inner state for battery management system, an improved online parameters identification algorithm for equivalent circuit battery model is researched. To reduce the computation cost, the existing methods regarded the open circuit voltage over time as a constant value. However, when the sampling intervals are bigger, the estimation error of the battery state-of-charge calculated by the traditional method can be reach to 10% or more. Compared with the existing battery model parameter identification method, this study proposes a new online estimation method and which can estimate the battery open-circuit voltage in different sampling intervals with high accuracy. The results of the experiment, which uses Federal Urban Driving Schedule test to verify the parameters identification approach, show the proposed approach can accurately identify the model parameters within 1% maximum terminal voltage estimation error, and the state-of-charge error which calculated by the open circuit voltage estimates can be efficiently reduced to an accepted level. (C) 2016 The Authors. Published by Elsevier Ltd.