An online model-based battery parameter and state estimation method using multi-scale dual adaptive particle filters

Accurate estimations of battery parameter and state are very important for battery management in electric vehicles. To improve estimation accuracy and robustness of battery parameter and state, and to reduce computational cost, an online model-based estimation approach is proposed, Firstly, the lithium-ion battery is modeled using the Thevenin model, Then, A multi-scale dual particle filters has been proposed and applied to the battery parameter and state estimation. Finally, to elevate the accuracy and the ability of convergence to initial states' offset, a multi-scale dual adaptive particle filter was proposed and applied to the battery parameter and state estimation. Experimental results on various degradation states of lithium-ion battery cells further verified the feasibility of the proposed approach. (C) 2017 The Authors. Published by Elsevier Ltd.