Adaptive state of charge estimation for battery packs

Abstract

Rechargeable batteries as an energy source in electric vehicles (EVs), hybrid electric vehicles (HEVs) and smart grids are receiving more attention with the worldwide demand for greenhouse reduction. In all of these applications, the battery management system needs to have an accurate online estimation of the state of charge (SOC) of the battery pack. This estimation is difficult, especially after substantial aging of batteries. In order to overcome this problem, this work addresses SOC estimation of Li-ion battery packs using fuzzy-improved extended Kalman filter (fuzzy-IEKF) from new to aged cells. In the proposed approach, a fuzzy method with a new class of membership function has been introduced and used to make the approximate initial value to estimate SOC. Later on, the IEKF method, considering the unit single model for the battery pack, is applied to estimate the SOC for the long working time of the pack. This approach uses a model adaptive algorithm to update each single cell's model in the battery pack. The algorithm's fast response and low computational burden, makes on-board estimation practical. A LiFePO4 single cell/battery pack consists of single/120 cells connected in series with a nominal voltage 3.6V/432 V is used to implement the experiments/simulations to verify the SOC estimation method's accuracy. The obtained results by the federal test procedure (FTP75) and the new European driving cycle (NEDC) reveal that the proposed approach's SOC and voltage estimation error do not exceed 1.5%.