Real-time Adaptive State of Energy Estimation of Lithium-ion Batteries Applied in Electric Vehicles

Abstract

State of energy estimation of lithium-ion batteries applied in electric vehicles is required for users to predict the battery recharge time. The paper developed a new mathematical model for estimating state of energy in real-time. The recursive least squares method with an optimal forgetting factor was used to identify model parameters, and the adaptive extended Kalman filter was used to estimate the state of energy. Experimental results indicated that the developed method can realize accurate model parameter estimation with modeling error less than 2 mV. The state of energy estimation error was less than 2%. The developed method can still estimate accurate state of energy even if an erroneous initial state of energy value was available.