An online model-based method for state of energy estimation of lithium-ion batteries using dual filters

Abstract The state-of-energy of lithium-ion batteries is an important evaluation index for energy storage systems in electric vehicles and smart grids. To improve the battery state-of-energy estimation accuracy and reliability, an online model-based estimation approach is proposed against uncertain dynamic load currents and environment temperatures. Firstly, a three-dimensional response surface open-circuit-voltage model is built up to improve the battery state-of-energy estimation accuracy, taking various temperatures into account. Secondly, a total-available-energy-capacity model that involves temperatures and discharge rates is reconstructed to improve the accuracy of the battery model. An extended-Kalman-filter and particle-filter based dual filters algorithm is then developed to establish an online model-based estimator for the battery state-of-energy. The extended-Kalman-filter is employed to update parameters of the battery model using real-time battery current and voltage at each sampling interval, while the particle-filter is applied to estimate the battery state-of-energy. Finally, the proposed approach is verified by experiments conducted on a LiFePO 4 lithium-ion battery under different operating currents and temperatures. Experimental results indicate that the battery model simulates battery dynamics robustly with high accuracy, and the estimates of the dual filters converge to the real state-of-energy within an error of ±4%.