State-of-charge estimation of lithium-ion battery using an improved neural network model and extended Kalman filter

Abstract

Abstract Accurate state-of-charge (SoC) estimation is remarkably difficult due to nonlinear characteristics of batteries and complex application environment in electric vehicles (EVs), particularly low temperature and low SoC. In this paper, an improved battery model is first built using a feedforward neural network (FFNN) by introducing newly defined inputs. Based on the FFNN model and the extended Kalman filter algorithm, a FFNN-based SoC estimation method is designed, and its robustness is verified and discussed using the experimental data obtained at different temperatures. Finally, a hardware-in-loop test bench is built to further evaluate the real-time and generalization of the designed FFNN model. The results show that the SoC estimation can converge to the reference value at erroneous settings of an initial SoC error and an initial capacity error, and the SoC estimation errors can be stabilized within 2% after convergence, which applies to all the cases discussed in this paper, including low temperature and low SoC. This indicates that the FFNN-based method is an effective method to estimate SoC accurately in complex EV application environment.