An Improved Self-Adaptive Flower Pollination-Backpropagation Neural Network for the State of Charge Estimation of Lithium-Ion Batteries with Output Sliding Average Window Strategy

Abstract

With the rapid development of electric vehicles and green energy sources, the use of backpropagation neural network (BPNN) to precisely estimate the state of charge (SOC) in lithium-ion batteries has become a popular research topic. However, traditionally BPNN has low prediction accuracy and large output fluctuations. To address the shortcomings of BPNN, self-adaptive flower pollination algorithm (SFPA) was proposed to optimize the initial weights and thresholds of BPNN, and an output sliding average window (OSAW) strategy is proposed to smooth SOC outputs in this research, which SOC estimation method is named SFPA-BP-OSAW. In addition, the performance of the newly-proposed method is compared with other common related algorithms under different working conditions to verify the effectiveness of SFPA-BP-OSAW. The experimental results show that the mean absolute error of SFPA-BP-OSAW is 0.771% and 0.897%, and the root mean square error is 0.236% and 0.37%, respectively, under HPPC and BBDST working conditions. Experimental data and error analysis show that the method proposed in this paper has fast convergence, high prediction accuracy, and curve smoothness.