International Journal of Energy Research, Vol.44, No.6, 4812-4826, 2020
Equalization of series connected lithium-ion batteries based on back propagation neural network and fuzzy logic control
In this article, a nondissipative equalization scheme is proposed to reduce the inconsistency of series connected lithium-ion batteries. An improved Buck-Boost equalization circuit is designed, in which the series connected batteries can form a circular energy loop, equalization speed is improved, and modularization is facilitated. This article use voltage and state of charge (SOC) together as equalization variables according to the characteristics of open-circuit voltage (OCV)-SOC curve of lithium-ion battery. The second-order RC equivalent circuit model and back propagation neural network are used to estimate the SOC of lithium-ion battery. Fuzzy logic control (FLC) is used to adjust the equalization current dynamically to reduce equalization time and improve efficiency. Simulation results show that the traditional Buck-Boost equalization circuit and the improved Buck-Boost equalization circuit are compared, and the equalization time of the latter is reduced by 34%. Compared with mean-difference algorithm, the equalization time of FLC is decreased by 49% and the energy efficiency is improved by 4.88% under static, charging and discharging conditions. In addition, the proposed equalization scheme reduces the maximum SOC deviation to 0.39%, effectively reducing the inconsistency of batteries.
Keywords:back propagation neural network;battery equalization;fuzzy logic control;improved Buck-Boost equalization circuit;state of charge