Prediction of lithium-ion battery SOC in EV based on genetic neural network

HUANG Yao-bo1， TANG Hai-ding1， ZHANG Huan2， WENG Guo-qing2
（1. Jianxing Honors College， Zhejiang University of Technology， Hangzhou 310023， China；
2. College of Information Engineering， Zhejiang University of Technology， Hangzhou 310023， China）

Abstract： Aiming at limits and insufficiency of orthodox methods in state of charge（SOC） estimation for lithium-ion battery of electric vehicle（EV），an algorithm based on genetic neural network was presented. Firstly，the realization of a lithium-ion battery condition monitoring system was introduced；the sample data of voltage， current and SOC of a lithium-ion battery were obtained by different rate discharge. Secondly，the genetic algorithm was used to train the weight values and threshold values of BP network considering its ability of global optimization；the BP network was trained with the data collected from experiments，and then the SOC was distinguished from the well-trained neural network；the feasibility of the algorithm was demonstrated by comparing real SOC with predicted SOC. The results indicate that this scheme not only obtains the residual capacity through the voltage and current but also has a quick convergent velocity，less error，wide adaptation range，and can estimate the SOC of a lithium-ion battery in EV effectively.
Key words： electric vehicle（EV）； lithium-ion battery； prediction of state of charge（SOC）； genetic neural network