摘要
搭建了混合动力汽车动力电池的性能实验平台,针对车辆实际行驶工况,在不同环境温度下对动力电池进行了相关充放电实验。利用实验系统采集到的动力电池电压与电流,采用自校正模糊神经网络控制算法对常温25℃下的动力电池荷电状态(State of Charge,SOC)进行计算,并与Arbin动力电池测试设备计算出的动力电池荷电状态进行了比较。理论分析和实验结果表明,采用自校正模糊神经网络控制算法计算出的电池SOC满足混合动力汽车电池SOC所需的精度要求。
A performance test bench for power batteries in hybrid electric vehicles is built. According to vehicles＇ real driving condition, the correlative charge and discharge experiments of power batteries are carried out in different environmental temperatures. A new algorithm about self correction fuzzy neural network control is used to calculate the state of charge（SOC）of power batteries at 25 °C, and the result is compared with that of Arbin test instrumentation. Theoretical analysis and experimental results suggest that the accuracy of SOC calculated by the algorithm of correction fuzzy neural network control under variable rate discharge for power batteries meets the requirements.
出处
《重庆大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2011年第5期19-25,共7页
Journal of Chongqing University
基金
国家自然科学基金资助项目(51075411)
重庆市重大科技专项(CSTC2008AA6025)
关键词
混合动力汽车
动力电池
荷电状态
模糊神经网络
hybrid electric vehicles （HEV）
power batteries
state of charge （SOC）
fuzzy neural network
