摘要
针对混合动力汽车在复杂工况下动力电池温度测量可靠性下降的问题,提出基于pso_FSVM的车用动力电池温度预测模型,该研究分别采集车辆key_on和key_off两种状态下的动力电池温度数据,采用粒子群优化的快速支持向量机算法,构建了稳定的动力电池温度预测模型。实验结果表明,在车辆key_on和key_off两种状态下,数据集的预测数据与实际测量数据的相关系数分别达到0.810 2和0.797 3,温度预测误差小于2℃,pso_FSVM模型提高了动力电池温度预测的精度和可靠性。
In allusion to the problem of the decline of temperature measurement reliability for power battery of the hybrid electric vehicle in complicated working conditions,the temperature data of power battery at two vehicle states of Key_on and Key_off is collected respectively. A stable power battery temperature prediction model is constructed by using the particle swarm optimization based fast support vector machine algorithm. The experimental results show that the correlation coefficient between the prediction data and actual measurement data of data sets reaches 0.810 2 and 0.797 3 respectively at the two vehicle states of Key_on and Key_off,and the temperature prediction error is less than 2 ℃,which indicates that the pso_FSVM model can improve the prediction accuracy and reliability of power battery temperature.
作者
刘荣
童亮
许永红
LIU Rong;TONG Liang;XU Yonghong(Sehool of Eleetromeehanieal Engineering, Beijing Information Seienee ＆ Teehnology University, Beijing 100192, China;Beijing Collaborative Innovation Center of Electric Vehicles, Beijing 100192, China)
出处
《现代电子技术》
北大核心
2018年第12期24-27,共4页
Modern Electronics Technique
基金
国家自然科学基金(51275053)
电动汽车北京市实验室项目(PXM_2013_014224_000005)~~
关键词
混合动力汽车
动力电池温度
粒子群
快速支持向量机
预测模型
热动力学模型
pso_FSVM
hybrid electric vehicle
power battery temperature
particle swarm optimization
fast support vector machine
prediction model
thermodynamics model
