摘要
为减少电动汽车(EV)质量估算模型在工程应用中对车辆参数的依赖性,提出了一种整车质量估算算法。该算法能适应空气阻力、迎风面积和传动比等部分车辆参数变化。基于车载终端获得的车辆运行信息,完成传动比和坡度的还原,并将空气阻力中的空气密度、迎风面积、风阻系数作为整体,与整车质量一同借助扩展Kalman滤波算法完成联合辨识。结果表明:在不同整车质量以及道路环境下,质量参数辨识的平均误差为3.74%。因此,该算法可以实时得到质量辨识结果,且对车辆参数具有较好的自适应能力。
A vehicle mass estimation algorithm, which can adapt to the variation of air resistance, windward area and transmission ratio, was proposed to reduce the dependence of mass estimation model on vehicle parameters for electric vehicle(EV) in engineering applications. The reduction of transmission ratio and slope were identified on the base of the vehicle operation information obtained through vehicle terminal. The air density, windward area and wind resistance coefficient extracted from air resistance were regarded as a parameter, which was identified by the extended Kalman filter algorithm together with the vehicle mass. The results show that the average error of quality parameter identification is 3.74% under different vehicle mass and road environments. The proposed algorithm can estimate vehicle mass in real time with good adaptability to variation of vehicle and environment parameters.
作者
谢辉
张宁
XIE Hui;ZHANG Ning(State Key Laboratory of Engines, School of Mechanical Engineering, Tianjin University, Tianjin 300072, China)
出处
《汽车安全与节能学报》
CAS
CSCD
2019年第2期219-225,共7页
Journal of Automotive Safety and Energy
