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汽车安全与节能学报

汽车安全与节能学报 ›› 2019, Vol. 10 ›› Issue (2): 219-225.DOI: 10.3969/j.issn.1674-8484.2019.02.010

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基于参数自适应的电动汽车整车质量辨识算法

谢  辉，张  宁   

  1. （内燃机燃烧学国家重点实验室，天津大学 机械工程学院，天津 300072，中国） 
  • 收稿日期:2018-12-16 出版日期:2019-06-29 发布日期:2019-07-05
  • 作者简介:第一作者：谢辉 (1970—)，男( 汉)，天津，教授。E-mail: xiehui@tju.edu.cn。 第二作者：张宁(1991—)，男( 汉)，河北，硕士研究生。E-mail: zhangning_530@tju.edu.cn。

Parameter-adaptive mass identification algorithm of electric vehicles

XIE Hui, ZHANG Ning   

  1. (State Key Laboratory of Engines, School of Mechanical Engineering, Tianjin University）
  • Received:2018-12-16 Online:2019-06-29 Published:2019-07-05

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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。

关键词: 电动汽车(EV) , 整车质量 , 参数自适应 , 辨识算法 , 拓展Kalman滤波算法(EKF) , 坡度重构

Abstract:

 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.

Key words: electric vehicle (EV) , vehicle mass , parameter-adaptive , identification algorithm ,  extended Kalman filter (EKF) , slope reconstruction