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中南大学学报(自然科学版)

Journal of Central South University

第50卷    第1期    总第293期    2019年1月

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文章编号:1672-7207(2019)01-0241-09
四轮独立驱动电动汽车行驶状态级联估计
陈特1,陈龙1, 2,蔡英凤1, 2,徐兴1, 2,江浩斌1, 2

(1. 江苏大学 汽车与交通工程学院,江苏 镇江,212013;
2. 江苏大学 汽车工程研究院,江苏 镇江,212013
)

摘 要: 建立三自由度车辆模型与轮胎模型,提出电驱动轮模型并将其应用到纵向力估计中,基于自适应高阶滑模观测器实现轮胎纵向力的估计,利用纵向力观测器(longitudinal force observer, LFO)输出值作为已知输入,结合信息融合滤波(information fusion filter, IFF)算法提出一种车辆状态级联估计方法。进行仿真实验、台架实验以及实车道路实验。研究结果表明:设计的纵向力观测器具有较高的纵向力估计精度,基于信息融合滤波的车辆状态估计方法能够实时跟踪车辆状态且估计性能优于扩展卡尔曼滤波(extended Kalman filter, EKF)。

 

关键字: 电动汽车;四轮独立驱动;纵向力估计;车辆状态;高阶滑模观测器

Cascaded method for running state estimation of four-wheel independent drive electric vehicles
CHEN Te1, CHEN Long1, 2, CAI Yingfeng1, 2, XU Xing1, 2, JIANG Haobing1, 2

1. School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China;
2. Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China

Abstract:The vehicle model with 3 degree of freedom and the tire model were established. An electric drive wheel model was presented and applied to the longitudinal force estimation. The estimation of tire longitudinal force was realized based on the adaptive high order sliding mode observer. Using the output values of longitudinal force observer as the known input and combining the information fusion filter algorithm, a vehicle state joint estimation method was proposed. The simulation, bench test and road test were carried out. The results show that the designed longitudinal force observer has high estimation accuracy, and the information fusion filter-based vehicle state estimation method can track the vehicle state in real time and has better estimation performance than extended Kalman filter.

 

Key words: electric vehicle; four-wheel independent drive; longitudinal force estimation; vehicle state; high order sliding mode observer

中南大学学报(自然科学版)
  ISSN 1672-7207
CN 43-1426/N
ZDXZAC
中南大学学报(英文版)
  ISSN 2095-2899
CN 43-1516/TB
JCSTFT
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