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汽车安全与节能学报 ›› 2021, Vol. 12 ›› Issue (3): 355-363.DOI: 10.3969/j.issn.1674-8484.2021.03.010

• 汽车安全 • 上一篇    下一篇

独立驱动电动汽车模型参考自适应稳定性控制

李家林1(), 奥迪2,*(), 王杨3, 熊锐1   

  1. 1.广东工业大学 机电工程学院,广州 510006,中国
    2.澳门大学 科技学院,澳门 999078,中国
    3.吉林大学汽车仿真与控制国家重点实验室,长春 130012,中国
  • 收稿日期:2021-01-14 出版日期:2021-09-30 发布日期:2021-10-09
  • 通讯作者: 奥迪
  • 作者简介:* 奥迪(1992—),博士研究生。E-mail: yb87413@um.edu.mo
    李家林(1994—),男(汉族),湖南,硕士研究生。E-mail: janjananlin@163.com
  • 基金资助:
    国家自然科学基金(62003238);分布式纯电动轿车底盘及整车产业化研发(2017YFB0103600)

Model reference adaptive stability control for independent driving electric vehicle

LI Jialin1(), AO Di2,*(), WANG Yang3, XIONG Rui1   

  1. 1. Department of Mechanical and Electrical Engineering, Guangdong University of Technology, Guangzhou 510006, China
    2. Faculty of Science and Technology, University of Macau, Macau, 999078, China
    3. State Key Laboratory of Automotive Simulation and Control, Jilin University,Changchun 130012, China
  • Received:2021-01-14 Online:2021-09-30 Published:2021-10-09
  • Contact: AO Di

摘要:

为提高四轮独立驱动电动车(EV)辆行驶中侧向稳定性和安全性,设计了一种基于模型参考自适应(MRAC)直接横摆力矩稳定性控制算法。上层控制器通过Lyapunov数设计并对系统轮胎侧偏刚度、车身质量参数进行实时估计以获得额外横摆力矩;下层控制器利用二次规划法优化所得力矩分配至各轮,以保证控制器动态响应的实时性。在双移线工况下进行Carsim与Simulink联合仿真的结果表明:在附着因数为0.85时,该算法的横摆角速度、质心侧偏角的平均绝对误差相比传统滑模控制分别下降43.0%、37.1%;在附着因数为0.40时,分别下降了25.3%、23.2%。因此,该算法提高了车辆行驶稳定性和安全性。

关键词: 电动汽车(EV), 四轮独立驱动, 模型参考自适应(MRAC), 参数不确定性, 力矩分配优化, 行驶稳定性

Abstract:

A direct yaw moment controller algorithm was designed based on a model reference adaptive control (MRAC) to improve the lateral stability and safety for four-wheel independent electric vehicles (EV). The upper-level controller was based on the Lyapunov function to estimate the tire corning stiffness and vehicle mass in real-time and obtain the additional yaw moment. The lower-level controller allocated the requested yaw moment to each wheel via an optimized quadratic programming algorithm, and guarantees the real-time dynamic response of the controller. The results from Carsim and Simulink co-simulation under double lane change driving mode show that the proposed MRAC controller algorithm outperforms the conventional sliding mode controller (SMC) with the reductions for mean absolute errors of yaw rate and sideslip angle by 43.0% and 37.1% under 0.85 adhesion ratio; with 25.3% and 23.2% reductions under 0.40 adhesion ratio. Therefore, the proposed MRAC controller algorithm improves the vehicle lateral stability and safety.

Key words: electric vehicles (EV), four-wheel independent driving, model reference adaptive control (MRAC), parameter uncertainties, torque distribution optimization, stability performance

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