四轮驱动电动汽车车速估计与DYC控制研究
|
摘要
四轮驱动电动汽车因每个车轮独立可控,在对车辆进行直接横摆力矩控制(Direct Yaw-moment Control,DYC)时需要根据车轮滑移率选择干预车轮而施加合适的控制转矩,而滑移率的获取有赖于精确的车速信息。设计了基于扩展卡尔曼滤波的车速估计算法,依据车轮滑移状态及车辆状态参数提出了直接横摆力矩控制策略及期望转矩与期望横摆力矩的计算方法。在CarSim与Simulink联合仿真环境下对所提出的车速估计算法及直接横摆力矩控制策略进行了离线仿真。仿真结果表明,车速估计算法能准确估计车速信息,绝对误差较小;直接横摆力矩控制策略能够根据横摆角速度的偏差,正确地选择干预车轮,及时地控制车轮转矩,从而保持车辆的横向稳定性。
Four wheel drive electric vehicle for each wheel can be controlled independently,the Direct Yaw-moment Control of vehicle need based on wheel slip ratio of wheels to select the Intervening wheel to apply appropriate control torque,The acquisition of slip rate depends on the precise speed information.The vehicle speed estimation algorithm based on Extended Kalman Filter is designed,and the direct yaw moment control strategy is presented based on the wheel slip state and the vehicle state parameters. In the Car Sim and Simulink simulation environment speed estimation algorithm and direct cross yaw moment control strategy are used in the off-line simulation of the proposed. The simulation results show that the speed estimation algorithm can accurately estimate the speed information,absolute error is small;the direct yaw moment control strategy can according to the deviation of the yaw angular velocity and correctly choose intervention wheel,in a timely manner to control wheel torque,so as to keep the vehicle lateral stability.
Four wheel drive electric vehicle for each wheel can be controlled independently,the Direct Yaw-moment Control of vehicle need based on wheel slip ratio of wheels to select the Intervening wheel to apply appropriate control torque,The acquisition of slip rate depends on the precise speed information.The vehicle speed estimation algorithm based on Extended Kalman Filter is designed,and the direct yaw moment control strategy is presented based on the wheel slip state and the vehicle state parameters. In the Car Sim and Simulink simulation environment speed estimation algorithm and direct cross yaw moment control strategy are used in the off-line simulation of the proposed. The simulation results show that the speed estimation algorithm can accurately estimate the speed information,absolute error is small;the direct yaw moment control strategy can according to the deviation of the yaw angular velocity and correctly choose intervention wheel,in a timely manner to control wheel torque,so as to keep the vehicle lateral stability.
引文
[1]褚文博,罗禹贡,赵峰.分布式驱动电动汽车驱动转矩协调控制[J].汽车工程,2012(3):185-189.(Chu Wen-bo,Luo Yu-gong,Zhao Feng.Distributed drive electric vehicle drive torque coordination control[J].Automobile Engineering,2012(3):185-189.)
[2]余卓平,高晓杰.车辆行驶过程中的状态估计问题综述[J].机械工程学报,2009,45(5):20-32.(Yu Zhuo-ping,Gao Xiao-jie.Review of vehicle state estimation problem under driving situation[J].Journal of Mechanical Engineering,2009,45(5):20-32.)
[3]宗新怡,李刚,邓伟文.四轮独立驱动电动汽车车速估计研究[J].机械设计与制造,2013(9):83-85.(Zong Xin-yi,Li Gang,Deng Wei-wen.Study on velocity estimation for four-wheel independent drive electric vehicle[J].Machinery Design&Manufacture,2013(9):83-85.)
[4]ANTONOVA S,FEHNB A,KUGIC A.Unscented Kalman filter for vehicle state estimation[J].Vehicle System Dynamics,2011,49(9):1496-1520.
[5]宗长富,胡丹,杨肖.基于扩展Kalman滤波的汽车行驶状态估计[J].吉林大学学报,2009,39(1):7-11.(Zong Chang-fu,Hu Dan,Yang Xiao.Vehicle driving state estimation based on Extended Kalman filter[J].Journal of Jilin Uiversity,2009,39(1):7-11.)
[6]Castro R,Araújo R E,Freitas D.Wheel slip control of EVs based onsliding mode technique with conditional integrators[J].IEEE Transactions on Industrial Electronic,2013,60(8):3256-3271.
[7]段振云,况卫平,李强.主动前轮转向横摆稳定性模糊控制系统设计[J].机械设计与制造,2010(1):39-41.(Duan Zhen-yun,Kang Wei-ping,Li Qiang.Design of fuzzy control system for yaw stability of active front wheel steering[J].Machinery Design&Manufacture,2010(1):39-41.)
[8]李刚,王超,石晶.基于模糊控制的汽车直接横摆力矩研究[J].计算机仿真,2014(12):151-155.(Li Gang,Wang Chao,Shi Jing.Study on direct yaw moment of automobile based on fuzzy control[J].Computer Simulation,2014(12):151-155.)
[9]林程,徐志峰,王文伟.基于直接滑动率分配的横摆稳定性控制策略研究[J].机械工程学报,2015(6):41-49.(Lin Cheng,Xu Zhi-feng,Wang Wen-wei.Research on control strategy of yaw stability based on direct sliding rate distribution[J].Journal of Mechanical Engineering,2015(6):41-49.)
[10]王立标,李军,范剑.车辆横向稳定性自适应神经网络控制策略研究[J].汽车工程,2010(6):493-496.(Wang Li-biao,Li Jun,Fan Jian.Research on adaptive neural network control strategy for vehicle lateral stability[J].Automobile Engineering,2010(6):493-496.)
[2]余卓平,高晓杰.车辆行驶过程中的状态估计问题综述[J].机械工程学报,2009,45(5):20-32.(Yu Zhuo-ping,Gao Xiao-jie.Review of vehicle state estimation problem under driving situation[J].Journal of Mechanical Engineering,2009,45(5):20-32.)
[3]宗新怡,李刚,邓伟文.四轮独立驱动电动汽车车速估计研究[J].机械设计与制造,2013(9):83-85.(Zong Xin-yi,Li Gang,Deng Wei-wen.Study on velocity estimation for four-wheel independent drive electric vehicle[J].Machinery Design&Manufacture,2013(9):83-85.)
[4]ANTONOVA S,FEHNB A,KUGIC A.Unscented Kalman filter for vehicle state estimation[J].Vehicle System Dynamics,2011,49(9):1496-1520.
[5]宗长富,胡丹,杨肖.基于扩展Kalman滤波的汽车行驶状态估计[J].吉林大学学报,2009,39(1):7-11.(Zong Chang-fu,Hu Dan,Yang Xiao.Vehicle driving state estimation based on Extended Kalman filter[J].Journal of Jilin Uiversity,2009,39(1):7-11.)
[6]Castro R,Araújo R E,Freitas D.Wheel slip control of EVs based onsliding mode technique with conditional integrators[J].IEEE Transactions on Industrial Electronic,2013,60(8):3256-3271.
[7]段振云,况卫平,李强.主动前轮转向横摆稳定性模糊控制系统设计[J].机械设计与制造,2010(1):39-41.(Duan Zhen-yun,Kang Wei-ping,Li Qiang.Design of fuzzy control system for yaw stability of active front wheel steering[J].Machinery Design&Manufacture,2010(1):39-41.)
[8]李刚,王超,石晶.基于模糊控制的汽车直接横摆力矩研究[J].计算机仿真,2014(12):151-155.(Li Gang,Wang Chao,Shi Jing.Study on direct yaw moment of automobile based on fuzzy control[J].Computer Simulation,2014(12):151-155.)
[9]林程,徐志峰,王文伟.基于直接滑动率分配的横摆稳定性控制策略研究[J].机械工程学报,2015(6):41-49.(Lin Cheng,Xu Zhi-feng,Wang Wen-wei.Research on control strategy of yaw stability based on direct sliding rate distribution[J].Journal of Mechanical Engineering,2015(6):41-49.)
[10]王立标,李军,范剑.车辆横向稳定性自适应神经网络控制策略研究[J].汽车工程,2010(6):493-496.(Wang Li-biao,Li Jun,Fan Jian.Research on adaptive neural network control strategy for vehicle lateral stability[J].Automobile Engineering,2010(6):493-496.)