大型客车横向稳定性仿真分析
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摘要
为了给营运客车横向稳定状态监测提供理论依据,针对极限工况下状态参数的临界值仿真结果,进行了营运客车稳定区域边界条件的研究。基于非线性三自由度车辆模型建立了基于扩展卡尔曼滤波(EKF)的状态参数估计器,对营运客车的质心侧偏角和横摆角速度进行实时估计,并利用Trucksim验证估计值具有较好的一致性和状态跟随能力。基于MATLAB/Simulink建立非线性七自由度车辆模型,分析不同行驶状态参数对质心侧偏角-质心侧偏角速度(β-6)β)相平面稳定区域边界的影响,基于仿真数据确定了以车速、前轮转角和路面附着系数为变量的稳定区域边界条件,结合状态估计模型获得以β-6)β决定的控制变量。在Trucksim中进行连续正弦方向盘转角输入标准稳定性试验,通过分析营运客车行驶过程中控制变量的曲线变化趋势是否超出稳定区域边界确定车辆的运行状态。结果表明:营运客车以60km·h~(-1)车速、小方向盘转角行驶在低附着系数(μ=0.3)路面和高附着系数(μ=0.85)路面时,横摆角速度对驾驶人的意图(方形盘转角曲线趋势)有很好的跟随能力,具有较小的延迟响应,车辆处于稳定状态,此时控制变量曲线一直处于稳定区域内;当相同工况下以大方向盘转角输入时,横摆角速度已经不能很好地跟随驾驶人意图,且低附着系数路面下,在3.5s左右时方向盘转角已经回正,但横摆角速度仍位于最大值,具有较大的延迟,营运客车发生急转侧滑;高附着系数路面下第2.5s和第6.2s左右车辆发生严重偏移,车辆处于失稳状态,而对应时刻的控制变量曲线部分超出稳定边界,验证了营运客车横向稳定状态判据的准确性。
To provide a theoretical basis for monitoring the lateral stability of an operating coach,based on the critical value simulation results of the state parameters under extreme conditions,the study of the boundary conditions for the stability of an operating coach was conducted.Based on the nonlinear three degree-of-freedom vehicle model,the extended Kalman filter state estimator was established to estimate the sideslip angle and the yaw rate of the operating coach in real time,and Trucksim was used to verify that the estimate has a better consistency and statusfollowing capability.Using MATLAB/Simulink to establish the nonlinear seven-freedom-degreevehicle model,the influence of different running state parameters on the boundary of theβ-6)βphase plane stability area was analyzed.Based on the simulation data,the boundary conditions of the stable region were based on the vehicle speed,the front wheel rotation angle,and the road surface adhesion coefficient,combining the control variable determined byβ-6)βthrough the state estimation model.Standard stability tests of continuous sine steering angle input for the operating coach were performed in Trucksim,by analyzing whether the curve trend of the control variable during the running of the passenger bus exceeds the boundary of the stable area;subsequently,the running status of the vehicle is determined.The results show that when the operating coach was driven at 60 km·h~(-1) with a low road surface adhesion coefficient(μ=0.3)and a high road surface adhesion coefficient(μ=0.85)under a small steering angle,the yaw rate had can well follow the driver's intention(the curve tendency of the steering wheel angle)with a smaller delay response,and the vehicle is in a steady state.At this point,the control variable curve is always in the stable region.While inputting at a large steering angle under the same conditions,the yaw rate had not been able to follow the driver's intentions well,When driving in approximately 3.5 sbelow the low adhesion coefficient,the steering wheel angle had returned to the normal position,but the yaw rate was still at the maximum value;further,with a large delay,the operating coach had a sharp sideslip.When driving on a high adhesion coefficient road,the operating coach would have a serious offset at 2.5 sand 6.2 s.The vehicle is in an unstable state,and the curve of the control variable at the corresponding time exceeds the stable boundary.Herein,we verified the accuracy of the criteria for the lateral stability of the passenger bus.
To provide a theoretical basis for monitoring the lateral stability of an operating coach,based on the critical value simulation results of the state parameters under extreme conditions,the study of the boundary conditions for the stability of an operating coach was conducted.Based on the nonlinear three degree-of-freedom vehicle model,the extended Kalman filter state estimator was established to estimate the sideslip angle and the yaw rate of the operating coach in real time,and Trucksim was used to verify that the estimate has a better consistency and statusfollowing capability.Using MATLAB/Simulink to establish the nonlinear seven-freedom-degreevehicle model,the influence of different running state parameters on the boundary of theβ-6)βphase plane stability area was analyzed.Based on the simulation data,the boundary conditions of the stable region were based on the vehicle speed,the front wheel rotation angle,and the road surface adhesion coefficient,combining the control variable determined byβ-6)βthrough the state estimation model.Standard stability tests of continuous sine steering angle input for the operating coach were performed in Trucksim,by analyzing whether the curve trend of the control variable during the running of the passenger bus exceeds the boundary of the stable area;subsequently,the running status of the vehicle is determined.The results show that when the operating coach was driven at 60 km·h~(-1) with a low road surface adhesion coefficient(μ=0.3)and a high road surface adhesion coefficient(μ=0.85)under a small steering angle,the yaw rate had can well follow the driver's intention(the curve tendency of the steering wheel angle)with a smaller delay response,and the vehicle is in a steady state.At this point,the control variable curve is always in the stable region.While inputting at a large steering angle under the same conditions,the yaw rate had not been able to follow the driver's intentions well,When driving in approximately 3.5 sbelow the low adhesion coefficient,the steering wheel angle had returned to the normal position,but the yaw rate was still at the maximum value;further,with a large delay,the operating coach had a sharp sideslip.When driving on a high adhesion coefficient road,the operating coach would have a serious offset at 2.5 sand 6.2 s.The vehicle is in an unstable state,and the curve of the control variable at the corresponding time exceeds the stable boundary.Herein,we verified the accuracy of the criteria for the lateral stability of the passenger bus.
引文
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[18]刘飞,熊璐,邓律华,等.基于相平面法的车辆行驶稳定性判定方法[J].华南理工大学学报:自然科学版,2014,42(11):63-70.LIU Fei,XIONG Lu,DENG Lu-hua,et al.Vehicle Stability Criterion Based on Phase Plane Method[J].Journal of South China University of Technology:Natural Science Edition,2014,42(11):63-70.
[2]VENHOVENS P J T H,NAAB K.Vehicle Dynamics Estimation Using Kalman Filters[J].Vehicle System Dynamics,1999,32(2/3):171-184.
[3]黄程程.基于自适应卡尔曼滤波的汽车质心侧偏角估算研究[D].长春:吉林大学,2011.HUANG Cheng-cheng.Research on Vehicle Sideslip Angle Estimation Based on Adaptive Kalman Filter[D].Changchun:Jilin University,2011.
[4]PIYABONGKARN D,RAJAMANI R,GROGG J A,et al.Development and Experimental Evaluation of a Slip Angle Estimator for Vehicle Stability Control[J].IEEE Transactions on Control System Technology,2009,17(1):78-88.
[5]刘伟.基于质心侧偏角相平面的车辆稳定性控制系统研究[D].长春:吉林大学,2013.LIU Wei.Vehicle Stability Control System Research Based on Side-slip Angle Phase[D].Changchun:Jilin University,2013.
[6]王健,余贵珍,张为,等.基于滑模观测理论的车辆质心侧偏角估算[J].北京工业大学学报,2011,37(3):335-341.WANG Jian,YU Gui-zhen,ZHANG Wei,et al.Vehicle Sideslip Angle Estimation Based on Principle of Sliding Mode[J].Journal of Beijing University of Technology,2011,37(3):335-341.
[7]SHRAIM H,OULADSINE M,FRIDMAN L.Vehicle Parameter Estimation and Stability Enhancement[J].IFAC Proceedings Volumes,2008,41(2):5700-5705.
[8]BEVLY D M.Global Positioning System(GPS):A Low-cost Velocity Sensor for Correcting Inertial Sensor Errors on Ground Vehicles[J].Journal of Dynamics Systems Control,2004,126(2):255-264.
[9]赵又群,林棻.基于UKF算法的汽车状态估计[J].中国机械工程,2010,21(5):615-619.ZHAO You-qun,LIN Yi.Vehicle State Estimation Based on Unscented Kalman Filter Algorithm[J].China Mechanical Engineering,2010,21(5):615-619.
[10]宗长富,胡丹,杨肖,等.基于扩展Kalman滤波的汽车行驶状态估计[J].吉林大学学报:工学版,2009,39(1):9-13.ZONG Chang-fu,HU Dan,YANG Xiao,et al.Estimation of Vehicle Running State Based on Extended Kalman Filtering[J].Journal of Jilin University:Engineering and Technology Edition,2009,39(1):9-13.
[11]HONG S,LI C,BORRELLI F,et al.A Novel Approach for Vehicle Inertial Parameter Identification Using a Dual Kalman Filter[J].IEEE Transactions on Intelligent Transportation Systems,2015,16(1):151-161.
[12]ZONG C,HU D,ZHENG H.Dual Extended Kalman Filter for Combined Estimation of Vehicle State and Road Friction[J].Chinese Journal of Mechanical Engineering,2013,26(2):313-324.
[13]WIELITZKA M,DAGEN M,ORTMAIER T.Joint Unscented Kalman Filter for State and Parameter Estimation in Vehicle Dynamics[C]//IEEE.Proceedings of IEEE Conference on Control Applications.New York:IEEE,2015:1945-1950.
[14]NAM K,FUJIMOTO H,HORI Y.Lateral Stability Control of In-wheel-motor-driven Electric Vehicles Based on Sideslip Angle Estimation Using Lateral Tire Force Sensors[J].IEEE Transactions on Vehicular Technology,2012,61(5):1972-1985.
[15]SADRI S,WU C.Stability Analysis of a Nonlinear Vehicle Model in Plane Motion Using the Concept of Lyapunov Exponents[J].Vehicle System Dynamics,2013,51(6):906-924.
[16]FARRONI F,RUSSO M,RUSSO R,et al.A Combined Use of Phase Plane and Handling Diagram Method to Study the Influence of Tyre and Vehicle Characteristics on Stability[J].Vehicle System Dynamics,2013,51(8):1265-1285.
[17]柳江,陈朋,李道飞.基于相平面方法的车辆稳定性控制[J].工程设计学报,2016,23(5):409-416.LIU Jiang,CHEN Peng,LI Dao-fei.Vehicle Stability Control Based on Phase Plane Method[J].Chinese Journal of Engineering Design,2016,23(5):409-416.
[18]刘飞,熊璐,邓律华,等.基于相平面法的车辆行驶稳定性判定方法[J].华南理工大学学报:自然科学版,2014,42(11):63-70.LIU Fei,XIONG Lu,DENG Lu-hua,et al.Vehicle Stability Criterion Based on Phase Plane Method[J].Journal of South China University of Technology:Natural Science Edition,2014,42(11):63-70.