Adaptive sliding mode acceleration slip regulation control based on road identification for distributed electric drive buses
Article Text (Baidu Translation)
-
摘要: 为解决分布式电驱动客车在低附着路面上起步时驱动轮易发生过度打滑的问题，提出一种基于路面识别和自适应滑模控制（ASMC）的驱动防滑（ASR）控制策略。建立非线性的车辆动力学模型和Dugoff轮胎模型，基于奇异值分解的高阶容积卡尔曼滤波算法设计了路面附着系数估计方法；结合目标车型的轮胎参数，利用TruckSim中的轮胎测试模块对轮胎特性进行测试，确定不同路面下的最佳滑转率，并基于此设计了ASR触发与退出机制；设计了基于ASMC的防滑控制算法，其趋近律中引入了指数自适应增益，能够依据误差大小自适应调整控制力度，加快滑转率跟踪速度并抑制超调；结合采集到的实车起步工况数据设置测试工况，基于MATLAB/Simulink与TruckSim的联合仿真平台，在不同起步工况和载重下对所提出的ASR控制策略的性能进行验证，并与传统模型预测控制（MPC）、一阶滑模控制（FOSMC）和积分滑模控制（ISMC）方法进行了对比。分析结果表明：在4种典型测试工况下，基于ASMC的ASR控制策略使滑转率跟踪的平均绝对误差和均方根误差均为最小值；使客车车速比MPC分别提升30.45%、10.01%、24.55%和13.45%，比FOSMC分别提升5.62%、5.08%、5.38%和6.35%，比ISMC分别提升4.09%、2.74%、3.21%和4.64%。所提出的ASR控制策略能够提升分布式电驱动客车的纵向稳定性和驱动性能，对分布式电驱动客车的扭矩控制系统设计具有重要参考价值。Abstract: To solve the problem of excessive slippage of the driving wheels when distributed electric drive buses start on low-adhesion roads, an acceleration slip regulation (ASR) control strategy based on road identification and adaptive sliding mode control (ASMC) was proposed. A nonlinear vehicle dynamics model and a Dugoff tire model were established. A road adhesion coefficient estimation method was designed based on the high-degree cubature Kalman filter algorithm with singular value decomposition. Combined with the tire parameters of the target vehicle model, the tire test module in TruckSim was utilized to test tire characteristics and determine the optimal slip ratios under different road surfaces, based on which an ASR trigger and exit mechanism was designed. An anti-slip control algorithm based on ASMC was designed, into the reaching law of which an exponential adaptive gain was introduced to adaptively adjust the control force according to the error size, accelerate the slip ratio tracking speed, and suppress overshoot. Test maneuvers were set combined with the collected real-vehicle starting data. Based on the joint simulation platform of MATLAB/Simulink and TruckSim, the performance of the proposed ASR control strategy was verified under different starting maneuvers and loads and compared with the traditional model predictive control (MPC), first-order sliding mode control (FOSMC), and integral sliding mode control (ISMC) methods. Analysis results indicate that under four typical test maneuvers, the ASR control strategy based on ASMC minimizes both the average absolute error and root mean square error of slip ratio tracking; it increases the bus speed by 30.45%, 10.01%, 24.55%, and 13.45% compared with MPC, by 5.62%, 5.08%, 5.38%, and 6.35% compared with FOSMC, and by 4.09%, 2.74%, 3.21%, and 4.64% compared with ISMC, respectively. The proposed ASR control strategy can improve the longitudinal stability and driving performance of distributed electric drive buses, providing an important reference for the torque control system design of distributed electric drive buses.
-
图 3 不同路面工况下SVD-CKF和SVD-HCKF估计结果
Figure 3. Estimation results of SVD-CKF and SVD-HCKF under different road maneuvers
图 4 分布式电驱动客车ASR控制策略框架
Figure 4. ASR control strategy framework for distributed electric drive buses
图 5 两个直线起步工况的加速踏板开度变化曲线
Figure 5. Acceleration pedal opening changing curves of two straight starting maneuvers
图 6 两个转弯起步工况的加速踏板开度变化曲线
Figure 6. Acceleration pedal opening changing curves of two steering starting maneuvers
图 7 不同路面下纵向力与滑转率曲线
Figure 7. Longitudinal force and slip ratio curves under different road surfaces
图 8 冰雪路面下不同控制方法的结果
Figure 8. Results of different control methods under ice and snow road
图 11 客车满载时积水路面下不同控制方法的结果
Figure 11. Results of different control methods under waterlogged road when the bus is fully loaded
表 1 不同垂直载荷下轮胎的纵向刚度和侧向刚度
Table 1. Longitudinal and lateral stiffness of tires under different vertical loads
Fz/N Cx/（N·m-1） Cy/（N·rad-1） 7 357.5 94 925 72 517 14 715.0 183 024 139 820 29 430.0 338 976 258 959 44 145.0 467 856 357 416 58 860.0 576 288 440 252 
下载: 导出CSV
表 2 路面附着系数-最佳滑转率的映射关系
Table 2. Mapping relationship between road adhesion coefficient and optimal slip ratio
路面附着系数 最佳滑转率 0.1 0.02 0.2 0.05 0.3 0.07 0.4 0.10 0.5 0.12 0.6 0.15 0.7 0.17 0.8 0.20 0.9 0.22 1.0 0.25
下载: 导出CSV
表 3 分布式电驱动客车参数
Table 3. Parameters of the distributed electric drive bus
参数 取值 m/kg 10 000 mfull/kg 15 000 lf/m 2.8 lr/m 2.2 wf/m 2.12 wr/m 2.09 Iz/（kg·m2） 40 365.6 Iω/（kg·m2） 14 Rω/m 0.477 Tm，max/（N·m） 360
下载: 导出CSV
表 4 控制器参数
Table 4. Controller parameters
参数 取值 c 4 $ \varepsilon $ 2 k 10 $ \sigma $ 2 $ {k}_{\omega } $ 0.5 $ \beta $ 1
下载: 导出CSV
表 5 不同控制方法下滑转率跟踪精度的性能指标
Table 5. Performance indicators of slip ratio tracking accuracy for different control methods
工况 性能指标 控制算法 MPC FOSMC ISMC ASMC 1 平均绝对误差 0.026 0.009 0.006 0.002 均方根误差 0.049 0.020 0.016 0.009 2 平均绝对误差 0.070 0.066 0.064 0.060 均方根误差 0.083 0.079 0.077 0.077 3 平均绝对误差 0.027 0.010 0.007 0.002 均方根误差 0.051 0.024 0.016 0.009 4 平均绝对误差 0.020 0.011 0.009 0.003 均方根误差 0.039 0.025 0.022 0.013
下载: 导出CSV
表 6 不同控制方法的耗时对比
Table 6. Time comparison of different control methods
s 工况 工况总仿真时间 不同控制方法的耗时 MPC FOSMC ISMC ASMC 1 16 13.55 4.33 4.63 4.79 2 10 8.27 3.03 3.14 3.15 3 10 8.76 3.00 3.26 3.30 4 10 8.49 2.94 3.46 3.42
下载: 导出CSV
-
[1] 王之中, 余荣杰, 邱斌. 国家自然科学基金委员会交通与运载工程学科2024年度管理工作综述与展望[J]. 交通运输工程学报, 2025, 25(1): 1-7. doi: 10.19818/j.cnki.1671-1637.2025.01.001WANG Zhi-zhong, YU Rong-jie, QIU Bin. Review and prospect on management of transportation and vehicle engineering discipline under NSFC in 2024[J]. Journal of Traffic and Transportation Engineering, 2025, 25(1): 1-7. doi: 10.19818/j.cnki.1671-1637.2025.01.001 [2] YUE M, SHANGGUAN J Y, GUO L, et al. All-in-one control framework for distributed drive electric buses path tracking subject to uncertain crosswind and varied passenger mass[J]. IEEE Transactions on Vehicular Technology, 2023, 72 (7): 8342-8353. doi: 10.1109/TVT.2023.3244980 [3] MESQUITA H C, LAGANÁ A A M, ANGÉLICO B A. An algebraic estimation-based model-free control for acceleration slip regulation of electric vehicles[J]. Journal of Control, Automation and Electrical Systems, 2024, 35 (6): 1097-1107. doi: 10.1007/s40313-024-01126-5 [4] DEGEL W, LUPBERGER S, ODENTHAL D, et al. Scalable slip control with torque vectoring including input-to-state stability analysis[J]. IEEE Transactions on Control Systems Technology, 2023, 31 (3): 1250-1265. doi: 10.1109/TCST.2022.3224839 [5] HORI Y. Future vehicle driven by electricity and control-research on four-wheel-motored "UOT electric march Ⅱ"[J]. IEEE Transactions on Industrial Electronics, 2004, 51 (5): 954-962. doi: 10.1109/TIE.2004.834944 [6] YIN D, OH S, HORI Y. A novel traction control for EV based on maximum transmissible torque estimation[J]. IEEE Transactions on Industrial Electronics, 2009, 56 (6): 2086-2094. doi: 10.1109/TIE.2009.2016507 [7] NGUYEN B M, HARA S, FUJIMOTO H, et al. Slip control for IWM vehicles based on hierarchical LQR[J]. Control Engineering Practice, 2019, 93: 104179. doi: 10.1016/j.conengprac.2019.104179 [8] SHI K, YUAN X F, LIU L. Model predictive controller-based multi-model control system for longitudinal stability of distributed drive electric vehicle[J]. ISA Transactions, 2018, 72: 44-55. doi: 10.1016/j.isatra.2017.10.013 [9] DING X L, WANG Z P, ZHANG L. Hybrid control-based acceleration slip regulation for four-wheel-independent-actuated electric vehicles[J]. IEEE Transactions on Transportation Electrification, 2021, 7 (3): 1976-1989. doi: 10.1109/TTE.2020.3048405 [10] ZHANG H Z, QI Y Y, SI W J, et al. An improved adaptive sliding mode control approach for anti-slip regulation of electric vehicles based on optimal slip ratio[J]. Machines, 2024, 12 (11): 769. doi: 10.3390/machines12110769 [11] LI B, E W J, FENG T L, et al. Road adhesion coefficient estimation based on LiDAR reflectance intensity[J]. IEEE Sensors Journal, 2024, 24 (18): 29135-29148. doi: 10.1109/JSEN.2024.3436897 [12] CHEN L, QIN Z B, BIAN Y G, et al. Data-driven tire-road friction estimation for electric-wheel vehicle with data category selection and uncertainty evaluation[J]. IEEE Transactions on Industrial Electronics, 2025, 72 (3): 3048-3060. doi: 10.1109/TIE.2024.3440510 [13] ZHAO S Y, ZHANG J Z, JIANG Y H, et al. Tire-road friction coefficients adaptive estimation through image and vehicle dynamics integration[J]. Mechanical Systems and Signal Processing, 2025, 224: 112039. doi: 10.1016/j.ymssp.2024.112039 [14] WANG Y, HU J Y, WANG F A, et al. Tire road friction coefficient estimation: Review and research perspectives[J]. Chinese Journal of Mechanical Engineering, 2022, 35(1): 6. doi: 10.1186/s10033-021-00675-z [15] LENG B, TIAN C, HOU X C, et al. Tire-road peak adhesion coefficient estimation based on multisource information assessment[J]. IEEE Transactions on Intelligent Vehicles, 2023, 8 (7): 3854-3870. doi: 10.1109/TIV.2023.3271867 [16] KANG S, CHEN J J, QIU G Q, et al. Slip ratio adaptive control based on wheel angular velocity for distributed drive electric vehicles[J]. World Electric Vehicle Journal, 2023, 14(5): 119. doi: 10.3390/wevj14050119 [17] LIU Y H, LI T, YANG Y Y, et al. Estimation of tire-road friction coefficient based on combined APF-IEKF and iteration algorithm[J]. Mechanical Systems and Signal Processing, 2017, 88: 25-35. doi: 10.1016/j.ymssp.2016.07.024 [18] WANG X D, RAN M P, ZHOU X L. Integration of in-wheel motor sensorless systems and hierarchical direct yaw moment control for distributed drive electric vehicles[J]. Engineering Applications of Artificial Intelligence, 2025, 139: 109600. doi: 10.1016/j.engappai.2024.109600 [19] ZHANG R Y, FENG Y. L, SHI P C, et al. Tire-road friction coefficient estimation for distributed drive electric vehicles using PMSM sensorless control[J]. IEEE Transactions on Vehicular Technology, 2023, 72 (7): 8672-8685. doi: 10.1109/TVT.2023.3248866 [20] SUN X M, XIAO Z H, WANG Z, et al. Acceleration slip regulation control method for distributed electric drive vehicles under icy and snowy road conditions[J]. Applied Sciences, 2024, 14 (15): 6803. doi: 10.3390/app14156803 [21] ATAEI M, KHAJEPOUR A, JEON S. Model predictive control for integrated lateral stability, traction/braking control, and rollover prevention of electric vehicles[J]. Vehicle System Dynamics, 2020, 58 (1): 49-73. doi: 10.1080/00423114.2019.1585557 [22] LI Q X, LIU L J, YUAN X F. Model predictive controller-based optimal slip ratio control system for distributed driver electric vehicle[J]. Mathematical Problems in Engineering, 2020: 8086590. [23] SAVITSKI D, IVANOV V, AUGSBURG K, et al. Wheel slip control for the electric vehicle with in-wheel motors: Variable structure and sliding mode methods[J]. IEEE Transactions on Industrial Electronics, 2020, 67 (10): 8535-8544. doi: 10.1109/TIE.2019.2942537 [24] 张利鹏, 刘欣, 刘帅帅, 等. 双模耦合驱动智能电动汽车对开坡道行驶稳定性控制[J]. 中国公路学报, 2024, 37(3): 204-215.ZHANG Li-peng, LIU Xin, LIU Shuai-shuai, et al. Driving stability control of a dual-mode coupling drive intelligent electric vehicle on bisectional slopes[J]. China Journal of Highway and Transport, 2024, 37 (3): 204-215. [25] LI Z X, PAN S J, MAO K, et al. Combined acceleration slip regulation for multi-wheel distributed electric drive vehicles considering torque loss factor[J]. Control Engineering Practice, 2024, 146: 105893. doi: 10.1016/j.conengprac.2024.105893 [26] YUAN S X, SHI Q, HE Z J, et al. Acceleration slip regulation by electric motor torque of battery electric vehicle with nonlinear model predictive control approach[J]. Vehicle System Dynamics, 2023, 61 (8): 1937-1953. doi: 10.1080/00423114.2022.2093758 [27] 吴勃夫, 徐晓, 陈自强, 等. 基于非线性MPC的电动赛车驱动防滑控制[J]. 合肥工业大学学报(自然科学版), 2024, 47(2): 182-188.WU Bo-fu, XU Xiao, CHEN Zi-qiang, et al. Acceleration slip regulation of electric racing cars based on nonlinear MPC[J]. Journal of Hefei University of Technology (Natural Science), 2024, 47 (2): 182-188. [28] 张荣芸, 凤永乐, 时培成, 等. 分布式电动汽车路面附着系数估计[J]. 机械科学与技术, 2025, 44(1): 187-198.ZHANG Rong-yun, FENG Yong-le, SHI Pei-cheng, et al. Estimation on road adhesion coefficient of distributed drive electric vehicles[J]. Mechanical Science and Technology for Aerospace Engineering, 2025, 44 (1): 187-198. [29] 汪洪波, 王春阳, 高含, 等. 基于FFUKF路面附着系数估计的汽车牵引力控制[J]. 力学学报, 2022, 54(7): 1866-1879.WANG Hong-bo, WANG Chun-yang, GAO Han, et al. Vehicle traction force control based on the road adhesion coefficient estimation by FFUKF[J]. Chinese Journal of Theoretical and Applied Mechanics, 2022, 54 (7): 1866-1879. [30] CHEN T, CAI Y F, CHEN L, et al. Sideslip angle fusion estimation method of three-axis autonomous vehicle based on composite model and adaptive cubature Kalman filter[J]. IEEE Transactions on Transportation Electrification, 2024, 10 (1): 316-330. doi: 10.1109/TTE.2023.3263592 [31] JIA B, XIN M, CHENG Y. High-degree cubature Kalman filter[J]. Automatica, 2013, 49 (2): 510-518. doi: 10.1016/j.automatica.2012.11.014 [32] 吴建洋, 王震坡, 张雷, 等. 四轮轮毂电机驱动电动汽车纵侧向稳定性协调控制策略研究[J]. 机械工程学报, 2023, 59 (4): 163-172.WU Jian-yang, WANG Zhen-po, ZHANG Lei, et al. Coordination stability control strategy for four-wheel-independent-actuated electric vehicles[J]. Journal of Mechanical Engineering, 2023, 59 (04): 163-172. [33] FU R, YANG B, ZHANG H L, et al. Lateral stability control of buses: A framework considering the steering hysteresis response using interleaved predictive control[J]. IEEE Transactions on Vehicular Technology, 2024, 73 (1): 216-231. doi: 10.1109/TVT.2023.3306800 [34] 胡海林, 虞诗焱, 黄伟毅, 等. 基于改进SMO的轨道交通直线感应电机无速度传感器MPTC[J]. 交通运输工程学报, 2025, 25(2): 94-107. doi: 10.19818/j.cnki.1671-1637.2025.02.006HU Hai-lin, YU Shi-yan, HUANG Wei-yi, et al. Speed sensorless MPTC of linear induction motors for rail transit based on improved SMO[J]. Journal of Traffic and Transportation Engineering, 2025, 25 (2): 94-107. doi: 10.19818/j.cnki.1671-1637.2025.02.006 [35] 傅琪涛. 分布式电驱动客车稳定性控制系统设计[D]. 杭州: 浙江大学, 2021.FU Qi-tao. Stability control system design for distributed drive electric bus[D]. Hangzhou: Zhejiang University, 2021. -
图(11) / 表(6)
计量
- 文章访问数: 46
- HTML全文浏览量: 23
- PDF下载量: 5
- 被引次数: 0
