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摘要: 针对四轮毂驱动电动汽车中局部轮毂电机发生故障后存在的安全隐患问题，提出了一种基于模型预测控制-多系统协同分配(MPC-MSCA)的容错控制方法，以应对局部轮毂电机发生故障后输出能力不足的运行工况；搭建了14自由度四轮毂驱动车辆动力学模型，包括六自由度车身模型和4个二自由度车轮模型；参考二自由度车辆模型设计了容错控制方法，包括运动跟踪层和力矩分配层，运动跟踪层用于计算车辆正常行驶所需的总纵向力和附加横摆力矩，力矩分配层设计了优化分配方案和MSCA方案，分别应对局部轮毂电机发生故障后输出能力充足和不足2种运行工况，重点研究了MSCA控制方法的目标函数、约束条件和控制变量；利用Simulink/MATLAB和CarSim联合仿真，分别设置了直线行驶和双移线行驶2种运行环境，验证了所提MPC-MSCA控制方法的有效性。研究结果表明：相较于传统方法，在直线行驶环境下，MPC-MSCA控制方法可使车辆横摆角速度平均误差降低了31.6%，有效保障了局部轮毂电机故障时车辆的直线行驶能力；在双移线行驶环境下，质心侧偏角和横摆角速度平均误差分别降低了7.4%和6.9%，提高了局部轮毂电机故障时车辆的操纵稳定性。可见，所提容错控制方法可以确保四轮毂驱动电动汽车在1个或2个轮毂电机故障工况下满足操纵稳定性和安全性要求。Abstract: In response to the safety hazards posed by local in-wheel motor failures in four-wheel hub-drive electric vehicles, a fault-tolerant control method based on model predictive control-multi system collaborative allocation (MPC-MSCA) was proposed. The method was designed to address operating conditions characterized by insufficient output capacity following local in-wheel motor failures. A 14-degree-of-freedom (DOF) four-wheel hub-drive vehicle dynamics model was constructed, comprising a 6-DOF vehicle body model and four 2-DOF wheel models. A fault-tolerant control method was designed by referencing a 2-DOF vehicle model, which included a motion tracking layer and a torque distribution layer. The motion tracking layer was utilized to calculate the total longitudinal force and additional yaw moment required for normal vehicle operations. In the torque distribution layer, an optimization allocation scheme and a MSCA scheme were designed to respectively address operating conditions of sufficient and insufficient output capacity following local in-wheel motor failures. The objective function, constraints, and control variables of the MSCA control strategy were highlighted. The proposed MPC-MSCA control method was validated through co-simulation with Simulink/MATLAB and CarSim under two operating conditions: straight-line driving and double lane change driving. Research results show that the method reduces the average yaw velocity error of vehicles by 31.6% compared with the traditional method in a straight-line driving environment, effectively ensuring the vehicle's ability to drive straight when there is a local in-wheel motor failure. In a double lane change driving environment, the average sideslip angle error and average yaw velocity error reduce by 7.4% and 6.9%, respectively, improving the vehicle's handling stability when there is a local in-wheel motor failure. Therefore, the proposed fault-tolerant controller can ensure the four-wheel hub-drive electric vehicle meeting the handling stability and safety requirements under the condition of one or two in-wheel motor failure.
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图 4 电机输出能力充足工况下车辆行驶状态
Figure 4. Vehicle driving status under sufficient motor output capacity
图 5 电机输出能力充足工况下车辆四轮驱动力矩
Figure 5. Vehicle four-wheel drive torques under sufficient motor output capacity
图 6 直线行驶环境下车辆行驶状态
Figure 6. Vehicle driving status in straight-line driving environment
图 7 直线行驶环境下车辆四轮驱动力矩
Figure 7. Vehicle four-wheel-drive torques in straight-line driving environment
图 9 双移线环境下车辆四轮驱动力矩
Figure 9. Vehicle four-wheel-drive torques in double lane change environment
表 1 车辆主要参数
Table 1. Main parameters of vehicle
参数 数值 m/kg 1 240 ms /kg 1 110 a/m 1.04 b/m 1.56 hg/m 0.52 Ix/(kg·m2) 460 Iy/(kg·m2) 1 343.1 Iz/(kg·m2) 1 343.1 Bf/m 1.48 Ksfl(Ksfr)/(N·m-1) 34 950 Ksrl(Ksrr) /(N·m-1) 34 950 Csfl(Csfr)/(N·s·m-1) 2 100 Csrl(Csrr)/(N·s·m-1) 2 000 Cf /(N·rad-1) 75 700 Cr /(N·rad-1) 75 700 
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表 2 直线行驶环境下车辆行驶状态的量化评价
Table 2. Quantitative evaluation of vehicle driving status in straight-line driving environment
方案 质心侧偏角平均误差/(°) 横摆角速度平均误差/(°·s-1) 平均车速/(km·h-1) 质心侧偏角最大偏差/(°) 横摆角速度最大偏差/(°·s-1) CBDSA 0.052 0.038 109.13 0.073 0 0.195 CSDSA 0.020 0.038 109.71 0.038 9 0.153 MSCA 0.022 0.026 109.69 0.038 6 0.135
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表 3 双移线环境下车辆行驶状态的量化评价
Table 3. Quantitative evaluation of vehicle driving status in double lane change environment
方案 质心侧偏角平均误差/(°) 横摆角速度平均误差/(°·s-1) 平均车速/(km·h-1) 质心侧偏角最大偏差/(°) 横摆角速度最大偏差/(°·s-1) CBDSA 0.208 5.136 69.60 0.54 10.76 CSDSA 0.215 5.443 69.82 0.40 9.77 MSCA 0.199 5.066 69.83 0.37 9.37
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