Creep control is a kind of start-up control for electric vehicle. In the paper, two closed-loop control is contained in the creep control strategy. Proportional control with torque limitation, which adjusts vehicle speed, is the outer-loop control; and anti-slip control is the inner-loop control. In this way, the vehicle speed indicates no overshoot and has uniform convergence with driving torque. Moreover, the vehicle can start up on low adhesion or split road, and the driver can control the creep speed by the brake pedal only, so that the driver’s operation is reduced. Subjective evaluation method is proposed to determine the control performance metrics, and then the mathematical relationship between the performance metrics and the control parameters is established. The tuning method of the control parameters is proposed according to those performance metrics, which mitigates the workloads of calibration and provides a better driving experience. Some simulations and real vehicle experiments are conducted to verify that the control strategy has an expected performance.
Vehicle velocity is an important parameter for vehicle dynamics control. Global Positioning System (GPS) and inertial measurement unit (IMU) information can be used for velocity estimation by taking advantage of each other through fusion. However, if GPS velocity is used as the only measurement like classic GPS/INS, it will cause a large velocity estimation error or even divergence. This paper, from the perspective of system observability, proves that the GPS course angle (GCA) plays an important role in vehicle velocity estimation based on low-cost GPS/IMU. Firstly, construct the velocity estimation algorithm in the vehicle coordinate system directly. Then, according to the characteristics of different working conditions, the observability of the system without GCA is fully analyzed from the Lie derivative-based observability proof and the observability basic definition. The results show that the weak observability of the yaw angle is the most serious threat in vehicle velocity estimation. After fusing the GCA, the yaw angle potential divergence can be suppressed and the estimation accuracy of velocity will be improved. Even though roll and pitch are still unobservable under some conditions after augmenting GCA, the error propagation analysis shows that their estimation errors have little effect on velocity estimation, so the accurate estimation of velocity can be guaranteed. Finally, the indispensable of GCA in velocity estimation based on low-cost GPS/IMU is verified by Carsim-Simulink joint simulations and vehicle tests.
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