Robust Speed Tracking Control for Future Electric Vehicles under Network-Induced Delay and Road Slope Variation

Area of Science:

• Automotive Engineering
• Control Systems

Background:

• Integrated motor-transmission (IMT) powertrains are key for electric vehicles, requiring precise speed control for optimal performance.
• Challenges in IMT speed control include network-induced time delays, unknown road variations, parameter uncertainties, and measurement noise.

Purpose of the Study:

• To propose a robust speed tracking control strategy for electric vehicles with IMT powertrains.
• To address challenges posed by network delays, parameter uncertainties, and external disturbances.

Main Methods:

• A disturbance observer and low-pass filter were developed to mitigate road slope variations and measurement noise, improving external load torque estimation.
• A network-induced delay speed tracking model was created, incorporating damping coefficient uncertainties using norm-bounded uncertainty reduction.
• A novel Lyapunov function and linear matrix inequality (LMI) algorithm were employed to design the robust controller, treating estimation errors and noise as external disturbances.

Main Results:

• The proposed controller demonstrated strong robustness against system uncertainties and external disturbances.
• Excellent speed tracking performance was achieved, leading to improved ride comfort.
• The controller effectively managed network-induced delays and parameter variations.

Conclusions:

• The developed robust speed tracking control strategy offers significant advantages for electric vehicle IMT powertrains.
• The approach enhances performance, robustness, and ride comfort compared to existing methods.
• This strategy provides a reliable solution for precise speed control in challenging electric vehicle operating conditions.