Model Predictive Energy Management for Induction Motor Drives and All-Wheel-Drive Battery Electric Vehicles

A Flatness Based Approach

Bernhard Rolle

• Publisher: Shaker
• Year: 2021
• eBook ISBN:
  9783844078978
• Edition: 1

Analytical models of the electric powertrain for a battery electric vehicle with two drive modules on the front and rear axle are investigated. The modeling approach focuses on loss processes associated with the energy conversion of the voltage source inverters and induction motors. New dynamical models are proposed that can be efficiently integrated into vehicle simulations and also be implemented on embedded systems, such as the motor control unit of the investigated vehicle. In doing so, an average value model of the voltage source inverter is derived, based on a double Fourier integral analysis of the semi-conductor switching signals. Furthermore, a widely used model of the induction motor, applied for motor analysis and control design, is reformulated into an equivalent differential flat system based on the definition of a new flat output. Both component models are integrated into a vehicle simulation of a Mercedes Benz EQC prototype and are thoroughly validated through experimental test series. With the help of the newly introduced models and with the assistance of modern vehicle sensor systems, control strategies of the electric powertrain are investigated that aim for the most energy efficient operation. In a first step, decentralized optimal control approaches are proposed that improve the efficiency of the electric drive module, not only during stationary operation, but also during transient torque conditions. This improvement is achieved by an appropriate field oriented control method. In a second step, optimization-based torque allocation strategies are investigated. Finally, a centralized predictive control approach is presented that exploits all operational degrees of freedom.