Development of Predictive and Nonlinear Control Designs for an Electric All Wheel Drive Powertrain System

Abstract

Vehicle electrification is a strong trend in the automotive industry. The use of electric motors for propulsion offers many opportunities and some technical difficulties. This thesis addresses the challenge of controlling and coordinating two electric motors as part of an electric all wheel drive (eAWD) powertrain to maintain vehicle stability during longitudinal and lateral motion of the vehicle. The control problem is broken into two pieces, vehicle level control and control of the electric motors. At the vehicle level, several control strategies are developed and compared. First, a weighted one step ahead (WOSA) controller is developed. Next, a nonlinear model predictive controller (NLMPC) is developed. Next, a feedback linearization controller (FLC) is developed. Finally, a sliding mode controller (SMC) is developed. In each case, the vehicle level controller is integrated with fuzzy rules to translate the vehicle level control signal into reference targets for the motor controllers. The integration of a physical model and fuzzy rules into a single controller is a unique concept that is explored in this thesis. Each of the electric motors consists of a traction motor and torque vectoring motor. WOSA controllers are developed for each type of motor and integrated with the vehicle level controller for overall vehicle control. The WOSA is a simple but powerful control approach for the motor controllers. Key system states are estimated using the controller output observer (COO) concept. The COO provides a good estimate of the system states with sensor data that is already available on typical vehicles. The COO also uses the WOSA control strategy. For both longitudinal and lateral motion, the effectiveness of the control strategies are demonstrated for launch and a high speed double lane change maneuver on high friction and low friction road surfaces using Simulink and CarSim