Research on Telematics Enabled Intelligent Eco-driving Technology for Electric Vehicles

The telematics was usually adopted for more straightforward applications such as video and audio amusement, electric toll collection and navigation etc., which are not usually coupled with the vehicle control unit (VCU), safety and power control systems. In recent years, its usage have been extended to more sophisticated applications involved with VCU due to the development of telematics, such as cooperative adaptive cruise control (CACC) which integrates vehicle to vehicle communication, sensor technique and control technique. In this research, a study on incorporating telematics to improve driving efficiency and extend the electric vehicle driving range is carried out.
Two methods to extend the electric vehicle driving range have been proposed in this thesis. The first approach is the driver assistance system (DAS) which can improve driving efficiency by providing suggested velocity profile for driver. The driving range of the EV could be improved if the driver follows its suggestion. In another approach, the powertrain is controlled directly by the VCU, the energy efficiency is improved by operating the motors at high efficiency operation point. In both parts, the method and impact of incorporating the real-time traffic information and road condition is studied.
The DAS which is called the green control unit (GCU) developed in this research is deployed on an electric vehicle driven by an induction motor. The model predictive control (MPC) theory which formulates the eco-driving strategy as an optimization problem with constraints is adopted in the development of the GCU. The GCU is able to provide a velocity suggestion that considers both energy efficiency and safety for the EV driver. In GCU, the prediction model includes vehicle longitudinal dynamics and induction motor loss model. The altitude profile ahead, speed limit and friction coefficient that can be updated via telematics are considered as constraints in the MPC formulation. The simulation is carried out and the GCU performances with different MPC formulation are compared. The impacts of parameter accuracy and information availability are also investigated.
The second part of the research is focused on developing intelligent control unit that utilizes telematics on the EV with multiple traction motors. The EV with multiple traction motors includes one front wheel driving traction motor which is a 40kW induction motor and two rear wheel driving in-wheel motors which are two 28kW permanent magnet synchronous motors (PMSM). The idea to improve energy efficiency is that the efficiency characteristics of these two motors are able to complement each other. The front traction motor provides traction force and is suitable for when the vehicle is driven at low speed but with high torque demand, due to the fact that induction motor demonstrates higher efficiency in the aforementioned operating region. The rear permanent magnet synchronous motors are suitable to provide traction force while vehicle is driven at high speed. In this work, two distribution strategies which distribute different traction force ratio based on current vehicle states are proposed to improve energy efficiency. The first method is the instantaneous distribution strategy and the second is the model predictive control based technique. These two distribution strategy are assessed by comparing them with other distribution strategies such as dynamic programming and even distribution strategy. The simulation is carried out with motor efficiency maps measured by experiment. The results show that both strategies developed in this research is able to improve the energy efficiency for the compound electric vehicle.