Next Generation Intelligent Driver-Vehicle-Infrastructure Cooperative System for Energy Efficient Driving in Connected Vehicle Environment
Transportation-related fossil fuel consumption and greenhouse gas emissions have received increasing public concern in recent years. To reduce energy consumption and mitigate the environmental impact of transportation activities, this dissertation research work aims at providing technical solutions by taking advantage of recent technology development in vehicle automation, vehicle connectivity and vehicle electrification.
More specifically, a driver-vehicle-infrastructure cooperative framework for energy efficient driving of plug-in electric vehicles (PEVs) is proposed in this dissertation. Within this framework, this research improves energy efficiency of PEVs in the following ways: vehicle dynamics optimization and powertrain optimization, as well as co-optimization between them.
For vehicle dynamics optimization, a connected ecodriving system has been designed for PEVs to optimize their speed profiles when travelling through signalized intersections, by receiving real-time signal phase and timing information obtained through wireless communications. The calculated optimal speed trajectory (in terms of energy efficiency) is provided to the driver through an in-vehicle display in real-time. The performance of this connected ecodriving system is implemented and evaluated at different automation levels: human driving without considering the driver error, human driving considering the driver error, and partial automated (longitudinal) driving. Numerical analysis with real-world driving data shows that there is 12% ,14% and 21% potential energy savings that can be achieved by these proposed strategies respectively.
For powertrain operation optimization, an evolutionary algorithm based power-split control system for plug-in hybrid electric vehicle has been designed and evaluated with real-world traffic data. The designed model is used to optimally control the power-split between two different power sources (i.e., battery and gas tank) by considering various traffic conditions to achieve the minimum fuel consumption when satisfying total power-demand. In addition, a reinforcement-learning based autonomous learning strategy is also proposed for learning the optimal power-split decision based on historical driving data. Approximately 14% and 12% energy savings are identified by these two different powertrain operation strategies respectively.
For co-optimization of the vehicle dynamics and powertrain optimization, a bi-level optimization strategy has been designed and tested with real-world driving data to achieve augmented energy benefits from the compound effect of vehicle dynamics and powertrain operations optimization. An average of 29% improvement of fuel efficiency for the tested PHEV is identified by combining the vehicle dynamics and powertrain operation optimization.
The main contribution of this dissertation research is the design and validation of a driver-vehicle-infrastructure framework for PEV energy efficient driving. To the best of our knowledge, this is one of the first efforts to systematically investigate the potential energy benefits of both vehicle dynamics and powertrain operation optimization as well as its compound effect with real-world driving data for PEVs. The designed connected eco-driving system and power-split control model are quite promising in improving PEV energy efficiency.