Optimal Routing and Ecological Driving Systems for EVs/HEVs

Electric Vehicle;Hybrid Electric Vehicle;Ecological Driving;Optimal Routing and Charging;Electrical Engineering;Electrical Engineering, College of Engineering and Computer Science

Zhang, BowenWan, Mengqi ;

University of Michigan

关键词: Electric Vehicle;    Hybrid Electric Vehicle;    Ecological Driving;    Optimal Routing and Charging;    Electrical Engineering;    Electrical Engineering, College of Engineering and Computer Science;   

Others  :  https://deepblue.lib.umich.edu/bitstream/handle/2027.42/136617/Thesis_FInal.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Nowadays, there has been an unprecedented growth of energy demand and environmental concerns, in terms of the vehicle emissions and fuel efficiency. Transportation electrification is seen as an effective way to substantially satisfy both requirements. By applying the secondary electric power source, Electric Vehicles (EVs) and Hybrid Electric Vehicles (HEVs) become the promising solutions, and have potential to revolutionize urban transportation systems. Therefore, the pre-trip planning and power management problems regarding EVs and HEVs have attracted many researchers’ attention. However, there is little work addressing the EV/HEV fuel economy with the consideration of detailed vehicle powertrain model and meet the constraints from various perspectives. To address these challenges, on one hand, we propose a simulation framework for the economic operation of pure EV considering dynamic electricity price. We address optimal routing and charging problems of Uber-like EV considering dynamic electricity price and passenger satisfaction. It aims not only at finding the best route over a finite driving cycle, but also optimizing the EV charging behavior, in order to achieve optimal fuel efficiency and reduce cost. On the other hand, the detailed powertrain model for the individual power-split HEV is proposed. Constraints from vehicle power flows, road conditions, and speed limits are included. Two stochastic methods, the generic algorithm (GA) and the estimation of distribution algorithm (EDA) are implemented to verify the feasibility, accuracy, robustness, and effectiveness of the proposed methods. Moreover, the proposed models for individual EV/HEV can be further tailored and extended to multiple considering other emerging technologies (e.g., connected and automated vehicles) in the near future.