An eco-driving system for electric vehicles with signal control under V2X environment |
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| Institution: | 1. School of Transportation Science and Engineering, Beihang University, Beijing 100191, China;2. Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing 100191, China;3. Department of Civil and Environmental Engineering, Rensselaer Polytechnic Institute, 110 8th St., Troy, NY 12180, United States;1. Office of Operation Research and Development, Federal Highway Administration, United States;2. Department of Mechanical Engineering, University of Minnesota, United States;3. Department of Transport & Planning and Department of BioMechanical Engineering, Delft University of Technology, The Netherlands;1. Univ. Orléans, PRISME, EA 4229, F45072, Orléans, France;2. PSA Peugeot Citroën, Direction Recherche Innovation & Technologies Avancées (DRIA), France;1. School of Transportation Science and Engineering, Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control, Beihang University, Beijing 100191, China;2. School of Automobile Engineering, Harbin Institute of Technology at Weihai, China;1. School of Transportation Science and Engineering, Beihang University, Beijing 100191, China;2. Department of Civil Engineering, California State Polytechnic University, Pomona, 3801 West Temple Ave., Pomona, CA 91768, United States;3. Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing 100191, China |
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| Abstract: | The benefit of eco-driving of electric vehicles (EVs) has been studied with the promising connected vehicle (i.e. V2X) technology in recent years. Whereas, it is still in doubt that how traffic signal control affects EV energy consumption. Therefore, it is necessary to explore the interactions between the traffic signal control and EV energy consumption. This research aims at studying the energy efficiency and traffic mobility of the EV system under V2X environment. An optimization model is proposed to meet both operation and energy efficiency for an EV transportation system with both connected EVs (CEVs) and non-CEVs. For CEVs, a stage-wise approximation model is implemented to provide an optimal speed control strategy. Non-CEVs obey a car-following rule suggested by the well-known Intelligent Driver Model (IDM) to achieve eco-driving. The eco-driving EV system is then integrated with signal control and a bi-objective and multi-stage optimization problem is formulated. For such a large-scale problem, a hybrid intelligent algorithm merging genetic algorithm (GA) and particle swarm optimization (PSO) is implemented. At last, a validation case is performed on an arterial with four intersections with different traffic demands. Results show that cycle-based signal control could improve both traffic mobility and energy saving of the EV system with eco-driving compared to a fixed signal timing plan. The total consumed energy decreases as the CEV penetration rate augments in general. |
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| Keywords: | Electric vehicle Signal control Connected vehicles Eco-driving system Bi-objective |
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