An eco-driving system for electric vehicles with signal control under V2X environment. (August 2018)
- Record Type:
- Journal Article
- Title:
- An eco-driving system for electric vehicles with signal control under V2X environment. (August 2018)
- Main Title:
- An eco-driving system for electric vehicles with signal control under V2X environment
- Authors:
- Li, Ming
Wu, Xinkai
He, Xiaozheng
Yu, Guizhen
Wang, Yunpeng - Abstract:
- Highlights: An optimization framework for connected EV system is investigated. A bi-objective and multi-stage model is built with eco-driving and signal control. A hybrid algorithm merging GA and PSO is employed to search for optimal efficiency. Various penetration rates of Connected EVs are investigated. 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 anHighlights: An optimization framework for connected EV system is investigated. A bi-objective and multi-stage model is built with eco-driving and signal control. A hybrid algorithm merging GA and PSO is employed to search for optimal efficiency. Various penetration rates of Connected EVs are investigated. 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. … (more)
- Is Part Of:
- Transportation research. Volume 93(2018)
- Journal:
- Transportation research
- Issue:
- Volume 93(2018)
- Issue Display:
- Volume 93, Issue 2018 (2018)
- Year:
- 2018
- Volume:
- 93
- Issue:
- 2018
- Issue Sort Value:
- 2018-0093-2018-0000
- Page Start:
- 335
- Page End:
- 350
- Publication Date:
- 2018-08
- Subjects:
- Electric vehicle -- Signal control -- Connected vehicles -- Eco-driving system -- Bi-objective
Transportation -- Periodicals
Transportation -- Technological innovations -- Periodicals
388.011 - Journal URLs:
- http://www.sciencedirect.com/science/journal/0968090X ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.trc.2018.06.002 ↗
- Languages:
- English
- ISSNs:
- 0968-090X
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 9026.274620
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 17054.xml