Abstract
Electrification alters the energy demand and environmental impacts of vehicles, which brings about new challenges for sustainability in the transport sector. To further enhance the energy economy of electric vehicles (EVs) and offer an energy-efficient driving strategy for next-generation intelligent mobility in daily synthetic traffic situations with mixed driving scenarios, the model predictive control (MPC) algorithm is exploited to develop a predictive cruise control (PCC) system for eco-driving based on a detailed driving scenario switching logic (DSSL). The proposed PCC system is designed hierarchically into three typical driving scenarios, including car-following, signal anticipation, and free driving scenario, using one linear MPC and two nonlinear MPC controllers, respectively. The performances of the proposed tri-level MPC-based PCC system for EV eco-driving were investigated by a numerical simulation using the real road and traffic data of Japan under three typical driving scenarios and an integrated traffic situation. The results showed that the proposed PCC system can not only realize driving safety and comfortability, but also harvest considerable energy-saving rates during either car-following (16.70%), signal anticipation (12.50%), and free driving scenario (30.30%), or under the synthetic traffic situation (19.97%) in urban areas of Japan.
Highlights
Itself, which means that the predictive cruise control (PCC) system manipulates the electric vehicles (EVs) to execute the eco-driving strategy that can be combined with the energy-saving technologies of the vehicle itself to maximize the energy-saving potential simultaneously [9]
Since the main instantaneous optimal vehicle velocity trying to avoid stopping at the red light is protarget of the proposed PCC system is to evaluate energy consumption, an artificial neural network (ANN)-based instantaneous energy consumption estimation model (ANN-instantaneous EV energy consumption model (IECM)) is applied
MATLAB/Simulink is more focused on the development of the control system
Summary
Following the Paris Agreement signed at COP21 in 2015 and to achieve the Sustainable Development Goals (SDGs), governments and industries around the world have been developing innovative solutions to intensify the development of a sustainable lowcarbon society. Energy consumption during vehicle driving is pertinent to the status of the vehicle itself, and subject to road conditions and the traffic situation. Eco-driving, as one of the conceptual control technologies, has been considerably noted due to its capability of reducing energy consumption in either the local microscopic or global macroscopic level [6,7]. The main objective of eco-driving is to attain the best match between the host vehicle speed and the vehicle surroundings, including road environment and traffic flow, through the appropriate operation controlled by a driver or an autonomous driving system. Following the developing trend of connected and automated vehicles (CAVs) and intelligent transportation systems (ITSs), eco-driving assistance systems (EDAS), as the extension of advanced driver assistance systems (ADASs), present a transcendent energy economy improvement potential due to higher levels of engagement with the driving surroundings [8]. The predictive cruise control (PCC) system is an ideal EDAS to take full advantage of the energy saving of eco-driving because of two reasons: (1) Equipped with an intelligent hardware system including controllers, sensors, and actuators, the cruise control system can partially or entirely replace the human driver to realize the energy saving objective automatically, which promotes the development of next-generation intelligent mobility; (2) Cruise control technology, as an embedded system into the EV, does not influence the energy-saving technologies of the EV itself, which means that the PCC system manipulates the EV to execute the eco-driving strategy that can be combined with the energy-saving technologies of the vehicle itself to maximize the energy-saving potential simultaneously [9]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
