脱炭素エネルギー先導人材育成フェローシップ 2022年度
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wolleQ-Energy Innovator Fellowship19総合理工学府総合理工学専攻博士後期課程1年総合理工学研究院 准教授The development of automotive industry brings benefits to modern transport meanwhile arouses energy shortage and emis-sion pollution problems. Next-generation connected and auto-mated vehicles bring opportunities for energy-efficient control. Under this circumstance, the intelligent eco-driving control has drawn increasing attention. The main idea is that, utilizing the vehicle electrification and intelligence, the reasonable matching between vehicle driving, traffic situation and vehicle performance can be achieved, and the energy economy can be improved on the premise of satisfying the travel requirement. Research works have shown that eco-driving control can reduce 15~20% energy con-sumption with the optimization of driving strategy and power-train by utilizing the ubiquitous traffic information through V2X communication. However, there are still challenges during real implementation of the intelligent eco-driving control as follows: (1) How to fully utilize the abundant external traffic and geograph-ic information combined with velocity profile optimization and powertrain control to establish a systematic control architecture to save energy is a most urgent problem to be solved. (2) To facilitate the real implementation, human driving style (personalization) should be considered so that the personalized energy-efficient au-tomated driving system can be not only realizing the energy sav-ing but also accepted by the human driver and passengers.Focusing on these challenges in energy-efficient automated driving control under the intelligent transportation system, an eco-driving predictive cruise control system for EVs is proposed considering surrounding traffic information to realize the ener-gy-saving autonomous driving in the vehicle longitudinal driv-ing direction; on the other hand, an energy-efficient lane-change motion planning for personalized autonomous driving has been developed recently. To further explore the impact of driving be-havior on the vehicle energy consumption level and realize the human-like automated driving, a personalized anticipatory eco-driving system for EVs is expected, to enhance the existing com-mercial ACC system.In the foreseeable future, we believe such an advanced automo-tive control strategy considering driving style, energy consump-tion, and vehicle motion control will provide guideline to the ve-hicle manufacturers to facilitate people-oriented, energy-aware, and smarter vehicles production, and lay the foundation of the application of the strong AI or artificial general intelligence to the future mobility.指導教員からメッセージGiven the recent advancements in smart and efficient mobility, our research at the Energy and Environmental Systems (EES) Laboratory focuses on real-time detecting, tracking, and predicting driver behavior by designing and developing personalized behavior learning systems that can be used to enhance the performance of adaptive cruise control systems in future autonomous vehicles. This is in light of eco-driving technology development, which seeks to perform energy-efficient speed planning for autonomous vehicles, taking into account the impact of driving behaviors in different traffic systems. The Q-Energy Innovation Fellowship has provided an excellent opportunity for Mr. Nie Zifei to conduct interdisciplinary-oriented research on eco-driving by establishing a robust theoretical model and experimental system to investigate the interaction between optimal driving behavior and vehicle driving motion control.Human-in-the-Loop Driving Simulator for Eco-Driving Control Algorithm TestFarzaneh HoomanToward Safer, Smarter and Decarbonized Mobility: Personalized Energy-efficient Autonomous DrivingToward Safe, Smart and Efficient Mobility: Energy-Aware Personalized Autonomous Driving Motion Control and Learning-Based Human-Machine Interactive Platform for Driving Behavior Analysis NIE Zifei14f

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