主讲人：Elnaz Irannezhad
邀请人：谢驰 教授
时间：2025年9月15日上午10：00-11：00（Beijing Time）
地点：通达馆103会议室
主讲人简介：
Dr Elnaz (Elli) Irannezhad is a Senior Lecturer in transport engineering at the School of Civil and Environmental Engineering at the University of New South Wales (UNSW), Sydney, Australia. Elli's research contributes to the advancement of science in cross-disciplinary fields of transport engineering, logistics engineering, automated vehicles, decarbonisation and blockchain technology. She has over 18 years of combined research and industry experience in modelling, simulation, optimisation, and machine learning.
She received her PhD at the School of Civil Engineering of the University of Queensland (UQ) (2014-2017). Prior to that, she worked in the transport industry as a senior transport engineer in Iran for nine years. Her PhD was on behavioural freight transport modelling in which she contributed to the field of behavioural agent-based modelling and operation research. From 2017 to 2020, she completed a three-year postdoctoral research fellowship at UQ’s Business Faculty on a partnership project with the Port of Brisbane. From 2020 to 2022, she acted as a Principal Engineer and the Portfolio Leader of Next Generation Transport Systems at the Australian Road Research Board (ARRB) where she led several projects in automated driving technologies and sensors, infrastructure readiness for autonomous vehicles, smart heavy vehicle access monitoring and heavy vehicle origin-destination modelling. Elli joined UNSW in March 2022 as a Senior Lecturer. She will start a three-year research fellowship developing a digital twin model for Port Sydney and its hinterland network.
主讲内容简介：
As global efforts to decarbonise the transport sector accelerate, the rail industry is transitioning from diesel-powered locomotives to more sustainable battery-electric alternatives. Although battery-electric technology offers a promising solution, widespread adoption, particularly in freight rail, is limited by range constraints and the need for strategically located charging infrastructure along corridors. To address these challenges, accurate energy consumption modelling and route-specific planning to determine the optimal number and placement of charging stations are required. By applying an optimisation algorithm to identify the most efficient charging locations and using real-world data, a case study on a freight route was undertaken. The model estimates the energy consumption of battery electric trains, incorporating operational parameters as well as regenerative braking efficiency. An optimisation model lays down the optimal location of charging/battery swapping stations as well as the number of battery consists on trains.
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