This PhD project addresses the question of the optimal setup on an electric bus, with primary focus on the battery and charging methods. The current lack of knowledge regarding energy consumption models has led to the prevalent but inefficient strategy of oversizing batteries, resulting in unnecessary costs and environmental externalities. The project aims to develop advanced learning mechanisms, using real-world data from electric bus fleets and applying federated learning to create energy consumption models that account for variable operating conditions, such as ambient temperature and door opening patterns. Additionally, it will explore how automation—both at the local (bus) and global (fleet) levels—can contribute to energy reduction, investigating whether isolated or system-level automation offers the greatest benefits
Requirements:
The candidate should possess an MSc degree or equivalent in Computer Science or any related engineering discipline. The ideal candidate should have some knowledge and/or experience in several of the following topics: Optimisation and/or machine learning algorithms Distributed AI (e.g., swarm intelligence, federated learning) Data Analytics and Management Statistic models/processing/analysis
Text:
PhD Candidate in Energy Storage Rightsizing for Electric Buses This PhD project addresses the question of the optimal setup on an electric bus, with primary focus on the battery and charging methods. The current lack of knowledge regarding energy consumption models has led to the prevalent but inefficient strategy of oversizing batteries, resulting in unnecessary costs and environmental externalities. The project aims to develop advanced learning mechanisms, using real-world data from electric bus fleets and applying federated learning to create energy consumption models that account for variable operating conditions, such as ambient temperature and door opening patterns. Additionally, it will explore how automation—both at the local (bus) and global (fleet) levels—can contribute to energy reduction, investigating whether isolated or system-level automation offers the greatest benefits The candidate should possess an MSc degree or equivalent in Computer Science or any related engineering discipline. The ideal candidate should have some knowledge and/or experience in several of the following topics: Optimisation and/or machine learning algorithms Distributed AI (e.g., swarm intelligence, federated learning) Data Analytics and Management Statistic models/processing/analysis
Please click here, if the job didn't load correctly.
Please wait. You are being redirected to the job in 1 seconds.