ATEM · Accelerating transport electrification by machine learning
Horizon 2020 — Marie Skłodowska-Curie Actions
- Duration
- 2021-08-01 → 2023-07-31
- EU contribution
- €191,852
- Participants
- 1
- Scheme
- MSCA-IF
Lines connect the coordinator with its partners.
Results in brief
Accelerating transport electrification by machine learning
Problem/Issue Being Addressed: This project seeks to address several interconnected challenges within the realm of transportation and environmental sustainability. These challenges include heavy reliance on fossil fuels, environmental pollution, energy inefficiency, and underutilization of emerging technologies in the road transport sector. The advent of Electric Vehicles (EVs) and Autonomous Electric Vehicles (AEVs) brings forth a promising solution, but optimal usage is constrained by limitations in battery management, route optimization, and overall vehicle efficiency. Importance for Society: Tackling these challenges holds significant societal importance. Transitioning from fossil fuels to electric transport contributes to the mitigation of climate change, improves air quality, and promotes energy sustainability. Moreover, optimizing the use of EVs and AEVs through advanced AI technologies can enhance transportation efficiency, user experience, and overall traffic management. The use of AI can contribute to smarter, safer, and more energy-efficient transportation systems, thereby positively impacting the quality of life and sustainability on a global scale. Overall Objectives: The overall objectives of this project can be summarized as follows: To enhance the lifespan and energy efficiency of batteries in EVs and AEVs. This will be achieved through the advanced design of battery management systems (BMS) that optimally control operations within the physical limits of the battery system, thereby ensuring safety and efficiency. To build a personalized AEV recommendation model leveraging AI technologies. The model will recommend optimal routes to users with the lowest energy consumption, further reducing energy cost and improving the user experience. To improve the efficiency of AEVs and overall user experience by integrating the electrochemical battery model (using a data-driven method), AEV speed control (using deep reinforcement learning), and the AEV recommendation model in real-time. This unified approach will contribute to safer, faster, and more environmentally friendly transport. By successfully achieving these objectives, the project will make significant strides towards more sustainable and efficient transportation, harnessing the potential of AI technologies to revolutionize the field of transport electrification.
Data: CORDIS, © European Union
Project objective
The technological blossom in artificial intelligence (AI) makes possible numerous advancements in various engineering disciplines. The applicant has been in the forefront of this AI innovation, and received seven world prizes in AI competitions. For this fellowship, he intends to use his AI expertise to examine the role that AI technologies can play in accelerating transport electrification, and subsequently contributing to climate action. In view of the strong vehicle industry in Gothenburg, Sweden, Chalmers and Scania AB collectively provide an outstanding research environment for this important topic. More specifically, this project will consolidate data-driven electrochemical battery model, deep reinforcement learning based automated electric vehicle (AEV) speed control and AEV personalized routing recommendation model, which are three biggest challenges in the transition process towards electrified transport systems. The fellowship will be co-supported by AI Innovation of Sweden, and Drive Sweden, with a hope to implement the expected findings in not only the vehicle industry but also transport management sectors. This research is interdisciplinary in nature, and requires expertise from computer science, artificial intelligence, electrochemical engineering, electrical engineering, and transport engineering. In this regard, the host institute, Chalmers University of Technology, has developed a well-designed career training and development plan for the applicant to work with renowned research leaders in relevant disciplines (e.g. Prof. Xiaobo Qu, Member of Academia Europaea; Prof. Karl Johansson, Fellow of Royal Swedish Academy of Engineering Sciences) to enhance his readiness to become an assistant professor after this fellowship. The applicant will also take advantage of the fertile research environment in Chalmers, and synchronize the workshops and reference groups with the host’s other EU and Swedish projects to disseminate the new findings.
Original text from CORDIS.
Participants
- CHALMERS TEKNISKA HOGSKOLA AB · GoteborgCoordinatorSweden
Links
Data: CORDIS, © European Union