This book details how to assess electric mobility characteristics within electric vehicles, discussing energy management methods, automated systems, and the enormous potential of data resources mined from software, navigation systems, and connectivity.
Big Data and Electric Mobility presents methods to mine data specifically for electric vehicles, to comprehend their performance and to present opportunities to develop data-driven technological advancements. Including contributions from experts across the world, the book will look at topics such as human mobile behavior, battery charging and health, powertrain simulation, energy management, and multiphysics-constrained optimal charging.
The book will be key reading for researchers and engineers in the fields of automotive engineering, electrical engineering, and data mining.
Table of Contents:
1 Introduction Part I Design, optimization, and management of the power sources in electric vehicles 2. E-fuels and their implementation in zero-emission propulsion system 3. Design and integration of energy storage devices for automotive applications 4. Vehicle energy storage devices and their second-life applications 5. Digital Twin: An Effective Big Data Processing Tool for the Optimization of Electric Vehicles Part II Design, management, and control of the powertrain of electric vehicles 6. Topologies design and component sizing of electrified powertrains 7.Dedicated thermal propulsion systems for electrified vehicles 8. Thermal management systems and methods for electrified vehicles Part III Prediction and planning of electric vehicles in real-world driving 9. Driver behaviour prediction and driver-oriented control of electric vehicles 10. Global control optimizations of electrified vehicles 11. Traffic-in-the-loop simulation, optimization, and evaluation for electric vehicles 12. Driving Control and Traffic Modelling for Automated, Connected and Electrified Vehicles (ACEVs)
About the Author :
Dr. Haoran Zhang is a researcher with an interdisciplinary background, holding dual Bachelor's degrees in Engineering and Economics, complemented by two Ph.D. degrees in Oil & Gas Storage and Transportation Engineering and Environmental science. His research is at the forefront of sustainable development, focusing on clean energy supply chains, green transportation, and smart urban energy systems. His innovative research has garnered prestigious accolades, including the Energy Globe Award, the R&D 100 Award, the John Tiratsoo Award for Young Achievement, and the Smart 50 Award among others.
Quan Zhou is Professor of Automotive Engineering in the School of Automotive Studies at Tongji University, Shanghai, China. He received his Ph.D. in mechanical engineering from the University of Birmingham, Birmingham, UK, in 2019. Before joining Tongji University with the NSFC International Fellowship, Prof. Zhou was Assistant Professor and Research Fellow at the University of Birmingham. His research interests include evolutionary computation, fuzzy logic, reinforcement learning, and their application in automotive systems. He is associate editor for IEEE Transactions on Transportation Electrification and IET Journal of Intelligent Transportation.
