iHorizon-Enabled Energy Management for Electrified Vehicles

  • ID: 4519438
  • Book
  • 420 Pages
  • Elsevier Science and Technology
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iHorizon-Enabled Energy Management for Electrified Vehicles proposes a realistic solution that assumes only scarce information is available prior to the start of a journey and that limited computational capability can be allocated for energy management. This type of framework exploits the available resources and closely emulates optimal results that are generated with an offline global optimal algorithm. In addition, the authors consider the present and future of the automotive industry and the move towards increasing levels of automation. Driver vehicle-infrastructure is integrated to address the high level of interdependence of hybrid powertrains and to comply with connected vehicle infrastructure.

This book targets upper-division undergraduate students and graduate students interested in control applied to the automotive sector, including electrified powertrains, ADAS features, and vehicle automation.

  • Addresses the level of integration of electrified powertrains
  • Presents the state-of-the-art of electrified vehicle energy control
  • Offers a novel concept able to perform dynamic speed profile and energy demand prediction
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1. Introduction
2. Integrated Energy Management for Electrified Vehicles
3. The Driver in the Loop
4. iHorizon in cycle-length Windows
5. iHorizon in Short-Term Windows
6. iHorizon Driver Energy Management for PHEV Real-Time Control
7. iHorizon Extension for Vehicle Applications
8. Conclusions, Discussions and Future Directions for Research
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Martinez, Clara Marina
Dr. Clara Marina Martínez is currently working for Porsche Engineering Service. Her current position involves virtual testing of driver assisstance and automated features. She obtained a 5-year degree in Industrial Engineering in the Seville University, an MSc degree in Automotive Mechatronics from Cranfield University and a PhD within industry and Cranfield University. She has contributed in over 10 publications during PhD studies and has collaborated in several reseach projects.
Cao, Dongpu
Dr. Dongpu Cao is an Associate Professor at University of Waterloo, Canada. His current research focuses on driver cognition, automated driving and parallel driving, where he has contributed over 160 publications. He received the ASME AVTT'2010 Best Paper Award and 2012 SAE Arch T. Colwell Merit Award. Dr. Cao serves as an Associate Editor for IEEE TVT, T-ITS, TIE, TMECH, JAS, and ASME JDSMC. He serves as the Co-Chair of IEEE ITSS Technical Committee on Cooperative Driving.
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