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Elsevier

iHorizon-Enabled Energy Management for Electrified Vehicles

  • 1st Edition - September 11, 2018
  • Latest edition
  • Authors: Clara Marina Martinez, Dongpu Cao
  • Language: English

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 a… Read more

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Description

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.

Key features

  • 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

Readership

Master and Doctorate levels in mechanical engineering, mechatronics, control engineering, electronics or similar

Table of contents

1. Introduction2. Integrated energy management for electrified vehicles3. The driver in the loop4. iHorizon in cycle-length windows5. iHorizon in short-term windows6. iHorizon driver energy management for PHEV real-time control7. iHorizon extension for vehicle applications8. Conclusions, discussion and future direction for research

Product details

  • Edition: 1
  • Latest edition
  • Published: September 11, 2018
  • Language: English

About the authors

CM

Clara Marina Martinez

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.
Affiliations and expertise
Development Engineer, Porsche Engineering Service, Germany

DC

Dongpu Cao

Dongpu Cao received the Ph.D. degree from Concordia University, Canada, in 2008. He is currently an Associate Professor at University of Waterloo, Canada. His research focuses on vehicle dynamics and control, automated driving and parallel driving, where he has contributed more than 100 publications and 1 US patent. 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 TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, IEEE/ASME TRANSACTIONS ON MECHATRONICS and ASME JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. He has been a Guest Editor for VEHICLE SYSTEM DYNAMICS, and IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS. He serves on the SAE International Vehicle Dynamics Standards Committee and a few ASME, SAE, IEEE technical committees.
Affiliations and expertise
Associate Professor, University of Waterloo, Canada

View book on ScienceDirect

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