Energy Efficient Non-Road Hybrid Electric Vehicles

Advanced Modeling and Control

  • Johannes Unger
  • Marcus Quasthoff
  • Stefan Jakubek

Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)

Table of contents

  1. Front Matter
    Pages i-ix
  2. Johannes Unger, Marcus Quasthoff, Stefan Jakubek
    Pages 1-9
  3. Johannes Unger, Marcus Quasthoff, Stefan Jakubek
    Pages 11-41
  4. Johannes Unger, Marcus Quasthoff, Stefan Jakubek
    Pages 43-65
  5. Johannes Unger, Marcus Quasthoff, Stefan Jakubek
    Pages 67-95
  6. Johannes Unger, Marcus Quasthoff, Stefan Jakubek
    Pages 97-105
  7. Johannes Unger, Marcus Quasthoff, Stefan Jakubek
    Pages 107-108
  8. Back Matter
    Pages 109-116

About this book


Analyzing the main problems in the real-time control of parallel hybrid electric powertrains in non-road applications, which work in continuous high dynamic operation, this book gives practical insight in to how to maximize the energetic efficiency and drivability of such powertrains.

The book addresses an energy management control structure, which considers all constraints of the physical powertrain and uses novel methodologies for the prediction of the future load requirements to optimize the controller output in terms of an entire work cycle of a non-road vehicle. The load prediction includes a methodology for short term loads as well as for an entire load cycle by means of a cycle detection. A maximization of the energetic efficiency can so be achieved, which is simultaneously a reduction in fuel consumption and exhaust emissions.

Readers will gain a deep insight into the necessary topics to be considered in designing an energy and battery management system for non-road vehicles and that only a combination of the management systems can significantly increase the performance of a controller.


Non-road hybrid electric vehicle (HEV) Model predictive control Nonlinear system identification Optimal model based design of experiments (DoE) Load and cycle prediction Hybrid power-train

Authors and affiliations

  • Johannes Unger
    • 1
  • Marcus Quasthoff
    • 2
  • Stefan Jakubek
    • 3
  1. 1.Technische Universität WienViennaAustria
  2. 2.Intellectual Property & Innovation ManagLiebherr Machines Bulle SABulleSwitzerland
  3. 3.Technische Universität WienViennaAustria

Bibliographic information

  • DOI
  • Copyright Information The Author(s) 2016
  • Publisher Name Springer, Cham
  • eBook Packages Engineering Engineering (R0)
  • Print ISBN 978-3-319-29795-8
  • Online ISBN 978-3-319-29796-5
  • Series Print ISSN 2191-530X
  • Series Online ISSN 2191-5318
  • Buy this book on publisher's site
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