Driving Mode Optimization for Hybrid Trucks Using Road and Traffic Preview Data

Yutao Chen, Nazar Rozkvas and Mircea Lazar
Additional contact information
Yutao Chen: College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China
Nazar Rozkvas: Lightyear, 5708 JZ Helmond, The Netherlands
Mircea Lazar: Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands

Energies, 2020, vol. 13, issue 20, 1-18

Abstract: This paper proposes a predictive driver coaching (PDC) system for fuel economy driving for hybrid electric trucks using upcoming static map and dynamic traffic data. Unlike traditional methods that optimize over engine torque and brake to obtain a speed profile, we propose to optimize over driving modes of trucks to achieve a trade-off between fuel consumption and trip time. The optimal driving mode is provided to the driver as a coaching recommendation. To obtain the optimal solution, the truck dynamics are firstly modeled as a hybrid controlled switching dynamical system with autonomous subsystems and then a hybrid optimal control problem (HOCP) is formulated. The problem is solved using an algorithm based on discrete hybrid minimum principle. A warm-start strategy to reduce algorithmic iterations is used by employing a shrinking horizon strategy. In addition, an extensive analysis of the proposed algorithm is provided. We prove that the the coasting mode is never optimal given the truck configuration and and we provide a guideline for tuning parameters to maintain the optimal mode sequence. Finally, the algorithm is validated using real-world data from baseline driving tests using a DAF hybrid truck. Significant reduction in fuel consumption is achieved when the data is perfectly available.

Keywords: eco-driving; hybrid vehicle; hybrid minimum principle (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/1996-1073/13/20/5341/pdf (application/pdf)
https://www.mdpi.com/1996-1073/13/20/5341/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:13:y:2020:i:20:p:5341-:d:427558

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().