Title
Online Optimization of Gear Shift and Velocity for Eco-Driving using Adaptive Dynamic Programming
Author
Li, Guoqiang (Beijing Institute of Technology)
Gorges, Daniel (Technische Universität Kaiserslautern)
Wang, M. (TU Delft Transport and Planning) 
Date
2022
Abstract
In this paper a learning-based optimization method for online gear shift and velocity control is presented to reduce the fuel consumption and improve the driving comfort in a car-following process. The continuous traction force and the discrete gear shift are optimized jointly to improve both the powertrain operation and the longitudinal motion. The problem is formulated as a nonlinear mixed-integer optimization problem and solved based on adaptive dynamic programming. A major difference compared to existing approaches is that the developed control method is model-free, i.e. it does not rely on vehicle models. It can address system nonlinearities and adapt to changes in engine characteristics (e.g. consumption map) during vehicle driving. The computation is efficient and enables possible real-time implementation. The proposed control method is studied for an urban driving cycle to evaluate the control performance with respect to the fuel economy and the driving comfort. Simulations indicate that the host vehicle can reduce the fuel consumption by 5.03% and 1.12% for two consumption maps in comparison to the preceding while keeping a desired inter-vehicle distance. The results further show a decrease of 1.59% and 2.32% in fuel consumption compared to a linear quadratic controller with the same gear shift schedule.
Subject
adaptive cruise control
adaptive dynamic programming
Biological system modeling
Eco-driving
Engines
Force
Fuel economy
gear shift schedule
Gears
Optimization
reinforcement learning
Vehicle dynamics
velocity optimization
To reference this document use:
http://resolver.tudelft.nl/uuid:19b6cf70-fc08-4fdd-b324-25f1815e3274
DOI
https://doi.org/10.1109/TIV.2021.3111037
Embargo date
2023-07-01
ISSN
2379-8858
Source
IEEE Transactions on Intelligent Vehicles, 7 (1), 123-132
Bibliographical note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Part of collection
Institutional Repository
Document type
journal article
Rights
© 2022 Guoqiang Li, Daniel Gorges, M. Wang