Look-Ahead Prediction-Based Real-Time Optimal Energy Management for Connected HEVs

Within the headway distance constraints, the potential for reduction of energy consumption by hybrid electric vehicles (HEVs) with connectivity could be achieved by optimizing the ego vehicle motion. This paper proposes a look-ahead traffic information-based real-time model predictive control scheme...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on vehicular technology 2020-03, Vol.69 (3), p.2537-2551
Main Authors: Xu, Fuguo, Shen, Tielong
Format: Article
Language:English
Subjects:
Computer simulation
Connected hybrid electric vehicle
Electric vehicles
Energy consumption
Energy management
Gaussian process
Hybrid electric vehicles
look-ahead prediction
Maximum principle
Mechanical power transmission
Nonlinear control
Optimal control
Optimization
Powertrain
Predictive control
Predictive models
Real time
Real-time systems
receding horizon control
Time optimal control
Traffic information
Traffic models
Traffic volume
Vehicle dynamics
Vehicles
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Within the headway distance constraints, the potential for reduction of energy consumption by hybrid electric vehicles (HEVs) with connectivity could be achieved by optimizing the ego vehicle motion. This paper proposes a look-ahead traffic information-based real-time model predictive control scheme to minimize total monetary cost of HEVs. A chain Gaussian process approach is employed to estimate the probability distribution of future increments of vehicle number over a look-ahead horizon from vehicle-to-vehicle and vehicle-to-infrastructure information. The future motion of preceding vehicles could be predicted by the evolution of the traffic density model and velocity tracking model. The above problem is formulated as a nonlinear optimal control problem with predicted disturbance input and dynamic constraints. Optimal solutions are derived through Pontryagin's maximum principle. The effectiveness of the proposed control scheme is evaluated on a traffic-in-the-loop powertrain simulation platform by integrating a commercial traffic platform and an enterprise-level powertrain simulator.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2020.2965163