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Look-Ahead Prediction-Based Real-Time Optimal Energy Management for Connected HEVs
- Source :
- IEEE Transactions on Vehicular Technology. 69:2537-2551
- Publication Year :
- 2020
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2020.
-
Abstract
- 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.
- Subjects :
- Computer Networks and Communications
Energy management
Powertrain
Computer science
Aerospace Engineering
Energy consumption
Model predictive control
symbols.namesake
Maximum principle
Control theory
Automotive Engineering
Headway
symbols
Probability distribution
Electrical and Electronic Engineering
Gaussian process
Subjects
Details
- ISSN :
- 19399359 and 00189545
- Volume :
- 69
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Vehicular Technology
- Accession number :
- edsair.doi...........407264f8d977020a1958a54977c01ae3