Lei, H., Chong, B. orcid.org/0000-0001-5977-7197 and Li, K. MPC Based Optimal Energy Management of Electrical Vehicles with Hybrid Energy Storage on Fixed Routine. In: UNSPECIFIED IEEE Transportation Electrification Conference and Expo + Electric Aircraft Technologies Symposium, 18-20 Jun 2025, Anaheim, California. IEEE (In Press)
Abstract
This article presents a model predictive control (MPC) based energy management strategy for electric vehicles powered by hybrid power sources, focusing on their performance improvements while running on fixed routines. The idea is to utilize the historic operation data to improve the energy allocation optimization solutions, with the goal of enhancing the energy efficiency of electric vehicles running on fixed driving routes by minimising losses through minimum battery current operations and extending the vehicles’ driving range. Key contributions include the formulation of a model predictive control based energy allocation problem, and the implementation of convex hull constrained MPC based on historic data. Simulation results demonstrate the continual performance improvements in the presence of uncertainties being introduced into the system.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 24 Jul 2025 10:46 |
Last Modified: | 24 Jul 2025 10:52 |
Status: | In Press |
Publisher: | IEEE |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:229436 |