Context-Aware Optimal Resource Management in Electric Vehicle Smart2Charge
Sharif, Muddsair (2025) Context-Aware Optimal Resource Management in Electric Vehicle Smart2Charge. Doctoral thesis, Birmingham City University.
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Muddsair Sharif PhD Thesis_Final Version_Final Award Jan 2025.pdf - Accepted Version Download (8MB) |
Abstract
This thesis presents a novel approach to optimizing electric vehicle (EV) charging systems through a context-aware framework powered by deep reinforcement learning (DRL). The research addresses critical challenges in the EV ecosystem, balancing the needs of multiple stakeholders including end-users, grid operators, fleet managers, and charging station operators. At its core, a Deep Q-Network (DQN) algorithm outperforms other state-of-the-art DRL methods in managing complex, multi-objective optimization scenarios.
This work advances the field by bridging theoretical DRL models with practical EV charging implementations, offering a framework that optimizes outcomes for multiple stakeholders while promoting sustainable transportation. Through the Smart2Charge application, the research demonstrates how context-aware solutions can enhance both user experience and environmental sustainability. The application integrates real-time data including grid conditions, user preferences, charging station availability, and environmental factors to optimize charging decisions. Comprehensive testing through simulations and real-world scenarios validates the system’s effectiveness and adaptability across diverse operating conditions.
The proposed system achieves a 15% increase in overall energy efficiency, 10% reduction in charging costs for EV owners, 20% decrease in grid strain, and 10% reduction in CO₂ emissions through optimal integration of renewable energy sources. These advancements significantly contribute to both user satisfaction and environmental sustainability. This research paves the way for more intelligent, user-centric, and environmentally conscious EV charging systems, marking a significant step towards sustainable urban mobility.
Item Type: | Thesis (Doctoral) |
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Dates: | Date Event 20 January 2025 Accepted |
Uncontrolled Keywords: | Deep Reinforcement Learning, Context-Aware, Smart Charging, Electric Vehicle, Multi-Modal, Multi-Objective, Resource Optimality, Resource optimisation, Multi-Agent DRL Model |
Subjects: | CAH10 - engineering and technology > CAH10-01 - engineering > CAH10-01-08 - electrical and electronic engineering CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence |
Divisions: | Doctoral Research College > Doctoral Theses Collection Faculty of Computing, Engineering and the Built Environment > College of Computing |
Depositing User: | Louise Muldowney |
Date Deposited: | 24 Jan 2025 16:30 |
Last Modified: | 24 Jan 2025 16:30 |
URI: | https://www.open-access.bcu.ac.uk/id/eprint/16096 |
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