Integrating Artificial Intelligence into Electric Vehicle is a M.Tech project topic for Electrical Engineering. It gives students a clear starting point for research, implementation planning, and documentation.
Integrating Artificial Intelligence into Electric Vehicle Project Details
| Abstract |
The fast growth of electric vehicles (EVs) is a key part of worldwide efforts to cut carbon emissions, but putting many EVs into existing power grids, charging networks, and supply chains creates tough operational problems. This research framework gives clear guidance for studying and applying artificial‑intelligence (AI) methods to improve EV energy systems. It looks at how machine learning, deep learning, and meta‑heuristic optimization can be combined to create smart algorithms for: * balancing load on the grid, * predicting how charging stations will be used, and * diagnosing battery health. The framework also stresses the need to measure life‑cycle emissions and to spot bottlenecks in current grid‑to‑vehicle (G2V) and
vehicle‑to‑grid (V2G) setups. Using systematic simulation and modeling, researchers can test AI‑based control strategies under different grid limits and fleet behaviors. The review helps postgraduate students pinpoint important research gaps, build solid mathematical models for energy dispatch, and define metrics that compare AI performance with traditional heuristic approaches. Overall, the approach moves ideas from theory to validated simulation models that support sustainable transport systems.
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| Reference Paper |
Integrating Artificial Intelligence into Electric Vehicle Energy Systems: A Survey |
| Domain |
Electrical Engineering |
| Sub-Domain |
Electrical Machines & Drives / Motor Drives / Electric Vehicles |
| PDF Download |
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| Get Help |
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