Machine learning-based energy management bidirectional is a M.Tech project topic for Electrical Engineering. It gives students a clear starting point for research, implementation planning, and documentation.
Machine learning-based energy management bidirectional Project Details
| Abstract |
This project presents a comprehensive implementation and evaluation framework for a machine learning-based energy management and bidirectional power control system designed for battery-fed electric vehicle (EV) traction drives. The proposed methodology integrates a lithium-ion battery model, a bidirectional direct current-to-direct current (DC-DC) converter, a DC-link capacitor, and an inverter-fed Permanent Magnet Synchronous Motor (PMSM) alongside battery state estimation and regenerative braking control. Unlike conventional split-loop architectures, this framework utilizes a single supervisory multilayer feedforward neural network with three hidden layers to simultaneously generate converter-level, traction-level, and battery-level control references from a unified multivariable input vector. The input vector incorporates critical parameters such as motor torque demand, motor speed, battery State
of Charge (SoC), State of Health (SoH), DC-link voltage, battery current, temperature, and road-gradient profiles. Training data is synthesized using Latin-hypercube sampling across diverse traction, cruising, and braking scenarios. The project provides structured guidance for modeling the electrical drive components, designing the neural network architecture, and evaluating the closed-loop performance under dynamic driving cycles, offering a robust foundation for advanced research in intelligent electric vehicle energy management.
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| Reference Paper |
Machine learning-based energy management and bidirectional power control for battery-fed electric vehicle traction drive |
| Domain |
Electrical Engineering |
| Sub-Domain |
Electrical Machines & Drives / Motor Drives / Regenerative Braking |
| PDF Download |
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| Get Help |
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