Fuel Cell Hybrid Electric Vehicles (FCHEVs) have emerged as encouraging substitutes for conventional vehicles due to their lower environmental impact and higher energy efficiency. Effective energy management is crucial for optimizing the performance of FCHEVs. This paper presents an Artificial Neural Network (ANN) based holistic approach for energy management controllers in FCHEVs. The proposed approach leverages ANN models trained on real-world driving data to predict optimal power distribution between the fuel cell and battery. By utilizing ANN's ability to learn complex patterns in data, the proposed approach aims to improve energy utilization, enhance vehicle performance, and reduce environmental impact. Simulation results demonstrate the effectiveness of the ANN -based approach, showing significant improvements in energy efficiency compared to traditional control strategies. The findings suggest that ANN -based energy management controllers have the potential to advance the efficiency and sustainability of FCHEVs, contributing to a cleaner and greener transportation ecosystem.
Artificial Neural Network Based Holistic Approach in Energy Management Controllers for Fuel Cell Hybrid Electric Vehicles
2024-07-31
461810 byte
Conference paper
Electronic Resource
English
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