AI-Driven Optimization of Fuel Cell Performance in Electric Vehicles

Abstract

The increasing adoption of electric vehicles (EVs) is driving the need for efficient and sustainable energy sources, such as fuel cells, to enhance vehicle range and performance. This paper explores the application of AI-driven optimization techniques to improve the performance of fuel cells in electric vehicles. By leveraging machine learning algorithms, particularly reinforcement learning and predictive modeling, the system can optimize key parameters such as temperature, pressure, and hydrogen consumption in real-time, thereby maximizing efficiency and extending the operational lifetime of the fuel cells. The study demonstrates that AI-based approaches can significantly enhance energy output and fuel utilization while adapting to dynamic driving conditions. This research provides a promising pathway for improving fuel cell performance, thus promoting the broader adoption of hydrogen-based electric vehicles as a viable alternative to traditional internal combustion engines.

Keywords

• ai-driven optimization
• fuel cells
• electric vehicles
• machine learning
• hydrogen economy