Optimization of Control Strategy for Fuel Cell Vehicles by Integrating Fuzzy Algorithm

Abstract

Fuel cell vehicles have rapidly occupied the market with advantages such as environmental protection and energy conservation. However, their battery technology is insufficient and their endurance is poor, making them unsuitable for use over long distances. To address the aforementioned issues, a fuel cell vehicle energy storage system based on super-capacitors was constructed. Meanwhile, a proportional integral derivative controller based on fuzzy algorithms was established. Finally, the particle swarm optimization algorithm was used to optimize the fuzzy control strategy that integrated the fuzzy algorithm. When using the optimized fuzzy control strategy for simulation, the peak power of the fuel cell output power was reduced from 3.8kW to 2.0kW. The remaining power of the super-capacitor remained stable within a reasonable range throughout the entire operating condition. Under the new European urban road cycle, the optimized control strategy improved energy recovery performance by 4.3% and reduced hydrogen consumption by 0.9964%. Under the United States federal environmental protection agency standardized urban cycle conditions, the optimized control strategy improved the braking energy recovery efficiency index and effective braking energy recovery efficiency by 8.9% and 6.3%, respectively. The percentage reduction in hydrogen consumption was 0.9433%. Therefore, this research method can effectively reduce hydrogen consumption and improve the product economy and market competitiveness of enterprises.

Keywords

• Fuzzy algorithm
• fuel cell vehicles
• optimization of control strategy
• particle swarm optimization algorithm
• super-capacitor