Improving a Fuel Cell System’s Thermal Management by Optimizing Thermal Control with the Particle Swarm Optimization Algorithm and an Artificial Neural Network

Abstract

The thermal management of proton exchange membrane fuel cell systems plays a significant role in a stack’s lifetime, performance, and reliability. However, it is challenging to manage the thermal system precisely due to the multiple coupling relationships between the stack’s components, its operating environment, and its thermal management system. In addition, temperature hysteresis (temporal inconsistency of temperature with electrochemical reactions and fluid mechanics) imposes more difficulties on thermal control. We aim to develop an effective thermal control model for the fuel cell system to improve the temperature regulation accuracy and response speed and thus achieve highly stable temperature control. A dynamic mechanistic model is first developed based on the physical processes of the stack and its thermal management system. The model is then validated through experiments. Based on this dynamic mechanistic model, a control model is proposed for stack thermal management with the particle swarm optimization algorithm and an artificial neural network. It is applied and compared with the traditional PID algorithm. The simulation results indicate that the regulation time of the coolant inlet temperature as the current changes is reduced by more than 74%, and the overshoot is reduced by more than 50%. Therefore, the control model can enhance the dynamic response capability and temperature control precision under complex operating conditions with constantly changing load current and preset stack temperature, ensuring the temperature’s stability and thus improving the fuel cell system’s reliability and durability.

Keywords

• proton exchange membrane fuel cell
• dynamic thermal management
• particle swarm optimization
• artificial neural network
• control model