Fault Diagnosis Strategy for Proton Exchange Membrane Fuel Cells Based on PSO-BP Neural Network Using Particle Swarm Optimization Algorithm Zhejiang University, Power Machinery and Vehicle Engineering

Format:

Book

Conference/Event

Author/Creator:

Wang, Ze, author.

Contributor:

Chen, Ping

Li, Congxin

Zhou, Wenhua

Zhu, Shaopeng

Conference Name:

SAE 2024 Vehicle Powertrain Diversification Technology Forum (2024-12-06 : Xi'An, China)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2025

Summary:

This paper presents a fault diagnosis strategy that integrates model-based and data-driven approaches for a 115 kW proton exchange membrane fuel cell used in vehicles. First, a stack subsystem model was developed in the MATLAB/Simulink platform based on the working principles and structure of PEMFC, and validated with experimental data. Subsequently, faults in the air and hydrogen inlet pipelines were simulated, and the resulting fault data were subjected to preprocessing steps, including cleaning, normalization, and feature extraction, to enhance the efficiency of subsequent data processing. Finally, a BP neural network optimized by particle swarm optimization was employed to achieve fault tree-based classification diagnosis. Experimental results indicate that the diagnosis accuracy of the BP neural network reached 96.04%, with an additional accuracy improvement of approximately 2.4% after PSO optimization

Notes:

Vendor supplied data

Publisher Number:

2025-01-7085

Access Restriction:

Restricted for use by site license