A fault diagnosis model for proton exchange membrane fuel cell based on impedance identification with differential evolution algorithm

• Published in: International journal of hydrogen energy Vol. 46; no. 78; pp. 38795 - 38808
• Main Authors: Du, Runben, Wei, Xuezhe, Wang, Xueyuan, Chen, Siqi, Yuan, Hao, Dai, Haifeng, Ming, Pingwen
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 11.11.2021
• Subjects: Fault diagnosis; Fuel cell; Parameter identification; Probabilistic neural network; Random mutation differential evolution; Fault diagnosis; Probabilistic neural network; Parameter identification; Fuel cell; Random mutation differential evolution
• ISSN: 0360-3199; 1879-3487
• DOI: 10.1016/j.ijhydene.2021.09.126

An effective online fault diagnosis system is of great significance to improve the reliability of fuel cell vehicles. In this paper, a fault diagnosis model for proton exchange membrane fuel cells is proposed. Firstly, the tests of electrochemical impedance spectroscopy under different fault types (flooding, drying, air starvation) and fault degrees (minor, moderate, severe) are carried out, and each polarization loss of the fuel cell is denoted by an equivalent circuit model (ECM). Then, the parameters of the ECM are identified by the proposed random mutation differential evolution algorithm. Furthermore, the parameters identified under different fault conditions are used to train and test a probabilistic neural network-based fault diagnosis model. The fault diagnosis model achieves diagnosis accuracies of 100% for the fault type and 96.67% for the fault degree. By setting operating conditions with different fault degrees, the fault diagnosis model proposed in this paper can realize the fault type and fault degree diagnosis, effectively avoiding the misjudgment of fault types, and is effective for improving the reliability of the fuel cell system. [Display omitted] •A RMDE algorithm is proposed to realize the impedance parameter identification.•A fault diagnosis model based on the probabilistic neural network is developed.•The diagnosis accuracies for fault type and fault degree reach 100% and 96.67%.