Fault Diagnosis for Proton Exchange Membrane Fuel Cell Systems via Improved Equivalent Circuit Model and Multiple Support Vector Machines

Fault diagnosis is of paramount importance in ensuring the reliability and stability of proton exchange membrane fuel cell systems. In this article, an innovative fault diagnosis method based on an improved equivalent circuit model (ECM) and multiple support vector machines (multi-SVMs) arranged in series is proposed. This method can enhance the interpretability of the ECM and feature data while achieving more efficient and accurate fault diagnosis. Initiating the process, the anode/cathode activation impedance, oxygen mass transfer impedance, and ohmic impedance are considered to design an improved ECM with a streamlined set of parameters for identification. The advantage of the improved ECM in terms of accuracy is demonstrated by comparison with two recently proposed ECMs. Then, a novel parameter identification method, integrating simulated annealing and complex nonlinear least square approach, is introduced to identify ECM parameters more efficiently and accurately. Finally, the multi-SVM classifier is built by concatenating multiple binary SVM models, then trained and validated using measured operating parameters from a 60 kW physical test bench and identified ECM parameters as feature data. Through a comparative evaluation with other classical classifiers, the advantages of multi-SVM in terms of training efficiency and classification accuracy can be demonstrated with training time of 0.3 s and test accuracy of 96.15%.