Online Diagnosis of PEMFC Based on Machine Learning Methods and EIS

The EIS can be effectively applied in Proton exchange membrane fuel cell (PEMFC) for online diagnosis of the system by machine learning methods to prolong the lifetime. The development of advanced EIS techniques lays a solid foundation for the flexibility of data collection based on EIS for the construction of diagnosis model in PEMFC. Feature extraction from EIS can be conducted using raw impedances and based on different interpretation methods of EIS. Different machine learning models show great possibilities for PEMFC diagnosis combining with EIS. Classical classification, extreme learning, simple fuzzy logic, and ensemble learning (without deep learning) can be adopted using countable 1D features of EIS which are interpretable, such as geometric information, ECM parameters, and DRT characteristic frequencies and peaks. Complex deep learning methods can adopt raw EIS for automatic feature extraction. Typically, CNNs can be used to convolve 2D EIS data, processing the real and imaginary parts across the frequency range to extract features automatically. Since impedance spectra are collected at regular or specific time points during cell operation for real-time diagnosis, RNN methods like LSTM can also be applied using features extracted from EIS in theory. The critical steps in developing data-driven diagnosis strategies for PEMFC with EIS and machine learning methods come from seven aspects, namely, determining applicable faults for diagnosis, achieving fast and reliable online EIS data collection, extracting potential features for constructing diagnosis models, expanding the choice of machine learning methods for online FDI of PEMFC based on EIS, conducting comprehensive performance evaluations of diagnosis models, making full use of big data for diagnosis, and diagnosing special faults in high-temperature PEMFCs. EIS combined with machine learning methods can also be applied for online prediction of fuel cell health index, such as ECSA and hydrogen crossover, which are not available by onboard sensors. This work hopes to provide inspirations and new insights for future research on using AI methods in fuel cell health management. Figure 1