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Abstract

Provided is a method and a system for accelerated life testing (ALT) of a proton exchange membrane fuel cell (PEMFC). In the present disclosure, a collected voltage-time sequence data of the PEMFC is filtered and subjected to empirical mode decomposition (EMD), such that a voltage data is decomposed to obtain K intrinsic mode functions. A constructed bidirectional long short-term memory-based artificial neural network (BiLSTM) shows majority input characteristics and can model each of the intrinsic mode functions independently, thereby reducing a difficulty of long-cycle life prediction in limited training data scenarios. In addition, optimal parameters of the BiLSTM are optimized through a sparrow search algorithm, which greatly improves a prediction accuracy of a remaining useful life for the PEMFC. The method and the system of the present disclosure exhibit low computing cost, simple parameter setting, and high prediction accuracy, and are extremely suitable for operation and maintenance of the PEMFC.

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