Parameter identification is a key task in the research of proton exchange membrane fuel cell (PEMFC)

which provides a basis for establishing an accurate and reliable PEMFC model. However

the nonlinear characteristics of the PEMFC model and the inevitable insufficient measurement data often make traditional optimization techniques difficult to solve. In particular

inadequate measurement data can introduce bias or lead to data loss. To solve the above problems

a new hybrid optimization strategy is proposed. Firstly

the semi-mechanism PEMFC model is established by SimuNPS

and the feedforward neural network (FNN) is used to expand and predict the data. Then

the teaching-learning-based algorithm (TLBO) is used to identify the expanded PEMFC parameters. Finally

the effectiveness of the FNN-TLBO strategy is verified by comparing the two identification methods. For example

under high temperature and low pressure

the identification of FNN expansion processing shows that TLBO is 99.21% and 85.93% higher than particle swarm optimization algorithm and Harris hawk algorithm respectively. The parameter identification of PEMFC model based on FNN-TLBO shows that FNN-TLBO has higher robustness and optimization quality in the simulation results by comparing the original data and the preprocessed data.