State of Health Estimation for Proton Exchange Membrane Fuel Cells Based on Particle Filtering Algorithm
The aging process of a proton exchange membrane fuel cell(PEMFC)affects its output performance,and in order to accurately control output power,it is necessary to consider the aging and power degradation trends of the PEMFC.In this paper,the power-current curve is used as an indicator of the state of health(SOH).Based on previous studies,improvements have been made by considering changes in open-circuit voltage during the aging process.The number of aging factors in the aging model has been increased and the mapping relationship between the PEMFC power and the aging of the internal components is established.A semi-mechanical power degradation model is derived based on polarization curves,and an aging rate model has been designed using the particle filter algorithm.Combining the power decay analysis,the paper estimated the fuel cell's SOH.Simulations were carried out on the test dataset and compared with experimental test data.The results show that the method can predict the long-term performance decay model.Furthermore,compared with existing research methods,the proposed method estimates the SOH and performance decay trend of PEMFCs more accurately through the use of aging rate reference values and the power decay model.With reduced training time,there is an improvement in estimation accuracy.Especially when the training time is 100 hours and the estimation time is 250 hours,the error's relative decrease rate reaches 65.69%.
fuel cellagingstate of health estimationparticle filter
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