A dual-channel predictive framework for fuel cell degradation based on Adaptive Extended Kalman Filter and optimized random forest

Accurately predicting the aging process of fuel cells and extending their lifespan through effective management strategies is crucial. This paper proposes a dual-channel prediction framework based on Adaptive Extended Kalman Filter (AEKF) and Random Forest (RF) models. The approach utilizes an AEKF algorithm, based on a semi-empirical aging model, to predict the irreversible aging trajectory of fuel cells under normal operating conditions. Simultaneously, a Random Forest model, optimized using the grey wolf optimizer, is employed to predict the reversible aging trajectory caused by improper operation. Specifically, the proposed method first applies the AEKF algorithm, derived from the semi-empirical aging model, to extract the baseline trend representing irreversible aging. Then, the dataset undergoes a detrending process to isolate the reversible aging component, which serves as the training datasets of RF model. This ensures that the RF model effectively captures the reversible aging characteristics of the fuel cell. The method is validated using experimental data from proton exchange membrane fuel cells under both constant and quasi-dynamic load conditions. Using 60% of the dataset under two different operating conditions, the proposed approach achieves a mean absolute percentage error of only 0.464% and 0.502%, respectively.