Search Results

To study the state of health (SOH) of the proton exchange membrane fuel cell (PEMFC), a novel hybrid method combining the advantages of both the model-based and data-driven methods is proposed. Firstly, the model-based method is proposed based on the voltage degradation model to estimate the variation trend, and three parameters reflecting the performance degradation are selected. Secondly, the data-driven (long short-term memory (LSTM)) method is presented to estimate the variation fluctuation. Moreover, the core step of the hybrid method is returning the results of the LSTM method to the power degradation model as the “observation” and modifying related parameters to improve the estimation accuracy. Finally, the sliding window method is applied to solve the problem of the data increase with the increase of the operating time. The results show that the power estimation is better than the current estimation for the SOH estimation.

The multi-stack fuel cell system (MFCS) has the advantages of higher efficiency, stronger robustness and longer life, and could be widely used in high-power application scenarios such as automobiles, airplanes, trains, and ships. The appropriate air mass flow and air pressure have a crucial impact on the output power performance indicators of the MFCS. Considering that the designed integrated air supply system for the MFCS has significant gas supply hysteresis and strong coupling between the inlet air mass flow and air pressure of each stack, this paper identifies multiple steady-state operating points of the fuel cell system to obtain corresponding linear predictive models and establishes corresponding predictive control algorithms. The Model Predictive Control (MPC) algorithms are switched in real-time based on the current load throughout the entire C-WTVC (China World Transient Vehicle Cycle) working condition.