Online extremum seeking-based optimized energy management strategy for hybrid electric tram considering fuel cell degradation

Abstract

In order to realize optimal power distribution between proton exchange membrane fuel cell and supercapacitor in hybrid electric tram, an online extremum seeking-based optimized energy management strategy is proposed in this work. Considering that the fuel cell is a complex nonlinear system, its performance will vary as the external parameters change, so it is necessary to consider the performance state of stack. An online extremum seeking algorithm is investigated in this work to seek the maximum power and maximum efficiency points by searching the variation in fuel cell performance. Besides, this work also updates its “safe operating zone” based on the results of the online extremum seeking. This process is achieved by the adaptive recursive least square algorithm. Furthermore, in order to limit the power dynamic of fuel cell, the degradation of the stack is considered in this study. To guarantee the stable and continued operation of the electric tram, the state of charge fluctuation range of supercapacitor is also limited. The effectiveness of the presented method is successfully verified under scaled-down operating condition of hybrid electric tram on the reduced-scale test platform. The proposed method is also compared with state machine control and equivalent consumption minimization strategy to further demonstrate that it has advantages in hydrogen consumption, state of charge fluctuation, efficiency, and fuel cell output power dynamics.