Improved efficiency maximization strategy for vehicular dual-stack fuel cell system considering load state of sub-stacks through predictive soft-loading

Abstract The durability of vehicular fuel cells can be affected by severe load conditions, such as ultrafast loading, frequent start/stop and idling/open-circuit operation. Currently, some fuel cell systems are developed based on dual-stack configuration. For dual-stack systems, not only the system efficiency, but also the load state of sub-stacks is crucial when determining the power distribution between sub-stacks. This study proposes an improved overall efficiency maximization strategy (I-OEMS) that combines a predictive soft-loading method to improve the load state of sub-stacks while ensuring the approximate maximum efficiency. Firstly, a conventional overall efficiency maximization strategy (C-OEMS) is formulated to analyze the load state and possible degradations of sub-stacks under dynamic driving cycles. Then, the proposed I-OEMS is developed accordingly, in which the short-term reference power of sub-stacks is pre-planned according to look-ahead vehicular demand power predicted by an iterative learning framework. Finally, simulations are conducted to analyze the effectiveness. The results show that I-OEMS can effectively limit the loading speed, reduce the start/stop frequency, and operate the auxiliary sub-stack away from idling/open-circuit, so as to enhance the durability. Furthermore, it achieves a better fuel economy compared with simply limiting the loading rate of sub-stacks under C-OEMS, especially for urban driving cycles.