Global path planning with lifetime constraint model-based offline optimized loading strategy for vehicle fuel cell system

Abstract

To promote the commercialization of fuel cell vehicles, simultaneously improving the dynamic response and durability of fuel cells under loading conditions are extremely important. Since it can seriously affect the system design and lifespan of the fuel cell system. Unfortunately, fast and reliable loading is a pair of contradictory relationships and it needs an optimal loading slope under variable loading conditions. Therefore, this paper proposes a global path planning (GPP) model by considering lifetime limitations to solve the problem. Firstly, the initial GPP model is established according to the initial power and target power, which is exploited to determine exploratory and calibration tests. Subsequently, the trade-off relationship between fast and reliable loading is addressed by the multi-objective cost function based on the dynamic weight coefficient adjustment method to construct a complete GPP model. Eventually, the optimal slopes solution is calculated by the Dijkstra algorithm. The superiority, robustness, and feasibility of the presented method are successfully verified under a 90 kW fuel cell system test bench. The verification test results show that compared with the reference solution, the utilization of optimal solution loading can significantly reduce the dynamic response time by 22.22% and improve the loading reliability by 40.93%. Moreover, the number of calibration tests determined based on the GPP model is 85.19% less than that of the traditional method. Thus, the proposed loading strategy can load quickly and reliably.