Degradation adaptive energy management strategy using fuel cell state-of-health for fuel economy improvement of hybrid electric vehicle

Most studies on fuel cell hybrid electric vehicle energy management have focused on fuel economy. However, it is also important to consider the rapid degradation of the fuel cell. Therefore, a degradation-adaptive energy management strategy is proposed in this paper. The strategy can adaptively change the power distribution between different power sources using the fuel cell state-of-health. First, a novel degradation model is established for the fuel cell. The degradation model combines the polarisation curves of the fuel cell system under different state-of-health conditions and fuel cell efficiency models. An unbalanced degradation of the fuel cell at different current densities is shown in the degradation model. The proposed strategy is modified from an instantaneous optimisation energy management strategy by including state-of-health data. Accordingly, it is possible to provide optimised control based on the decrease in efficiency, thereby taking advantage of the unbalanced degradation. The proposed strategy can adaptively adjust the power distribution during degradation to get a higher energy efficiency over entire lifetime of fuel cell. The proposed strategy is adaptive to different degradation rates and consumes a small amount of computing resources, which ensure the feasibility of real-world implication. The performance of the proposed strategy is compared with that of the original strategy via simulation. The proposed strategy can optimise the fuel economy by 1.52-2.06% and 2.26-2.90% for a half and seriously degraded fuel cell, respectively. The results reveal that the proposed strategy provide an effective approach to improving the fuel economy of degraded fuel cell hybrid electric vehicles.

Automated summary

This paper proposes a degradation-adaptive energy management strategy for fuel cell hybrid electric vehicles. The strategy adjusts power distribution adaptively based on the state-of-health of the fuel cell, improving energy efficiency over its entire lifetime. It is adaptive to different degradation rates and consumes minimal computing resources, providing optimized control by considering the decrease in efficiency due to degradation.