Degradation-oriented real-time power control of fuel cell hybrid vehicles under uncertain driving conditions.

This work provides a real-time power allocation algorithm to address uncertain actual driving situations for fuel cell hybrid vehicles. To predict the vehicle speed under nondeterministic driving conditions, a fusion prediction model is developed based on the advantages of the Markov chain and neural network. The optimal power splitting decision in each receding horizon is then solved using the Pontryagin's minimum principle (PMP) method, considering fuel consumption, State of Charge (SOC), and performance degradation. A degradation model of electrochemical active surface area (ECSA) based on Pt catalyst dissolution was developed. Then the effect of the energy management algorithm on fuel cell degradation was evaluated using the degradation model. Compared with the two conventional real-time power splitting strategies, the approach suggested in this research can better reduce the fuel consumption and maintain the stability of battery SOC with a lower fluctuation while taking into account the degradation of the fuel cell. • Real-time power allocation for FC hybrid vehicle under non-deterministic conditions. • Model fusion velocity prediction based on historical datas. • Power optimization in rolling prediction horizon using PMP method. • Using ECSA to assess the effect of power allocation strategies on FC durability. [ABSTRACT FROM AUTHOR]