Degradation-considering adaptive equivalent consumption minimization strategy for fuel cell electric vehicles

In order to improve the economy and durability of fuel cell vehicle, a degradation-considering adaptive equivalent consumption minimization strategy (D-ECMS) was proposed for fuel cell/lithium battery hybrid power system. Aiming at enhancing the adaptability of the equivalent consumption minimum strategy, four different vehicle speed conditions were considered including low speed, medium speed, high speed, and ultra-high speed. Particle swarm optimization (PSO) algorithm was used to optimize the penalty factor under different vehicle speed conditions, and BP neural network was used to identify real-time conditions to form an adaptive equivalent consumption minimum strategy (A-ECMS). The voltage decay model of the fuel cell was established according to the voltage decay rate of the fuel cell under the three adverse conditions of idle speed, high power and dynamic load cycle. The voltage decay of the fuel cell was transformed to hydrogen consumption in the cost function of the A-ECMS strategy to obtain the D-ECMS considering durability degradation. The simulation and the hardware-in-the-loop (HIL) test based on the dSPACE real-time platform was carried out. The results show that: compared with A-ECMS, the hydrogen consumption of the D-ECMS increased by 5.2%, 9.7%, 10.2%, and 0.2% respectively under WLTC, NEDC, UDDS, and HWFET cycles. However, D-ECMS can reduce the voltage decay by 38.3%, 59.7%, 35.2%, and 26% under the above four test conditions, and their total comprehensive cost loss is reduced by 8.7%, 6.7%, 9.6%, and 6.3% respectively. As result, the D-ECMS can reduces the voltage attenuation of fuel cells and improve the durability of fuel cells significantly with a small hydrogen consumption increase,which can reduce the operating cost of fuel cells finally. Furthermore, the average error between the results of HIL test and offline simulation results is less than 5%, which validates the adaptability and real-time application feasibility of D-ECMS.