Trip distance adaptive power prediction control strategy optimization for a Plug-in Fuel Cell Electric Vehicle

Abstract

The driving energy of a plug-in fuel cell electric vehicle (PFCEV) is provided by the fuel cell and battery. The hydrogen consumption (HC) is minimized through the optimization of the ratio of energy provided by the fuel cell and battery, respectively. Such a ratio may vary with the control of the state of charge (SOC) and the expected energy consumption dominated by the forthcoming trip distance. This research develops a trip distance SOC adaptive (TDSA) power prediction control strategy for a PFCEV based equivalent consumption minimization strategy (ECMS). The required power is estimated using Markov Chain Monte Carlo (MCMC). An off-line global optimization model is developed to derive the correction coefficient of equivalent factor. The advantage of the proposed strategy is numerically verified. The validation results confirm that the implementation of the proposed method could significantly decrease the HC for variable trip distances. . The HC, validated by using the TDSA is improved by 45.76%, 37.75% and 37.19% compared with Rule-based strategy at a trip distance of 100 km, 300 km and 500 km, respectively. The combination of the MCMC with ECMS makes it possible to develop the TDSA strategy capable of significantly decreasing the HC of the PFCEV.