Energy management strategy integrating self-adaptive adjustment and Pontryagin's minimum principle-based optimization for fuel-cell hybrid electric vehicle

A hierarchical energy management strategy (EMS) integrating self-adaptive adjustment and Pontryagin's minimum principle-based optimization is proposed for a fuel cell hybrid electrical vehicle. First, the proposed EMS estimate the future power requirement by using Markov chain Metropolis-Hastings sampling, second the parameters of the low-pass filter is adjusted according to the state of charge estimation of the super capacitor, finally the best fuel economy is realized through Pontryagins minimum principle-based optimization. The evaluation of the EMS is verified via simulation and real driving cycle, the results are compared with another global optimization EMS. The results show that the proposed EMS can fully exert the function of the super capacitor and improve the fuel economy 0.3% and 8.8% for simulation and real driving cycle, respectively.