  Fuel cell electric vehicles have earned substantial attentions in recent
decades due to their high-efficiency and zero-emission features, while the high
operating costs remain the major barrier towards their large-scale
commercialization. In such context, this paper aims to devise an energy
management strategy for an urban postal-delivery fuel cell electric vehicle for
operating cost mitigation. First, a data-driven dual-loop spatial-domain
battery state-of-charge reference estimator is designed to guide battery energy
depletion, which is trained by real-world driving data collected in postal
delivery missions. Then, a fuzzy C-means clustering enhanced Markov speed
predictor is constructed to project the upcoming velocity. Lastly, combining
the state-of-charge reference and the forecasted speed, a model predictive
control-based cost-optimization energy management strategy is established to
mitigate vehicle operating costs imposed by energy consumption and power-source
degradations. Validation results have shown that 1) the proposed strategy could
mitigate the operating cost by 4.43% and 7.30% in average versus benchmark
strategies, denoting its superiority in term of cost-reduction and 2) the
computation burden per step of the proposed strategy is averaged at 0.123ms,
less than the sampling time interval 1s, proving its potential of real-time
applications.
