The　stochasticity　of　vehicle　velocity　poses　a　significant　challenge　to　enhancing　fuel　cell　energy　management　strategy　(EMS).　Under　these　circumstances,　a　self-learning　Markov　algorithm-based　EMS　with　stochastic　velocity　prediction　capability　is　proposed.　First,　building　upon　the　traditional　offline-trained　Markov　model,　a　real-time　self-learning　Markov　predictor　(SLMP)　is　proposed,　which　collects　historical　data　during　the　vehicle＇s　driving　process　and　continuously　updates　the　state　transition　matrix　on　a　rolling　basis.　It　provides　excellent　prediction　performance　under　stochastic　driving　cycles.　and　the　impact　of　different　prediction　time-steps　is　analyzed.　Subsequently,　by　employing　sequential　quadratic　programming　for　optimal　power　allocation,　the　Stochastic　Velocity-Prediction　Conscious　EMS　for　fuel　cell　hybrid　electrical　vehicle　based　on　SLMP　is　constructed.　Finally,　the　predictors　and　EMSs　based　on　back-propagation　neural　network　and　offline-trained　Markov　are　selected　for　performance　comparison.　The　validation　results　indicate　that　the　performance　of　SLMP　improves　as　driving　mileage　accumulates.　Meanwhile,　the　proposed　Stochastic　Velocity-Prediction　Conscious　EMS　significantly　improves　economic　performance　in　different　driving　cycles.　Hardware-in-the-Loop　experiments　further　validate　the　superior　fuel　cell　efficiency　and　robustness　of　the　proposed　EMS.　The　key　contribution　lies　in　the　real-time　adaptability　of　the　SLMP,　which　ensures　improved　prediction　accuracy　and　economic　performance　as　driving　mileage　accumulates.