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.　©　2021　Elsevier　Ltd