Both　the　stochastic　traffic　information　and　state　of　charge　(SOC)　greatly　impact　the　plug-in　parallel　hybrid　electric　vehicle　performance.　Uncertain　cycles　and　driving　styles　affect　the　effectiveness　of　velocity　prediction　and　further　cause　the　instability　of　SOC　estimate.　These　uncertain　stochastic　factors　interfere　with　the　solution　of　torque　demand　in　different　degrees　in　each　control　cycle.　To　address　this　issue,　a　stochastic　model　predictive　control　(SMPC)　considering　short-term　forecast　optimal　SOC　is　proposed.　Firstly,　multiple　linear　regression　of　engine　and　battery　is　developed　for　energy　management　strategy　(EMS),　respectively.　Then,　the　velocity　prediction　model　is　developed　based　Markov　chain　considering　the　driver　styles,　and　reference　SOC　is　optimized　by　dynamic　programming　with　the　forthcoming　information.　Finally,　the　SMPC-based　EMS　with　the　short-term　optimal　SOC　is　constituted.　The　verification　results　show　Markov　based　on　driver　styles　has　better　predictive　performance　than　radial　basis　function　neural　networks　and　back　propagation　neural　networks.　The　fuel　economy　of　the　proposed　strategy　improves　by　about　11.8%　compared　with　normal　model　predictive　control　and　is　close　to　that　of　the　globally　optimal　dynamic　programming.　The　test　results　indicate　that　the　SMPC　with　the　short-term　optimal　SOC　can　promote　EMS　to　improve　the　fuel　economy.　IEEE