The　environment　pollution　and　petroleum　problems,　which　are　increasingly　becoming　serious,　have　caused　the　vehicle　industry　to　transition　into　a　low-carbon　and　energy-saving　industry.　During　processes,　plug-in　fuel-cell　electric　vehicles　(PFCEVs)　play　an　important　role　due　to　their　advantages　of　rapid　fueling,　high　energy　density　and　efficiency,　low　operating　temperature,　and　zero　onboard　emissions.　PFCEVs　use　high-capacity　rechargeable　batteries　to　avoid　working　in　low-efficiency　areas.　However,　a　robust　energy　management　strategy　that　can　achieve　reliable　energy　distribution　by　regulating　the　output　power　of　the　fuel　cell　and　battery　within　the　hybrid　powertrain　merits　further　investigation.　Considering　the　close　relationship　between　the　driving　cycle,　state　of　charge　(SOC),　equivalent　factor,　and　hydrogen　consumption,　a　trip　distance　adaptive　equivalent　consumption　minimum　strategy　integrating　driving　cycle　prediction　is　proposed.　A　backpropagation-based　neural　network　is　used　to　predict　short-term　vehicle　velocity　and　analyze　future　changes　in　vehicle　demand　power.　Planning　a　path　to　the　destination　with　the　help　of　the　global　positioning　system,　the　intelligent　transportation　system　can　also　obtain　traffic　flow　information　for　the　entire　trip.　The　equivalent　factor　is　dynamically　corrected　in　real　time　using　the　driving　distance　and　SOC　to　realize　the　adaptability　of　the　energy　management　strategy.　Finally,　the　velocity　prediction　sequence　is　combined　with　the　objective　function.　The　sequential　quadratic　programming　algorithm　is　used　to　optimize　the　equivalent　hydrogen　consumption　of　the　objective　function　and　to　obtain　the　distributed　power　of　the　fuel　cell　and　battery.　The　vehicle　simulation　model　is　built　and　compared　with　a　traditional　energy　management　strategy　based　on　MATLAB/Simulink　software.　The　simulation　results　show　that　the　driving　cycle　prediction　algorithm　based　on　the　backpropagation-based　neural　network　predicts　future　short-term　conditions　better,　with　a　12.5%　higher　accuracy　than　the　Markov　method.　The　proposed　energy　management　strategy　allows　the　fuel　cell　to　operate　in　high-efficiency　areas.　The　hydrogen　consumption　is　55.6%　less　than　that　of　the　CD/CS　strategy　under　the　UDDS　cycle.　The　hardware　in　the　loop　experiment　verifies　a　hydrogen　consumption　that　is　26.8%　less　than　that　of　the　CD/CS　strategy　under　the　EUDC　cycle.　The　numerical　validation　results　demonstrate　the　superior　performance　of　the　proposed　strategy　in　terms　of　hydrogen　consumption　over　the　CD/CS　strategy.　The　effectiveness　of　the　proposed　strategy　is　validated　by　hardware　during　the　loop　experiment.　©　2024　Science　Press.　All　rights　reserved.