The　trajectory　of　the　battery　state　of　charge　(SOC)　optimized　by　using　dynamic　programming　(DP)　is　the　global　optimization　solution　to　enhance　the　economy　performance　of　the　fuel　cell　hybrid　electric　vehicles　under　various　driving　cycles,　however,　this　method　requires　prior　knowledge　of　the　future　driving　cycles.　To　utilize　the　solutions　of　DP,　a　SOC-trajectory　online　learning　generation　algorithm　based　approximate　global　optimization　energy　management　control　strategy　is　proposed.　Initially,　the　global　optimality　of　DP　is　used　to　extract　the　optimal　SOC　gradients　for　diverse　driving　scenarios.　Real-time　generation　of　optimal　gradient　factors　for　SOC　trajectories　is　facilitated　through　the　training　of　a　backpropagation　neural　network　with　DP　solutions.　Subsequently,　the　deterministic　rules　are　designed　to　plan　SOC　under　actual　driving　conditions,　with　a　dynamically　updated　threshold　by　the　trained　agents.　Finally,　based　on　the　above,　the　optimal　calculation　of　energy　allocation　is　performed　by　combining　sequence　quadratic　programming.　Numerical　verification,　inclusive　of　hardware-in-the-loop　experiments,　show　the　effectiveness　of　the　proposed　strategy.　The　results　demonstrate　that　the　proposed　strategy　improves　fuel　economy　by　7.39%　compared　to　ECMS.　Additionally,　it　reduces　the　cost　of　fuel　cell　life　loss　by　32.09%　and　achieves　over　90%　optimization　of　global　driving　cost.　©　2024