The　main　contribution　of　this　study　is　to　introduce　deep　reinforcement　learning　(DRL)　within　the　model　prediction　control　(MPC)　framework,　and　consider　comprehensive　economic　objectives　including　fuel　cell　degradation　costs,　lithium　battery　aging　costs,　hydrogen　consumption　costs,　etc.　This　approach　successfully　mitigated　the　inherent　shortcomings　of　deep　reinforcement　learning,　namely　poor　generalization　and　lack　of　adaptability,　thereby　significantly　enhancing　the　robustness　of　economic　driving　decision　in　unknown　scenarios.　In　this　study,　an　MPC　framework　was　developed　for　the　energy　management　problem　of　fuel　cell　vehicles,　and　Bi-directional　Long　Short-Term　Memory　(Bi-LSTM)　neural　network　was　used　to　construct　a　vehicle　speed　predictor　The　accuracy　of　its　prediction　was　verified　through　comparative　analysis,　and　then　it　was　regarded　as　a　DRL　model.　Different　from　the　overall　strategy　of　the　entire　driving　cycle,　the　model　based　DRL　agent　can　learn　the　optimal　action　for　each　vehicle　state.　The　simulation　evaluated　the　impact　of　different　predictors　and　prediction　ranges　on　hydrogen　economy,　and　verified　the　adaptability　of　the　proposed　strategy　in　different　driving　environments,　the　stability　of　battery　state　maintenance,　and　the　advantages　of　delaying　energy　system　degradation　through　comprehensive　comparative　analysis.