The　driving　trip　pattern　is　of　great　significance　in　hydrogen　consumption　and　battery　Longevity　of　the　plug-in　fuel　cell　hybrid　electric　vehicles　(PFCHEV).　However,　the　traditional　energy　management　strategy　failed　to　consider　the　uncertainty　of　driving　patterns.　To　overcome　this　drawback,　a　deep　Q-learning　network　based　trip　pattern　adaptive　(DQN-TPA)　battery　longevity-conscious　strategy　is　proposed　in　this　study.　To　begin　with,　the　trip　pattern　recognition　based　Learning　Vector　Quantization　Neural　Network　is　devised　for　pattern　identification,　and　the　adaptive-equivalent　consumption　minimizes　strategy　(A-ECMS)　is　conducted　to　improve　the　hydrogen　consumption.　Then,　a　TPA　longevity-conscious　strategy　is　developed　and　compared　with　the　conventional　multi-criteria　(MC)　optimization　strategy　to　investigate　the　discrepancy　brought　by　the　pattern　adaptation.　And　finally,　in　combination　with　the　above　efforts,　an　improved　DQN-TPA　based　battery　longevity-conscious　strategy　has　been　established　accordingly.　The　advances　are　confirmed　by　the　validation　results　that,　the　A-ECMS　makes　an　11.76%　promotion　in　fuel　economy　by　taking　the　deviation　among　different　driving　patterns　into　concern.　The　TPA　strategy　shows　more　adaptiveness　than　the　MC　optimization　strategy　in　which,　the　effective　Ah-throughput　is　5.17%　lower　than　MC-based　while　keeping　the　same　economy.　Further　improvement　can　be　achieved　by　the　modified　DQN-TPA　based　approach　by　remedying　the　imperfection　of　TPA-based　recognition　delay　and　performing　the　economy　and　durability　conscious　actions　with　5.87%　further　reduction　of　effective　Ah-throughput　without　observably　sacrificing　the　fuel　economy.　Furthermore,　the　effectiveness　and　adaptiveness　of　the　proposed　strategy　are　validated　by　the　Hardware-in-the-Loop　experiments.　Both　the　numerical　validation　and　semi-physical　validation　results　indicate　that　the　DQN-TPA　based　approach　made　it　possible　to　develop　the　battery　longevity-conscious　strategy　capable　of　significantly　adapting　various　driving　patterns　and　improving　the　hydrogen　consumption　and　battery　durability　performance　of　the　PFCHEV.　©　2022　Elsevier　Ltd