An　effective　energy　management　strategy　(EMS)　is　crucial　for　the　reliable　operation　of　fuel　cell　hybrid　electric　vehicles　(FCHEVs).　This　study　proposes　a　power　distribution　optimization　strategy　for　FCHEVs　that　leverages　deep　reinforcement　learning　(DRL)　and　Pontryagin＇s　minimum　principle　(PMP).　The　DRL　algorithm　effectively　balances　fuel　economy,　battery　durability,　and　fuel　cell　durability　objectives.　The　degradation　mechanisms　of　battery　and　fuel　cell　under　extreme　working　conditions　are　considered　in　the　PMP　optimization.　A　comprehensive　evaluation　framework　is　established　with　degradation　and　energy　consumption　models　to　serve　as　a　reward　for　deep　reinforcement　learning　to　balance　fuel　economy　and　power　sources＇　lifetime.　Simulation　results　show　that　the　proposed　EMS　framework　reduces　FC　degradation　by　18.4%　and　battery　degradation　by　71.1%　compared　to　traditional　PMP-based　EMS　under　the　NEDC　driving　condition.　Hardware-in-the-loop　(HIL)　testing　demonstrates　that　the　proposed　EMS　framework　has　the　potential　for　real-time　application,　with　an　average　relative　error　between　experiment　and　simulation　of　approximately　0.0203.　This　research　highlights　the　significance　of　the　proposed　EMS　framework　in　ensuring　the　reliable　operation　of　FCHEVs　with　enhanced　performance　and　reduced　cost.　©　2023　Elsevier　Ltd