With　increasingly　serious　environmental　pollution　and　the　energy　crisis,　fuel　cell　hybrid　electric　vehicles　have　been　considered　as　an　ideal　alternative　to　traditional　hybrid　electric　vehicles.　Nevertheless,　the　total　costs　of　fuel　cell　systems　are　still　too　high,　thus　limiting　the　further　development　of　fuel　cell　hybrid　electric　vehicles.　This　paper　presents　an　energy　management　strategy　(EMS)　based　on　deep　reinforcement　learning　for　the　energy　management　of　fuel　cell　hybrid　electric　vehicles.　The　energy　management　model　of　a　fuel　cell　hybrid　electric　bus　and　its　main　components　are　established.　Considering　the　power　response　characteristics　of　the　fuel　cell　system,　the　power　change　rate　of　the　fuel　cell　system　is　reasonably　limited　and　introduced　as　action　variables　into　the　network　of　Double　Deep　Q-Learning　(DDQL),　and　a　novel　DDQL-based　EMS　is　developed　for　the　fuel　cell　hybrid　electric　bus.　Subsequently,　a　comparative　test　is　conducted　with　the　DP-based　and　the　Rule-based　EMS　to　analyze　the　performance　of　the　DDQL-based　EMS.　The　results　indicate　that　the　proposed　EMS　achieves　good　fuel　economy　performance,　with　an　improvement　of　15.4%　compared　to　the　Rule-based　EMS　under　the　training　scenarios.　In　terms　of　generalization　performance,　the　proposed　EMS　also　achieves　good　fuel　economy　performance,　which　improves　by　13.3%　compared　to　the　Rule-based　energy　management　strategy　under　the　testing　scenario.　©　The　Author(s)　2025.