To　cope　with　the　increasingly　stringent　emission　regulations,　major　automobile　manufacturers　have　been　focusing　on　the　development　of　new　energy　vehicles.　Fuel-cell　vehicles　with　advantages　of　zero　emission,　high　efficiency,　diversification　of　fuel　sources,　and　renewable　energy　have　been　the　focus　of　international　automotive　giants　and　Chinese　automotive　enterprises.　Establishing　a　reasonable　energy　management　strategy,　effectively　controlling　the　vehicle　working　mode,　and　reasonably　using　battery　energy　for　hybrid　fuel-cell　vehicles　are　core　technologies　in　domestic　and　foreign　automobile　enterprises　and　research　institutes.　To　improve　the　equilibrium　between　fuel-cell　hydrogen　consumption　and　battery　consumption　and　realize　the　optimal　energy　distribution　between　fuel-cell　systems　and　batteries　for　plug-in　fuel-cell　electric　vehicles　(PFCEVs),　considering　vehicles　as　the　environment　and　vehicle　control　as　an　agent,　an　energy　management　strategy　for　the　PFCEV　based　on　reinforcement　learning　algorithm　was　proposed　in　this　paper.　This　strategy　considered　the　immediate　return　and　future　cumulative　discounted　returns　of　a　fuel-cell　vehicle＇s　real-time　energy　allocation.　The　vehicle　simulation　model　was　built　by　Matlab/Simulink　to　carry　out　the　simulation　test　for　the　proposed　strategy.　Compared　with　the　rule-based　strategy,　the　battery　can　store　a　certain　amount　of　electricity,　and　the　integrated　energy　consumption　of　the　vehicle　was　notably　reduced　under　different　mileages.　The　energy　consumption　in　100　km　was　reduced　by　8.84%,　29.5%,　and　38.6%　under　100,　200,　and　300　km　mileages,　respectively.　The　hardware-in-loop-test　was　performed　on　the　D2P　development　platform,　and　the　final　energy　consumption　of　the　vehicle　was　reduced　by　20.8%　under　urban　dynamometer　driving　schedule　driving　cycle.　The　hardware-in　loop-test　results　are　consistent　with　the　simulation　findings,　indicating　the　effectiveness　and　feasibility　of　the　proposed　energy　management　strategy.　©　All　right　reserved.