The　battery　degradation　and　equivalent　hydrogen　consumption　are　of　great　significance　in　vehicle　performance　improvement　for　fuel　cell　hybrid　electric　vehicle.　However,　few　energy　management　strategies　proposed　by　previous　researchers　can　optimize　the　objectives　at　the　same　time.　To　overcome　this　drawback,　a　battery　degradation　-aware　energy　management　strategy　with　a　driving　pattern　severity　factor　feedback　correction　algorithm　is　developed　to　achieve　the　optimal　trade-off　between　power　battery　degradation　and　vehicle　economy　improvement.　The　proposed　strategy　adjusts　the　equivalent　factor　according　trip　distance　and　correct　the　battery　power　to　directly　address　the　battery　degradation　and　vehicle　economy　problem.　First,　a　cost　function　using　weighting　factor　to　trade　off　the　battery　degradation　and　vehicle　economy　is　formulated.　The　severity　factor　based　on　the　battery　aging　model　with　effective　ampere　-hour　throughput　is　used　to　measure　the　degree　of　battery　degradation.　Then,　the　genetic　algorithm　back　propagation　neural　network　for　driving　pattern　recognition　is　developed.　The　trip　distance　adaptive　equivalent　consumption　minimization　strategy　is　introduced　to　correct　the　equivalent　factor　according　to　the　driving　pattern　information　and　the　battery　degradation　feedback　correction　strategy　of　the　severity　factor　integrated　with　driving　patterns　recognition　is　constructed.　Finally,　a　comparison　analysis　study　and　hardware　-in　-the　-loop　experiment　are　conducted　to　validate　the　effectiveness　of　the　proposed　strategy.　Experimental　results　under　Urban　Dynamometer　Driving　Schedule　and　Extra　Urban　Driving　Cycle　combined　with　the　equivalent　consumption　minimum　strategy　indicate　that　the　battery　degradation　feedback　correction　algorithm　can　significantly　reduce　the　battery　degradation　degree　while　sacrificing　hydrogen　consumption　optimization　to　a　small　extent.