Road　gradients　not　only　affect　the　actual　performance　of　control　strategies　but　also　impact　battery　life　due　to　the　drastic　changes　in　power　demands.　To　balance　battery　degradation　with　fuel　economy　using　gradient　information,　this　study　proposes　a　gradient-aware　trade-off　control　strategy.　Initially,　a　vehicle　dynamics　model　and　a　battery　degradation　model　are　established.　Based　on　the　characteristics　of　known　road　information　and　remaining　driving　distance,　state　of　charge　planning　of　the　battery　is　conducted.　Subsequently,　the　Nondominated　Sorting　Genetic　Algorithm-II　is　applied　for　bi-objective　optimization,　yielding　a　set　of　Pareto　solutions　that　represent　different　levels　of　energy　consumption　and　battery　degradation.　Thereafter,　by　introducing　a　real-time　battery　degradation　severity　factor,　an　optimized　bias　coefficient　is　obtained,　which　adjusts　in　accordance　with　the　gradient　changes.　Through　the　optimization　of　the　bias　line,　the　optimal　bias　solution　set　under　different　working　conditions　is　determined,　achieving　the　optimal　control　for　power　system.　The　fuel　economy　of　the　proposed　strategy　is　improved　by　6.8%　relative　to　the　mileage　adaptive　Equivalent　Consumption　Minimization　Strategy,　and　the　battery　degradation　inhibition　is　improved　by　9.3%.　After　real-world　conditions　validation,　the　proposed　strategy　demonstrates　good　performance　in　both　economic　efficiency　and　battery　life.