The　power　transients　caused　by　switching　from　drive　mode　to　brake　mode　in　fuel　cell　hybrid　electric　vehicles　(FCHEV)　can　result　in　significant　degradation　cost　losses　to　the　fuel　cell.　To　address　this　issue,　this　study　proposes　a　self-learning　Markov　prediction　algorithm　based　aging-oriented　gradient　drop　power　control　strategy.　First,　a　real-time　self-learning　Markov　predictor　(SLMP)　based　on　the　traditional　offline　training　Markov　improvement　is　designed　to　predict　the　demand　power　and　combined　with　the　sequential　quadratic　programming　(SQP)　optimization　algorithm　to　solve　for　the　inner　optimal　demand　power　based　on　its　global　cost　function　minimization　characteristic.　On　this　basis,　the　fuel　cell　gradient　drop　power　(FGDP)　strategy　is　proposed　to　optimize　the　operating　state　of　the　vehicle　powertrain　under　vehicle　mode　switching.　This　involves　establishing　a　power　gradient　drop　step　based　on　considering　the　fuel　cell　hydrogen　consumption　cost　and　its　lifetime　degradation　cost　to　further　obtain　the　outer　fuel　cell　demand　power　at　the　optimal　step.　And　three　execution　modes　are　designed　to　trigger　the　FGDP　strategy.　Finally,　by　combining　the　above　efforts,　the　SLMP-FGDP　optimization　control　strategy　is　constructed.　The　numerical　verification　and　hardware　in　loop　experiments　results　show　that　the　proposed　improved　SLMP　can　predict　the　vehicle　demand　power　more　accurately.　Compared　with　the　non-FGDP　system,　the　SLMP-FGDP　strategy　can　effectively　near-eliminate　the　fuel　cell　power　transient　due　to　any　braking　scenario,　thus　effectively　controlling　the　fuel　cell　lifetime　degradation　cost　in　a　lower　range　and　realizing　a　reduction　of　up　to　52.21%　of　the　fuel　cell　usage　costs　without　significantly　sacrificing　the　hydrogen　fuel　economy.　©　2024　Elsevier　Ltd