Deep　reinforcement　learning　(DRL)　has　been　widely　used　in　the　field　of　automotive　energy　management.　However,　DRL　is　computationally　inefficient　and　less　robust,　making　it　difficult　to　be　applied　to　practical　systems.　In　this　article,　a　customized　energy　management　strategy　based　on　the　deep　reinforcement　learning-model　predictive　control　(DRL-MPC)　self-regulation　framework　is　proposed　for　fuel　cell　electric　vehicles.　The　soft　actor　critic　(SAC)　algorithm　is　used　to　train　the　energy　management　strategy　offline,　which　minimizes　system　comprehensive　consumption　and　lifetime　degradation.　The　trained　SAC　policy　outputs　the　sequence　of　fuel　cell　actions　at　different　states　in　the　prediction　horizon　as　the　initial　value　of　the　nonlinear　MPC　solution.　Under　the　MPC　framework,　iterative　computation　is　carried　out　for　nonlinear　optimization　problems　to　optimize　action　sequences　based　on　SAC　policy.　In　addition,　the　vehicle＇s　usual　operation　dataset　is　collected　to　customize　the　update　package　for　further　improvement　of　the　energy　management　effect.　The　DRL-MPC　can　optimize　the　SAC　policy　action　at　the　state　boundary　to　reduce　system　lifetime　degradation.　The　proposed　strategy　also　shows　better　optimization　robustness　than　SAC　strategy　under　different　vehicle　loads.　Moreover,　after　the　update　package　application,　the　total　cost　is　reduced　by　5.93%　compared　with　SAC　strategy,　which　has　better　optimization　under　comprehensive　condition　with　different　vehicle　loads.