Energy　management　strategies　play　an　essential　role　in　improving　fuel　economy　and　extending　battery　lifetime　for　fuel　cell　hybrid　electric　vehicles.　However,　the　traditional　energy　management　strategy　ignores　the　lifetime　of　the　battery　for　good　fuel　economy.　To　overcome　this　drawback,　a　battery　longevity-conscious　energy　management　predictive　control　strategy　is　proposed　based　on　the　deep　reinforcement　learning　algorithm　predictive　equivalent　consumption　minimization　strategy　(DRL-PECMS)　in　this　study.　To　begin　with,　the　back-propagation　neural　network　is　devised　for　predicting　demand　power,　and　the　predictive　equivalent　consumption　minimum　strategy　(PECMS)　is　proposed　to　improve　the　hydrogen　consumption.　Then,　in　order　to　improve　the　battery　durability　performance,　the　deep　reinforcement　learning　algorithm　is　utilized　to　optimize　the　battery　power　and　improve　battery　lifetime.　Finally,　numerical　verification　and　hard-ware　in　the　loop　experiments　are　conducted　to　validate　hydrogen　consumption　and　battery　durability　performance　of　the　proposed　strategy.　The　validation　results　show　that,　compared　with　CD/CS　and　SQP(Sequential　Quadratic　Programming),　the　PECMS　combined　can　achieve　better　fuel　economy　with　the　fuel　consumption　reduction　by　55.6　%　and　5.27　%,　which　effectively　improves　the　fuel　economy.　The　DRL-PECMS　can　reduce　the　effective　Ah-throughput　by　3.1　%　compared　with　the　PECMS.　The　numerous　validations　and　comparisons　demonstrate　that　the　proposed　strategy　effectively　accomplishes　the　trade-off　optimization　between　energy　consumption　and　battery　durability　performance.　©　2023