Nowadays,　the　trend　of　powertrain　electrification　in　the　public　transportation　sector　is　clear.　To　meet　the　dramatic　load　variation　and　relatively　high　handling　stability　requirements　for　battery　electric　buses,　the　dual-motor　four-wheel　powertrain　architecture　attracts　great　attention　in　recent　years.　Although　the　bus　routes　are　fixed,　the　driving　speed　and　load　vary　significantly　with　time,　season,　passenger　capacity,　and　traffic　conditions,　which　presents　a　serious　challenge　for　efficient　power　coupling　in　a　dual-motor　system　to　reduce　energy　consumption.　This　study　provides　a　data-driven　fitting　cycle　for　the　specific　bus　route.　Then,　Deep　Deterministic　Policy　Gradient　(DDPG)　algorithm　is　introduced　in　Energy　Management　Strategy　(EMS)　design　to　improve　the　vehicle＇s　economic　performance　with　uncertain　demand　in　the　unknown　cycle.　The　simulation　results　show　that　the　proposed　DDPG-EMS　achieves　93.91%-97.66%　of　the　benchmark　Dynamic　Programming　(DP)　-　based　EMS　under　various　testing　cycles.　In　addition,　the　comparison　of　DDPG-EMS　agent　trained　by　fitting　cycle,　standard　cycle,　and　real　driving　data　reached　97.2%-97.66%,　93.91%-97.0%,　and　94.41%-96.0%　of　DP,　respectively,　which　demonstrates　the　effectiveness　of　data-driven　fitting　cycle　and　reinforcement　learning　algorithm　combination　in　EMS　design　for　dual-motor　electrified　bus.