With　decades?　development　of　energy　management　strategy　in　hybrid　electric　vehicle,　learning-based　method　has　been　deemed　as　a　key　solution　for　energy　economy　and　real　time.　However,　current　energy　management　strategy　cannot　reach　an　optimal　energy　economy　performance　and　online　update　in　a　tolerable　time　lag.　Aiming　at　solving　these　problems,　an　accelerated　reinforcement　learning　method　and　an　online-updated　strategy　are　proposed　in　present　work.　Firstly,　prioritized　replay　is　applied　in　deep　Q　network　with　normalized　advantage　function　for　a　fast　convergence　to　an　optimal　strategy.　Prioritized　replay　module　endows　weight　to　trained　history　data　which　is　utilized　in　neural　network　training.　The　neural　network　is　updated　towards　optimal　strategy　by　weight　in　an　effective　way.　Secondly,　the　online　updated　strategy　for　fix-line　hybrid　electric　vehicle　is　designed　based　on　the　accelerated　reinforcement　learning　method　and　model　predictive　control.　The　predicted　future　road　information　generated　by　model　predictive　control　in　each　time　interval　is　delivered　to　the　accelerated　reinforcement　learning　module　for　online　energy　management　strategy　generating.　Finally,　with　all　efforts　above,　the　online　updated　strategy　is　carried　out　and　validated　through　hardware-in-the-loop　simulation.　The　results　show　that　this　approach　promotes　the　energy　economic　performance　while　updating　strategy　in　real　time.　?　2021　Elsevier　Ltd.　All　rights　reserved.