In　this　paper,　we　propose　an　energy　management　strategy　based　on　deep　reinforcement　learning　for　a　hybrid　battery　system　in　electric　vehicles　consisting　of　a　high-energy　and　a　high-power　battery　pack.　The　energy　management　strategy　of　the　hybrid　battery　system　was　developed　based　on　the　electrical　and　thermal　characterization　of　the　battery　cells,　aiming　at　minimizing　the　energy　loss　and　increasing　both　the　electrical　and　thermal　safety　level　of　the　whole　system.　Primarily,　we　designed　a　novel　reward　term　to　explore　the　optimal　operating　range　of　the　high-power　pack　without　imposing　a　rigid　constraint　of　state　of　charge.　Furthermore,　various　load　profiles　were　randomly　combined　to　train　the　deep　Q-learning　model,　which　avoided　the　overfitting　problem.　The　training　and　validation　results　showed　both　the　effectiveness　and　reliability　of　the　proposed　strategy　in　loss　reduction　and　safety　enhancement.　The　proposed　energy　management　strategy　has　demonstrated　its　superiority　over　the　reinforcement　learning-based　methods　in　both　computation　time　and　energy　loss　reduction　of　the　hybrid　battery　system,　highlighting　the　use　of　such　an　approach　in　future　energy　management　systems.