In　order　to　effectively　solve　the　problem　of　EVElectric　Vehicle　charging　destination　optimization　and　charging　path　planningas　well　as　the　online　real-time　decision　making　problem　of　EV　charging　navigationa　double-layer　stochastic　optimization　model　for　EV　charging　navigation　considering　a　variety　of　uncertainty　factors　is　establishedand　an　EV　charging　navigation　method　based　on　HEDQNHierarchical　Enhanced　Deep　Q　Network　is　proposed.　The　proposed　HEDQN　algorithm　adopts　double　competitive　deep　Q　network　algorithm　based　on　the　Huber　loss　functionincluding　two　layers　of　eDQNenhanced　Deep　Q　Network　algorithms.　The　upper　eDQN　is　used　to　optimize　the　EV　charging　destination.　On　this　basisthe　lower　eDQN　is　utilized　to　optimize　the　EV　charging　path　in　real　time.　Finallythe　proposed　HEDQN　algorithm　is　simulated　and　verified　in　a　city　transportation　network.　The　simulative　results　illustrate　that　compared　with　the　nearest　recommendation　algorithm　based　on　Dijkstra’s　shortest　pathsingle-layer　deep　Q　network　algorithm　and　traditional　hierarchical　deep　Q　network　algorithmthe　proposed　HEDQN　algorithm　can　effectively　decrease　the　EV　charging　costso　as　to　realize　the　online　real-time　EV　charging　navigation.　In　additionthe　adaptability　of　the　proposed　HEDQN　algorithm　is　verified　after　the　simulation　environment　changes.　©　2022　Electric　Power　Automation　Equipment　Press.　All　rights　reserved.