In　order　to　effectively　solve　the　problem　of　EV（Electric　Vehicle）　charging　destination　optimization　and　charging　path　planning，as　well　as　the　online　real-time　decision　making　problem　of　EV　charging　navigation，a　double-layer　stochastic　optimization　model　for　EV　charging　navigation　considering　a　variety　of　uncertainty　factors　is　established，and　an　EV　charging　navigation　method　based　on　HEDQN（Hierarchical　Enhanced　Deep　Q　Network）　is　proposed.　The　proposed　HEDQN　algorithm　adopts　double　competitive　deep　Q　network　algorithm　based　on　the　Huber　loss　function，including　two　layers　of　eDQN（enhanced　Deep　Q　Network）　algorithms.　The　upper　eDQN　is　used　to　optimize　the　EV　charging　destination.　On　this　basis，the　lower　eDQN　is　utilized　to　optimize　the　EV　charging　path　in　real　time.　Finally，the　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　path，single-layer　deep　Q　network　algorithm　and　traditional　hierarchical　deep　Q　network　algorithm，the　proposed　HEDQN　algorithm　can　effectively　decrease　the　EV　charging　cost，so　as　to　realize　the　online　real-time　EV　charging　navigation.　In　addition，the　adaptability　of　the　proposed　HEDQN　algorithm　is　verified　after　the　simulation　environment　changes.　©　2022　Electric　Power　Automation　Equipment　Press.　All　rights　reserved.