Most　of　the　existing　electric　vehicle　(EV)　charging　navigation　methods　do　not　simultaneously　take　into　account　the　electric　vehicle　charging　destination　optimization　and　path　planning.　Moreover,　they　are　unable　to　provide　online　real-time　decision-making　under　a　variety　of　uncertain　factors.　To　address　these　problems,　this　paper　first　establishes　a　bilevel　stochastic　optimization　model　for　EV　charging　navigation　considering　various　uncertainties,　and　then　proposes　an　EV　charging　navigation　method　based　on　the　hierarchical　enhanced　deep　Q　network　(HEDQN)　to　solve　the　above　stochastic　optimization　model　in　real-time.　The　proposed　HEDQN　contains　two　enhanced　deep　Q　networks,　which　are　utilized　to　optimize　the　charging　destination　and　charging　route　path　of　EVs,　respectively.　Finally,　the　proposed　method　is　simulated　and　validated　in　two　urban　transportation　networks.　The　simulation　results　demonstrate　that　compared　with　the　Dijkstra　shortest　path　algorithm,　single-layer　deep　reinforcement　learning　algorithm,　and　traditional　hierarchical　deep　reinforcement　learning　algorithm,　the　proposed　HEDQN　algorithm　can　effectively　reduce　the　total　charging　cost　of　electric　vehicles　and　realize　online　real-time　charging　navigation　of　electric　vehicles,　that　shows　excellent　generalization　ability　and　scalability.　©　2024