With　the　rapid　increase　in　electric　vehicle　(EV)　adoption,　there　is　an　escalating　need　for　coordinated　planning　between　power　and　transportation　systems　to　address　the　additional　stress　on　urban　infrastructure.　This　study　tackles　the　critical　challenge　of　aligning　investment　and　planning　between　these　systems,　specifically　considering　the　impact　of　EV　charging　behaviors.　To　address　this　issue,　we　propose　a　novel　tri-level　optimization　framework　that　incorporates　a　quasi-variational　inequality　(QVI)　model　to　effectively　capture　the　interactions　between　user　equilibrium　(UE)　in　transportation　networks　and　elastic　EV　charging　demand.　The　upper　level　of　the　model　optimizes　investments　in　power　systems,　including　distributed　generation　and　charging　infrastructure.　The　middle　level　concentrates　on　expanding　road　capacity,　while　the　lower　level　resolves　the　traffic　flow　patterns　under　user　equilibrium　with　elastic　charging　behaviors　(UE-ECB)　using　the　QVI　approach.　By　transforming　the　tri-level　model　into　a　bi-level　optimization　program　with　quasi-variational　inequality　(OP-QVI)　formulation,　we　achieve　computational　tractability　and　provide　efficient　solutions.　Results　show　that　the　elastic　charging　demand　model　outperforms　the　non-elastic　assumption,　reducing　total　costs　by　22.2　%,　with　power　system　investments　down　by　19.1　%　and　transportation　system　investments　down　by　39.8　%.　These　findings　demonstrate　the　model＇s　superior　accuracy,　avoiding　the　overestimation　of　infrastructure　needs.　Furthermore,　the　proposed　algorithm　efficiently　solves　complex　integrated　planning　problems,　ensuring　both　computational　effectiveness　and　economic　viability　in　system-wide　optimization.