Electric vehicles (EVs) play a significant role in the current transportation systems. The main factor that affects acceptance of existing EV models is the range anxiety problem caused by limited charging stations and long recharge times. Recently, the solar-powered EV has drawn many attentions due to being free of charging limitations. However, the solarpowered EVs may still struggle with the limited use because of unpredictable solar availability. For example, shadings caused by buildings and trees also possibly decrease the solar panel cell efficiency. To address this, we propose a route planning method for solar-powered EVs to balance the energy harvesting and consumption subject to time constraint. The idea behind our solution is to offer power-aware optimal routing, which maximizes the on-road energy input given solar availability on each road segment. We first build a solar access estimation model using 3D geographic data and then employ a multi-criteria search method to generate a set of Pareto candidate routes. In order to reduce the size of the set, we leverage the bisect kmeans clustering algorithm to extract the most representative Pareto routes with better solar availability. In the evaluation, we developed a validation platform on the vehicle and leveraged mobile sensing techniques to examine our proposed model in real road environments. We conducted simulations to evaluate our proposed route planning algorithm using real life scenarios. Experimental results demonstrate that our solar input model is robust to real road scenarios, and the routing algorithm has great potential to provide efficient services for solar-powered EV in the future.