Smart transportation shall address utility waste, traffic congestion, and air pollution problems with least human intervention in future smart cities. To realize the sustainable operation of smart transportation, we leverage solar-harvesting charging stations and rooftops to power electric autonomous vehicles(AVs) solely via design. With a fixed budget, our framework first optimizes the locations of charging stations based on historical spatial-temporal solar energy distribution and usage patterns, achieving (2+epsilon) factor to the optimal. Then a stochastic algorithm is proposed to update the locations online to adapt to any shift in the distribution. Based on the deployment, a strategy is developed to assign energy requests in order to minimize their traveling distance to stations while not depleting their energy storage. Equipped with extra harvesting capability, we also optimize route planning to achieve a reasonable balance between energy consumed and harvested en-route. As a promising application, utility optimization of shared electric AVs is discussed, and (2k+1)-approx algorithm is proposed to manage k vehicles simultaneously. Our extensive simulations demonstrate the algorithm can approach the optimal solution within 10-15% approximation error, improve the operating range of vehicles by up to 2-3 times, and improve the utility by more than 50% compared to other competitive strategies.
Recommended citation: Pengzhan Zhou, Cong Wang, Yuanyuan Yang. “Design and Optimization of Solar-Powered Shared Electric Autonomous Vehicle System for Smart Cities.” IEEE Transactions on Mobile Computing (TMC), 2021.