Siting and Operational Optimization of Mixed Wireless Charging Facilities for Electric Buses

Abstract: Electric buses (EBs) have emerged as a critical solution to reduce reliance on fossil fuels, mitigate climate change, promote sustainable energy, and achieve carbon neutrality goals. However, despite their growing adoption, widespread deployment remains constrained by challenges such as limited driving range and lengthy recharging times. These constraints are especially pronounced in large transit systems that serve extensive areas, where the operational demands are higher and the need for reliable, efficient charging solutions becomes critical. Wireless charging technologies, including static charging at bus stops (SC) and dynamic charging lanes (DC), present a promising solution by enabling buses to charge during service. DC allows for charging while in motion, and SC provides faster charging when buses are stationary at designated stops. These technologies have the potential to alleviate range anxiety, which could accelerate the integration of EBs into modern transit systems.
This study develops an optimization model for the mixed siting of SC and DC facilities and the management of EB charging schedules. The objective function extends beyond minimizing construction and charging costs; it also incorporates travel time, aiming to enhance the operational efficiency of EB systems. A unique feature of this model is its consideration of mixed wireless charging facilities, nonlinear and different charging rates at these facilities, as well as variable charging times. These factors are often overlooked in existing studies but are essential for improving the feasibility and effectiveness of the EB charging network.
The optimization model is evaluated using a real-world transit network at Athens Clarke County. Results show that the model delivers optimal siting strategies for both SC and DC facilities, while also providing managed EB charging schedules that meet service demands. Sensitivity analysis is conducted on key parameters such as charging rates, travel times, and service demand fluctuations. The results confirm the model’s stability and adaptability across various scenarios, highlighting its potential applicability in a wide range of transit environments.