Addressing charging infrastructure location problem considering a dynamic and supply
Last modified: 2024-04-05
Abstract
Known for their environmentally friendly, cost-effective, and quiet operation, electric buses come forward as ideal public transportation solutions for urban areas. On the other hand, their limited driving range, depending on their battery capacities, hinders their wider adoption. Implementing the opportunity charging strategy can serve as a solution to address this inherent challenge of electric buses. This strategy allows buses to charge at bus stops using wireless chargers during the dwell time. However, it introduces another challenge, known as the Charging Infrastructure Location Problem (CILP), which involves optimizing the charger locations on transit networks.
Several studies have approached CILP, adopting the opportunity charging strategy. Nevertheless, these studies either evaluate energy levels concerning bus routes or assume predetermined charging durations at stops. However, it is a fact that each electric bus operating on the same route is supplied a different amount of energy due to the total number of passengers boarding and alighting throughout the entire operating period. Furthermore, each bus consumes different amounts of energy due to its schedule throughout the operation period.
This study develops an optimization model to minimize the charging infrastructure investment cost in an electric bus network under dynamic supply and demand. The model assesses the energy level of each bus individually by calculating the recharging duration at each stop for each bus depending on the number of boarding and alighting passengers and taking into account the impact of time-dependent link travel times on energy consumption. The developed optimization model is tested on a small-scale network. The results demonstrate that when the energy level of each bus is assessed individually, notable differences exist in the energy levels among buses operating on the same route. Additionally, solving the model more realistically using charging durations obtained from passenger boarding/alighting numbers instead of predetermined charging duration leads to significant increases in the charging infrastructure investment cost.
Several studies have approached CILP, adopting the opportunity charging strategy. Nevertheless, these studies either evaluate energy levels concerning bus routes or assume predetermined charging durations at stops. However, it is a fact that each electric bus operating on the same route is supplied a different amount of energy due to the total number of passengers boarding and alighting throughout the entire operating period. Furthermore, each bus consumes different amounts of energy due to its schedule throughout the operation period.
This study develops an optimization model to minimize the charging infrastructure investment cost in an electric bus network under dynamic supply and demand. The model assesses the energy level of each bus individually by calculating the recharging duration at each stop for each bus depending on the number of boarding and alighting passengers and taking into account the impact of time-dependent link travel times on energy consumption. The developed optimization model is tested on a small-scale network. The results demonstrate that when the energy level of each bus is assessed individually, notable differences exist in the energy levels among buses operating on the same route. Additionally, solving the model more realistically using charging durations obtained from passenger boarding/alighting numbers instead of predetermined charging duration leads to significant increases in the charging infrastructure investment cost.
Keywords
Charging infrastructure location problem,electric bus, opportunity charging strategy