Robust eBuses Charging Location Problem

Abstract

The implementation of a sustainable and efficient electric bus (eBus) transportation network requires addressing multiple concerns, such as limited driving range and battery charging/discharging time. Currently, eBuses can travel between 200 to 300 km on a single charge, and fast charging stations can fully recharge a battery in a matter of minutes. However, a failure in a charging station might negatively impact the operation of the system with unnecessary delays for the users. Taking this into account, we propose and implement a model for the Robust eBuses Charging Location problem that takes into account potential vulnerabilities of the transportation system. Our model incorporates a protection mechanism that allows eBuses to reach a backup charging station in case the regular one is down. We propose a MIP model to tackle this problem with minimal disruptions in the regular operation of the eBuses. Furthermore, we also present a Large Neighbourhood Search framework to efficiently tackle the problem. Our empirical evaluation suggests that our framework can operate a robust service with a small number of charging stations for three Irish cities and our Large Neighbourhood Search approach largely outperforms a popular commercial MIP solver.

Online Optimization of Vehicle-to-Grid Scheduling to Mitigate Battery Aging

“…In simulation, we use the Monte Carlo algorithm to obtain the expected value of E eν according to Equation (9). We generate the SoC when the EV is connected to the grid based on Equation (10), and use Equation (11) to guide the provision of the total amount of electricity to be charged for participating in V2G. The dataset generated based on the above formula is shown in Table 1.…”

“…In [9], the authors focus on optimizing coordinated schedules for recharging electric buses to enhance charging efficiency and operational cost-effectiveness. In [10], the authors design a robust charging positioning system for electric buses to maintain operational continuity in the event of a charging station failure. The proposed model optimizes the charging plan and considers the mirroring behavior between the primary and standby routes to enhance system resilience in a dynamic environment.…”

Optimizing EV Battery Management: Advanced Hybrid Reinforcement Learning Models for Efficient Charging and Discharging

“…Quintana et al (2022) [31] presented a model addressing the robust bus charging location problem, considering system vulnerabilities. It includes a safeguard mechanism enabling buses to access backup charging stations if primary ones fail.…”

Non-Iterative Coordinated Optimisation of Power–Traffic Networks Based on Equivalent Projection

“…As an illustration, the researchers in [27] conducted a research study to analyse the effect of charging station placements on PDNs. The failure of a charging station does not impact the charging behaviour of electric buses, as seen in [28]. In a previous study, scientists employed genetic algorithms to forecast forthcoming charging requirements for EVs and strategise the most advantageous sites for charging stations [29].…”