Model Predictive Control for Charging Management Considering Mobile Charging Robots

Energies, vol. 18, pp. 3948

Abstract

Mobile Charging Robots (MCRs), essentially high-voltage batteries mounted on mobile platforms, offer a flexible solution for electric vehicle (EV) charging, particularly in environments like supermarket parking lots with photovoltaic (PV) generation. Unlike fixed charging stations, MCRs must be strategically dispatched and recharged to maximize operational efficiency and revenue. This study investigates a Model Predictive Control (MPC) approach using Mixed-Integer Linear Programming (MILP) to coordinate MCR charging and movement, accounting for the additional complexity that EVs can park at arbitrary locations. The performance impact of EV arrival and demand forecasts is evaluated, comparing perfect foresight with data-driven predictions using long short-term memory (LSTM) networks. A slack variable method is also introduced to ensure timely recharging of the MCRs. Results show that incorporating forecasts significantly improves performance compared to no prediction, with perfect forecasts outperforming LSTM-based ones due to better-timed recharging decisions. The study highlights that inaccurate forecasts—especially in the evening—can lead to suboptimal MCR utilization and reduced profitability. These findings demonstrate that combining MPC with predictive models enhances MCR-based EV charging strategies and underlines the importance of accurate forecasting for future smart charging systems.

Authors 5

1. Max Faßbender Aachen Faculty of Mechanical Engineering

   RWTH Aachen University

   Affiliation as printed

   Teaching and Research Area Mechatronics in Mobile Propulsion , Faculty of Mechanical Engineering , RWTH Aachen University ;

   Teaching and Research Area Mechatronics in Mobile Propulsion, Faculty of Mechanical Engineering, RWTH Aachen University, 52062 Aachen, Germany

2. Nicolas Rößler Aachen Faculty of Mechanical Engineering

   RWTH Aachen University

   Affiliation as printed

   Teaching and Research Area Mechatronics in Mobile Propulsion , Faculty of Mechanical Engineering , RWTH Aachen University ;

   Teaching and Research Area Mechatronics in Mobile Propulsion, Faculty of Mechanical Engineering, RWTH Aachen University, 52062 Aachen, Germany

3. Christoph Wellmann Aachen Faculty of Mechanical Engineering

   RWTH Aachen University

   Affiliation as printed

   Teaching and Research Area Mechatronics in Mobile Propulsion , Faculty of Mechanical Engineering , RWTH Aachen University ;

   Teaching and Research Area Mechatronics in Mobile Propulsion, Faculty of Mechanical Engineering, RWTH Aachen University, 52062 Aachen, Germany

4. Markus Eisenbarth Aachen Faculty of Mechanical Engineering

   RWTH Aachen University

   Affiliation as printed

   Teaching and Research Area Mechatronics in Mobile Propulsion , Faculty of Mechanical Engineering , RWTH Aachen University ;

   Teaching and Research Area Mechatronics in Mobile Propulsion, Faculty of Mechanical Engineering, RWTH Aachen University, 52062 Aachen, Germany

5. Jakob Andert corresponding Aachen Faculty of Mechanical Engineering

   RWTH Aachen University

   Affiliation as printed

   Teaching and Research Area Mechatronics in Mobile Propulsion , Faculty of Mechanical Engineering , RWTH Aachen University ;

   Teaching and Research Area Mechatronics in Mobile Propulsion, Faculty of Mechanical Engineering, RWTH Aachen University, 52062 Aachen, Germany