Model Predictive Control for Charging Management Considering Mobile Charging Robots

Max Faßbender (), Nicolas Rößler, Christoph Wellmann, Markus Eisenbarth and Jakob Andert ()
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Max Faßbender: Teaching and Research Area Mechatronics in Mobile Propulsion, Faculty of Mechanical Engineering, RWTH Aachen University, 52062 Aachen, Germany
Nicolas Rößler: Teaching and Research Area Mechatronics in Mobile Propulsion, Faculty of Mechanical Engineering, RWTH Aachen University, 52062 Aachen, Germany
Christoph Wellmann: Teaching and Research Area Mechatronics in Mobile Propulsion, Faculty of Mechanical Engineering, RWTH Aachen University, 52062 Aachen, Germany
Markus Eisenbarth: Teaching and Research Area Mechatronics in Mobile Propulsion, Faculty of Mechanical Engineering, RWTH Aachen University, 52062 Aachen, Germany
Jakob Andert: Teaching and Research Area Mechatronics in Mobile Propulsion, Faculty of Mechanical Engineering, RWTH Aachen University, 52062 Aachen, Germany

Energies, 2025, vol. 18, issue 15, 1-23

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.

Keywords: smart charging; electric vehicles; mobile charging robot; DC charging; model predictive control; long short-term memory network; energy management (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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