Jan Engelhardt

Electric vehicle (EV) smart charging in cluster setups, where multiple electric vehicle supply equipment (EVSE) units operate simultaneously under a shared but limited system capacity, is gaining increasing attention. Yet, practical validation of EV smart charging strategies in such environments remains limited. This paper presents an adaptive control method for charging load management in EV clusters, addressing three key dimensions: (i) system-level variability, (ii) grid-side coordination, and (iii) EV-specific diversity. The proposed approach ensures continuous power distribution with reallocation of charging power based on identified EV charging characteristics, while respecting grid constraints, including cluster power limits and three-phase unbalance, bridging the gap between research concepts and real-world implementation. Moving from theory to practice, experimental testing is conducted in a workplace charging cluster under two operating modes: constant mode and renewable energy source (RES) mode as well as a long-term operational test for a week. Results demonstrate that the approach effectively identifies distinct EV charging characteristics, prioritizes urgent users, minimizes unnecessary allocation to non-urgent users, and improves system phase balance. Compared to a commonly deployed power-sharing approach, the proposed algorithm increases energy delivery to urgent users by 31.3% in constant mode. In RES mode, it successfully tracks local renewable generation while maximizing the utilization of limited resources. On the other hand, long-term investigation results demonstrate that the proposed approach managed to ensure fair charging performance among the users by reaching the fairness index at 93.39%.

Electric vehicle (EV) smart charging in cluster setups, where multiple electric vehicle supply equipment (EVSE) units operate simultaneously under a shared but limited system capacity, is gaining increasing attention. Yet, practical validation of EV smart charging strategies in such environments remains limited. This paper presents an adaptive control method for charging load management in EV clusters, addressing three key dimensions: (i) system-level variability, (ii) grid-side coordination, and (iii) EV-specific diversity. The proposed approach ensures continuous power distribution while respecting grid constraints, including cluster power limits and three-phase unbalance, bridging the gap between research concepts and real-world implementation. Moving from theory to practice, experimental testing is conducted in a workplace charging cluster under two operating modes: constant mode and renewable energy source (RES) mode. Results demonstrate that the approach effectively identifies distinct EV charging characteristics, prioritizes urgent users, minimizes unnecessary allocation to non-urgent users, and improves system phase balance. Compared to a standard powersharing method, the proposed algorithm increases energy delivery to urgent users by 31.3% in constant mode. In RES mode, it successfully tracks local renewable generation while maximizing the utilization of limited resources.

The demand for electric vehicle supply equipment (EVSE) is increasing because of the rapid shift toward electric transport. Introducing EVSE on a large scale into the power grid can increase power demand volatility, negatively affecting frequency stability. A viable solution to this challenge is the development of smart charging technologies capable of performing frequency regulation. This paper presents an experimental proof of concept for a new frequency regulation method for EVSE utilizing a distributed control architecture. The architecture dynamically adjusts the contribution of electric vehicles (EVs) to frequency regulation response based on the charging urgency assigned by the EV users. The method is demonstrated with two Renault ZOEs responding to frequency fluctuation with a combined power range of 6 kW in the frequency range of 50.1 to 49.9 Hz. The results confirm consistent power sharing and effective frequency regulation, with the system controlling the engagement of the EVs in frequency regulation based on priority. The delay and accuracy analyses reveal a fast and accurate response, with the cross-correlation indicating an 8.48 s delay and an average undershoot of 0.17 kW. In the conclusions, the paper discusses prospective improvements and outlines future research directions for integrating EVs as service providers.

Reconfigurable batteries can change their cell topology in real time, which enables them to adapt their voltage during operation. This unique capability makes interfacing power converters redundant in applications where batteries are directly coupled with other DC components or systems. The present paper characterizes a 104 kWh prototype of a reconfigurable battery for high power applications, and derives equations for calculating the battery efficiency for the complete operating area. The battery can adapt its voltage from 0 V up to 1200 V, and reaches power values of 240 kW for charging, and 280 kW for discharging. The results are presented in efficiency maps, showing the dependency on voltage, power, and state of charge. Moreover, the efficiency characteristic is compared to a conventional battery with fixed cell topology and DC-DC converter. The reconfigurable battery can operate at a wider voltage range and achieves better efficiency up to an average power of 44.6 kW during charging, and 46.7 kW during discharging. Conversely, the conventional system performs better above these thresholds. Finally, the presented model can be used to optimize the design of reconfigurable battery strings, and to accurately size such systems for specific applications and purposes.