Peukert's Law Based Adaptive Control Strategy for EVs in VPP Based Energy Market

The increasing integration of electric vehicles (EVs) into power grids presents new opportunities for enhancing energy efficiency and grid stability through vehicle-to-grid (V2G) and grid-to-vehicle (G2V) technologies. This paper proposes a Peukert's Law-based adaptive control strategy for optimizing bidirectional power transfer in a virtual power plant (VPP) network. The approach leverages fuzzy logic decision-making to dynamically regulate energy exchange based on grid demand, EV battery state-of-charge (SOC), and load conditions. By incorporating Peukert's Law into the control framework, the strategy accurately estimates battery discharge behavior, ensuring efficient energy utilization while minimizing degradation. A key aspect of this research is load balancing, where aggregated EVs actively support grid operations by strategically adjusting charging and discharging cycles. The control system integrates a bidirectional DC/DC converter model, constant current mode PI control, and a Peukert's Lawbased time estimation method using the Levenberg-Marquardt algorithm. These components enable real-time decision-making to optimize energy transfer, prevent grid instability, and extend battery life. By implementing this adaptive strategy within a VPP-based network, the proposed method enhances grid resilience, reduces peak load stress, and facilitates greater penetration of renewable energy sources. The findings contribute to the development of smart energy management systems, enabling seamless integration of EVs as distributed energy resources. Future work will focus on refining optimization techniques and expanding the system's real-world applicability.