A Swarm Intelligence Approach to Optimize Vehicle-to-Vehicle Charging Considering Dynamic Pricing Scenario

This paper investigates how a swarm intelligence meta-heuristic algorithm can be applied in vehicle electrification, focusing on the coordination of vehicle-to-vehicle charging (V2V) operations. A V2V charging method realizes a flexible and rapid power recharging manner for EVs regardless of traditional charging facilities. The main technical challenges for charging are to determine how much it costs to charge, maximum energy efficiency, and guarantee the stability of the grid while taking into account dynamic pricing, penalties related to unbalancing power flow, and time-varying energy constraints. In this paper, a bio-inspired algorithm called Red Deer Algorithm (RDA) with swarm intelligence has been introduced, and the V2V problem has been modelled in this work as a strictly constrained nonlinear multiobjective optimization to cope with complex constraints of the system for a distributed power sharing mechanism among multiple EVs. The simulations were conducted, and the results demonstrate the exceptional performance of the proposed framework in managing constraints while attaining the global minimum. Furthermore, the work analyzes operational criteria, including power sharing, battery state of charge (SoC), and power grid demand. Furthermore, the technique is flexible, adaptive, more robust, and computationally viable, allowing its implementation on a broad scale under varying conditions.