Spider Wasp Optimization-based Power Management for Connected Electric Vehicles

Power Management (PM) in connected Electric Vehicles (EVs) entails coordinating the energy flow between the vehicles, charging stations, and the power grid in such a way as to achieve optimal functioning. Yet, energy supply in connected EVs would remain tricky, especially with the grid's constraints, variability in renewable energy generation, and variation in charging demand. This paper proposes a Spider Wasp Optimization (SWO) for enhancing PM in connected EVs. The SWO is used to minimize Cost of Energy (COE) in EVs. By optimizing the operational parameters, SWO minimizes the energy cost involved in charging EVs while maximizing the use of PV energy and managing energy distribution from various sources. Its adaptive optimization ensures that COE is minimized across various operating conditions, enhancing overall efficiency and responsiveness of PM of EVs. By then the proposed SWO method is implemented in MATLAB platform and evaluated their performance with various existing methods such as Multi Island Genetic Algorithm (MIGA), Genetic Algorithm (GA), Deep Deterministic Policy Gradient Algorithm (D3PG), Stochastic Multi Objective Optimization (SMOO), and Grey Sail Fish Optimization (GSFO). The proposed SWO method outperforms all the others with the lowest COE of $0.043/kWh, indicating its superior performance cost effectiveness of PM in connected EVs.