Adaptive Polar Lights Optimizer for Smart Electric Vehicle Charging Under Price Uncertainty and Battery Degradation

This paper investigates an algorithmic redesign tailored to cost minimization with degradation awareness EV charging under an uncertainty framework for coordinated grid-to-vehicle (G2V) and vehicle-to-grid (V2G) scheduling. An improved variant of the Polar Lights Optimizer (IPLO) is developed through the integration of Random Walk Exploitation (RWE) to enhance local refinement and Periodic Random Parameter Tuning (PRPT) to improve adaptability under uncertainty. In addition, an adaptive control mechanism is incorporated to adjust charging and discharging actions based on battery capacity degradation and dynamic electricity price signals. The presented framework is evaluated through simulation-based case studies and compared with several recent metaheuristic algorithms. The results demonstrate cost reductions of up to 25.42% over the original PLO and 80.78% relative to a non-optimized baseline, faster convergence, and improved robustness to price uncertainty, while mitigating adverse battery degradation effects. A statistical analysis over multiple independent runs confirms the reliability and consistency of the presented approach, highlighting its suitability for smart EV charging optimization in dynamic operating environments.