User-Centric Adaptive Charging System: Enabling Personalized Charging in Electric Vehicle Chargers

Abstract

The rapid growth of electric vehicle (EV) adoption had increased the demand for charging systems that were not only technically efficient but also responsive to diverse user needs. Most existing smart-charging solutions had optimized grid stability and operational cost while treating users as passive system participants. This study developed and evaluated a User-Centric Adaptive Charging Systemthat enabled personalized charging based on user-defined priorities such as minimizing cost, reducing charging time, or preserving battery health. The system integrated preference modeling with adaptive control logic to adjust real-time charging rates while maintaining technical and safety constraints. A simulation-based experimental design had been employed to compare the proposed model with constant-rate and non-personalized smart-charging strategies across 120 charging sessions involving three user types. Results indicated that the adaptive system consistently reduced charging duration, session cost, and peak-load contribution, while significantly increasing the proportion of sessions that met user-specified objectives. These findings demonstrated that personalization enhanced both user satisfaction and grid-level efficiency rather than creating a trade-off between the two. The study concluded that embedding user preferences within adaptive charging algorithms was feasible, beneficial, and strongly aligned with emerging human-centric transport and energy system design principles. Although the evaluation had been simulation-based, the results provided a strong foundation for real-world pilot testing and policy exploration to support equitable, flexible, and sustainable EV charging ecosystems.