Optimization of Electric Vehicle Charging Schedules Based on Individual Driving Habits and Real-World Scenarios.

This study aims to address the issue of range anxiety in electric vehicle (EV) drivers by developing optimal charging schedules that are based on individual driving habits, proximity to charging facilities, and environmental factors. A scheduling approach was employed to match the usage profile of the EV with charging windows, with the objective of minimizing charging cost, time loss, and overall degradation of the EV. The development of a driving profile was undertaken for three scenarios: short commute, long commute, and senior citizen, and charging windows were defined to reflect real-world situations. With this, an optimization model was implemented using Python's pyworkforce package, with the constraint of charging rate per hour. The results provide tabulated quantitative values for optimal charging schedules on a weekly basis and can assist EV drivers in adapting to an EV lifestyle and reducing range anxiety. This research provides valuable insights into addressing the critical issue of range anxiety in EV adoption and has the potential to encourage more individuals to switch to EVs by alleviating their concerns regarding limited driving range. [ABSTRACT FROM AUTHOR]