The widespread adoption of battery electric vehicles is widely recognized as a key strategy for reducing emissions in the transport sector. However, the environmental benefits of electric mobility strongly depend on the temporal variability of the electricity generation mix and its associated emission intensity. This study investigates the potential of optimizing vehicle charging schedules based on time-varying carbon dioxide emissions factor, rather than grid-oriented criteria, to further reduce the indirect emissions linked to the operation of electric vehicle charging. To this end, an environmentally driven charging optimisation algorithm is developed, explicitly accounting for both the hourly variability of grid emission factors, derived from processing historical real Italian electricity production data, and the air temperature-dependent energy consumption of the BEV. The proposed framework is applied to a representative residential charging scenario, assuming overnight charging. Within this predefined charging window, the optmisation algorithm allocates the required charging energy over time so as to minimise the total carbon dioxide emissions. Results show that emission-aware charging strategies can reduce annual carbon dioxide emissions by approximately 5–6% compared to conventional uncontrolled charging, while ensuring that the vehicle is always fully charged and ready for use at the predefined departure time. In addition, by its design, the proposed environmentally driven charging optimization features as a scalable structure that can be integrated with existing smart charging strategies, contributing to the decarbonization of the transport sector.
Perugini et al. (Sun,) studied this question.