Study of disturbances of human-induced errors on the optimal decisions of smart charging and energy sharing of zero-emission vehicles with quantified traffic congestion impact

Abstract

The rapid growth of diversified X2V/V2X technologies promotes smart charging of zero electric/emission vehicles (ZEVs). The existing EV smart charging framework primarily involves the interaction between vehicles and individual buildings, while overlooking their potential in energy sharing and the impact of traffic on road infrastructure. The previous studies on smart charging decision-making also ignore the uncertainty of human-induced errors in the optimal decision. This project develops a smart decision-making system (DMS) for distant energy sharing in ZEVs, incorporating multiple objectives, including the traffic impact, CO<inf>2</inf> emissions, and charging costs. By the proposed weighted decision-making index (DMI), the DMS converts multi-objective decision-making into single-objective decision-making, providing the optimal decision at each time step. Furthermore, this study investigates the impact of human-induced errors on optimal decisions with different scenarios and degrees, including random, timely, and ∆DMI-based errors. According to the simulation results, compared to the case of human decision-making, the proposed DMS can help to reduce the charging cost by up to 66 % for EV users and alleviate the negative impact on traffic by up to 12 %. Through the sensitivity analysis, it is concluded that the DMS can help EVs achieve a maximum 19 % reduction in the negative traffic impact on road resources compared with the decisions made by EV drivers, when the traffic congestion level of road-1 increases to level 3. Designed for dynamic environments where energy and information are continuously exchanged, this multi-objective DMS enables efficient ZEV charging by optimising the decision for users and maximising overall charging performance.