Planejamento logístico de rede de abastecimento para veículos elétricos: previsão de demanda da cidade de São Paulo

Abstract

Taking into account the emission of polluting gases by the transport sector and the electric vehicle as an alternative to solve this problem, it is necessary to encourage its largescale use. However, consumer insecurity regarding autonomy and lack of service from the supply network are difficulties in accepting electric vehicles, making this solution increasingly distant. As a solution, the use of logistical concepts and tools such as demand forecasting can support decisionmaking and guarantee a better infrastructure of charging points in order to serve all electric vehicles in circulation. This work identified the demand forecast model that best fits the historical series, analyzing the evaluation criteria, chose the demand forecast model with the lowest errors. After the predictive analysis of the behaviors present in the historical series and verification of the nonstationarity of the series, the exponential smoothing methods were defined. Using comparative methods, the Holt model with damped trend presented better evaluation criteria, compared to the simple exponential smoothing models, Holt model and HoltWinters model. Subsequently, a study of the capacity of the number of recharging points was carried out to meet the expected demand of the electric vehicle fleet in the city of São Paulo, thus scoring the number of necessary recharging points. In view of the results, it was considered that the study contributed to the proposed objective and indicated the ideal number of recharge points.