Energy Consumption Prediction in Battery Electric Vehicles: A Systematic Literature Review

The prediction of energy consumption in battery electric vehicles (BEV) is a complex task due to the large number of influencing factors and their intercorrelation. However, it is a necessary endeavour to reduce range anxiety, facilitate route planning, manage charging infrastructure, and enable more effective travel decisions that lower operational risks in transportation; this would lead to greater adoption of BEV in the global vehicle fleet. In this regard, the present paper examines the available evidence on the methodologies employed for predicting the energy consumption of electric cars using the systematic literature review (SLR) protocol of Denyer and Tranfield together with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement for the selection and evaluation of studies. The analysis addresses modelling methods, computational tools employed, model accuracy metrics, the topology of the variables used, their sampling frequency and period of analysis, the modelling scale, and the data source. In addition, a classification of the different methodologies and variables is proposed, providing a reference framework for further studies. This article closes the research gap and complements previous literature, allowing the identification of current shortcomings and directions for future research related to energy consumption in BEV.