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Recently, there has been an increasing interest in the roll-out of electricvehicles (EVs) in the global automotive market. Compared to conventionalinternal combustion engine vehicles (ICEVs), EVs can not only help users reducemonetary costs in their daily commuting, but also can effectively help mitigatethe increasing level of traffic emissions produced in cities. Among manyothers, battery electric vehicles (BEVs) exclusively use chemical energy storedin their battery packs for propulsion. Hence, it becomes important tounderstand how much energy can be consumed by such vehicles in various trafficscenarios towards effective energy management. To address this challenge, wepropose a novel framework in this paper by leveraging the federated learningapproaches for modelling energy consumption for BEVs (Fed-BEV). Morespecifically, a group of BEVs involved in the Fed-BEV framework can learn fromeach other to jointly enhance their energy consumption model. We present thedesign of the proposed system architecture and implementation details in aco-simulation environment. Finally, comparative studies and simulation resultsare discussed to illustrate the efficacy of our proposed framework for accurateenergy modelling of BEVs.

Fed-BEV: A Federated Learning Framework for Modelling Energy Consumption of Battery Electric Vehicles