  Recently, there has been an increasing interest in the roll-out of electric
vehicles (EVs) in the global automotive market. Compared to conventional
internal combustion engine vehicles (ICEVs), EVs can not only help users reduce
monetary costs in their daily commuting, but also can effectively help mitigate
the increasing level of traffic emissions produced in cities. Among many
others, battery electric vehicles (BEVs) exclusively use chemical energy stored
in their battery packs for propulsion. Hence, it becomes important to
understand how much energy can be consumed by such vehicles in various traffic
scenarios towards effective energy management. To address this challenge, we
propose a novel framework in this paper by leveraging the federated learning
approaches for modelling energy consumption for BEVs (Fed-BEV). More
specifically, a group of BEVs involved in the Fed-BEV framework can learn from
each other to jointly enhance their energy consumption model. We present the
design of the proposed system architecture and implementation details in a
co-simulation environment. Finally, comparative studies and simulation results
are discussed to illustrate the efficacy of our proposed framework for accurate
energy modelling of BEVs.
