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, Mingming Liu and his team 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. They 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 the proposed framework for accurate energy modelling of BEVs.
Link to publication https://ieeexplore.ieee.org/document/9625535