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

TL;DR

This paper introduces Fed-BEV, a federated learning framework that enables battery electric vehicles to collaboratively improve energy consumption models without sharing raw data, enhancing energy management accuracy.

Contribution

The paper presents a novel federated learning framework specifically designed for modeling energy consumption in BEVs, including system architecture and implementation in a co-simulation environment.

Findings

Fed-BEV improves energy consumption prediction accuracy.

Collaborative learning enhances model robustness.

Framework demonstrates effective energy modeling in simulations.

Abstract

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.