# Privacy-Aware Energy Consumption Modeling of Connected Battery Electric Vehicles using Federated Learning

arXiv: https://arxiv.org/abs/2312.07371 · HTML: https://mlsanity.com/paper/2312.07371/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 64 references that are in the map.
- First posted on arXiv 12 Dec 2023.

## Builds on (most cited first, 9 of 9)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Federated Learning with Personalization Layers](https://mlsanity.com/paper/1912.00818/) (2019)
- [Exploiting Shared Representations for Personalized Federated Learning](https://mlsanity.com/paper/2102.07078/) (2021)
- [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/1909.11875/) (2019)
- [A Review and Outlook of Energy Consumption Estimation Models for Electric Vehicles](https://mlsanity.com/paper/2003.12873/) (2020)
- [Estimation of energy consumption of electric vehicles using Deep Convolutional Neural Network to reduce driver's range anxiety](https://mlsanity.com/paper/2008.11112/) (2020)
- [Fed-BEV: A Federated Learning Framework for Modelling Energy Consumption of Battery Electric Vehicles](https://mlsanity.com/paper/2108.04036/) (2021)
- [A Perspective on the Challenges and Opportunities for Privacy-Aware Big Transportation Data](https://mlsanity.com/paper/1811.09486/) (2018)

## Built on by (most cited first, 4 of 4)

- [A review of federated learning in renewable energy applications: Potential, challenges, and future directions](https://mlsanity.com/paper/2312.11220/) (2023)
- [Optimal Design and Implementation of an Open-source Emulation Platform for User-Centric Shared E-mobility Services](https://mlsanity.com/paper/2403.07964/) (2024)
- [Data-driven Energy Consumption Modelling for Electric Micromobility using an Open Dataset](https://mlsanity.com/paper/2403.17632/) (2024)
- [Machine Learning in Micromobility: A Systematic Review of Datasets, Techniques, and Applications](https://mlsanity.com/paper/2508.16135/) (2025)

## Abstract (arXiv)

Battery Electric Vehicles (BEVs) are increasingly significant in modern cities due to their potential to reduce air pollution. Precise and real-time estimation of energy consumption for them is imperative for effective itinerary planning and optimizing vehicle systems, which can reduce driving range anxiety and decrease energy costs. As public awareness of data privacy increases, adopting approaches that safeguard data privacy in the context of BEV energy consumption modeling is crucial. Federated Learning (FL) is a promising solution mitigating the risk of exposing sensitive information to third parties by allowing local data to remain on devices and only sharing model updates with a central server. Our work investigates the potential of using FL methods, such as FedAvg, and FedPer, to improve BEV energy consumption prediction while maintaining user privacy. We conducted experiments using data from 10 BEVs under simulated real-world driving conditions. Our results demonstrate that the FedAvg-LSTM model achieved a reduction of up to 67.84% in the MAE value of the prediction results. Furthermore, we explored various real-world scenarios and discussed how FL methods can be employed in those cases. Our findings show that FL methods can effectively improve the performance of BEV energy consumption prediction while maintaining user privacy.