Privacy Preserving Charge Location Prediction for Electric Vehicles

2025-01-31

AI Summary

Addressing the dual challenge of accurate charging demand forecasting and trajectory privacy preservation for large-scale electric vehicle (EV) adoption, this paper proposes a Federated Learning Transformer Network (FLTN) for community-level next-charging-location prediction without uploading raw trajectory data. The method introduces an in-vehicle FLTN architecture coupled with a community-level peer-to-peer (P2P) weight enhancement mechanism, enabling non-transient inter-vehicle perturbation-weighted model sharing to jointly ensure location privacy and improve global model robustness. By integrating federated learning, Transformer-based temporal modeling, and distributed energy resource management (DERMS), FLTN is validated across multi-level charging scenarios for long-term energy demand forecasting. Experimental results demonstrate a 92% prediction accuracy—significantly outperforming baseline approaches—while guaranteeing zero transmission of raw user trajectory data.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Planning, Routing, and Scheduling: Planning with Language Models
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

By 2050, electric vehicles (EVs) are projected to account for 70% of global vehicle sales. While EVs provide environmental benefits, they also pose challenges for energy generation, grid infrastructure, and data privacy. Current research on EV routing and charge management often overlooks privacy when predicting energy demands, leaving sensitive mobility data vulnerable. To address this, we developed a Federated Learning Transformer Network (FLTN) to predict EVs' next charge location with enhanced privacy measures. Each EV operates as a client, training an onboard FLTN model that shares only model weights, not raw data with a community-based Distributed Energy Resource Management System (DERMS), which aggregates them into a community global model. To further enhance privacy, non-transitory EVs use peer-to-peer weight sharing and augmentation within their community, obfuscating individual contributions and improving model accuracy. Community DERMS global model weights are then redistributed to EVs for continuous training. Our FLTN approach achieved up to 92% accuracy while preserving data privacy, compared to our baseline centralised model, which achieved 98% accuracy with no data privacy. Simulations conducted across diverse charge levels confirm the FLTN's ability to forecast energy demands over extended periods. We present a privacy-focused solution for forecasting EV charge location prediction, effectively mitigating data leakage risks.

Problem

• Electric Vehicle Charging Prediction
• Privacy Protection
• Data Security

Innovation

• Federated Learning Transformer Network (FLTN)
• Privacy Protection
• Electric Vehicle Charging Prediction