This paper reviews the current state of cybersecurity for EV charging infrastructure, analyzing existing machine learning countermeasures and proposing future directions to overcome data limitations i…

This paper introduces a method for adapting pretrained battery prediction models in Electric Vehicles using on-device learning, achieving significant improvements in forecasting performance.

The paper proposes a scalable, market-analysis-driven methodology to assess national charging station cybersecurity by extrapolating field test results from a manageable subset of stations to estimate…

This paper proposes a new method for AC/DC Power Factor Correction in single-phase On-Board Chargers for Electric Vehicles using a duty cycle predictive Model Predictive Current Control with real-time…

The FALCON-C framework proposes a flow-based autoencoder approach to detect cyber anomalies and label malicious flows in connected vehicular networks, achieving high accuracy in identifying attacks on…

This paper develops FGDSE, a feature-governed dynamic stacking ensemble for climate-resilient charging-asset management in electric vehicles, which predicts daily fault risk over a multi-week horizon…

The paper models how AI-driven data center demand stresses the electrical grid, finding that relying solely on renewable energy certificates (RECs) is insufficient and that on-site storage and spatial…

SolarChain is a platform that ensures verifiable trust in decentralized solar energy markets by anchoring digital energy credits to the hard physical limits of solar yield, thereby preventing data man…

This paper provides a comprehensive review of network optimization aspects for Connected and Autonomous Vehicles (CAVs), aiming to clarify misconceptions and outline future research directions.

This paper compares the efficiency of different routing algorithms for collectively routed fleets of autonomous vehicles and proposes a market design to encourage social welfare oriented cooperation.

This paper proposes a hybrid certificate solution combining Elliptic-Curve Cryptography and Post-Quantum Cryptography for secure vehicular communications.

The paper introduces a genetic algorithm framework to calibrate complex urban traffic simulations using only sparse real-world traffic observations, eliminating the need for detailed employment data.

This paper proposes a Model-Based Systems Engineering workflow for creating Digital Twins of Renewable Energy Communities using SysML and the SAREF4ENER ontology.

This paper introduces FORESEE, a cooperative lane change model for connected and automated driving that utilizes V2X data to organize vehicles and improve traffic flow, enhancing average vehicle speed…

The paper introduces CityGen, a diffusion-based framework that enables zero-label city adaptation for autonomous driving by synthesizing city-style data conditioned on HD maps and visual prompts, sign…

The paper introduces Battery-Sim-Agent, an LLM-based framework that reframes the difficult inverse problem of battery parameter estimation as a reasoning task, significantly outperforming traditional…

This paper proposes an Explainable Deep Reinforcement Learning (XRL) framework to optimize energy management in complex buildings, demonstrating that on-policy algorithms provide superior cost reducti…

This paper surveys how integrating Edge Computing, Machine Learning, and Deep Learning can enhance the security and resilience of complex Internet of Vehicles (IoV) networks.