Related papers: Transfer Learning Assisted XgBoost For Adaptable Cyberattack Detection In Battery Packs

Transfer Learning Assisted XgBoost For Adaptable Cyberattack Detection In Battery Packs

URL: http://arxiv.org/abs/2504.10658v1
Date: Mon, 14 Apr 2025 19:15:32 GMT
Title: Transfer Learning Assisted XgBoost For Adaptable Cyberattack Detection In Battery Packs
Authors: Sanchita Ghosh, Tanushree Roy,
Abstract summary: An adversary could corrupt the voltage sensor data during transmission, potentially causing local to wide-scale disruptions.<n>It is essential to detect sensor cyberattacks in real-time to ensure secure EV charging.<n>We propose fine-tuning of an XgBoost-based cell-level model using limited pack-level data to use for voltage prediction and residual generation.
Score: 3.4530027457862
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Optimal charging of electric vehicle (EVs) depends heavily on reliable sensor measurements from the battery pack to the cloud-controller of the smart charging station. However, an adversary could corrupt the voltage sensor data during transmission, potentially causing local to wide-scale disruptions. Therefore, it is essential to detect sensor cyberattacks in real-time to ensure secure EV charging, and the developed algorithms must be readily adaptable to variations, including pack configurations. To tackle these challenges, we propose adaptable fine-tuning of an XgBoost-based cell-level model using limited pack-level data to use for voltage prediction and residual generation. We used battery cell and pack data from high-fidelity charging experiments in PyBaMM and `liionpack' package to train and test the detection algorithm. The algorithm's performance has been evaluated for two large-format battery packs under sensor swapping and replay attacks. The simulation results also highlight the adaptability and efficacy of our proposed detection algorithm.

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