Transfer Learning Assisted XgBoost For Adaptable Cyberattack Detection In Battery Packs
This addresses the need for real-time, adaptable cyberattack detection to ensure secure EV charging, though it appears incremental as it builds on existing XgBoost methods with fine-tuning.
The paper tackled the problem of detecting sensor cyberattacks in electric vehicle battery packs by proposing an adaptable fine-tuning method using XgBoost for voltage prediction and residual generation, achieving efficacy in simulations under sensor swapping and replay attacks.
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.