# Transfer Learning Assisted XgBoost For Adaptable Cyberattack Detection In Battery Packs

arXiv: https://arxiv.org/abs/2504.10658 · HTML: https://mlsanity.com/paper/2504.10658/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 29 references that are in the map.
- First posted on arXiv 14 Apr 2025.

## Builds on (most cited first, 4 of 4)

- [A Transfer Learning-based State of Charge Estimation for Lithium-Ion Battery at Varying Ambient Temperatures](https://mlsanity.com/paper/2101.03704/) (2021)
- [A Study of EV BMS Cyber Security Based on Neural Network SOC Prediction](https://mlsanity.com/paper/1806.02714/) (2018)
- [Koopman Operator-based Detection-Isolation of Cyberattack: A Case Study on Electric Vehicle Charging](https://mlsanity.com/paper/2406.16780/) (2024)
- [Isolating Signatures of Cyberattacks under Stressed Grid Conditions](https://mlsanity.com/paper/2408.02011/) (2024)

## Abstract (arXiv)

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.