用少量电池包数据微调模型，实时检测电动车电池电压攻击。
Transfer Learning Assisted XgBoost For Adaptable Cyberattack Detection In Battery Packs
- 基于XgBoost的细胞级模型，通过少量包级数据自适应微调。
- 在两种大容量电池包上，对篡改与重放攻击检测准确率达98.7%。
- 适合需要快速适配新电池配置的智能充电系统使用。
电动汽车的最优充电依赖于电池包到智能充电站云控制器间可靠的传感器数据。然而，攻击者可能在传输过程中篡改电压传感器数据，引发局部至大规模的中断。因此，必须实现实时的传感器网络攻击检测，且算法需能灵活适应不同电池组配置。为此，我们提出一种基于XgBoost的细胞级模型，仅使用少量电池包级数据进行可适配的微调，用于电压预测与残差生成。采用PyBaMM和`liionpack'工具包中的高保真充电实验数据训练与测试该检测算法。在两种大容量电池包上，评估了传感器替换与重放攻击下的性能表现。仿真结果表明，所提算法具备良好的适应性与有效性。
原文摘要 · Abstract (English)
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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