基于自学习Koopman算子的电池组电压稳健估计
Resilient Voltage Estimation for Battery Packs Using Self-Learning Koopman Operator
AI总结：
本文提出一种基于自学习Koopman算子的两阶段误差校正方案，用于在传感器攻击下实现大容量电池组的高精度实时电压估计。
摘要：
基于云的电池管理系统（BMS）依赖实时电压测量数据，以确保电动汽车（EV）通过车辆到电网技术的双向充电协调。不幸的是，攻击者可以在本地BMS到云BMS的数据传输过程中篡改测量数据，导致EV充电中断。因此，为确保在这样的传感器攻击下可靠电压数据，本文提出了一种两阶段误差校正的自学习Koopman算子安全电压估计方案，用于大容量电池组。第一阶段的校正补偿Koopman近似误差。第二阶段旨在从自学习反馈中缺乏更高阶电池动力学信息而导致的误差累积中恢复，使用两种替代方法：一种适应性经验策略，利用单个电池开路电压到状态的映射来实现组级估计；另一种基于高斯过程回归的数据驱动方法，利用最小的数据训练。在使用高保真电池仿真包'PyBaMM-liionpack'的全面案例研究中，我们提出的安全估计器在变化的组拓扑、充电设置、电池老化水平和攻击策略下，能够可靠地生成高精度的实时电压估计。因此，可扩展且适应性强的算法可以轻松应用于各种电池配置和运行条件，无需进行显著修改、大量数据或传感器冗余，以确保在受损传感下的最优电动汽车充电。
英文摘要：
Cloud-based battery management systems (BMSs) rely on real-time voltage measurement data to coordinate bi-directional electric vehicle (EV) charging in vehicle-to-grid (V2G) applications. Unfortunately, an adversary can corrupt the transmitted measurement data, leading to disrupted charging/discharging of EVs. To ensure reliable voltage data under such sensor attacks, this paper proposes a secure voltage estimation scheme for large-format battery packs based on a self-learning Koopman operator with two-stage error corrections. The first stage compensates for the Koopman approximation error, and the second stage aims to recover the error amassed from the lack of higher-order battery dynamics information in the self-learning feedback. The latter is obtained from two alternative methods: an adaptable heuristic correction that leverages cell-level open-circuit voltage to state-of-charge mapping, and a Gaussian process regression-based correction. We tested our proposed secure estimator using the high-fidelity battery simulation package 'PyBaMM-liionpack', and the results show high accuracy under varying pack topologies, charging settings, battery aging, and attack policies. These findings highlight the scalability and adaptability of our algorithm to diverse battery configurations and operating conditions without requiring significant modifications, excessive data, or sensor redundancy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
