Resilient Voltage Estimation for Battery Packs Using Self-Learning Koopman Operator

Abstract

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.

9 figures, 2 tables