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 ensure coordinated bi-directional charging of electric vehicles (EVs) with vehicle-to-grid technology. Unfortunately, an adversary can corrupt the measurement data during transmission from the local-BMS to the cloud-BMS, leading to disrupted EV charging. Therefore, to ensure reliable voltage data under such sensor attacks, this paper proposes a two-stage error-corrected self-learning Koopman operator-based secure voltage estimation scheme for large-format battery packs. The first stage of correction compensates for the Koopman approximation error. The second stage aims to recover the error amassing from the lack of higher-order battery dynamics information in the self-learning feedback, using two alternative methods: an adaptable empirical strategy that uses cell-level knowledge of open circuit voltage to state-of-charge mapping for pack-level estimation, and a Gaussian process regression-based data-driven method that leverages minimal data-training. During our comprehensive case studies using the high-fidelity battery simulation package 'PyBaMM-liionpack', our proposed secure estimator reliably generated real-time voltage estimation with high accuracy under varying pack topologies, charging settings, battery age-levels, and attack policies. Thus, the scalable and adaptable algorithm can be easily employed to diverse battery configurations and operating conditions, without requiring significant modifications, excessive data or sensor redundancy, to ensure optimum charging of EVs under compromised sensing.