Detection of False Data Injection Attacks in Battery Stacks Using Input Noise-Aware Nonlinear State Estimation and Cumulative Sum Algorithms

Author:

Obrien Victoria1ORCID,Rao Vittal S.1ORCID,Trevizan Rodrigo D.2ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA

2. Energy Storage Technology and Systems Group, Sandia National Laboratories, Albuquerque, NM, USA

Funder

U.S. Department of Energy

National Technology & Engineering Solutions of Sandia, LLC

Honeywell

U.S. Department of Energy's National Nuclear Security Administration

U.S. Department of Energy, Office Electricity, Energy Storage Program

U.S. Department of Education's Program on Graduate Assistance in Areas of National Need to Texas Tech University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Control and Systems Engineering

Reference41 articles.

1. Observability Analysis and State Estimation of Lithium-Ion Batteries in the Presence of Sensor Biases

2. Battery firmware hacking: Inside the innards of a smart battery;miller;Proc BlackHat,2011

3. The unscented Kalman filtering in extended noise environments;zhou;Proc Amer Control Conf,2009

4. Battery Energy Storage System (BESS) and Battery Management System (BMS) for Grid-Scale Applications

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