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

Obrien, Victoria Ann; Rao, Vittal S.; Trevizan, Rodrigo Daniel

doi:10.1109/tia.2023.3308548

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

Journal Article · Fri Aug 25 00:00:00 EDT 2023 · IEEE Transactions on Industry Applications

DOI:https://doi.org/10.1109/tia.2023.3308548· OSTI ID:2311650

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Texas Tech Univ., Lubbock, TX (United States); Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Texas Tech Univ., Lubbock, TX (United States)
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Grid-scale battery energy storage systems (BESSs) are vulnerable to false data injection attacks (FDIAs), which could be used to disrupt state of charge (SoC) estimation. Inaccurate SoC estimation has negative impacts on system availability, reliability, safety, and the cost of operation. In this article a combination of a Cumulative Sum (CUSUM) algorithm and an improved input noise-aware extended Kalman filter (INAEKF) is proposed for the detection and identification of FDIAs in the voltage and current sensors of a battery stack. The series-connected stack is represented by equivalent circuit models, the SoC is modeled with a charge reservoir model and the states are estimated using the INAEKF. Further, the root mean squared error of the states’ estimation by the modified INAEKF was found to be superior to the traditional EKF. By employing the INAEKF, this article addresses the research gap that many state estimators make asymmetrical assumptions about the noise corrupting the system. Additionally, the INAEKF estimates the input allowing for the identification of FDIA, which many alternative methods are unable to achieve. The proposed algorithm was able to detect attacks in the voltage and current sensors in 99.16% of test cases, with no false positives. Utilizing the INAEKF compared to the standard EKF allowed for the identification of FDIA in the input of the system in 98.43% of test cases.

View Accepted Manuscript (DOE)

Research Organization:: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: NA0003525

OSTI ID:: 2311650

Report Number(s):: SAND--2023-08566J

Journal Information:: IEEE Transactions on Industry Applications, Journal Name: IEEE Transactions on Industry Applications Journal Issue: 6 Vol. 59; ISSN 0093-9994

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

References (36)

Change Detection with Kalman Filter and CUSUM Severo, Milton; Gama, João Discovery Science https://doi.org/10.1007/11893318_25	book	January 2006
A Security Perspective on Battery Systems of the Internet of Things Lopez, Anthony Bahadir; Vatanparvar, Korosh; Deb Nath, Atul Prasad Journal of Hardware and Systems Security, Vol. 1, Issue 2 https://doi.org/10.1007/s41635-017-0007-0	journal	May 2017
An innovations approach to fault detection and diagnosis in dynamic systems Mehra, R. K.; Peschon, J. Automatica, Vol. 7, Issue 5 https://doi.org/10.1016/0005-1098(71)90028-8	journal	September 1971
Sensor fault detection and isolation for a lithium-ion battery pack in electric vehicles using adaptive extended Kalman filter Liu, Zhentong; He, Hongwen Applied Energy, Vol. 185 https://doi.org/10.1016/j.apenergy.2015.10.168	journal	January 2017
Revisiting the dual extended Kalman filter for battery state-of-charge and state-of-health estimation: A use-case life cycle analysis Wassiliadis, Nikolaos; Adermann, Jörn; Frericks, Alexander Journal of Energy Storage, Vol. 19 https://doi.org/10.1016/j.est.2018.07.006	journal	October 2018
Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs Plett, Gregory L. Journal of Power Sources, Vol. 161, Issue 2 https://doi.org/10.1016/j.jpowsour.2006.06.003	journal	October 2006
Survey of machine learning methods for detecting false data injection attacks in power systems Sayghe, Ali; Hu, Yaodan; Zografopoulos, Ioannis IET Smart Grid, Vol. 3, Issue 5 https://doi.org/10.1049/iet-stg.2020.0015	journal	October 2020
Characterization of Kalman filter residuals in the presence of mismodeling Hanlon, P. D.; Maybeck, P. S. IEEE Transactions on Aerospace and Electronic Systems, Vol. 36, Issue 1 https://doi.org/10.1109/7.826316	journal	January 2000
The unscented Kalman filtering in extended noise environments No authors listed 2009 American Control Conference https://doi.org/10.1109/ACC.2009.5159886	conference	January 2009
Energy Management and Optimization Methods for Grid Energy Storage Systems Byrne, Raymond H.; Nguyen, Tu A.; Copp, David A. IEEE Access, Vol. 6 https://doi.org/10.1109/ACCESS.2017.2741578	journal	January 2018
Cyberphysical Security of Grid Battery Energy Storage Systems Trevizan, Rodrigo D.; Obert, James; De Angelis, Valerio IEEE Access, Vol. 10 https://doi.org/10.1109/ACCESS.2022.3178987	journal	January 2022
The Cyber Security of Battery Energy Storage Systems and Adoption of Data-driven Methods Kharlamova, Nina; Hashemi, Seyedmostafa; Traholt, Chresten 2020 IEEE Third International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) https://doi.org/10.1109/AIKE48582.2020.00037	conference	December 2020
An Event Detection Approach Based on Improved CUSUM Algorithm and Kalman Filter Fang, Kaijie; Huang, Yixuan; Huang, Qifeng 2020 IEEE 4th Conference on Energy Internet and Energy System Integration (EI2) https://doi.org/10.1109/EI250167.2020.9346980	conference	October 2020
Cybersecurity for Battery Management Systems in Cyber-Physical Environments Kumbhar, Sourabh; Faika, Tasnimun; Makwana, Darshan 2018 IEEE Transportation Electrification Conference and Expo (ITEC) https://doi.org/10.1109/ITEC.2018.8450159	conference	June 2018
Battery Energy Storage System (BESS) and Battery Management System (BMS) for Grid-Scale Applications Lawder, Matthew T.; Suthar, Bharatkumar; Northrop, Paul W. C. Proceedings of the IEEE, Vol. 102, Issue 6 https://doi.org/10.1109/JPROC.2014.2317451	journal	June 2014
Detecting False Data Injection Attacks to Battery State Estimation Using Cumulative Sum Algorithm Obrien, Victoria; Trevizan, Rodrigo D.; Rao, Vittal S. 2021 North American Power Symposium (NAPS) https://doi.org/10.1109/NAPS52732.2021.9654567	conference	November 2021
On the Performance Degradation of Cyber-Physical Systems Under Stealthy Integrity Attacks Mo, Yilin; Sinopoli, Bruno IEEE Transactions on Automatic Control, Vol. 61, Issue 9 https://doi.org/10.1109/TAC.2015.2498708	journal	September 2016
Battery Energy Storage State-of-Charge Forecasting: Models, Optimization, and Accuracy Rosewater, David; Ferreira, Summer; Schoenwald, David IEEE Transactions on Smart Grid, Vol. 10, Issue 3 https://doi.org/10.1109/TSG.2018.2798165	journal	May 2019
Battery Energy Storage Models for Optimal Control Rosewater, David M.; Copp, David A.; Nguyen, Tu A. IEEE Access, Vol. 7 https://doi.org/10.1109/access.2019.2957698	journal	January 2019
False Data Injection Attacks Against State Estimation in Smart Grids: Challenges and Opportunities Youssef, El-Nasser S.; Labeau, Fabrice 2018 IEEE Canadian Conference on Electrical & Computer Engineering (CCECE) https://doi.org/10.1109/ccece.2018.8447683	conference	May 2018
Deep Learning-Based False Sensor Data Detection for Battery Energy Storage Systems Lee, Hyunjun; Bere, Gomanth; Kim, Kyungtak 2020 IEEE CyberPELS (CyberPELS) https://doi.org/10.1109/cyberpels49534.2020.9311542	conference	October 2020
Robustness of attack-resilient state estimators Pajic, Miroslav; Weimer, James; Bezzo, Nicola 2014 ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS) https://doi.org/10.1109/iccps.2014.6843720	conference	April 2014
Online SoC estimation for Li-ion batteries: A survey explore the distributed secure cloud management to battery packs Han, Fengling; See, Khay; Feng, Yong 2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA) https://doi.org/10.1109/iciea.2017.8283137	conference	June 2017
Detection of False Data Injection Attacks in Battery Stacks Using Physics-Based Modeling and Cumulative Sum Algorithm Obrien, Victoria; Rao, Vittal; Trevizan, Rodrigo D. 2022 IEEE Power and Energy Conference at Illinois (PECI) https://doi.org/10.1109/peci54197.2022.9744036	conference	March 2022
Kalman filtering in extended noise environments Diversi, R.; Guidorzi, R.; Soverini, U. IEEE Transactions on Automatic Control, Vol. 50, Issue 9 https://doi.org/10.1109/tac.2005.854627	journal	September 2005
Secure Estimation and Control for Cyber-Physical Systems Under Adversarial Attacks Fawzi, Hamza; Tabuada, Paulo; Diggavi, Suhas IEEE Transactions on Automatic Control, Vol. 59, Issue 6 https://doi.org/10.1109/tac.2014.2303233	journal	June 2014
Simultaneous Estimation of the State, Unknown Input, and Output Disturbance in Discrete-Time Linear Systems Alenezi, Badriah; Zhang, Mukai; Hui, Stefen IEEE Transactions on Automatic Control, Vol. 66, Issue 12 https://doi.org/10.1109/tac.2021.3061993	journal	December 2021
Observability Analysis and State Estimation of Lithium-Ion Batteries in the Presence of Sensor Biases Zhao, Shi; Duncan, Stephen R.; Howey, David A. IEEE Transactions on Control Systems Technology, Vol. 25, Issue 1 https://doi.org/10.1109/tcst.2016.2542115	journal	January 2017
Summation Detector for False Data-Injection Attack in Cyber-Physical Systems Ye, Dan; Zhang, Tian-Yu IEEE Transactions on Cybernetics, Vol. 50, Issue 6 https://doi.org/10.1109/tcyb.2019.2915124	journal	June 2020
Convolutional Neural Network-Based False Battery Data Detection and Classification for Battery Energy Storage Systems Lee, Hyun-Jun; Kim, Kyoung-Tak; Park, Joung-Hu IEEE Transactions on Energy Conversion, Vol. 36, Issue 4 https://doi.org/10.1109/tec.2021.3061493	journal	December 2021
A Joint Online Strategy of Measurement Outliers Diagnosis and State of Charge Estimation for Lithium-Ion Batteries Chen, Hui; Tian, Engang; Wang, Licheng IEEE Transactions on Industrial Informatics, Vol. 19, Issue 5 https://doi.org/10.1109/tii.2022.3202949	journal	May 2023
A Sensor Fault Diagnosis Method for a Lithium-Ion Battery Pack in Electric Vehicles Xiong, Rui; Yu, Quanqing; Shen, Weixiang IEEE Transactions on Power Electronics, Vol. 34, Issue 10 https://doi.org/10.1109/tpel.2019.2893622	journal	October 2019
Optimal Filtering Anderson, Brian D. O.; Moore, John B.; Eslami, Mansour IEEE Transactions on Systems, Man, and Cybernetics, Vol. 12, Issue 2 https://doi.org/10.1109/tsmc.1982.4308806	journal	January 1982
False data injection attacks against state estimation in electric power grids Liu, Yao; Reiter, Michael K.; Ning, Peng Proceedings of the 16th ACM conference on Computer and communications security - CCS '09 https://doi.org/10.1145/1653662.1653666	conference	January 2009
Battery Management System Hardware Concepts: An Overview Lelie, Markus; Braun, Thomas; Knips, Marcus Applied Sciences, Vol. 8, Issue 4 https://doi.org/10.3390/app8040534	journal	March 2018
SOC and SOH Joint Estimation of the Power Batteries Based on Fuzzy Unscented Kalman Filtering Algorithm Zeng, Miaomiao; Zhang, Peng; Yang, Yang Energies, Vol. 12, Issue 16 https://doi.org/10.3390/en12163122	journal	August 2019

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

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