Convolutional Neural Network-Based False Battery Data Detection and Classification for Battery Energy Storage Systems
- Authors
- Lee, H.; Kim, K.; Park, J.; Bere, G.; Ochoa, J.J.; Kim, T.
- Issue Date
- Dec-2021
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Batteries; Battery energy storage systems; Circuit faults; communication failure; Convolution; convolutional neural network; cyber-attacks; Data models; deep learning; false data inject attack; fault diagnosis; Safety; sensor fault; State of charge; Temperature sensors
- Citation
- IEEE Transactions on Energy Conversion, v.36, no.4, pp.3108 - 3117
- Journal Title
- IEEE Transactions on Energy Conversion
- Volume
- 36
- Number
- 4
- Start Page
- 3108
- End Page
- 3117
- URI
- http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/40715
- DOI
- 10.1109/TEC.2021.3061493
- ISSN
- 0885-8969
- Abstract
- Battery energy storage systems (BESSs) rely on battery sensor data and communication. It is crucial to evaluate the trustworthiness of battery sensor and communication data in (BESS) since inaccurate battery data caused by sensor faults, communication failures, and even cyber-attacks can not only impose serious damages to BESSs, but also threaten the overall reliability of BESS-based applications (e.g., electric vehicles (EVs), power grids). This paper proposes a battery data trust framework that enables detect and classify false battery sensor data and communication data by using a deep learning algorithm. The proposed convolutional neural network (CNN)-based false battery data detection and classification (FBD<sup>2</sup>C) model could potentially improve safety and reliability of the BESSs. The proposed algorithm is validated by simulation and experimental results. IEEE
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Collections - College of Engineering > School of Electrical Engineering > 1. Journal Articles
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