Deep Learning-Based False Sensor Data Detection for Battery Energy Storage Systems

Lee, Hyunjun; Bere, Gomanth; Kim, Kyungtak; Ochoa, Justin J.; Park, Joung‐Hu; Kim, Taesic

doi:10.1109/cyberpels49534.2020.9311542

Cited by 7 publications

(4 citation statements)

References 21 publications

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“…In this regard, one effective solution is to apply ML techniques to analyze large volumes of BMS data and to identify patterns of attacked and nonattacked conditions and distinguish between them [67]. An example of ML-based attack detection is presented in [68], in which an ML-based trust framework for battery sensory data was proposed. The framework is based on false sensor data detection (FSDD) which enables detection of undependable battery data using deep learning algorithms.…”

Section: Cbms Attack Detection Methods and Mitigation Strategiesmentioning

confidence: 99%

“…It was initially developed to secure cryptocurrency transactions, but lately, it has been explored for new cloud applications including CBMSs. Concerning the CBMSs, it has been discussed that the blockchain can be used to enhance both software and hardware aspects [68]. For instance, the blockchain can be used to manage critical activities related to the transaction and sharing of battery data between the CBMS and the BMS terminal nodes [52].…”

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confidence: 99%

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Cyber-Physical Cloud Battery Management Systems: Review of Security Aspects

et al. 2023

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Battery management systems (BMSs) are critical to ensure the efficiency and safety of high-power battery energy storage systems (BESSs) in vehicular and stationary applications. Recently, the proliferation of battery big data and cloud computing advancements has led to the development of a new generation of BMSs, named Cloud BMS (CBMS), aiming to improve the performance and safety of BESSs. The CBMS is a cyber-physical system with connectivity between the physical BMS and a cloud-based virtual BMS, which is realized through a communication channel such as Internet of Things. Compared to the traditional BMS, the CBMS offers significantly higher computational resources, leveraging the implementation of advanced digital twin models and best-in-class algorithms in the BMS software, which will provide superior performances. However, as for any other CPS, the CBMS creates vulnerabilities against cyberattacks and if not properly secured, could end up damaging the BESS and/or causing dangerous, expensive, and life-threatening situations. Cybersecurity of the CBMSs has thus become a trending topic and several works have been published in this area in recent years. This paper conducts a scoping review to address different topics related to BMS cybersecurity. The CBMS architecture is presented, and the potential cyberattack surfaces are identified. Different possible attack scenarios, including attack points, attack types, and their impact at the component level (BMS and BESS) and system level (vehicle or grid), are discussed. In addition, the paper provides a review of potential countermeasures to protect the CBMS against cyberattacks. The paper also includes a review of the applicable standards and regulations that relate to this trending topic. Finally, based on the reviewed gaps, potential future research domains on BMS cybersecurity topics are identified and presented at the end of the paper.

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Section: Cbms Attack Detection Methods and Mitigation Strategiesmentioning

confidence: 99%

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confidence: 99%

Cyber-Physical Cloud Battery Management Systems: Review of Security Aspects

et al. 2023

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“…This parameter involves adopting machine learning (ML) interpretable methodologies for analyzing characteristics of chemical materials, energy systems, and batteries. It captures various dimensions of "Materials for Energy Storage and Systems," including using interpretable machine learning (IML) for estimating decomposition enthalpy that measures the durability of Chevrel phases for batteries (Singstock et al, 2021), forecasting material characteristics using IML models to provide transparency (Allen and Tkatchenko, 2022), and trustworthy approach using DL for data evaluation of battery energy storage systems (Lee et al, 2020). Additional dimensions comprise modeling and explainability of the formation energy of inorganic chemicals using DL (Huang and Ling, 2020) and developing a predictive model using IML to predict the Fermi energy level needed to build electrically conductive materials, heterostructures, and devices (Motevalli et al, 2022).…”

Section: Materials For Energy Storage and Systemsmentioning

confidence: 99%

AI explainability and governance in smart energy systems: A review

2023

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Traditional electrical power grids have long suffered from operational unreliability, instability, inflexibility, and inefficiency. Smart grids (or smart energy systems) continue to transform the energy sector with emerging technologies, renewable energy sources, and other trends. Artificial intelligence (AI) is being applied to smart energy systems to process massive and complex data in this sector and make smart and timely decisions. However, the lack of explainability and governability of AI is a major concern for stakeholders hindering a fast uptake of AI in the energy sector. This paper provides a review of AI explainability and governance in smart energy systems. We collect 3,568 relevant papers from the Scopus database, automatically discover 15 parameters or themes for AI governance in energy and elaborate the research landscape by reviewing over 150 papers and providing temporal progressions of the research. The methodology for discovering parameters or themes is based on “deep journalism,” our data-driven deep learning-based big data analytics approach to automatically discover and analyse cross-sectional multi-perspective information to enable better decision-making and develop better instruments for governance. The findings show that research on AI explainability in energy systems is segmented and narrowly focussed on a few AI traits and energy system problems. This paper deepens our knowledge of AI governance in energy and is expected to help governments, industry, academics, energy prosumers, and other stakeholders to understand the landscape of AI in the energy sector, leading to better design, operations, utilisation, and risk management of energy systems.

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“…Two possible strategies can limit the danger of cyberattacks and undue access to sensible data [122][123][124]:…”

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confidence: 99%

Exploring the Synergy of Artificial Intelligence in Energy Storage Systems for Electric Vehicles

Miraftabzadeh,

Longo,

Di Martino

et al. 2024

Electronics

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The integration of Artificial Intelligence (AI) in Energy Storage Systems (ESS) for Electric Vehicles (EVs) has emerged as a pivotal solution to address the challenges of energy efficiency, battery degradation, and optimal power management. The capability of such systems to differ from theoretical modeling enhances their applicability across various domains. The vast amount of data available today has enabled AI to be trained and to predict the behavior of complex systems with a high degree of accuracy. As we move towards a more sustainable future, the electrification of vehicles and integrating electric systems for energy storage are becoming increasingly important and need to be addressed. The synergy of AI and ESS enhances the overall efficiency of electric vehicles and plays a crucial role in shaping a sustainable and intelligent energy ecosystem. To the best of the authors’ knowledge, AI applications in energy storage systems for the integration of electric vehicles have not been explicitly reviewed. The research investigates the importance of AI advancements in energy storage systems for electric vehicles, specifically focusing on Battery Management Systems (BMS), Power Quality (PQ) issues, predicting battery State-of-Charge (SOC) and State-of-Health (SOH), and exploring the potential for integrating Renewable Energy Sources with EV charging needs and optimizing charging cycles. This study examined all topics to identify the most commonly used methods, which were analyzed based on their characteristics and potential. Future trends were identified by exploring emerging techniques introduced in recent literature contributions published since 2017.

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Deep Learning-Based False Sensor Data Detection for Battery Energy Storage Systems

Cited by 7 publications

References 21 publications

Cyber-Physical Cloud Battery Management Systems: Review of Security Aspects

Cyber-Physical Cloud Battery Management Systems: Review of Security Aspects

AI explainability and governance in smart energy systems: A review

Exploring the Synergy of Artificial Intelligence in Energy Storage Systems for Electric Vehicles

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