Performance of the current battery management systems is limited by the on-board embedded systems as the number of battery cells increases in the large-scale lithium-ion (Li-ion) battery energy storage systems (BESSs). Moreover, an expensive supervisory control and data acquisition system is still required for maintenance of the large-scale BESSs. This paper proposes a new cloud-based battery condition monitoring and fault diagnosis platform for the large-scale Li-ion BESSs. The proposed cyber-physical platform incorporates the Internet of Things embedded in the battery modules and the cloud battery management platform. Multithreads of a condition monitoring algorithm and an outlier mining-based battery fault diagnosis algorithm are built in the cloud battery management platform (CBMP). The proposed cloud-based condition monitoring and fault diagnosis platform is validated by using a cyber-physical testbed and a computational cost analysis for the CBMP. Therefore, the proposed platform will support the on-board health monitoring and provide an intelligent and cost-effective maintenance of the large-scale Li-ion BESSs.

Lithium-ion (Li-ion) batteries are a key energy storage component in various electrical and electronic systems, such as mobile phones and electric vehicles. A properly designed battery management system (BMS) is crucial to guarantee the safety, reliability, and optimal performance of the battery, as well as to interconnect the battery systems with each other and external systems through communication channels. However, security threats of the Li-ion battery systems are often overlooked by BMS developers in the design phase. The cybersecurity of BMSs is an essential factor to consider as more battery systems require internet connectivity for functionality, such as intelligent monitoring, control, and maintenance. This article discusses the overall security vulnerabilities from potential cyber-attacks and defense strategies, as well as the adoption of current blockchain technology in BMSs, which will be used as a cybersecurity baseline reference to BMS developers. The implementation of blockchain technology is promising to protect BMSs from malicious cyber-physical attacks and ensure the secure utilization of battery systems for numerous applications in cyber-physical environments.

Recently, devices in real-time systems, such as residential facilities, vehicles, factories, and social infrastructure, have been increasingly connected to communication networks. Although these devices provide administrative convenience and enable the development of more sophisticated control systems, critical cybersecurity concerns and challenges remain. In this paper, we propose a hybrid anomaly detection method that combines statistical filtering and a composite autoencoder to effectively detect anomalous behaviors possibly caused by malicious activity in order to mitigate the risk of cyberattacks. We used the SWaT dataset, which was collected from a real water treatment system, to conduct a case study of cyberattacks on industrial control systems to validate the performance of the proposed approach. We then evaluated the performance of the proposed hybrid detection method on a dataset with two time window settings for the composite autoencoder. According to the experimental results, the proposed method improved the precision, recall, and F1-score by up to 0.008, 0.067, and 0.039, respectively, compared to an autoencoder-only approach. Moreover, we evaluated the computational cost of the proposed method in terms of execution time. The execution time of the proposed method was reduced by up to 8.03% compared to that of an autoencoder-only approach. Through the experimental results, we show that the proposed method detected more anomalies than an autoencoder-only detection approach and it also operated significantly faster.

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 ² C) model could potentially improve safety and reliability of the BESSs. The proposed algorithm is validated by simulation and experimental results.

Quantum computing is a game-changing technology that affects modern cryptography and security systems including distributed energy resources (DERs) systems. Since the new quantum era is coming soon in 5–10 years, it is crucial to prepare and develop quantum-safe DER systems. This paper provides a comprehensive review of vulnerabilities caused by quantum computing attacks, potential defense strategies, and remaining challenges for DER networks. First, new security vulnerabilities and attack models of the cyber-physical DER systems caused by quantum computing attacks are explored. Moreover, this paper introduces potential quantum attack defense strategies including Quantum Key Distribution (QKD) and Post-Quantum Cryptography (PQC), which can be applied to DER networks and evaluates defense strategies. Finally, remaining research opportunities and challenges for next-generation quantum-safe DER are discussed.

With high penetration of distributed energy resources (DERs), power systems are increasingly transforming into distributed power grids, which provide grid automation, decarbonization, and decentralization of critical assets. Smart inverters are key power-electronic devices that connect renewable energy and energy storage equipment to power grids. DER includes several intelligent grid functions, such as fault ride through, grid-voltage support, and reactive-power compensation, typically with real-time remote access, data exchange, and seamless over-the-air firmware updates in a cyber-physical environment. However, cybersecurity concerns arise due to extensive information exchange among DER and multiple stakeholders (e.g., utilities, aggregators, vendors, operators, and owners). Therefore, smart inverters account for a growing attack surface for the power grid. This article reviews the cybersecurity best practices and current recommendations for smart inverters and explores emerging cyber threats for smart inverters, including malware attacks and hardware attacks. Finally, we propose a new smart inverter security and resilience framework for developing cyber-resilient smart inverters against the advanced/future threat actors. This article establishes a resilience-by-design baseline reference for smart inverter cybersecurity teams, which bridges the gap between cybersecurity and power-electronics’ communities.