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Predicting the remaining useful life (RUL) of a battery is critical to ensure the safe management of its manufacture and operation. In this study, a comprehensive investigation of the effect of data partitioning methods on RUL prediction was performed. To confirm the generality and transferability, the charge–discharge information of cathode materials with different chemical elements was adopted from previous research, including lithium iron phosphate, lithium nickel cobalt aluminum oxide, and lithium nickel cobalt manganese oxide cells. Among the partitioning procedures, the method of adding predicted data from the surrogate model to the training set exhibited the best accuracy, with an average mean absolute error (MAE) of 47 cycles. In contrast, the slide BOX method, which only used certain cycles before the test set as the training set, exhibited the worst MAE value of 60 cycles. In conclusion, the proposed data partitioning method could be implemented to predict the RUL of batteries to develop next-generation cathode materials with improved performance and stability, shorten the quality assessment time, and achieve stable predictive maintenance.

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 paper discusses overall security vulnerabilities from potential cyber-attacks and defense strategies as well as adoption of current blockchain technology in BMSs, which will be used as a cyber security 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.

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 (FBD2C) model could potentially improve safety and reliability of the BESSs. The proposed algorithm is validated by simulation and experimental results.

In this paper, the challenges and a future vision of the cyber-physical security of photovoltaic (PV) systems are discussed from a firmware, network, PV converter controls, and grid security perspective. The vulnerabilities of PV systems are investigated under a variety of cyber-attacks, ranging from data integrity attacks to software-based attacks. A success rate metric is designed to evaluate the impact and facilitate decision making. Model-based and data-driven methods for threat detection and mitigation are summarized. In addition, the blockchain technology that addresses cyber-attacks in software and cyber networks is described. Simulation and experimental results that show the impact of cyber-attacks at the converter (device) and grid (system) levels are presented. Finally, potential research opportunities are discussed for next-generation, cyber-secure power electronics systems. These opportunities include multi-scale controllability, self-/event-triggering control, artificial intelligence/machine learning, hot patching, and online security. As of today, this study will be one of the few comprehensive studies in this emerging and fast-growing area.

A high-fidelity battery model capable of accurately predicting real-time battery behavior for various operating conditions is crucial for the design and operation of battery-powered systems. Based upon a prior work of the authors, this paper proposes an enhanced hybrid battery model to provide accurate predictions for the runtime behaviors of rechargeable electrochemical battery cells under various ambient temperatures and charge/discharge current conditions. The proposed battery model is validated by simulation and experimental studies for lithium-ion battery cells in various current and temperature conditions. The proposed battery model is computationally effective for design, simulation, condition monitoring, and power management of various battery systems.

As the traditional inverters are transforming toward more intelligent inverters with advanced information and communication technologies, the cyber-attack surface has been remarkably expanded. Specifically, securing firmware of smart inverters from cyber-attacks is crucial. This paper provides expanded firmware attack surface targeting smart inverters. Moreover, this paper proposes a security module for transforming a conventional inverter to a firmware security built-in smart inverter by preventing potential malware and unauthorized firmware update attacks as well as fast automated inverter recovery from zero-day attacks. Furthermore, the proposed security module as a client of blockchain is connected to blockchain severs to fully utilize blockchain technologies such as membership service, ledgers, and smart contracts to detect and mitigate the firmware attacks. The proposed security module framework is implemented in an Internet-of-Thing (IoT) device and validated by experiments.

A battery balancing circuit is a key component of a battery management system (BMS) that ensures safe and reliable operations of the multicell battery where imbalanced cell states are present, specifically as more battery cells are aged or eXtreme fast charging (XFC) is adopted. This paper explores how to apply artificial intelligence (AI) methods on measured battery cell data from a BMS to detect a defective battery balancing circuit. Several AI algorithms are evaluated by simulation and experiments using a real BMS hardware. Among them, Convolutional Neural Network (CNN) model-based detection meets the highest accuracy and about 95.31% of F-1 score.

Cybersecurity of photovoltaic (PV) systems entails a much larger scope than just encryption and firewall of communications. For instance, integrity of data in transit between inverters and a cloud server can be compromised by authorized third-party, devices, and internal network within security perimeter (i.e., man-in-the-middle (MITM) attack). To address this challenge, this paper proposes a blockchain-based MITM attack detection method for a PV system. A breakthrough method includes screening network data, network intrusion detection, and hash comparison of in-transit data using distributed ledgers. The proposed method is implemented in Internet-of-Thing (IoT) security modules as clients of a blockchain network and validated by experiments.

Advanced Persistent Threat (APT) is a professional stealthy threat actor who uses continuous and sophisticated attack techniques which have not been well mitigated by existing defense strategies. This paper proposes an APT-style cyber-attack tested for distributed energy resources (DER) in cyber-physical environments. The proposed security testbed consists of: 1) a real-time DER simulator; 2) a real-time cyber system using real network systems and a server; and 3) penetration testing tools generating APT-style attacks as cyber events. Moreover, this paper provides a cyber kill chain model for a DER system based on a latest MITRE’s cyber kill chain model to model possible attack stages. Several real cyber-attacks are created and their impacts in a DER system are provided to validate the feasibility of the proposed security testbed for DER systems.

This paper introduces a novel health monitoring framework using Gaussian process clustering and the proposed hybrid bar-delta filter for multicell Lithium-ion battery systems. The Gaussian process clustering is applied to screen all cell voltages and cluster cells into a normal cell cluster or an abnormal cell cluster. Then, the proposed hybrid bar-delta filter is designed for battery cell health monitoring using a triple filter (i.e., bar filter) estimating states and parameters of an average cell model built by the averaged voltage and current of the normal cell cluster and hybrid delta filters (i.e., delta filters) for estimation of individual cell states and critical parameters including an internal short circuit resistance and capacity used for fault diagnosis. We validate the proposed health monitoring algorithm by using simulation results.

Quantum computing is a game-changing technology that will push the boundaries of cybersecurity and cryptography of power infrastructures including distributed energy resources (DER). This paper explores new security vulnerabilities caused by potential quantum computing attacks and potential defense strategies using quantum key distribution (QKD) and post-quantum cryptography (PQC) in DER network systems. This paper will be used as a cybersecurity baseline reference toward a quantum-safe DER system.

Cybersecurity of power electronics (PE) is increasingly essential as more systems from renewable energy systems, energy storage systems, and electric vehicle charging stations utilize PE devices to connect them into power grids with complex communication and computation systems for advanced control and situational awareness improvement in smart grid environments. However, cybersecurity research and development (RD 2) a real-time cyber system using real network systems and a server; and 3) penetration testing tools generating real Cyber-attacks as cyber events. Several real cyber-attacks are created and their impacts in a PE system are provided to validate the feasibility of the proposed security testbed.

The importance of cybersecurity for inverters has been significantly increasing as inverters become smarter by using advanced network and computing power from cyber systems. This paper explores potential attacks targeting firmware of smart inverters and how blockchain technology can be applied to mitigate the firmware modification attacks. A breakthrough method includes an automated firmware integrity check, an anomaly detection algorithm, and recovery creating patch using local distributed ledgers. The proposed method is implemented in Internet-of-Thing (IoT) security modules as clients of a blockchain network and validated by experiments. The proposed method is transformative to other networked power electronic devices.

Accurate state-of-power (SOP) estimation is critical for building battery systems with optimized performance and longer life in electric vehicles and hybrid electric vehicles. This paper proposes a novel parameter identification method and its implementation on SOP prediction for lithium-ion batteries. The extremum seeking algorithm is developed for identifying the parameters of batteries on the basis of an electrical circuit model incorporating hysteresis effect. A rigorous convergence proof of the estimation algorithm is provided. In addition, based on the electrical circuit model with the identified parameters, a battery SOP prediction algorithm is derived, which considers both the voltage and current limitations of the battery. Simulation results for lithium-ion batteries based on real test data from urban dynamometer driving schedule (UDDS) are provided to validate the proposed parameter identification and SOP prediction methods. The proposed method is suitable for real operation of embedded battery management system due to its low complexity and numerical stability.

Due to the efforts of digitization of power grids and integration of online diagnosis systems, today's power transformers have been promoted to deploy seamless communications and information technology, which potentially presents growing threats from cyber-attacks. This paper provides security vulnerabilities and potential threats of power transformers in cyber-physical environments through a security threat modeling methodology. The proposed security threat modeling includes three steps: 1) system identification, 2) threat modeling, 3) and attack modeling. The devised security threat model can identify potential vulnerabilities and threats as well as simulate attack success rate, which will be used to develop device-level defense strategies to protect power transformers from malicious cyber-attacks, ensuring the secure utilization of the cyber-physical power transformers in modern power infrastructures.

Increasing penetration of networked distributed energy resources (DER) in electric power systems managed by multiparty are expanding the power system attack surface. An outstanding threat is sophisticated attackers who possess extensive knowledge of the systems and keep trying to attack DER systems, which have not been well mitigated by existing defense strategies. This paper explores a cyber kill chain model for a DER aggregation system based on a latest METRE's cyber kill chain model to model possible attack phases and requirement of defensive actions per each attack step, eventually toward detecting and neutralizing the intent of the sophisticated attackers.

The rapidly increasing expansion of distributed energy resources (DER), such as renewable energy systems and energy storage systems into the electric power system and the integration of advanced information and communication technologies enable DER owners to participate in the electricity market for grid services. For more efficient and reliable power system operation, the concept of peer-to-peer (P2P) energy trading has recently been proposed. The adoption of blockchain technology in P2P energy trading has been considered to be the most promising solution enabling secure smart contracts between prosumers and users. However, privacy concerns arise because the sensitive data and transaction records of the participants, i.e., the prosumers and the distribution system operator (DSO), become available to the blockchain nodes. Many efforts have been made to resolve this issue. A recent breakthrough in a P2P energy trading system on an Ethereum blockchain is that all bid values are encrypted using functional encryption and peer matching for trading is performed securely on these encrypted bids. Their protocol is based on a method that encodes integers to vectors and an algorithm that securely compares the ciphertexts of these vectors. However, the comparison method is not very efficient in terms of the range of possible bid values because the amount of computation grows linearly according to the size of this range. This paper addresses this challenge by proposing a new bid encoding algorithm called dual binary encoding, which dramatically reduces the amount of computation as it is only proportional to the square of the logarithm of the size of the encoding range. Moreover, we propose a practical mechanism for rebidding the remaining amount caused when the amounts from the two matching peers are not equal. Finally, the feasibility of the proposed method is evaluated by using a virtual energy trade testbed and a private Ethereum blockchain platform.

This paper proposes a cooperative control approach using blockchain technology for solar inverters in a photovoltaic system. Moreover, a co-simulation method for smart inverters and a blockchain network is studied. The blockchain assisted-smart inverter (BASI) consists of a solar inverter and an internet of things device as a client node of a blockchain network, which can fully utilize emerging blockchain technologies such as distributed ledger, security functions, and smart contract. The control includes a primary-level droop control in a BASI and smart contract-defined secondary-level supervisory control in a secured blockchain network. The concept of the proposed smart contract-defined control approach is validated by simulation studies using the cyber-physical co-simulation testbed built-in MATLAB/Simulink in a PC interfacing with a Hyperledger-Fabric blockchain software implemented in a PC.

Battery energy storage systems are facing risks of unreliable battery sensor data which might be caused by sensor faults in an embedded battery management system, communication failures, and even cyber-attacks. It is crucial to evaluate the trustworthiness of battery sensor data since inaccurate sensor data could lead to not only serious damages to battery energy storage systems, but also threaten the overall reliability of their applications (e.g., electric vehicles or power grids). This paper introduces a battery sensor data trust framework enabling detecting unreliable data using a deep learning algorithm. The proposed sensor data trust mechanism could potentially improve safety and reliability of the battery energy storage systems. The proposed deep learning-based battery sensor fault detection algorithm is validated by simulation studies using a convolutional neural network.

This paper proposes a blockchain-powered battery data management and analytics platform which fully utilizes blockchain technology for battery health monitoring in battery energy storage systems (e.g., electric vehicles) and accelerating battery cell development. The proposed platform consists of five distinct components: 1) blockchain clients using application SDK such as battery energy storage systems and a battery tester; 2) a multichannel blockchain network for enhanced data security, privacy, and management; 3) data preprocessing; 4) data analytics engine executing analytics tools and health monitoring algorithms; and 5) user-friendly service visualization. The proposed platform is implemented in an AWS cloud and tested using real-time battery data from a battery energy storage system (BESS) client and batch data from a battery cell tester client. The results show that each blockchain channels can independently manage and analyze data sources from two clients in a blockchain network. The proposed platform will enable a new level of data security and privacy preserved battery intelligence system used for both battery product development and lifetime battery health monitoring.

Blockchain technology has many beneficial properties that can advance electric vehicles in cyber-physical environments, especially for security-related purposes for EV battery management systems (BMSs). This paper explores firmware security vulnerabilities of a current BMS through reverse engineering and how the blockchain technology can be applied toward a next-generation BMS by managing critical activities and tasks including BMS firmware security check, recovery, and patch generation. A breakthrough method for blockchain-based automated detection of the firmware vulnerabilities and a patch generation is implemented in Internet-of-Thing (IoT) security modules as nodes of a blockchain network and validated by experiments. The proposed methods transformative to other cyber-physical system applications.