Increasing distributed topology design implementations, uncertainties due to solar photovoltaic systems generation intermittencies, and decreasing battery costs, have shifted the direction towards integration of battery energy storage systems (BESSs) with photovoltaic systems to form renewable microgrids (MGs). Specific benefits include, but are not limited to, seamless switching and islanding operations during outages and ancillary grid services. The evolution of battery chemistries and other components has also further enhanced practicality; however, developing these multifaceted MGs involves complexity in the design process. Consequently, stakeholders rely on connection standards and operational requirements to guarantee reliable and safe grid-connected operations. This paper presents a technical overview of battery system architecture variations, benchmark requirements, integration challenges, guidelines for BESS design and interconnection, grid codes and standards, power conversion topologies, and operational grid services. In addition, a comprehensive review of the control strategies for battery equalization, energy management systems, communication, control of multiple BESSs, as well as a discussion on protection blinding and intentional islanding using BESSs is also provided. Finally, a discussion of the islanded and black start operation results for time-based analysis and standard validation of a 3MW/9MWh BESS in a grid-connected MG at the Florida International University (FIU) Engineering Campus is presented.

Lithium-Ion batteries require step-ahead information to apply contingency plans to prevent them from operating beyond their safe operation thresholds in grid storage and electric vehicle applications. Recently, machine learning techniques have been increasingly applied to forecast one such battery information metric, State-of-Charge % ( SOC ). Conventional standalone machine learning techniques applied in recent works suffer from an accuracy standpoint and thus have been replaced by high-fidelity hybrid machine learning techniques. Existing works on hybrid techniques either perform in-sample predictions, provide limited information of the underlying model, or do not consider varying charging-discharging rate (C-rate) dynamics of the battery. To address this issue, this article presents unified SOC forecasting models using a Minimized Akaike Information Criterion ( ${m}$ -AIC) algorithm. Initially, the ${m}$ -AIC algorithm is used in a standalone manner to precisely tune and search ARIMA (Autoregressive Integrated Moving Average) terms’ order automatically to accurately forecast the battery’s current, voltage, and SOC parameters in a univariate manner at a lower C-rate from given C-rates. The ${m}$ -AIC algorithm based univariate models’ ( m-AIC ) accuracy is further enhanced by first modeling and unifying with Multilayer Perceptron (MLP) and then with Nonlinear Autoregressive Neural Network with external input (NARX) neural networks respectively using the previously forecasted parameters (from m-AIC ). This results in unified-MLP ( u- MLP) and unified-NARX ( u- NARX) models which provide higher accuracy out-of-sample SOC forecasting at the lower C-rate for different optimizer variants. Results show that the proposed u- MLP and u- NARX models reduce the mean squared error to 0.1048% and 0.0175% in comparison to their standalone counterparts which show the lowest corresponding error values of 0.271% and 0.0236% respectively. Furthermore, an additional reference univariate model, Holt-Winters Exponential Smoothing (HWES), is analyzed (by replacing m-AIC ) for a comparative performance evaluation in both standalone and unified manners. Recommendations for usage of the preferred respective models in either manner for SOC forecasting are also presented based on epochs and error values.

Regulations and standards define requirements for the development of every component in an Electric Vehicle (EV). Standards are meant to ensure healthy competition among the stakeholders and enhance consumer safety. Frameworks for the development and testing of the overall EVs have been developed, but they do not address the requirements of each component individually from cybersecurity perspective. With the advent of the Internet of Things (IoT) and connected devices, cybersecurity issues and attacks are coming into notice. This evolution requires a defined framework which addresses cybersecurity issues in EVs. Till date, only a generic EV standard (SAE J3061) has been developed. Battery Management Systems (BMS) which control the key component of an EV, the battery stacks, currently do not have a standard that could address their cybersecurity issues. The Framework for Analysis, Comparison, and Test of Standards (FACTS), is presented in this paper after identifying the key player in EV cybersecurity by comparing existing frameworks. The development approach and steps applicable to EV BMS’s for obtaining technical requirements and comparing testing standards are also presented. Finally, a fault tree analysis is also developed to validate the approach based on the technical requirements considered for the framework development.

Software-Defined Networking (SDN) is a network architecture that decouples the control plane from the data plane, enabling centralized, programmable management of network traffic. SDN introduces centralized control and programmability to modern networks, improving flexibility while also exposing new security vulnerabilities across the application, control, and data planes. This paper provides a comprehensive overview of SDN security threats and defenses, covering recent developments in controller hardening, trust management, route optimization, and anomaly detection. Based on these findings, we present a comparative analysis of SDN controllers in terms of performance, scalability, and deployment complexity. This culminates in the introduction of the Cloud-to-Edge Layer Two (CELT)-Secure switch, a virtual OpenFlow-based data-plane security mechanism. CELT-Secure detects and blocks Internet Control Message Protocol flooding attacks in approximately two seconds and actively disconnects hosts engaging in Address Resolution Protocol-based man-in-the-middle attacks. In comparative testing, it achieved detection performance 10.82 times faster than related approaches.

With increased usage, individual batteries within the battery pack will begin to show disparate voltage and State of Charge (SOC) profiles, which will impact the time at which batteries become balanced. Commercial battery management systems (BMSs), used in electric vehicles (EVs) and microgrids, typically send out signals suggesting removal of individual batteries or entire packs to prevent thermal runaway scenarios. To reuse these batteries, this paper presents an analysis of an off-the-shelf Orion BMS with a constrained cycling approach to assess the voltage and SOC balancing and thermal performances of such near-to-second life batteries. A scaled-down pack of series-connected batteries in 6s1p and 6s2p topologies are cycled through a combination of US06 drive and constant charge (CC) profiles using an OPAL-RT real-time Hardware-in-the-loop (HIL) simulator. These results are compared with those obtained from the Matlab/Simulink model to present the error incurred in the simulation environment. Results suggest that the close-to-second life batteries can be reused if operated in a constrained manner and that a scaled-up battery pack topology reduces incurred error.