Through multidisciplinary doctoral education in Cybersecurity for Electric Power Systems (CEPSE), North Carolina State University (NCSU) will increase its commitment to graduate training in two areas designated by the GAANN Program as critical to national need: Cybersecurity and Electrical Engineering. The goal of is to enlarge the pool of U.S. citizens and permanent residents who will pursue teaching and research careers in cybersecurity for electric power systems, thereby promoting workforce development and technological innovation impacting, national security, energy security, and environmental sustainability.

In this project, we will develop high temperature (> 600 C) embedded/integrated sensors (HiTEIS) for wireless monitoring of reactor and fuel cycle systems. HT pressure sensors, vibration sensors and liquid level sensors will be designed, fabricated, embedded and characterized, followed by nuclear structure integration and evaluations. The proposed technique will likely be used to enhance the safety and efficiency of nuclear power systems.

To enhance the microgrid������������������s scalability, reliability, and resilience, this project aims to develop a collaborative and distributed energy management system (CoDEMS) that can determine globally optimal control commands without the need for a central coordinator. This phase of the project will develop a 2-node system that can provide an optimal charging and discharging schedule of the energy storage system using information from the grid support point and load forecast.

This Accelerating Innovation Technology Translation project focuses on translating a novel Smart Battery Gauge technology to fill the increasing need for accurate battery state of charge (SOC) and remaining useful life (RUL) estimations for stationary energy storage of renewable energy. Although the large-scale integration of renewable energy into the power grid is driving for the growing demand in stationary energy storage, the reliability and safety concern remains as the major barrier that prevents its widespread deployment. The Smart Battery Gauge technology aims to improve the reliability and safety of energy storage systems. As compared to the existing battery monitoring methods, the reliably accurate estimation date generated by this technology will provide systems management and operations with the advantages of improved energy storage system efficiency, reliability, cost-effectiveness, longer lifespan, and reduced capital and operation/maintenance costs. In order to determine the technical feasibility and functional requirements of applying this technology in the stationary energy storage market and provide a commercially valuable solution, this project will result in a software prototype of the Smart Battery Gauge technology to demonstrate its real-time adaptive battery SOC and RUL estimations with market-leading accuracy and reliability, and its flexible customization for multiple different battery chemistries. The objectives of this project are to: 1) Extract the relevant data and models that are needed for RUL estimation, 2) Design the adaptive predictive battery RUL estimation algorithm that can adjust battery parameters with real-time measurement feedback, and 3) Implement and demonstrate the Smart Battery Gauge technology prototype in software and benchmark its performance with existing approaches.

The penetration of energy storage into the grid is low due to the inability to accurately and constantly assess the State of Charge (SOC) and State of Health (SOH). The Smart Battery Gauge can constantly monitor the batteries to provide live and accurate updates about the battery������������������s status to alleviate these issues

The objective of this proposal is to formulate a novel methodology for creating secure algorithms in cyber-physical systems and to develop metrics for evaluating the security of composed systems. Cyber-physical systems are composed of interconnected, semi-autonomous devices. The inherently open nature of a CPS implies a susceptibility to attacks that differ fundamentally from conventional cyber attacks. CPS-specific attack vectors exist as purely cyber, cyber-enabled physical attacks, and physically enabled cyber attacks. As such, the endpoints may be fundamentally unsecurable (such as the sensed information from physical resources) or may be compromised (as in computational resources). Creating a secure communications channel between two nodes is inadequate if one of the endpoints of the communication is insecure. Therefore, new methodologies are needed to ensure that the system is protected in the presence of open information flows from physical resources and possibly malicious entities inside the system.

The energy storage systems at Butler Farms are currently being monitored and maintained by NCEMC using the Smart Battery Gauge developed during the Smart Battery Gauge for Continuous Battery Assessment at Butler Farm and Smart Battery Gauge for Continuous Battery Health Assessment at Butler Farm projects. The Smart Battery Gauge was developed to continuously monitor and provide live feedback about the State of Charge and State of Health of the energy storage system at a rack level. This project will develop a Remaining Useful Life (RUL) Assessment algorithm to assist in determining State of Function (SOF).

The energy storage systems at Butler Farms are currently being monitored and maintained by NCEMC using the Smart Battery Gauge developed during Phase I of this project. The Smart Battery Gauge was developed to continuously monitor and provide live feedback about the State of Charge of the energy storage system at a rack level. This project will develop a State of Health (SOH) estimation algorithm that can provide meaningful insights into appropriate energy storage system operation in the microgrid to increase Remaining Useful Life (RUL) and State of Function (SOF).

The energy storage systems at Butler Farms are currently being monitored and maintained by NCEMC. This project is to develop a Smart Battery Gauge to constantly monitor the energy storage setup and provide live and accurate feedback regarding battery State of Charge (SOC), State of Health (SOH), Remaining Useful Life (RUL), and State of Function (SOF).

Online Adaptive Parameter Identification and SoC Co-Estimation This project will develop 1. A battery model for online parameter identification and SOC co-estimation 2. Online parameter identification and SOC co-estimation including battery degradation 3. Online parameter identification and SOC co-estimation algorithm considering noise 4. Online parameter identification and SOC co-estimation algorithm considering temperature 5. The algorithm integration test in battery pack