The practical application of incipient fault detection in Battery Energy Storage Systems (BESS) is challenged by complex fault characteristics, limited data, and the need for scalable real-time processing. Early faults like SEI growth are difficult to detect with conventional methods but critical for preventing catastrophic failures.

In response, we have developed the Battery Incipient Fault Digital Twin (BIF-DT), an integrated framework combining an electrochemical model and a physics-based circuit model. This validated platform enables high-fidelity, efficient simulation of internal processes for accurate early fault detection. Looking ahead, it holds strong promise for improving BESS reliability and safety, with future work focusing on enhancing its scalability and real-time capabilities for deployment as a proactive diagnostic tool in commercial and grid-scale systems.

Publications:

[1] Ziqi Wang and Mo-Yuen Chow, “Battery Modeling of SEI and Metal Dendrite Growth: A Transmission Line Circuit Framework with Genetic Algorithm-Identified Parameters ,” 2025 IEEE 20th Conference on Industrial Electronics and Applications (ICIEA), Yantai, China, 2025, pp. 1-6, doi: 10.1109/ICIEA65512.2025.11149030.

[2] Junya Shao, Mo-Yuen Chow, Zhiping Tan and Huiqin Jin, “Solid Electrolyte Interface Growth Fault Modeling for Battery State of Health Simulation,” 2025 IEEE International Conference on Industrial Technology (ICIT), Wuhan, China, 2025, pp. 1-6, doi: 10.1109/ICIT63637.2025.10965289.

[3] Ziqi Wang, Mo-Yuen Chow, Zhiping Tan and Huiqin Jin, “Modelling of the Solid Electrolyte Interface Growth Using Physics-Based Equivalent Circuit Model,” 2025 IEEE International Conference on Industrial Technology (ICIT), Wuhan, China, 2025, pp. 1-6, doi: 10.1109/ICIT63637.2025.10965250.

In the wake of a natural disaster, such as a hurricane, earthquake, or flood, the immediate aftermath often leaves communities devastated, with their energy infrastructure severely compromised or completely offline. Access to reliable energy sources is crucial for various aspects of disaster relief efforts, from providing power to emergency shelters to ensuring critical infrastructure like hospitals and communication networks remain operational. Consequently, the distributed control technologies have received a lot of interest from academia and industry recently.

To address the limitations of current disaster response strategies, our research lab (ADAC) is advancing the Smart Collaborative Distributed Energy Management System (S-CoDEMS) — an AI-enhanced framework designed to enhance power restoration during disasters. S-CoDEMS dynamically adapts distributed energy resource (DER) dispatching in the physical layer and routing in the cyber layer to achieve optimal performance of disaster relief microgrids under rapidly changing conditions. By leveraging AI-driven methods coupled with consensus mechanisms, S-CoDEMS strengthens system resilience against both natural and human-induced disruptions. Through intelligent coordination and integration of distributed energy resources into disaster recovery protocols, this approach aims to accelerate power restoration and improve overall recovery efficiency in post-disaster scenarios.

Publications:

[1]. A. Joshi, S. Capezza, A. Alhaji and M. -Y. Chow, “Survey on AI and Machine Learning Techniques for Microgrid Energy Management Systems,” in IEEE/CAA Journal of Automatica Sinica, vol. 10, no. 7, pp. 1513-1529, July 2023, doi: 10.1109/JAS.2023.123657.

[2]. A. Joshi, T. Wu and M. -Y. Chow, “Hierarchical Ensemble Based Clustering For Networked Microgrids,” IECON 2025 – 51st Annual Conference of the IEEE Industrial Electronics Society, Madrid, Spain, 2025.

[3] Tian H, Joshi A, Chow M Y, et al. Microgrid Communication Network Optimization During Disaster Relief: A Connectivity Rank Index Based Approach[C]//2025 IEEE International Conference on Industrial Technology (ICIT). IEEE, 2025: 1-6.