The practical application of incipient fault detection and diagnosis in Battery Energy Storage Systems (BESS) faces significant challenges, including complex fault characteristics, limited data availability, safety concerns, and the need for scalable real-time processing. Incipient faults such as Solid Electrolyte Interface (SEI) growth and metal dendrite growth are particularly difficult to detect using conventional methods, yet they are critical to preventing catastrophic battery failures. These challenges highlight the urgent need for advanced, reliable diagnostic tools that can operate accurately under real-world conditions, ensuring both the safety and longevity of BESS installations.
In response to these challenges, we have been develping the Battery Incipient Fault Digital Twin (BIF-DT), an advanced integrated framework designed to simulate and detect early-stage battery degradation. The BIF-DT platform uniquely combines an electrochemical model with a physics-based circuit model, enabling a comprehensive and multi-faceted approach to fault diagnosis. This dual-model structure allows for high-fidelity simulation of internal battery processes while maintaining computational efficiency suitable for practical applications. To date, development of the BIF-DT has progressed significantly, with both constituent models successfully validated using publicly available datasets, demonstrating remarkable accuracy in detecting incipient faults. This progress has been documented through multiple publications, establishing a solid foundation of scientific credibility for the platform. In the IEEE IESES 2025 tutorial, our self-developed BIF-DT is also presented to the audience.
Looking forward, the BIF-DT platform holds considerable promise for enhancing the reliability and safety of battery energy storage systems across various applications. The validated accuracy of our models provides a strong basis for transitioning this technology from research to real-world implementation. Future work will focus on refining the platform’s scalability and real-time processing capabilities, ultimately aiming to deploy the BIF-DT as a proactive diagnostic tool that can predict and prevent battery failures in commercial and grid-scale energy storage systems, thereby contributing to more secure and efficient renewable energy integration.
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 an era where climate-driven disasters and energy disruptions are becoming more frequent and severe, building a power grid that is intelligent, resilient, and adaptive is no longer a luxury—it’s a necessity. Conventional, centrally controlled energy systems often fall short in times of crisis, struggling with rigidity, bottlenecks, and single points of failure.
At ADAC Lab, we are pioneering the Hierarchical Collaborative Distributed Energy Management System (H-CoDEMS)—a cutting-edge framework designed to revolutionize how microgrids operate and coordinate. H-CoDEMS adopts a self-organizing, hierarchical architecture that enables scalable, fast, and resilient energy management across distributed energy resources. By leveraging situational awareness and a distributed consensus-based control strategy, H-CoDEMS allows microgrids to make intelligent, cooperative decisions in real time. This makes it exceptionally effective in a range of critical applications:
Disaster Relief: Rapidly restores power to essential services such as hospitals, emergency operations centers, and communication networks, even when centralized infrastructure is compromised.
Networked Microgrids: Enables seamless coordination and reconfiguration across interconnected microgrids, enhancing resilience and operational efficiency.
Virtual Power Plants (VPPs): Coordinates distributed energy resources to act as a unified, flexible grid asset, improving reliability and grid balance.
The YD-EMSC 2025 Summer Seminar of the Yangtze River Delta Energy Management System Alliance, the fourth edition of this annual event, was successfully held on June 7-8, 2025. Organized by Professor Mo-Yuen Chow of Shanghai Jiao Tong University, the seminar focused on the latest advancements in energy management systems, bringing together leading experts and scholars to engage in meaningful discussions on industry developments.
The Yangtze Delta Energy Management Systems Consortium (YD-EMSC) is a collaborative effort comprising five distinguished universities in the Yangtze Delta region: Nanjing University of Posts and Telecommunications (NJUPT), South East University (SEU), Shanghai Jiao Tong University (SJTU), Zhejiang University (ZJU), and Zhejiang University of Technology (ZJUT).
Key highlights of the seminar included:
Industrial Perspective on EMS: Experts from Nanjing University of Posts and Telecommunications, Guodian Nanzi Automation, NR Electric, and CATL shared practical insights and innovations in energy management.
Academic Perspectives on EMS: Leading scholars, including Professor Cao Xianghui from Southeast University and Professor Guo Fanghong from Zhejiang University of Technology, presented cutting-edge research and explored future trends in energy management systems.
Alliance Strategy Discussion: The YD-EMSC Alliance outlined strategic goals for future research collaborations, industry influence, and member growth, shaping the long-term development of EMS in the region.
Talent Development: A dedicated session on the career paths and development opportunities for young professionals in EMS featured global academic leaders offering valuable guidance and insights.
The workshop fostered cross-industry collaboration, academic research, and talent development, reinforcing the commitment to advancing energy management system innovation in the Yangtze River Delta region.
A Vision for a Resilient Energy Future:
YD-EMSC’s mission is to pioneer distributed and collaborative technologies for reconfigurable, reliable, scalable, and resilient energy management systems of evolving networked microgrids, particularly in contexts of disaster relief and emergent dynamic adhoc power systems.
With a total of twenty-eight dedicated faculty members, YD-EMSC is set to make significant strides in the field of energy management systems.
Campus directors:
NJUPT: Yue Dong (岳东) Professor, Dean of Automation/AI college, IEEE Fellow
SEU: Cao Xianghui ( 曹 向 辉 )Professor in Automation department
SJTU: Chow Mo-Yuen (周武元) Professor in JI, Director of ASPS research center in GIFT, IEEE Fellow
ZJU Power Electronics: Ma Hao (⻢皓)Professor in Electrical Engineering department
ZJU Systems and Control: Chen Jiming (陈积明) Professor, Vice-chancellor of ZJUT, IEEE Fellow
ZJUT: Guo Fanghong (郭方洪)Associate Professor, Director of Automation Department
Campus coordinators:
NJUPT: Zhang Huaipin ( 张 怀 品 ) Associate research professor, Institute of Advanced Technology
SEU: Yang Chaoqun (杨超群) Associate research professor, Department of Automation
SJTU: Li Yiyan (李亦言)Assistant professor, College of Smart Energy
ZJU: Deng Ruilong (邓瑞⻰)Research Professor, Dean Assistant of Control Science & Engineering Department
ZJUT: Guo Fanghong (郭方洪)Associate Professor, Director of Automation Department
The electric power system is vital to national infrastructure, yet vulnerable to natural disasters. Traditional centralized control strategies are prone to single points of failure and high computational costs. In contrast, distributed energy management systems (EMS) offer improved resilience by reducing failure risks and easing communication bottlenecks—crucial for emergency response.
During disasters, when grid infrastructure is damaged or destroyed, distributed EMS-enabled microgrids can restore power to critical facilities like hospitals and emergency centers. However, these systems face challenges in scalability, convergence speed, and real-time reconfigurability—especially in large-scale, time-sensitive scenarios.
To overcome the state of the art challenges, our research lab (ADAC) has proposed a novel Dynamic Energy Management System (D-EMS) framework for microgrids to perform self-reorganization for the Hierarchical Collaborative Distributed Energy Management Systems (H-CoDEMS) automatically and in a hierarchical structure to achieve log-scale scalability, fast convergence and reconfigurability. Leveraging situational awareness and intelligent management, the system adopts a hierarchical distributed consensus framework to enable fast and collaborative decision-making. This ensures efficient power system scheduling operations, allowing for rapid power restoration and ultimately saving lives.
To address the challenges posed by faults in Battery Energy Storage Systems (BESS), the ADAC Lab has developed advanced monitoring and fault detection solutions.
We have developed a comprehensive Battery Incipient Fault Detection and Diagnosis (BIF-DD) Platform, which utilizes real-time monitoring and advanced algorithms for early fault detection and root-cause diagnosis. This platform, implemented on a Raspberry Pi, performs parameter identification to visualize battery fault statuses based on data from a Battery Fault Simulator.
Current Developments:
The platform is continuously evolving with the following enhancements:
Expansion to Multiple Fault Types: The system is being expanded to detect a broader range of faults, enhancing the comprehensiveness of the BIF-DD platform.
Integration with AI and Big Data: We are integrating advanced AI technologies and large-scale models to further improve fault prediction accuracy and system intelligence.
Connection with Power Systems and Microgrids (MGs): The platform will be linked with power systems and microgrids, enabling real-time communication of BESS status for optimized energy dispatch and grid management.
Through these advancements, the BIF-DD platform is poised to provide a robust solution for proactive BESS maintenance, ensuring safe, reliable, and efficient energy storage operations.
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).(accepted)
[2] Skieler Capezza and Mo-Yuen Chow, “Real-Time SOH Estimation via Online Identification of Temperature and SOC Dependent Electric Circuit Model Parameters,” in IECON 2025- 51st Annual Conference of the IEEE Industrial Electronics Society, 2025.(accepted)
[3] 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.
[4] 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.
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