Control and Management

Battery Incipient Fault Digital Twin (BIF-DT)

by Solomon Nkrumah Adasah | Jul 21, 2025 | Control and Management, Energy Storage, Uncategorized

Battery Energy Storage Systems (BESS) are critical for maintaining the stability and reliability of microgrids. By balancing power supply, smoothing fluctuations, and regulating voltage and frequency, BESS play a key role in modern energy systems. However, safety concerns, such as fire, explosion, and large-scale outages due to battery malfunctions, pose significant risks to both personnel and property.

ADAC’s innovative solution addresses these safety challenges by integrating cutting-edge Battery Digital Twin technology to provide real-time fault detection and diagnosis for BESS. By combining the COMSOL Electrochemical Model with the Physics-Based Equivalent Circuit Model (PECM), our system simulates both the internal processes of the battery and its electrical behavior, ensuring accurate and fast fault tracking.

Key Features:

Integrated Battery Digital Twin: Combines electrochemical and circuit models for real-time, comprehensive battery performance simulation.
Fuzzy Logic Fault Detection: Uses fuzzy logic to estimate changes in battery parameters, detecting faults at their incipient stages for early intervention.
Intelligent Parameter Identification: The system processes fault data to train an intelligent algorithm that monitors battery health in real-time. By mapping changes in battery parameters to emerging faults, the system ensures timely detection.
Real-Time Monitoring: Continuously tracks battery conditions, such as current and temperature, providing ongoing health assessments and immediate identification of potential issues.
Fault Simulation & Prevention: Simulates battery performance under various fault scenarios, improving detection accuracy and preventing failures.

Results:

ADAC Hardware-in-Loop (HIL) Platform

To showcase our work, we have set up a Hardware-in-Loop (HIL) platform to demonstrate the integration of Energy Management Systems (EMS) and Battery Fault Simulation.
Dynamic energy management algorithms run on Raspberry Pi microcontrollers, enabling real-time microgrid scheduling visualized via a microgrid sandbox. The Battery Fault Simulator monitors battery status, while real-time demand and supply data are fed into big data analytics to predict and adjust the system’s next steps.
By combining HIL and Software-in-Loop (SIL) methods, this platform effectively demonstrates the feasibility of our research in dynamic energy management and Battery Fault Detection and Diagnosis (BIF-DD).

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Hierarchical Collaborative Distributed Energy Management Systems (H-CoDEMS)

by Solomon Nkrumah Adasah | Jul 19, 2025 | Control and Management, Smart Grid, Uncategorized

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.

Battery Incipient Fault Detection and Diagnosis (BIF-DD)

by Solomon Nkrumah Adasah | Jul 18, 2025 | Control and Management, Energy Storage, Uncategorized

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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iSpace

by chow | Oct 20, 2020 | Artificial Intelligence, Control and Management, Mechatronics, Uncategorized

iSpace Robots

Intelligent Space (iSpace) is a relatively new concept to effectively use distributed sensors, actuators, robots, computing processors, and information technology over communication networks. iSpace is a large scale Mechatronics System by integrating sensors, actuators, and control algorithms in a communication system using knowledge from various engineering disciplines such as automation, control, hardware and software design, image processing, communication and networking.

Documentations

Collaborative Distributed Energy Management Systems (CoDEMS)

by chow | Oct 1, 2020 | Control and Management, Smart Grid, Uncategorized

Typically, the distributed energy resources (DER) are controlled by the utility distribution management system (DMS) or DER management system (DERMS). If hosted by microgrid, the microgrid energy management system (MG-EMS) will be added between the DMS/DERMS and DERs. This type of top-down hierarchical control chain is heavily constrained by the communication latency, quality, bandwidth, and availability. These systems are not positioned to embrace the DER boom and will be a bottle-neck for undergoing DER integration. The solution to the scalability is decentralization. Current academic and industry efforts are made to push control to the “edge”, namely on on-site DERs. With built-in edge autonomy in DERs, they can seamlessly work together and the system becomes more scalable. Another downside of the conventional centralized control scheme is the lack of resilience against natural and man-made disasters. The typical industry practice for resilience is by adding redundant central controllers. However, this redundancy is expensive yet cannot rapidly restore electric service in parallel. Therefore, the distributed control technologies have attracted significant academic and industry attention in recent years. Our lab has been developing distributed EMS, called Collaborative Distributed Energy Management Systems (CoDEMS), since 2008.

Publications:

[1]Z. Cheng, J. Duan, and M.-Y. Chow, “To Centralize or to Distribute: That Is the Question: A Comparison of Advanced Microgrid Management Systems,” EEE Ind. Electron. Mag., vol. 12, no. 1, pp. 6–24, Mar. 2018, doi: 10.1109/MIE.2018.2789926.

[2]N. Rahbari-Asr, Y. Zhang, and M.-Y. Chow, “Consensus-based distributed scheduling for cooperative operation of distributed energy resources and storage devices in smart grids,” IET Generation, Transmission & Distribution, vol. 10, no. 5, pp. 1268–1277, Apr. 2016, doi: 10.1049/iet-gtd.2015.0159.

[3]Y. Zhang, N. Rahbari-Asr, J. Duan, and M.-Y. Chow, “Day-Ahead Smart Grid Cooperative Distributed Energy Scheduling With Renewable and Storage Integration,” IEEE Trans. Sustain. Energy, vol. 7, no. 4, pp. 1739–1748, Oct. 2016, doi: 10.1109/TSTE.2016.2581167.

Resilient Energy Magament in ugrid Simulator (REMμS)

by chow | Oct 1, 2020 | Control and Management, Smart Grid, Uncategorized

The microgrid is envisioned to be the building block of the future smart grid, for its abilities to host distributed energy resources, to improve grid reliability, and to enhance system resiliency. One of the most studied research topics of the microgrid is the distributed microgrid energy management system. However, the algorithm prototyping and hardware validation still remain great challenges at the current stage. Our lab has been developing a highly scalable, customizable, and low-cost DC microgrid testbed framework that enables fast distributed MG-EMS prototyping and provides proof-of-concept validation.

Publications:

[1]Cheng and M. Chow, “The Development and Application of a DC Microgrid Testbed for Distributed Microgrid Energy Management System,” IECON 2018 – 44th Annual Conference of the IEEE Industrial Electronics Society, Washington, DC, 2018, pp. 300-305, doi: 10.1109/IECON.2018.8591816.

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