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).
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.
The amount of data collected in Electric Vehicles has been growing fast because we have many more sensors, higher bandwidth communication systems, and cheaper memory to monitor and measure real-time driving range related data and store the data on the vehicles, in connected clouds, etc. This massive amount of data can have different levels of accuracy, resolutions, and relevance in unstructured ways. Big Data technologies have been emerging to address huge, diverse and unstructured data to substantially improve the overall system performance. With proper use of Big Data concepts and techniques, the remaining driving range estimation of the vehicle can be substantially improved.
The range estimation needs the incorporation and synchronization of all standard, real-time and historical data. Usually, the standard and historical data provides an initial prediction of the driving range; and the real-time data updates the estimation during the driving. However, under different conditions, some data are more relevant than others for the range estimation. This data can be historical, standard, or real-time depending on different situations. The big data analytics helps us identify the relevant data and discover its correlation to the remaining driving range estimation.
Publication
[1] H. Rahimi-Eichi and M.-Y. Chow, “Big-Data Framework for Electric Vehicle Range Estimation,” presented at the 40th Annual Conference of the IEEE Industrial Electronics Society (IECON2014), IEEE, Dallas, TX , 2014.
[2] Z. Cheng, M. Chow, D. Jung and J. Jeon, “A big data based deep learning approach for vehicle speed prediction,” 2017 IEEE 26th International Symposium on Industrial Electronics (ISIE), Edinburgh, 2017, pp. 389-394, doi: 10.1109/ISIE.2017.8001278.
[3] D. Jung, M. Chow, Z. Cheng, and J. Jeon, “Method and apparatus for estimating driving information,“US10215579B2, 2019.
The First Principle Based Four Dimensional Battery Degradation Model (4DM) is computer simulation model for battery dynamics studies under different degradation and operating conditions. The 4DM is designed based on the physics of operation of the battery, i.e., the actual components such as anode, cathode, electrolyte, separator and current collector, are used to construct the model. This particular approach is used to bridge the gap between material science, electrochemical and electrical engineering.
The 4DM, because of the design, is capable of simulating:
different battery chemistries,
batteries of different capacities,
progressive component degradation,
different operating conditions – C-rates, temperatures, depth of discharge, partial charging and discharging effects,
component degradation over time.
The 4DM provides a platform to study the sensitivity of the battery’s rate of change of voltage and capacity with respect to the degradation of different physical and electrochemical components. This feature/capability of the 4DM enables users to better understand the impact of different operating conditions on the degradation of their battery and determine appropriate use cases for their batteries to prolong the remaining useful life.
The 4DM has an intuitive user-interface that assists the user to perform different tests on the model under different operating conditions. The user interface is designed to be simple, yet intuitive and capable of providing the user with sufficient options to understand the working of the 4DM with access to the core back-end tool with all the features.
Real-time estimation of the state of charge (SOC) of the battery is a crucial need in the growing fields of plug-in hybrid electric vehicles and smart grid applications. The SOC estimation accuracy depends on the accuracy of the model used to describe the characteristics of the battery. To accurately estimate the SOC of the battery, a Co-Estimation algorithm is proposed. The Co-Estimation algorithm is developed based on a resistance–capacitance (RC)-equivalent circuit model to model the battery dynamics. Considering the parameters of the battery model are functions of the SOC, C-rate, temperature, and aging, the Co-Estimation algorithm adopts an adaptive online parameter-identification algorithm to identify and update the model’s parameters as they change. We also deployed a piecewise linearized mapping of the VOC–SOC curve along with continuously updating the parameters to accurately represent all of the battery’s static and dynamic characteristics. Using this adaptive structure, we design an observer based on the updating model to estimate the SOC as one of the states of the battery model.
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