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.
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.
Kelola interaksi pelanggan secara efisien lewat sistem CRM MTP di KAYARAYA, yang dirancang untuk menyatukan data dan strategi dalam satu dasbor cerdas.
