On the morning of November 21, the launch ceremony of the Advanced Energy Interdisciplinary Research Center (hereinafter referred to as the “Center”) of the Global College, Shanghai Jiao Tong University, was held in the Zhongji Lecture Hall of Longbin Building. Present at the event were Hesheng Wang, Dean of the Global College, Mo-Yuen Chow, Head of the Center, as well as faculty members Songliang Chen, Yuljae Cho, Yunlong Guo, Yulian He, Li Jin, Chengbin Ma, Dezhi Zhou, and others. The ceremony was hosted by faculty member Li Jin.

In his opening speech, Hesheng Wang stated that the College will establish a series of interdisciplinary research centers, and the establishment of such centers is an important strategic initiative to promote high-level scientific research development. The Center will actively foster collaboration and exchange among various research teams within the College, forming a synergistic innovation force that will provide strong support to the existing independent Principal Investigator (PI) research model. Based on the actual operation of the Center, the College will provide ample support in terms of student recruitment quotas, laboratory space, and startup funding.

Mo-Yuen Chow provided a detailed introduction to the Center’s mission, vision, and future plans. The Center will leverage the College’s dual advantages of internationalized education and interdisciplinary integration, strongly encourage academic innovation, and support the growth of young faculty and students. In the future, the Center will regularly organize academic exchange activities, gather high-level academic resources, actively promote international research collaboration and industry-academia partnerships, and strive to build an academic hub with global influence.

Subsequently, several faculty members and research group representatives from the College introduced the core research directions of their respective teams. The poster session was lively, with each team showcasing their latest research findings. Faculty and students engaged in enthusiastic discussions, actively exploring potential collaboration opportunities.

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Circuit modeling is an efficient method for analyzing early-stage faults in lithium-ion batteries. It simulates key aging mechanisms while balancing detail and computational speed. For early degradation, the model explicitly captures two critical faults: SEI growth using a 1RC equivalent circuit and metal dendrite growth using a transmission line model.

Our research develops a coupled model of SEI growth and metal dendrite growth within this circuit framework. By formulating model components as functions of key electrochemical parameters, it captures the distinct electrical effects of both degradation modes on terminal voltage. The metal dendrite growth model, represented by the transmission line circuit, is validated with long-term aging data, effectively capturing gradual resistance increase. Parameters for the extended coupled model are calibrated using a genetic algorithm and validated against experimental voltage data. The close match between simulation and measurement confirms the model’s ability to reproduce degradation behavior, providing a computationally efficient foundation for incipient fault prediction and future online diagnostics.

Electrochemical modeling is a key approach for detecting and diagnosing incipient faults in lithium-ion batteries. Despite its computational cost, the pseudo-two-dimensional (P2D) model enables high-fidelity multi-physics simulations and explicitly captures early degradation mechanisms such as solid electrolyte interphase (SEI) growth and metal dendrite formation, thereby providing a physics-based foundation for accurate state-of-health (SOH) estimation and incipient fault analysis.

In this work, a coupled SEI growth and metal dendrite growth model is developed based on the P2D framework and implemented in COMSOL Multiphysics. By incorporating both side reactions into the total volumetric current density, the model captures their coupled effects on overpotential, terminal voltage, and capacity degradation under incipient fault conditions. The SEI submodel is validated using public NMC battery SOH data, showing high accuracy across different aging stages. Parameters of the extended coupled model are further calibrated using a particle swarm optimization (PSO) algorithm and validated under dynamic load conditions, confirming the capability to reproduce battery degradation behavior.

The practical application of incipient fault detection and diagnosis in Battery Energy Storage Systems (BESS) faces major challenges, including complex fault characteristics, limited data, safety issues, and the demand for scalable real-time processing. Incipient faults such as Solid Electrolyte Interface (SEI) growth and metal dendrite growth are particularly hard to detect with conventional methods, yet they are crucial for preventing catastrophic battery failures. This underscores the urgent need for advanced, reliable diagnostic tools that can operate accurately under real-world conditions.

In response, we have developed the Battery Incipient Fault Digital Twin (BIF-DT), an integrated framework that combines an electrochemical model with a physics-based circuit model. This dual-model approach enables high-fidelity simulation of internal battery processes while maintaining computational efficiency suitable for practical use. To date, the BIF-DT platform has been significantly advanced, with both models validated using public datasets, demonstrating high accuracy in detecting incipient faults. This progress has been documented in multiple publications, establishing a solid scientific foundation. The BIF-DT was also presented at the IEEE IESES 2025 tutorial.

Looking ahead, the BIF-DT platform holds strong promise for improving the reliability and safety of BESS across various applications. Its validated accuracy provides a firm basis for moving from research to real-world implementation. Future work will focus on enhancing the platform’s scalability and real-time processing capabilities, aiming to deploy it as a proactive diagnostic tool that can predict and prevent battery failures in commercial and grid-scale energy storage systems, thereby supporting 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.