Three conference papers from the ADAC lab are accepted and presented at the 2025 IEEE International Conference on Industrial Technology (ICIT 2025) in Wuhan, China.
1. Solid Electrolyte Interface Growth Fault Modeling for Battery State of Health Simulation (Junya Shao, Mo-Yuen Chow) Link
With the extensive application of lithium-ion batteries in energy storage and electric vehicles, safety concerns have become increasingly prominent. Many accidents such as deflagration in battery energy storage stations and fires in electric vehicles have been traced back to performance deterioration and fault generation within the batteries. Therefore, auxiliary fault protection systems for lithium-ion batteries are urgently required to be developed. Incipient fault diagnosis systems, with the benefits of early detection, predictive maintenance, and minimized economic loss, have received increasing attention from battery manufacturers.
To implement effective incipient fault diagnosis and early warning systems for battery protection, it is essential to investigate the underlying degradation mechanisms that govern the evolution of battery health. Among these mechanisms, the formation and growth of the solid electrolyte interface (SEI) on the anode surface play a critical role in the gradual loss of active lithium ions, which directly influences the state of health (SOH) of lithium-ion batteries. Given its importance, an accurate SEI growth model is fundamental to simulating the SOH evolution and enabling incipient fault diagnosis.
To this end, a framework integrating a pseudo-two-dimensional (P2D) electrochemical model with lumped parameter SEI growth mechanisms is developed for battery SOH simulation. The proposed model is validated using the public data from the UCL cycling test on NMC811 batteries. To reflect capacity recovery phenomena observed in experiments, the simulation is divided into five distinct segments, each corresponding to different aging stages with recalibrated initial conditions and parameter sets. The results demonstrate that the infinity norm, the mean absolute error and the root mean square error of SOH simulation are no more than 0.9657%, 0.2227% and 0.3243% respectively. Additionally, the analysis of parameter variation at different aging segments reveals consistent findings aligned with microscopic electrochemical mechanisms. This study confirms that the proposed method provides a reliable and robust framework for SOH simulation and fault evolution analysis in lithium-ion batteries.
2. Modelling of the Solid Electrolyte Interface Growth Using Physics-Based Equivalent Circuit Model (Ziqi Wang, Mo-Yuen Chow) Link
Lithium-ion batteries are critical for modern applications like electric vehicles and renewable energy storage, making accurate battery models essential for ensuring their safety and longevity. A key factor in battery degradation is the growth of the Solid Electrolyte Interface (SEI), a passive layer that forms on the electrode and consumes active lithium, leading to capacity fade. While high-fidelity electrochemical models can describe this process in detail, they are often too computationally intensive for practical, real-time applications, creating a need for simpler yet physically representative alternatives.
This paper presents a physics-based equivalent circuit model (PECM) to simulate the SEI growth in lithium-ion batteries. The study addresses a gap in existing literature by integrating SEI growth dynamics into an equivalent circuit framework, offering a computationally efficient alternative to complex electrochemical models while retaining physical relevance. Key electrochemical parameters are abstracted into circuit elements, allowing the model to capture the impact of SEI thickening on battery performance over time.
The proposed model is validated using experimental battery aging datasets, demonstrating high accuracy in voltage estimation after applying cubic spline interpolation to the open-circuit voltage. Analysis reveals strong correlations between SEI thickness and circuit parameters, showing increased resistance and decreased capacitance as the SEI layer grows. This approach provides a practical tool for predicting battery degradation and supports future development of more comprehensive aging models.
3. Microgrid Communication Network Optimization During Disaster Relief: A Connectivity Rank Index Based Approach (Hengrui Tian, Aditya Joshi, Mo-Yuen Chow)Â Link
In the aftermath of disasters, the rapid restoration of power is critically needed, particularly for medical facilities. However, both power and communication systems are often severely damaged during such events, making power recovery with limited communication resources a key challenge in disaster relief efforts. This paper proposes a novel strategy for optimizing communication networks within a microgrid energy management system (EMS). The approach utilizes the Connectivity Rank Index (CRI) to evaluate the importance of communication links. The proposed strategy aims to achieve fast energy management response using minimal communication resources during disaster recovery. It iteratively identifies and removes redundant edges from the original network based on CRI. Numerical simulations conducted on a 14-node system demonstrate that the method improves convergence speed by 79.12% and increases communication efficiency by 14.98%, confirming its effectiveness.
