Lithium-ion battery performance and longevity are critically impacted by mechanical degradation, such as particle cracking within the anode. This incipient fault gradually reduces capacity and increases safety risks. Our research tackles this challenge with a novel and computationally efficient fuzzy logic model that predicts maximum crack damage. Traditional physics-based models for crack propagation, while accurate, are computationally expensive and data-intensive. This limits their application for rapid design iteration or large-scale degradation studies. A simpler, yet intelligent, alternative was needed to bridge this gap.

Fuzzy logic is uniquely suited for modeling complex, nonlinear degradation processes. It handles the inherent uncertainty in system behavior and provides a transparent, interpretable framework based on intuitive ‘if-then’ rules. Unlike black-box AI models, fuzzy logic is easily understandable, allowing for actionable insights without sacrificing performance. A key contribution of our work is the systematic methodology used to optimize the granularity of the model’s membership functions. This process carefully balanced model complexity with predictive accuracy, ensuring that the fuzzy logic system robustly captures the underlying physics of crack-induced degradation without overfitting.

The significance of this research lies in the development of a predictive fault model for particle crack damage in lithium-ion batteries. This model provides a valuable tool for proactive design optimization and early-stage degradation assessment, helping to create more durable, safe, and efficient energy storage systems. By enhancing battery longevity in applications such as electric vehicles and renewable energy grids, it also serves as a key component for integration with digital twins, enabling smarter battery management.

After disasters, restoring power to critical facilities like hospitals is an urgent priority. However, disasters often severely damage both power and communication infrastructure, making restoration efforts particularly challenging when communication resources are limited. Therefore, how to optimize the communication network of a microgrid to achieve high-efficiency, and resilience is a problem worth investigating.

To address the problem, we have applied edge centrality metrics to identify critical links as well as nonessential links. We have shown that Connectivity Rank Index (CRI) is an effective centrality metric to measure the importance of communication links for distributed structure. Based on this, we also proposed a CRI-based network optimization strategy by pruning. In addition, we defined a variant of CRI, called Local-CRI, to estimate the impact of link failures on hierarchical distributed structure.

In our future work, we will explore the integration of edge centrality metrics from complex network filed and physical constraints from power system. The strategic application of edge centrality metrics can be a practical and quantitative tool for us to design more efficient and resilient communication networks for microgrids in disaster relief.

The increasing integration of distributed energy resources (DERs)—such as renewable generation, energy storage systems, and responsive loads—has introduced significant variability and uncertainty into modern power systems. Traditional static energy management frameworks are often inadequate to cope with these rapid fluctuations in generation and demand. Learning-based Dynamic Energy Management Systems have emerged as a key paradigm for achieving flexible, adaptive, and real-time coordination of energy resources.

In this context, we mainly focus on applying multi-agent and learning-based methods such as reinforcement learning, deep learning under DEMS’s framework. In order to produce a more intelligent power system, we will focus on intent-driven DEMS in the future, which endow the power system with the ability of self-configuring, self-managing, and self-optimizing.

Our research addresses the critical challenge of restoring electricity in the aftermath of disasters, where conventional grid infrastructure is often compromised. We propose an innovative solution that leverages the rapidly growing fleet of Electric Vehicles (EVs) as a decentralized, mobile energy resource. Through Vehicle-to-Grid (V2G) technology, this distributed network of batteries can be coordinated to supply power, significantly enhancing community resilience. The core of our project is an AI-assisted coordination framework, which uses Multi-Agent Reinforcement Learning (MARL) to intelligently manage this system.

In this framework, we model each EV as an autonomous agent that learns optimal charging and discharging strategies through interaction with a simulated post-disaster environment. These agents are trained to pursue a dual-objective goal: to minimize the economic costs while maximizing the amount of power delivered to support disaster-stricken areas. This is achieved by developing a novel reward mechanism that incentivizes agents to charge during low-cost periods and discharge when the grid is most in need, thereby aligning private economic interests with the public good. This decentralized, AI-assisted approach is inherently more robust and scalable than traditional centralized control methods, making it ideally suited for the chaotic and unpredictable conditions of a post-disaster scenario.

The significance of this work lies in its potential to transform disaster response, providing a faster and more adaptive method for power restoration that can save lives and mitigate economic loss. Looking forward, our research will focus on scaling our simulations to model more complex, real-world urban environments, exploring more advanced reinforcement learning algorithms to enhance inter-agent cooperation, and developing hardware-in-the-loop testbeds to validate our findings.