Researchers from the University of Michigan, led by Jingbo Qu, have developed a new approach to improve the operation and maintenance of large-scale Battery Energy Storage Systems (BESSs). Their work, published in the journal Nature Energy, aims to address the challenges of reactive and expert-dependent diagnostics that currently dominate the industry.
Battery Energy Storage Systems are becoming increasingly vital for power-system stability. However, their operation and maintenance often rely on reactive diagnostics performed by experts. This can be time-consuming and costly. The researchers identified that cell-level inconsistencies can serve as early warning signals for degradation and safety risks. However, translating these signals into practical operational actions has been hindered by the lack of scalable and interpretable decision-support frameworks.
The team introduced a new inconsistency-driven operation and maintenance paradigm for large-scale BESSs. This framework systematically transforms routine monitoring data into explainable, decision-oriented guidance. It integrates multi-dimensional inconsistency evaluation with large language model-based semantic reasoning to bridge the gap between quantitative diagnostics and practical maintenance decisions.
Using eight months of field data from an in-service battery system comprising 3,564 cells, the researchers demonstrated how electrical, thermal, and aging-related inconsistencies can be distilled into structured operational records. These records are then converted into actionable maintenance insights through a multi-agent framework. The proposed approach enables accurate and explainable responses to real-world operation and maintenance queries, reducing response time and operational cost by over 80% compared with conventional expert-driven practices.
This research establishes a scalable pathway for intelligent operation and maintenance of battery energy storage systems. It has direct implications for enhancing reliability, safety, and cost-effective integration of energy storage into modern power systems. The practical applications of this work could significantly improve the efficiency and cost-effectiveness of energy storage solutions, benefiting both energy providers and consumers.
Source: Qu, J., Wang, Y., Fu, Y. et al. From inconsistency to decision: explainable operation and maintenance of battery energy storage systems. Nat Energy (2023).
This article is based on research available at arXiv.