TimeSeries2Report prompting enables adaptive large language model management of lithium-ion batteries
This addresses the challenge of using LLMs for real-world battery energy storage system operation and maintenance, offering a practical solution for adaptive battery intelligence.
The paper tackles the problem of applying large language models (LLMs) to lithium-ion battery management by introducing TimeSeries2Report (TS2R), a framework that converts raw time-series data into structured reports, enabling LLMs to improve performance in tasks like anomaly detection and state-of-charge prediction, achieving expert-level decision quality without retraining.
Large language models (LLMs) offer promising capabilities for interpreting multivariate time-series data, yet their application to real-world battery energy storage system (BESS) operation and maintenance remains largely unexplored. Here, we present TimeSeries2Report (TS2R), a semantic translation framework that converts raw lithium-ion battery operational time-series into structured, semantically enriched reports, enabling LLMs to reason, predict, and make decisions in BESS management scenarios. TS2R encodes short-term temporal dynamics into natural language through a combination of segmentation, semantic abstraction, and rule-based interpretation, effectively bridging low-level sensor signals with high-level contextual insights. We benchmark TS2R across both lab-scale and real-world datasets, evaluating report quality and downstream task performance in anomaly detection, state-of-charge prediction, and charging/discharging management. Compared with vision-, embedding-, and text-based prompting baselines, report-based prompting via TS2R consistently improves LLM performance in terms of across accuracy, robustness, and explainability metrics. Notably, TS2R-integrated LLMs achieve expert-level decision quality and predictive consistency without retraining or architecture modification, establishing a practical path for adaptive, LLM-driven battery intelligence.