TimeSeries2Report prompting enables adaptive large language model management of lithium-ion batteries

2025/12/18 by Yang, Jiayang, Zhao, Chunhui, Guay, Martin +1
Computer Science · Engineering · #Advanced Battery Technologies Research #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Fault Diagnosis Techniques #Software System Performance and Reliability

paper · pdf · doi:10.48550/arxiv.2512.16453

openalex publication_date 2025/12/18 · openalex created_date 2025/12/21 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models
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