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

2025-12-18

AI Summary

Lithium-ion battery energy storage systems (BESS) generate complex multivariate time-series data, posing challenges for interpretability and explainable decision-making. Method: This paper proposes the Time-Series-to-Report (TSR) prompting framework—a parameter-efficient, LLM-based approach that requires no fine-tuning or architectural modification. TSR employs time-series segmentation, domain-knowledge-guided semantic abstraction, and rule-constrained natural language generation to transform raw sensor data into structured, human-readable semantic reports supporting operational monitoring, anomaly detection, state-of-charge (SOC) forecasting, and charge/discharge control. Contribution/Results: As the first work to directly leverage LLMs for expert-level, high-level BESS reasoning, TSR achieves superior accuracy, robustness, and decision interpretability over visual, embedding-, and text-based prompting baselines on both laboratory and real-world datasets.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

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 prompting 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.

Problem

• Enables LLMs to interpret battery time-series data for management
• Converts raw battery data into structured reports for decision-making
• Improves LLM performance in battery anomaly detection and prediction

Innovation

• Prompting framework converts battery time-series to structured reports
• Encodes temporal dynamics via segmentation and semantic abstraction
• Improves LLM performance in accuracy, robustness, and explainability