LiBrain: LLM‐powered Li‐ion battery diagnostics with time‐series‐aware retrieval‐augmented framework for E‐bikes

Abstract

The rapid proliferation of smart‐city ecosystems has significantly amplified the demand for Li‐ion batteries, which now serve as the primary energy source for sustainable transportation systems such as e‐bikes. Ensuring battery safety and optimal performance is crucial, yet challenging due to complex intrinsic dynamics and extrinsic operating conditions. This paper presents LiBrain, an innovative LLM‐powered, time‐series‐aware retrieval‐augmented framework designed to simultaneously address both safety and performance challenges through three synergistic components: (1) a distributed IoT‐enabled edge network for continuous real‐time battery monitoring and data acquisition, (2) a pretrained deep multi‐task diagnostic engine capable of comprehensive battery performance forecasting, and (3) a knowledge‐base augmentation module that transforms technical diagnostics into clear, actionable guidance tailored for e‐bike users. Functioning as an intelligent battery management assistant, LiBrain effectively bridges the gap between expert‐level real‐time analytics and practical, user‐friendly instructions. Extensive validation across a real‐world operational e‐bike battery‐swap network demonstrates LiBrain's exceptional capabilities, achieving a 95% adoption rate in hazardous alarm detection and 92% in battery‐status prediction. In real application, LiBrain has processed over 500 million battery events, managed almost 10 million inquiries and 1 million alarms annually, and identified 10% of on‐site batteries daily for proactive replacement, thereby maintaining operational safety and reliability.