LiBrain: LLM-Powered Li-ion Battery Diagnostics with Time-Series-Aware Retrieval-Augmented Framework for E-bikes - Li, Zhao; Lin, Zixin; Ding, Donghui; Zhong, Yichen; Wang, Biao; Xu, Haitao; Cai, Peng (Proceedings of the AAAI Conference on Artificial Intelligence, 2026)
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, Li-Brain 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.