Electric bikes powered by lithium-ion batteries are increasingly used in smart cities to promote sustainable mobility and efficient delivery services. However, limited battery range and slow plug-in charging remain key challenges. Shared electric bike battery systems, facilitated by battery swapping stations, offer a promising solution by enabling quick and efficient battery replacements. However, their success hinges on accurate anomaly detection, battery health estimation and remain range prediction. These tasks remain challenging due to data scarcity, battery diversity and environmental variability. Here we show that a large-scale lithium-ion battery model trained on over ten million battery time series data enables robust and adaptable battery management across diverse real-world scenarios. The model learns complex battery behavior through unsupervised pretraining. Importantly, after efficient finetuning, the model significantly outperforms existing approaches in the three critical tasks. Deployed on cloud servers, our model enables real-time data processing, enhancing the safety, reliability and efficiency of battery swapping services. This advancement accelerates electric bike adoption, fostering sustainable urban mobility and green smart city development.