Battery life prediction holds significant practical value for battery research and development. Currently, many data-driven models rely on early electrical signals from specific target batteries to predict their lifespan. A common shortfall is that most existing methods are developed based on specific aging conditions, which not only limits their model's capability but also diminishes their effectiveness in predicting degradation under varied conditions. As a result, these models often miss out on fully benefiting from the rich historical data available under other conditions. Here, to address above, we introduce an approach that explicitly captures differences between electrical signals of a target battery and a reference battery, irrespective of their materials and aging conditions, to forecast the target battery life. Through this inter-cell difference, we not only enhance the feature space but also pave the way for a universal battery life prediction framework. Remarkably, our model that combines the inter- and intra-cell differences shines across diverse conditions, standing out in its efficiency and accuracy using all accessible datasets. An essential application of our approach is its capability to leverage data from older batteries effectively, enabling newer batteries to capitalize on insights gained from past batteries. This work not only enriches the battery data utilization strategy but also sets the stage for smarter battery management system in the future.

预测电池寿命的关键是基于数据驱动模型以及不同电池间的电信号差异来提高预测的准确性，并为未来更智能的电池管理系统奠定基础。

利用细胞内外差异：全面精确的跨条件电池寿命估算方法