State-of-Health (SOH) estimation of lithium-ion batteries is critical for reliable and efficient energy storage and usage. Traditional SOH estimation approaches either compromise on real-time performance or on estimation accuracy. This paper introduces an end-cloud collaboration approach that synergistically combines a cloud-based deep learning model with an end-based empirical model to achieve both high-accuracy and real-time SOH estimation. Utilizing the Transformer architecture, the cloud-side model employs multiple features extracted through signal analysis technology. On the other hand, the end-side employs a fast, iterative double exponential empirical model that provides real-time estimates. Two methods of end-cloud collaborative updating are presented, both leveraging Kalman Filter and Unscented Kalman Filter (UKF) to integrate and iteratively update the models. Evaluation shows that the cloud-side model achieves a Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of around 0.8 %, while the end-cloud collaboration system maintains these metrics at 1 %, successfully capturing nonlinear capacity rebound phenomena. The work contributes to the domain by offering an approach that balances computational load and latency, making it suitable for online applications. Under the Cyber Hierarchy and Interactional Network (CHAIN) framework, this end-cloud collaboration architecture holds promise for high-accuracy, real-time health management of lithium-ion batteries across their full lifecycle.

中文翻译：

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基于多特征和变压器的锂离子电池健康状态在线评估的端云协作方法


锂离子电池的健康状况 (SOH) 评估对于可靠、高效的能源存储和使用至关重要。传统的 SOH 估计方法要么会牺牲实时性能，要么会牺牲估计精度。本文介绍了一种端云协作方法，将基于云的深度学习模型与基于端的经验模型协同结合，以实现高精度和实时的 SOH 估计。云端模型利用Transformer架构，采用信号分析技术提取的多种特征。另一方面，端侧采用快速迭代双指数经验模型来提供实时估计。提出了两种端云协同更新方法，均利用卡尔曼滤波器和无迹卡尔曼滤波器（UKF）来集成和迭代更新模型。评估结果显示，云侧模型的均方根误差（RMSE）和平均绝对误差（MAE）均在0.8%左右，而端云协作系统则将这些指标保持在1%，成功捕获了非线性容量反弹现象。这项工作通过提供一种平衡计算负载和延迟的方法来对该领域做出贡献，使其适合在线应用程序。在网络层次和交互网络（CHAIN）框架下，这种端云协作架构有望在锂离子电池的整个生命周期内实现高精度、实时的健康管理。