As a significant ingredient regarding health status, data-driven state of health (SOH) estimation has become dominant for lithium-ion batteries. To handle data discrepancy across batteries, current SOH estimation models engage in transfer learning (TL), which reserves a priori knowledge gained through reusing partial structures of the offline trained model. However, multiple degradation patterns of a complete life cycle of a battery make it challenging to pursue TL. The concept of the stage is introduced to describe the collection of continuous cycles that present a similar degradation pattern. A transferable multistage SOH estimation model is proposed to perform TL across batteries in the same stage, consisting of four steps. First, with identified stage information, raw cycling data from the source battery are reconstructed into the phase space with high dimensions, exploring hidden dynamics with limited sensors. Next, domain invariant representation across cycles in each stage is proposed through cycling discrepancy subspace with reconstructed data. Third, considering the unbalanced discharge cycles among different stages, a switching estimation strategy composed of a lightweight model with the long short-term memory network and a powerful model with the proposed temporal capsule network is proposed to boost estimation accuracy. Finally, an updating scheme compensates for estimation errors when the cycling consistency of target batteries drifts. The proposed method outperforms its competitive algorithms in various transfer tasks for a run-to-failure benchmark with three batteries. Especially through transferring the estimation model from batteries B7 to B6, the proposed method improves the estimation accuracy by as high as 42.6% in the third stage in terms of the root mean square error, compared to the other state-of-the-art approaches. In addition, similar conclusions can be drawn from other contributed experiments.

中文翻译：

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用于锂离子电池健康状态估计的具有循环差异学习的可迁移多阶段模型

作为健康状态的重要组成部分，数据驱动的健康状态（SOH）估计已成为锂离子电池的主导。为了处理电池之间的数据差异，当前的 SOH 估计模型采用迁移学习 (TL)，它保留通过重用离线训练模型的部分结构获得的先验知识。然而，电池完整生命周期的多种退化模式使得追求 TL 具有挑战性。引入阶段的概念来描述呈现类似退化模式的连续循环的集合。提出了一种可转移的多级 SOH 估计模型，用于在同一阶段跨电池执行 TL，包括四个步骤。首先，通过确定的阶段信息，来自源电池的原始循环数据被重建到高维度的相空间中，用有限的传感器探索隐藏的动态。接下来，通过具有重构数据的循环差异子空间提出了每个阶段跨循环的域不变表示。第三，考虑到不同阶段之间的不平衡放电周期，提出了一种由具有长短期记忆网络的轻量级模型和具有所提出的时间胶囊网络的强大模型组成的切换估计策略，以提高估计精度。最后，当目标电池的循环一致性漂移时，更新方案可以补偿估计误差。所提出的方法在使用三个电池的运行至故障基准的各种传输任务中优于其竞争算法。特别是通过将估计模型从电池 B7 转移到 B6，与其他最先进的方法相比，所提出的方法在第三阶段的均方根误差方面提高了高达 42.6% 的估计精度. 此外，从其他贡献的实验中也可以得出类似的结论。