Lifetime and Aging Degradation Prognostics for Lithium-ion Battery Packs Based on a Cell to Pack Method
Lifetime and Aging Degradation Prognostics for Lithium-ion Battery Packs Based on a Cell to Pack Method
复制标题
基于电池组方法的锂离子电池组的寿命和老化退化预测
DOI：
10.1186/s10033-021-00668-y
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发表时间：
2022-01
影响因子：
4.2
通讯作者：
Xiaosong Hu
中科院分区：
文献类型：
--
作者：
Yunhong Che;Zhongwei Deng;Xiaolin Tang;Xianke Lin;Xianghong Nie;Xiaosong Hu
摘要蓄电池的老化诊断是确保储能系统在安全区域内运行的关键。提出了一种新的基于转移深度学习和高斯过程回归相结合的细胞包装健康和寿命预测方法。从局部放电过程中提取一般健康指标。基于深度学习框架建立了健康指标的序贯退化模型，并将其用于电池组退化预测。对电池组和每个电池组的未来退化容量进行了概率预测，以提供全面的寿命预测。此外，只需要源域中的几个独立的电池单元和目标域中的电池组的早期数据就可以进行建模。实验结果表明，电池组寿命预测误差不超过25次，即使只有50次模型微调，也能为老化实验节省约90%的时间。因此，大大减少了电池组调查的时间和人力。预测的电池组中连接的电池组的容量趋势准确地反映了每个电池组的实际退化情况，可以提前揭示最薄弱的单元进行维护。
AbstractAging diagnosis of batteries is essential to ensure that the energy storage systems operate within a safe region. This paper proposes a novel cell to pack health and lifetime prognostics method based on the combination of transferred deep learning and Gaussian process regression. General health indicators are extracted from the partial discharge process. The sequential degradation model of the health indicator is developed based on a deep learning framework and is migrated for the battery pack degradation prediction. The future degraded capacities of both battery pack and each battery cell are probabilistically predicted to provide a comprehensive lifetime prognostic. Besides, only a few separate battery cells in the source domain and early data of battery packs in the target domain are needed for model construction. Experimental results show that the lifetime prediction errors are less than 25 cycles for the battery pack, even with only 50 cycles for model fine-tuning, which can save about 90% time for the aging experiment. Thus, it largely reduces the time and labor for battery pack investigation. The predicted capacity trends of the battery cells connected in the battery pack accurately reflect the actual degradation of each battery cell, which can reveal the weakest cell for maintenance in advance.
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