原文信息：
Machine learning enables rapid state of health estimation of each cellwithin battery pack
原文链接：
https://www.sciencedirect.com/science/article/pii/S0306261924015484
Highlights
•开发了一种高精度的分支充电容量估计器。
•提取老化、不一致和操作条件特征。
•可以实现电池组内所有电池的SOH的高精度估计。
•在不同操作条件下验证了模型的泛化能力。
Abstract
The health and safety of the battery pack are directly influenced by the state of health of its cells. However, dueto the aging inconsistency among cells and the limited measurability of physical quantities for cells within thebattery pack, traditional approaches to state of health estimation of cell have significant limitations. This studyintroduces a machine learning approach for evaluating the state of health of cells within the battery pack. Firstly,a branch charging capacity estimator utilizing BiGRU is formulated, facilitating precise estimation of batterypack branch charging capacity across diverse charging conditions. Then, three categories of features, includingaging features, inconsistency features, and operating condition features, are extracted based on aging experimental data at the battery pack level and battery pack branch charging capacity. These features are input into thesupport vector regression-based generic model, facilitating precise state of health estimation for all cells withinthe battery pack. The generalization of the model is validated under both five-stage constant current chargingconditions and two-stage constant current charging conditions. Additionally, the discussion includes how thechoice of model parameters affects the precision of cell state of health estimation. The method proposed enablesprecise monitoring of cell state of health within the battery pack, offering valuable potential for ensuring overallbattery pack safety and issuing safety alerts for cells.
Keywords
State of health estimation；
Battery pack；
Branch charging capacity；
Multi-stage constant current charging；
Graphics
Fig. 3. The flowchart of proposed framework.
Fig. 6. Aging features.
Fig. 7. Operating condition features.
Fig. 8. The charging capacity estimation results of battery pack branch during FSCC condition.
Fig. 9. The result in estimating SOH of cells within the battery pack during FSCC condition.
Fig. 10. The result in estimating SOH of cells within the battery pack during TSCC condition.
Fig. 11. The curve of the average MAE value in SOH estimation for all cells within the battery pack as the model parameters vary.
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