Generalized impact of data distribution diversity on SOC prediction of lithium battery

doi:10.19799/j.cnki.2095-4239.2024.0003

Abstract

Abstract:

The prediction of state-of-charge (SOC) in batteries in data-driven models relies on high-quality experimental data. However, when considering lithium battery packs with diverse distributions in real-world scenarios, the accuracy of prediction of SOC in these packs becomes unstable, leading to poor generalization ability and limiting the practical application of the models. Therefore, it is of great significance to investigate the impact of the diverse distribution of large-scale datasets from actual operating scenarios on the generalization ability of SOC prediction models. Consequently, in this study, we conducted research on 32 datasets of operational data associated with lithium battery packs. Classic algorithms were combined with a multi-input multi-output (MIMO) strategy to predict multi-step SOC. Separate models were established for each dataset to analyze the application effects of different algorithms and the influence of data distribution diversity on model generalization ability. The results demonstrated that for large-scale lithium battery pack datasets, the LR-MIMO model generally exhibited higher training accuracy compared to the RF-MIMO, KNN-MIMO, and LSTM-MIMO models. The LR-MIMO model achieved R² values above 0.98 and MAPE values below 0.05 for predicting SOC in the next half hour. Compared to other models, the LR-MIMO model demonstrated excellent predictive performance, with R² values above 0.95 for predicting other datasets. The prediction accuracy of the KNN-MIMO model is comparable to that of the RF-MIMO model, with R² values roughly above 0.7, while the prediction performance of the LSTM-MIMO model differs significantly compared to other models due to the use of different datasets. The accuracy of the model can be improved when the data satisfies certain conditions, such as a correlation coefficient between SOC and voltage exceeding 0.9, a wide range of SOC and voltage distributions, a left-skewed kernel density curve, and a relatively uniform distribution.

Key words: lithium-ion battery, state of charge, data-driven, distribution diversity, generalization

CLC Number:

TM 912

Lin HE, Jiangyan LIU, Bin LIU, Kuining LI, Shuai DAI. Generalized impact of data distribution diversity on SOC prediction of lithium battery[J]. Energy Storage Science and Technology, 2024, 13(5): 1677-1687.

Figures/Tables 11

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序号	主要参数	量值
1	单电池数量	14
2	电池连接方式	串联
3	标称电压/V	58
4	标称容量/Ah	32
5	充电方式	恒流恒压
6	电池冷却	被动式空气冷却
7	电池加热	无

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