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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References 33

1	YANG R X, XIONG R, WEIXIANG S, et al. Extreme learning machine-based thermal model for lithium-ion batteries of electric vehicles under external short circuit[J]. Engineering, 2021, 7(3): 266-289.
2	XIONG R, MA S X, LI H L, et al. Toward a safer battery management system: A critical review on diagnosis and prognosis of battery short circuit[J]. iScience, 2020, 23(4): 101010.
3	CAI Z H, LIU G F, LUO J. Research state of charge estimation tactics of nickel-hydrogen battery[C]//2010 International Symposium on Intelligence Information Processing and Trusted Computing. Huanggang, China. IEEE, 2010: 184-187.
4	XIONG R, CAO J Y, YU Q Q, et al. Critical review on the battery state of charge estimation methods for electric vehicles[J]. IEEE Access, 2018, 6: 1832-1843.
5	BIAN C, HE H, YANG S, et al. State-of-charge sequence estimation of lithium-ion battery based on bidirectional long short-term memory encoder-decoder architecture[J]. Journal of Power Sources, 2020, 449: 227558.
6	CHEMALI E, KOLLMEYER P J, PREINDL M, et al. State-of-charge estimation of Li-ion batteries using deep neural networks: A machine learning approach[J]. Journal of Power Sources, 2018, 400: 242-255.
7	TIAN Y, LAI R, LI X, et al.. A combined method for state-of-charge estimation for lithium-ion batteries using a long short-term memory network and an adaptive cubature Kalman filter[J]. Applied Energy, 2020, 265: 114789.
8	BABAEIYAZDI I, REZAEI-ZARE A, SHOKRZADEH S. State of charge prediction of EV Li-ion batteries using EIS: A machine learning approach[J]. Energy, 2021, 223: 120116.
9	LI Y, ZOU C, BERECIBAR M, et al. Random forest regression for online capacity estimation of lithium-ion batteries[J]. Applied Energy, 2018, 232: 197-210.
10	MENG J H, STROE D I, RICCO M, et al. A simplified model-based state-of-charge estimation approach for lithium-ion battery with dynamic linear model[J]. IEEE Transactions on Industrial Electronics, 2019, 66(10): 7717-7727.
11	HONG J, WANG Z, CHEN W, et al. Online joint-prediction of multi-forward-step battery SOC using LSTM neural networks and multiple linear regression for real-world electric vehicles[J]. Journal of Energy Storage, 2020, 30: 101459.
12	WANG G, LYU Z, LI X. An optimized random forest regression model for Li–ion battery prognostics and health management[J]. Batteries, 2023, 9(6): 332.
13	WANG X, HU B, SU X, et al. State of health estimation for lithium-ion batteries using random forest and gated recurrent unit[J]. Journal of Energy Storage, 2024, 76: 109796.
14	YANG N K, SONG Z Y, HOFMANN H, et al. Robust state of health estimation of lithium-ion batteries using convolutional neural network and random forest[EB/OL]. 2020: arXiv: 2010.10452. http://arxiv.org/abs/2010.10452
15	HU C, JAIN G, ZHANG P Q, et al. Data-driven method based on particle swarm optimization and k-nearest neighbor regression for estimating capacity of lithium-ion battery[J]. Applied Energy, 2014, 129: 49-55.
16	ZHOU Y, HUANG M, PECHT M, et al. Remaining useful life estimation of lithium-ion cells based on knearest neighbor regression with differential evolution optimization[J]. Journal of Cleaner Production, 2020, 249: 119409.
17	CHEN J X, ZHANG Y, WU J, et al. SOC estimation for lithium-ion battery using the LSTM-RNN with extended input and constrained output[J]. Energy, 2023, 262: 125375.
18	LI W, SENGUPTA N, DECHENT P, et al. Online capacity estimation of lithium-ion batteries with deep long short-term memory networks[J]. Journal of Power Sources, 2021, 482: 228863.
19	VIDAL C, KOLLMEYER P, CHEMALI E, et al. Li-ion battery state of charge estimation using long short-term memory recurrent neural network with transfer learning[C]//2019 IEEE Transportation Electrification Conference and Expo (ITEC). Detroit, MI, USA. IEEE, 2019: 1-6.
20	LIU Y, SHU X, YU H Z, et al. State of charge prediction framework for lithium-ion batteries incorporating long short-term memory network and transfer learning[J]. Journal of Energy Storage, 2021, 37: 102494.
21	WANG Y, CHEN Z, ZHANG W. Lithium-ion battery state-of-charge estimation for small target sample sets using the improved GRU-based transfer learning[J]. Energy, 2022, 244: 12317.
22	Pro machine learning algorithms[M]. Apress, Berkeley, CA.
23	TORGO L. Chapman & Hall/CRC data mining and knowledge discovery series[M]. Data Mining with R Volume, 2010.
24	BREIMAN L, CUTLER A. Random forests[J]. Machine Learning, 2001, 45(1): 5-32.
25	BREIMAN L. Bagging predictors[J]. Machine Learning, 1996, 24(2): 123-140.
26	HO T K. The random subspace method for constructing decision forests[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(8): 832-844.
27	BREIMAN L, FREIDMANJ H, OLSHEN R A, et al. Classification and regression trees[M]. Chapman & Hau/CRC, 1984.
28	HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735-1780.
29	DR K S, DR C B. Data analytics: Why data normalization[J]. International Journal of Engineering & Technology, 2018, 7(4.6): 209.
30	MURRAY R. Remarks on some nonparametric estimates of a density function[J]. The Annals of Mathematical Statistics, 1956, 27(3): 832-837.
31	PARZEN E. On estimation of a probability density function and mode[J]. Annals of Mathematical Statistics, 1962, 33: 1065-1076.
32	EPANECHNIKOV V A.Nonparametric estimation of a multidimensional proability density[J]. Theory of Proability and Application,1969, 14: 153-158.
33	SILVERMAN B W. Density estimation for statistics and data analysis[M]. London: Chapman and Hall, 1986.

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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