改进Sine混沌映射CO-ELM锂离子电池RUL预测

doi:10.19799/j.cnki.2095-4239.2024.0990

摘要/Abstract

摘要：

针对锂离子电池采用极限学习机进行剩余使用寿命预测时，存在预测结果不稳定和预测准确度不高的问题，提出采用猎豹优化算法优化ELM对锂离子电池剩余使用寿命进行预测。提取锂离子电池数据集中等压降放电时间作为间接健康因子；引入猎豹优化算法对ELM模型参数进行优化，并使用改进的Sine混沌映射优化猎豹初始种群；最后采用NASA卓越预测中心提供的电池数据集和牛津大学提供的电池老化数据集对该模型有效性和准确性进行验证。通过原始ELM模型进行多次实验，得到该数据集进行预测的最佳训练数据量以及最佳神经元数量；利用所提出的SCO-ELM模型进行电池的剩余使用寿命预测，对比原始ELM与遗传算法优化ELM模型，均方根误差在0.004以下，且具有较快的预测时间；之后进行电池全周期寿命预测，预测精度平均提升40%，预测速度提升78%以上；使用B0005号电池训练结果对同类型电池组进行预测，预测精度平均提升25%，预测速度提升75%以上。实验结果表明，所提方法具有预测准确度高、预测速度快、操作复杂度低和模型稳定等优势。

关键词: 锂离子电池, 剩余使用寿命, 极限学习机, 猎豹优化, 混沌映射

Abstract:

To address the challenges of unstable predictions and accuracy when using extreme learning machines (ELMs) to predict the remaining useful life of lithium-ion batteries, this study proposes a cheetah optimization (CO) algorithm to optimize the ELM model performance. The equal voltage drop discharge time, extracted from the lithium-ion battery dataset, is employed as an indirect health factor. Furthermore, the CO algorithm is introduced to optimize the ELM parameters. This initial population of the CO algorithm is improved using sine chaotic mapping. The effectiveness and accuracy of the proposed model are verified using the battery dataset provided by the NASA Center for Excellence Prediction and the Oxford Battery Degradation Dataset from Oxford University. The optimal amount of training data and the ideal number of neurons are obtained through multiple experiments with the original ELM model. The residual service life of batteries is predicted using the proposed SCO-ELM model. Compared with the original ELM and the genetic algorithm-optimized ELM model, the proposed SCO-ELM model achieves a root mean square error below 0.004 and significantly faster prediction times. The prediction accuracy improves by 40% on average and the prediction speed is improved by more than 78%. Using the training results of battery B0005 to predict the performance of similar battery packs, the prediction accuracy improves by 25% on average, and the prediction speed increases by more than 75%. Thus, the experimental results confirm that the proposed method offers high prediction accuracy, fast computation speed, low operation complexity, and a stable model.

Key words: lithium-ion battery, remaining useful life, extreme learning machine, cheetah optimization, chaotic mapping

中图分类号:

TM 912

王鹏, 周俊, 伍星, 刘韬. 改进Sine混沌映射CO-ELM锂离子电池RUL预测[J]. 储能科学与技术, 2025, 14(4): 1603-1616.

Peng WANG, Jun ZHOU, Xing WU, Tao LIU. Remaining useful life prediction of a lithium-ion battery based on a cheetah optimization-extreme learning machine with improved Sine chaotic mapping[J]. Energy Storage Science and Technology, 2025, 14(4): 1603-1616.

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电池数据集	相关性	偏相关性
B0005	0.998271	0.931276
B0006	0.994482	0.931649
B0007	0.998413	0.947518
B0018	0.997773	0.962799

训练数据量	RMSE	r²
70	0.005686	0.996488
80	0.003693	0.995305
90	0.003714	0.997775
100	0.003937	0.996634

神经元数量	RMSE	r²
1	0.165125	0.995686
2	0.003755	0.994720
3	0.003666	0.995497
4	0.003582	0.995422

训练模型	RMSE	r²	训练时间 /s	ELM模型预测精度提升/%	ELM模型预测速度提升/%
ELM	0.010503	0.995182	0.000483	—	—
LSTM	0.017830	0.963152	2.241374	41.0937	99.9784
Transformer	0.029974	0.890339	9.276232	64.9596	99.9948

训练模型	RMSE	r²	训练时间/s
ELM	0.014614	0.993549	0.000358
GA-ELM	0.007035	0.995531	0.253348
SCO-ELM	0.003783	0.995071	0.058393