Lithium battery is a new energy equipment. Because of its long service life and high energy density, it is widely used in various industries. However, as the number of uses increases, the life of the energy battery gradually decreases. Aging of battery will bring security risks to energy storage system. Through the life prediction of energy lithium battery, the health status of energy battery is assessed, so as to improve the safety of energy storage system. Therefore, a hybrid model is proposed to predict the life of the energy lithium battery. The lithium-ion battery capacity data are always divided into two scales, which are predicted by extreme learning machine and support vector machine model. The energy lithium-ion battery capacity attenuation data were obtained through experiments. The original signal is decomposed into five layers by using the wavelet basis function to denoise the signal. Finally, the denoised signal is synthesized. The noise reduction effect of each wavelet was analyzed. The analysis results show that the mean square error value of the Haar wavelet is 5.31e-28, which indicates that the Haar wavelet has the best noise reduction effect. Finally, the combined model was tested by using two sets of experiments. The prediction results of the combined model are compared with those of the single model. The test results show that the prediction results of the combined model are better than the single model for either experiment 1 or experiment 2. Experiment 1 indicated the root mean square error values are 29.58 and 79.68% smaller than the root mean square error values of extreme learning machine and support vector machine. The model proposed in this study has positive significance for the safety improvement of energy storage system and can promote the development and utilization of energy resources.

中文翻译：

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基于混合模型的锂离子电池寿命预测

锂电池是一种新能源设备。由于其使用寿命长、能量密度高，被广泛应用于各个行业。但是，随着使用次数的增加，能量电池的寿命逐渐降低。电池老化会给储能系统带来安全隐患。通过对能量锂电池的寿命预测，评估能量电池的健康状况，从而提高储能系统的安全性。因此，提出了一种混合模型来预测能量锂电池的寿命。锂离子电池容量数据总是分为两个尺度，分别通过极限学习机和支持向量机模型进行预测。能量锂离子电池容量衰减数据是通过实验得到的。利用小波基函数对信号进行去噪，将原始信号分解为五层。最后，合成去噪后的信号。分析了每个小波的降噪效果。分析结果表明，Haar小波的均方误差值为5.31e-28，说明Haar小波的降噪效果最好。最后，通过两组实验对组合模型进行了测试。将组合模型的预测结果与单一模型的预测结果进行比较。测试结果表明，无论是实验 1 还是实验 2，组合模型的预测结果均优于单一模型。 实验 1 表明均方根误差值分别为 29.58 和 79。比极限学习机和支持向量机的均方根误差值小68%。本研究提出的模型对于提高储能系统的安全性具有积极意义，可以促进能源资源的开发利用。