Abstract
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.
Highlights
As environmental pollution becomes more and more dangerous, countries around the world are keen to develop and utilize new energy sources
The objectives of this study are as follows: (1) different wavelet basis functions are used to denoise the original signal, and the denoising effects of different wavelet basis functions are compared; (2) the SVM model and extreme learning machine (ELM) model are used to predict the remaining life of energy lithium batteries, and the prediction trends of the two models are compared; (3) the ELM-support vector machine model is established to predict the remaining life of lithium batteries, and the BSA algorithm is used to optimize the parameters of SVM; (4) different evaluation indicators are used to evaluate the method proposed in this study
With more and more attention paid to the development and utilization of new energy in the world, as well as the continuous maturity of new energy power generation technology, the security of energy storage system is very important
Summary
As environmental pollution becomes more and more dangerous, countries around the world are keen to develop and utilize new energy sources. The development of clean energy (such as solar, wind, etc.) has an important impact on improving environmental quality (Baleta et al, 2019; Karchiyappan, 2019; Tseng et al, 2018; Wang, 2016; Wang et al, 2019a, 2018). With the increase of service time, the service life of energy lithium battery will be reduced and the battery will appear aging problems. If the aging battery cannot be replaced in time, it will affect the safe and stable operation of the energy storage system. The health status of energy lithium battery is an important indicator of energy storage system and the health status is evaluated by predicting the remaining life of the lithium battery to provide reliable data support for the energy system (Ungurean et al, 2017). Improving the health assessment level of lithium batteries is of considerable significance to the electric vehicle industry
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