English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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.

Life prediction of lithium-ion battery based on a hybrid model