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

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.

Metal-related electrocatalysts for Li–CO2batteries: an overview of the fundamentals to explore future-oriented strategies

“…146 At present, the research on lithium batteries with the aid of machine learning has achieved fruitful results. [147][148][149][150] Based on the previous successful experience, the use of machine learning to develop advanced metal-based catalysts for Li-CO 2 batteries is a new and bright road.…”

Hybrid Modeling of Lithium-Ion Battery: Physics-Informed Neural Network for Battery State Estimation

“…We observe that in the literature, the topic hybrid models does not necessarily indicate a combination of physics and a data-driven model. For example, the approach taken by [21] involves the combination of an extreme learning machine and a support vector machine to predict the battery capacity. The implementation of hybrid battery models poses several challenges that require attention.…”

Prediction of State of Charge for Lead-acid Batteries Based on GRU Network and Isolated Forest

“…The ampere-time integration method is the most commonly used SOC estimation method, which essentially treats the battery as a black box and considers that the power flowing into the battery has a proportional relationship with the power flowing out. This method does not consider the inner configuration and external electrical properties of the battery [5][6][7]. However, in practice, the inaccurate calculation of current often leads to errors.…”