The Lithium-Ion Battery Remaining Useful Life(RUL)Prediction Method Based on Machine Learning
With the development of electrochemical energy storage in new energy vehicles and power systems,lithium-ion batteries are getting widespread attention from the society.However,the energy storage capacity of lithium-ion batteries will gradually weaken due to their internal chemical reactions,so this study focuses on the remaining life of energy storage batteries and conducts in-depth research based on neural networks.First,the basic neural network structure is introduced,and then the neural network is used to train and learn from a large amount of data.Through this process,a prediction model of the remaining life of the energy storage battery is successfully constructed,which provides a new way to solve the battery life problem.By establishing a prediction model,we can not only better understand the life characteristics of energy storage batteries,but also provide an effective reference for their use and management.The research results are of great significance for the rational use of energy storage batteries in power grid peak regulation and frequency regulation.
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