Open AccessLi-ion battery state of health Prediction based on Long Short-Term Memory Recurrent Neural Network
Author(s) -
Jianguo Lin,
Guansong Yan,
Chang Wang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2010/1/012133
Subject(s) - state of health , recurrent neural network , battery (electricity) , computer science , particle swarm optimization , state of charge , artificial neural network , lithium ion battery , artificial intelligence , machine learning , power (physics) , physics , quantum mechanics
The state of health (SOH) prediction of lithium-ion battery is essential for the health management of batteries. At present, the prediction method combined with the extraction of health indicators in charge-discharge process has received extensive attention, however, many studies ignored that the extraction of battery discharge data will be affected by the actual operating conditions, which will affect the effectiveness of health indicators extraction. In this work, a type of recurrent neural network (RNN), which is long short-term memory-RNN(LSTM-RNN), is proposed to prediction the SOH of Li-ion batteries through the data of charging process and capacity. Because the different choice of network parameters will also affect the performance of the model, particle swarm optimization (PSO) algorithm is used to optimize LSTM model. The test results show that this method can effectively predict SOH of battery, and the maximum RMSE is less than 0.01. Compared with the traditional LSTM algorithm, it has higher accuracy.