Open AccessHealth State Prediction of Lithium Ion Battery Based On Deep Learning Method
Author(s) -
Sheng Lu,
Fei Wang,
Changhao Piao,
Yi-Wei Ma
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/782/3/032083
Subject(s) - battery (electricity) , state of health , voltage , state of charge , lithium ion battery , computer science , lithium (medication) , test data , ion , artificial neural network , artificial intelligence , electrical engineering , engineering , power (physics) , chemistry , physics , medicine , organic chemistry , quantum mechanics , endocrinology , programming language
To predict the health status of lithium-ion batteries, long and short-term memory (LSTM) recurrent neural networks are used to build two types of battery SOH evaluation models. The discharge capacity is as input to a single feature model. While, the charge capacity, the charge time, the average charge temperature, the charge average voltage, the discharge temperature, and the discharge average voltage are as input to a multiple feature input model. The data samples are separated to training and test dataset. The test results show that the maximum absolute error of the LSTM-based model is less than 2%, which satisfies the industry standard (less than 5%). Meanwhile, this study transfers the NASA-based lithium-ion battery model to the University of Maryland lithium-ion battery data, which can reduce model training iterations and get good performance as well. The experimental results validate the effectiveness of transfer learning in the field of lithium-ion battery SOH prediction and provide a reference for future work in this field.