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http://dx.doi.org/10.7471/ikeee.2019.23.4.1328

Comparison of Learning Techniques of LSTM Network for State of Charge Estimation in Lithium-Ion Batteries  

Hong, Seon-Ri (Dept. of Electrical Engineering, Chungnam National University)
Kang, Moses (Dept. of Electrical Engineering, Korea Institute of Energy Research)
Kim, Gun-Woo (Dept. of Electrical Engineering, Chungnam National University)
Jeong, Hak-Geun (Dept. of Electrical Engineering, Korea Institute of Energy Research)
Beak, Jong-Bok (Dept. of Electrical Engineering, Korea Institute of Energy Research)
Kim, Jong-Hoon (Dept. of Electrical Engineering, Chungnam National University)
Publication Information
Journal of IKEEE / v.23, no.4, 2019 , pp. 1328-1336 More about this Journal
Abstract
To maintain the safe and optimal performance of batteries, accurate estimation of state of charge (SOC) is critical. In this paper, Long short-term memory network (LSTM) based on the artificial intelligence algorithm is applied to address the problem of the conventional coulomb-counting method. Different discharge cycles are concatenated to form the dataset for training and verification. In oder to improve the quality of input data for learning, preprocessing was performed. In addition, we compared learning ability and SOC estimation performance according to the structure of LSTM model and hyperparameter setup. The trained model was verified with a UDDS profile and achieved estimated accuracy of RMSE 0.82% and MAX 2.54%.
Keywords
State of charge estimation; battery management system; long short-term memory; recurrent neural network; lithium ion battery;
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