State-of-charge estimation method of lithium-ion batteries based on long-short term memory network

2018 International Conference on Air Pollution and Environmental Engineering

生态环境科学

Zhang, Qichang^1,2 ; Liu, Bing^1,2 ; Zhou, Fei^1,2 ; Wang, Qianzhi^1,2 ; Kong, Jizhou^1,2

State Key Laboratory of Mechanics and Control of Mechanical Structure, Nanjing University of Aeronautics and Astronautics, Nanjing

210016, China^1

College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing

210016, China^2

关键词: Chinese Standard;    Equivalent circuit model;    Life span;    Linear neural network;    Short term memory;    State of charge;    State-of-charge estimation;    working condition;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/208/1/012001/pdf
DOI  :  10.1088/1755-1315/208/1/012001

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

This paper presents a method to estimate the state-of-charge (SOC) of lithium-ion batteries based on long-short term memory network (LSTM). The method is mainly composed of two parts: (1) A linear neural network is used to identify the parameters of second-order equivalent circuit model. (2) A LSTM network is built to estimate the SOC of lithium-ion battery. The linear neural network is trained using the American Dynamic Stress Test Condition (DST) dataset of battery (1st cycle), while the LSTM network is trained using the Chinese Standard Operating Condition (QCT) datasets of battery (1st, 10th and 20th cycle). After that, the trained LSTM network is tested using DST datasets of the batteries and QCT datasets under different temperatures. The results show that the LSTM network can accurately estimate the SOC of lithium battery under different temperatures, different working conditions and lifespan.