A Hybrid Method for State-of-Charge Estimation for Lithium-Ion Batteries Using a Long Short-Term Memory Network Combined with Attention and a Kalman Filter

• Published in: Energies (Basel) Vol. 15; no. 18; p. 6745
• Main Authors: Zhang, Xinghao, Huang, Yan, Zhang, Zhaowei, Lin, Huipin, Zeng, Yu, Gao, Mingyu
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2022
• Subjects: Algorithms; attention mechanism; Automobiles, Electric; Batteries; charge state; Deep learning; Electric vehicles; Environmental impact; Estimation; Information processing; Kalman filter; Kalman filters; Lithium; Lithium-ion batteries; lithium-ion battery; Long short-term memory; Methods; Neural networks; Power management; Root-mean-square errors; State of charge; Support vector machines
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en15186745

A battery management system (BMS) is an important link between on-board power battery and electric vehicles. The BMS is used to collect, process, and store important information during the operation of a battery pack in real time. Due to the wide application of lithium-ion batteries in electric vehicles, the correct estimation of the state of charge (SOC) of lithium-ion batteries (LIBS) is of great importance in the battery management system. The SOC is used to reflect the remaining capacity of the battery, which is directly related to the efficiency of the power output and management of energy. In this paper, a new long short-term memory network with attention mechanism combined with Kalman filter is proposed to estimate the SOC of the Li-ion battery in the BMS. Several different dynamic driving plans are used for training and testing under different temperatures and initial errors, and the results show that the method is highly reliable for estimating the SOC of the Li-ion battery. The average root mean square error (RMSE) reaches 0.01492 for the US06 condition, 0.01205 for the federal urban driving scheme (FUDS) condition, and 0.00806 for the dynamic stress test (DST) condition. It is demonstrated that the proposed method is more reliable and robust, in terms of SOC estimation accuracy, compared with the traditional long short-term memory (LSTM) neural network, LSTM combined with attention mechanism, or LSTM combined with the Kalman filtering method.