State of energy estimation of lithium-ion battery based on long short-term memory optimization Adaptive Cubature Kalman filter

• Published in: PloS one Vol. 19; no. 7; p. e0306165
• Main Authors: Hou, Enguang, Song, Heyan, Wang, Zhen, Zhu, Jingshu, Tang, Jiarui, Shen, Gang, Wang, Jiangang
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 10.07.2024; Public Library of Science (PLoS)
• Subjects: Accuracy; Adaptability; Algorithms; Analysis; Artificial intelligence; Batteries; Big Data; Biology and Life Sciences; Computation; Decomposition; Electric Power Supplies; Energy storage; Engineering and Technology; Error analysis; Error correction; Estimation errors; Ions; Kalman filters; Lithium; Lithium batteries; Lithium-ion batteries; Long short-term memory; Memory, Short-Term; Noise; Optimization; Parameter identification; Physical Sciences; Product safety; Rechargeable batteries; Reliability; Research and Analysis Methods; Safety; Simulation methods; State of charge
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0306165

State of energy (SOE) is an important parameter to ensure the safety and reliability of lithium-ion battery (LIB) system. The safety of LIBs, the development of artificial intelligence, and the increase in computing power have provided possibilities for big data computing. This article studies SOE estimation problem of LIBs, aiming to improve the accuracy and adaptability of the estimation. Firstly, in the SOE estimation process, adaptive correction is performed by iteratively updating the observation noise equation and process noise equation of the Adaptive Cubature Kalman Filter (ACKF) to enhance the adaptive capability. Meanwhile, the adoption of high-order equivalent models further improves the accuracy and adaptive ability of SOE estimation. Secondly, Long Short-term Memory (LSTM) is introduced to optimize Ohmic internal resistance (OIR) and actual energy (AE), further improving the accuracy of SOE estimation. Once again, in the process of OIR and AE estimation, the iterative updating of the observation noise equation and process noise equation of ACKF were also adopted to perform adaptive correction and enhance the adaptive ability. Finally, this article establishes a SOE estimation method based on LSTM optimized ACKF. Validate the LSTM optimized ACKF method through simulation experiments and compare it with individual ACKF methods. The results show that the ACKF estimation method based on LSTM optimization has an SOE estimation error of less than 0.90% for LIB, regardless of the SOE at 100%, 65%, and 30%, which is more accurate than the SOE estimation error of ACKF alone. It can be seen that this study has improved the accuracy and adaptability of LIB's SOE estimation, providing more accurate data support for ensuring the safety and reliability of lithium batteries.