Research on SOC estimation for lithium ion batteries based on improved PNGV equivalence model and AF-UKF algorithm.

Zhou, Heng; Wang, Shunli; Yu, Chunmei; Xia, Lili; Fernandez, Carlos

Abstract

Accurately estimating the state of charge of lithium-ion batteries is of great significance for real-time monitoring and safety control of batteries. To solve the problems of difficult real-time estimation and low estimation accuracy of lithium batteries under various operating conditions, the ternary lithium-ion battery is used as the research object to establish an improved partnership for a new generation of vehicles(PNGV) equivalent circuit model to characterize the operating characteristics of the battery and to study and analyze the operating characteristics of the lithium battery by comprehensive experiments under various operating conditions. Considering the importance of state of charge accuracy at the early stage of estimation for the later estimation, the initial value of estimation is firstly calibrated using the open-circuit voltage method, and then the adaptive fading unscented Kalman filter algorithm is used for estimation tracking to achieve high accuracy estimation of lithium battery state of charge in real-time. A simulation model is built in MATLAB/Simulink and the performance analysis is carried out with a variety of operating conditions. The experimental results show that the improved PNGV model can better estimate the state of charge of lithium batteries with fast convergence, good tracking effect, and a maximum error of 0.485%. Comparing the state of charge results estimated using the adaptive fading unscented Kalman filter (AF-UKF) algorithm with the unscented Kalman filter algorithm, the maximum error was reduced by 0.354% in the HPPC condition and 1.978% in the BBDST condition, improving the accuracy and convergence speed of the filter.

Citation

ZHOU, H., WANG, S., YU, C., XIA, L. and FERNANDEZ, C. 2022. Research on SOC estimation for lithium ion batteries based on improved PNGV equivalence model and AF-UKF algorithm. International journal of electrochemical science [online], 17(8), article ID 220836. Available from: https://doi.org/10.20964/2022.08.31

• Journal Article Type: Article
• Acceptance Date: Jun 8, 2022
• Online Publication Date: Jul 4, 2022
• Publication Date: Aug 31, 2022
• Deposit Date: Jul 28, 2022
• Publicly Available Date: Jul 28, 2022
• Journal: International journal of electrochemical science
• Electronic ISSN: 1452-3981
• Publisher: Electrochemical Science Group
• Peer Reviewed: Peer Reviewed
• Volume: 17
• Issue: 8
• Article Number: 220836
• DOI: https://doi.org/10.20964/2022.08.31
• Keywords: Adaptive fading unscented Kalman filtering algorithm; Improve PNGV model; Lithium-ion battery; State of charge
• Public URL: https://rgu-repository.worktribe.com/output/1721576