Adaptive iterative working state prediction based on the double unscented transformation and dynamic functioning for unmanned aerial vehicle lithium-ion batteries

Shi, Haotian; Wang, Shunli; Fernandez, Carlos; Yu, Chunmei; Li, Xiaoxia; Zou, Chuanyun

Abstract

In lithium-ion batteries, the accuracy of estimation of the state of charge is a core parameter which will determine the power control accuracy and management reliability of the energy storage systems. When using unscented Kalman filtering to estimate the charge of lithium-ion batteries, if the pulse current change rate is too high, the tracking effects of algorithms will not be optimal, with high estimation errors. In this study, the unscented Kalman filtering algorithm is improved to solve the above problems and boost the Kalman gain with dynamic function modules, so as to improve system stability. The closed-circuit voltage of the system is predicted with two non-linear transformations, so as to improve the accuracy of the system. Meanwhile, an adaptive algorithm is developed to predict and correct the system noises and observation noises, thus enhancing the robustness of the system. Experiments show that the maximum estimation error of the second-order Circuit Model is controlled to less than 0.20V. Under various simulation conditions and interference factors, the estimation error of the unscented Kalman filtering is as high as 2%, but that of the improved Kalman filtering algorithm are kept well under 1.00%, with the errors reduced by 0.80%, therefore laying a sound foundation for the follow-up research on the battery management system.

Citation

SHI, H., WANG, S., FERNANDEZ, C., YU, C., LI, X. and ZOU, C. 2020. Adaptive iterative working state prediction based on the double unscented transformation and dynamic functioning for unmanned aerial vehicle lithium-ion batteries. Measurement and control [online], 53(9-10), pages 1760-1773. Available from: https://doi.org/10.1177/0020294020923057

• Journal Article Type: Article
• Acceptance Date: Apr 7, 2020
• Online Publication Date: Jul 8, 2020
• Publication Date: Dec 31, 2020
• Deposit Date: Jul 24, 2020
• Publicly Available Date: Jul 24, 2020
• Journal: Measurement and control
• Print ISSN: 0020-2940
• Electronic ISSN: 2051-8730
• Publisher: SAGE Publications
• Peer Reviewed: Peer Reviewed
• Volume: 53
• Issue: 9-10
• Pages: 1760-1773
• DOI: https://doi.org/10.1177/0020294020923057
• Keywords: Lithium-ion batteries; Working state; Double unscented transformation; Unscented Kalman filter; Adaptive noise correction
• Public URL: https://rgu-repository.worktribe.com/output/951787