A novel streamlined particle-unscented Kalman filtering method for the available energy prediction of lithium-ion batteries considering the time-varying temperature-current influence.

Zhang, Liang; Wang, Shunli; Zou, Chuanyun; Fan, Yongcun; Jin, Siyu; Fernandez, Carlos

Abstract

Effective energy prediction is of great importance for the operational status monitoring of high-power lithium-ion battery packs. It should be embedded in the battery system performance evaluation, energy management, and safety protection. A new Streamlined Particle-Unscented Kalman Filtering method is proposed to predict the available energy of lithium-ion batteries, in which an Adaptive-Dual Unscented Transform treatment is conducted to realize the precise mathematical expression of its working conditions. For the accurate mathematical description purpose, an improved Synthetic-Electrical Equivalent Circuit modeling method is introduced into the internal effect equivalent process considering the influence of time-varying temperature and current conditions. As can be known from the experimental results, the proposed prediction method has a maximum estimation error of 2.27% and an average error of 0.80%, for the complex varying-current Beijing Bus Dynamic Stress Test. Under the Urban Dynamometer Driving Schedule working conditions, the available energy prediction has high accuracy with a maximum error of 1.83% and a voltage traction error of 3.28%. It provides vehicle-mounted available energy prediction schemes for effective management and safety protection of high-power lithium-ion batteries.

Citation

ZHANG, L., WANG, S., ZOU, C., FAN, Y., JIN, S. and FERNANDEZ, C. 2021. A novel streamlined particle-unscented Kalman filtering method for the available energy prediction of lithium-ion batteries considering the time-varying temperature-current influence. International journal of energy research [online], 45(12): environmentally friendly energy solutions, pages 17858-17877. Available from: https://doi.org/10.1002/er.6930

• Journal Article Type: Article
• Acceptance Date: May 22, 2021
• Online Publication Date: Jul 4, 2021
• Publication Date: Oct 10, 2021
• Deposit Date: Aug 9, 2021
• Publicly Available Date: Jul 5, 2022
• Journal: International journal of energy research
• Print ISSN: 0363-907X
• Electronic ISSN: 1099-114X
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• Volume: 45
• Issue: 12
• Pages: 17858-17877
• DOI: https://doi.org/10.1002/er.6930
• Keywords: Available energy prediction; Lithium-ion battery; Streamlined particle-unscented Kalman filtering; Synthetic-electrical circuit modeling; Temperature-current influence
• Public URL: https://rgu-repository.worktribe.com/output/1391094