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
Doctor Carlos Fernandez firstname.lastname@example.org
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. Highlights: A new Streamlined Particle-Unscented Kalman Filtering method is proposed to predict the available energy of lithium-ion batteries. Improved Synthetic-Electrical Equivalent Circuit modeling strategies are established to describe the nonlinear battery characteristics. Adopted predictive correction is investigated by considering the time-varying temperature and current influence. For effective convergence, an adaptive windowing function factor is introduced into the correction process with a maximum estimation error of 2.27% and an average error of 0.80% for the complex varying-current Beijing Bus Dynamic Stress Test working conditions. The vehicle battery available energy prediction is realized with a maximum error of 1.83% and a maximum voltage traction error of 3.28% for the Urban Dynamometer Driving Schedule working conditions.
ZHANG, L., WANG, S., ZOU, C., FAN, Y., JIN, S. and FERNANDEZ, C. . 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], Early View. Available from: https://doi.org/10.1002/er.6930
|Journal Article Type||Article|
|Acceptance Date||May 22, 2021|
|Online Publication Date||Jul 4, 2021|
|Deposit Date||Aug 9, 2021|
|Publicly Available Date||Jul 5, 2022|
|Journal||International journal of energy research|
|Publisher||Wiley Open Access|
|Peer Reviewed||Peer Reviewed|
|Keywords||Available energy prediction; Lithium-ion battery; Streamlined particle-unscented Kalman filtering; Synthetic-electrical circuit modeling; Temperature-current influence|
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