A novel streamlined particle-unscented Kalman filtering method for the available energy prediction of lithium-ion batteries considering the time-varying temperature-current influence
| Autor: | Yongcun Fan, Shunli Wang, Chuan-Yun Zou, Liang Zhang, Siyu Jin, Carlos Fernandez |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Battery (electricity)
Schedule Renewable Energy Sustainability and the Environment Energy management Computer science Energy Engineering and Power Technology Kalman filter lithium-ion battery temperature-current influence Fuel Technology Nuclear Energy and Engineering Control theory streamlined particle-unscented Kalman filtering Available energy Equivalent circuit Unscented transform synthetic-electrical circuit modeling available energy prediction Voltage |
| Zdroj: | Zhang, L, Wang, S, Zou, C, Fan, Y, Jin, S & 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, vol. 45, no. 12, pp. 17858-17877 . https://doi.org/10.1002/er.6930 |
| DOI: | 10.1002/er.6930 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|