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.
|Journal||International Journal of Energy Research|
|Number of pages||20|
|Publication status||Published - 10 Oct 2021|
Bibliographical noteFunding Information:
Fund of Robot Technology Used for Special Environment Key Laboratory of Sichuan Province, Grant/Award Number: 18kftk03; China Scholarship Council, Grant/Award Number: 201908515099; Sichuan Science and Technology Program, Grant/Award Number: 2019YFG0427; Natural Science Foundation of Southwest University of Science and Technology, Grant/Award Numbers: 18zx7145, 17zx7110; National Natural Science Foundation of China, Grant/Award Number: 61801407 Funding information
© 2021 John Wiley & Sons Ltd.
- available energy prediction
- lithium-ion battery
- streamlined particle-unscented Kalman filtering
- synthetic-electrical circuit modeling
- temperature-current influence