| Autor: |
Chao Yu, Jiangong Zhu, Wenxue Liu, Haifeng Dai, Xuezhe Wei |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Green Energy and Intelligent Transportation, Vol 3, Iss 4, Pp 100152- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2773-1537 |
| DOI: |
10.1016/j.geits.2024.100152 |
| Popis: |
The safe and efficient operation of the electric vehicle significantly depends on the accurate state-of-charge (SOC) and state-of-temperature (SOT) of Lithium-ion (Li-ion) batteries. Given the influence of cross-interference between the two states indicated above, this study establishs a co-estimation framework of battery SOC and SOT. This framwork is based on an innovative electrothermal model and adaptive estimation algorithms. The first-order RC electric model and an innovative thermal model are components of the electrothermal model. Specifically, the thermal model includes two lumped-mass thermal submodels for two tabs and a two-dimensional (2-D) thermal resistance network (TRN) submodel for the main battery body, capable of capturing the detailed thermodynamics of large-format Li-ion batteries. Moreover, the proposed thermal model strikes an acceptable compromise between the estimation fidelity and computational complexity by representing the heat transfer processes by the thermal resistances. Besides, the adaptive estimation algorithms are composed of an adaptive unscented Kalman filter (AUKF) and an adaptive Kalman filter (AKF), which adaptively update the state and noise covariances. Regarding the estimation results, the mean absolute errors (MAEs) of SOC and SOT estimation are controlled within 1% and 0.4 °C at two temperatures, indicating that the co-estimation method yields superior prediction performance in a wide temperature range of 5–35 °C. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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