| Autor: |
Xingzi Qiang, Wenting Liu, Zhiqiang Lyu, Haijun Ruan, Xiaoyu Li |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Green Energy and Intelligent Transportation, Vol 3, Iss 5, Pp 100169- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2773-1537 |
| DOI: |
10.1016/j.geits.2024.100169 |
| Popis: |
The estimation of State of Health (SOH) for battery packs used in Electric Vehicles (EVs) is a complex task with significant importance, accompanied by several challenges. This study introduces a data-fusion model approach to estimate the SOH of battery packs. The approach utilizes dual Gaussian Process Regressions (GPRs) to construct a data-driven and non-parametric aging model based on charging-based Aging Features (AFs). To enhance the accuracy of the aging model, a noise model is established to replace the random noise. Subsequently, the state-space representation of the aging model is incorporated. Additionally, the Particle Filter (PF) is introduced to track the unknown state in the aging model, thereby developing the data-fusion-model for SOH estimation. The performance of the proposed method is validated through aging experiments conducted on battery packs. The simulation results demonstrate that the data-fusion model approach achieves accurate SOH estimation, with maximum errors less than 1.5%. Compared to conventional techniques such as GPR and Support Vector Regression (SVR), the proposed method exhibits higher estimation accuracy and robustness. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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