Battery state-of-health estimation incorporating model uncertainty based on Bayesian model averaging
文献类型：期刊论文
| 作者 | Zou, Qingrong3; Wen, Jici1,2 ; Wen JC(温济慈)
|
| 刊名 | ENERGY
 |
| 出版日期 | 2024-11-01 |
| 卷号 | 308页码:12 |
| 关键词 | State-of-health estimation Model uncertainty Parameter uncertainty Lithium-ion batteries Bayesian model averaging |
| ISSN号 | 0360-5442 |
| DOI | 10.1016/j.energy.2024.132884 |
| 通讯作者 | Wen, Jici(wenjici@lnm.imech.ac.cn) |
| 英文摘要 | Accurately estimating the state-of-health (SOH) of lithium-ion batteries is crucial for efficient, reliable, and safe use. However, the degradation of these batteries involves complex and intricate failure mechanisms that cannot be fully captured by a single model. To tackle this challenge, we propose an SOH estimation method based on Bayesian Model Averaging (BMA), which effectively accounts for both parameter and model uncertainties by combining estimations from different model implementations. It provides both point-value estimates and the probability distribution estimates, delivering results in a fraction of a second. Evaluated on three open-source datasets, the maximum absolute error of SOH estimation is within 0.03, and the Continuous Ranked Probability Score is within 0.015. Compared to optimal individual models, the proposed BMA method reduces the prediction error of point estimates by half to two-thirds. Additionally, the prediction error for probability estimation decreases by an order of magnitude. Moreover, the comparison studies with respect to the Gaussian Process Regression model and the Quantile Regression Forests model demonstrate the applicability and superiority of proposed method. These results highlight the potential of the BMA method to advance battery SOH estimation and facilitate reliable battery management. |
| WOS关键词 | CHARGE ; PREDICTION ; ELECTRODES ; REGRESSION ; DIAGNOSIS ; CAPACITY ; RULES |
| 资助项目 | R&D Program of Beijing Municipal Education Commission[KM202311232008] ; National Natural Science Foundation of China[12002343] ; Young Elite Scientists Sponsorship Program by the Chinese Society of Theoretical and Applied Mechanics[CSTAM2022-XSC-QN4] ; Young Backbone Teacher Support Plan of Beijing Information Science & Technology University[YBT 202446] |
| WOS研究方向 | Thermodynamics ; Energy & Fuels |
| 语种 | 英语 |
| WOS记录号 | WOS:001301040400001 |
| 资助机构 | R&D Program of Beijing Municipal Education Commission ; National Natural Science Foundation of China ; Young Elite Scientists Sponsorship Program by the Chinese Society of Theoretical and Applied Mechanics ; Young Backbone Teacher Support Plan of Beijing Information Science & Technology University |
| 源URL | [http://dspace.imech.ac.cn/handle/311007/96436]  |
| 专题 | 力学研究所_非线性力学国家重点实验室 |
| 通讯作者 | Wen, Jici |
| 作者单位 | 1.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech, Beijing 100190, Peoples R China 3.Beijing Informat Sci & Technol Univ, Sch Appl Sci, Beijing 102206, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zou, Qingrong,Wen, Jici,Wen JC. Battery state-of-health estimation incorporating model uncertainty based on Bayesian model averaging[J]. ENERGY,2024,308:12. |
| APA | Zou, Qingrong,Wen, Jici,&温济慈.(2024).Battery state-of-health estimation incorporating model uncertainty based on Bayesian model averaging.ENERGY,308,12. |
| MLA | Zou, Qingrong,et al."Battery state-of-health estimation incorporating model uncertainty based on Bayesian model averaging".ENERGY 308(2024):12. |
入库方式： OAI收割
来源：力学研究所
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