| Battery state-of-health estimation incorporating model uncertainty based on Bayesian model averaging | |
Zou, Qingrong1; Wen JC(温济慈)2,3
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| Corresponding Author | Wen, Jici([email protected]) |
| Source Publication | ENERGY
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| 2024-11-01 | |
| Volume | 308Pages:12 |
| ISSN | 0360-5442 |
| Abstract | 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. |
| Keyword | State-of-health estimation Model uncertainty Parameter uncertainty Lithium-ion batteries Bayesian model averaging |
| DOI | 10.1016/j.energy.2024.132884 |
| Indexed By | SCI ; EI |
| Language | 英语 |
| WOS ID | WOS:001301040400001 |
| WOS Keyword | CHARGE ; PREDICTION ; ELECTRODES ; REGRESSION ; DIAGNOSIS ; CAPACITY ; RULES |
| WOS Research Area | Thermodynamics ; Energy & Fuels |
| WOS Subject | Thermodynamics ; Energy & Fuels |
| Funding Project | 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] |
| Funding Organization | 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 |
| Classification | 一类 |
| Ranking | 1 |
| Contributor | Wen, Jici |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://dspace.imech.ac.cn/handle/311007/96437 |
| Collection | 非线性力学国家重点实验室 |
| Affiliation | 1.Beijing Informat Sci & Technol Univ, Sch Appl Sci, Beijing 102206, Peoples R China; 2.Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech, Beijing 100190, Peoples R China; 3.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China |
| Recommended Citation GB/T 7714 | Zou, Qingrong,Wen JC. Battery state-of-health estimation incorporating model uncertainty based on Bayesian model averaging[J]. ENERGY,2024,308:12.Rp_Au:Wen, Jici |
| APA | Zou, Qingrong,&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. |
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