| 1. |
- Alipour, Mohammad, et al.
(författare)
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A surrogate-assisted uncertainty quantification and sensitivity analysis on a coupled electrochemical–thermal battery aging model
- 2023
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Ingår i: Journal of Power Sources. - : Elsevier BV. - 0378-7753 .- 1873-2755. ; 579
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Tidskriftsartikel (refereegranskat)abstract
- High-fidelity physics-based models are required to comprehend battery behavior at various operating conditions. This paper proposes an uncertainty quantification analysis on a coupled electrochemical–thermal aging model to improve the reliability of a battery model, while also investigating the impact of parametric model uncertainties on battery voltage, temperature, and aging. The coupled model's high computing cost, however, is a significant barrier to perform uncertainty quantification (UQ) and sensitivity analysis (SA). To address this problem, a surrogate model – i.e, by simulating the outcome of a quantity of interest that cannot be easily computed or measured – based on the Gaussian process regression (GPR) theory and principle component analysis (PCA) is built, using a small collection of finite element simulation results as synthetic training data. In total, 43 variable electrochemical–thermal parameters as well as 13 variable aging parameters are studied and estimated. Moreover, the trained surrogate model is also used in the parameterization of the electrochemical and thermal models. The results show that the uncertainties in the input parameters significantly affect the estimations of battery voltage, temperature, and aging. Based on this sensitivity analysis, the most influential parameters affecting the above mentioned battery outputs are reported. This approach is thereby helpful for developing robust and reliable high-fidelity battery aging models with potential applications in digital twins as well as for synthetic data generation.
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| 2. |
- Alipour, Mohammad, et al.
(författare)
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Improved Battery Cycle Life Prediction Using a Hybrid Data-Driven Model Incorporating Linear Support Vector Regression and Gaussian
- 2022
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Ingår i: ChemPhysChem. - : Wiley. - 1439-4235 .- 1439-7641. ; 23:7
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Tidskriftsartikel (refereegranskat)abstract
- The ability to accurately predict lithium-ion battery life-time already at an early stage of battery usage is critical for ensuring safe operation, accelerating technology development, and enabling battery second-life applications. Many models are unable to effectively predict battery life-time at early cycles due to the complex and nonlinear degrading behavior of lithium-ion batteries. In this study, two hybrid data-driven models, incorporating a traditional linear support vector regression (LSVR) and a Gaussian process regression (GPR), were developed to estimate battery life-time at an early stage, before more severe capacity fading, utilizing a data set of 124 battery cells with lifetimes ranging from 150 to 2300 cycles. Two type of hybrid models, here denoted as A and B, were proposed. For each of the models, we achieved 1.1 % (A) and 1.4 % (B) training error, and similarly, 8.3 % (A) and 8.2 % (B) test error. The two key advantages are that the error percentage is kept below 10 % and that very low error values for the training and test sets were observed when utilizing data from only the first 100 cycles.The proposed method thus appears highly promising for predicting battery life during early cycles.
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| 3. |
- dos Santos, Arthur, et al.
(författare)
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An experiment on an automated literature survey of data-driven speech enhancement methods
- 2024
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Ingår i: Acta Acustica. - : EDP Sciences. - 2681-4617. ; 8:2, s. 1-8
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Tidskriftsartikel (refereegranskat)abstract
- The increasing number of scientific publications in acoustics, in general, presents difficulties in conducting traditional literature surveys. This work explores the use of a generative pre-trained transformer (GPT) model to automate a literature survey of 117 articles on data-driven speech enhancement methods. The main objective is to evaluate the capabilities and limitations of the model in providing accurate responses to specific queries about the papers selected from a reference human-based survey. While we see great potential to automate literature surveys in acoustics, improvements are needed to address technical questions more clearly and accurately.
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