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- W4313563998 abstract "Machine learning is an useful approach estimating the state-of-health (SOH) of the lithium-ion batteries and had been successfully implemented on battery management systems (BMS). However, the SOH estimation accuracy of the machine learning relies highly on the similarity of training dataset and battery data of estimation. In other words, the machine learning approach may fail if the battery of estimation is operated in a different condition from the condition set for collecting the training dataset. This paper aims on solving such a problem by proposing a transfer component analysis-random forest regression (TCA-RF) approach. The robustness of this proposed approach is ensured by the random forest regression, which is an ensemble learning approach. The generalization of this proposed approach is enhanced by transfer component analysis since it achieves the domain adaption with well transformation of features between two datasets. Two battery ageing datasets collected in different operating conditions are selected in this paper to validate the success of the proposed work, where these two datasets are collected by the Center for Advanced Life Cycle Engineering (CALCE) and the National Aeronautics and Space Administration (NASA) Prognostics Center of Excellence (PCoE). The proposed work is further compared with multiple commonly adopted intelligent algorithms to showcase the high performance of the proposed work. The experimental results shows that the proposed TCA-RF approach has a better accurate SOH estimation comparing to other estimation algorithms by 2% to more 50%." @default.
- W4313563998 created "2023-01-06" @default.
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- W4313563998 date "2022-08-22" @default.
- W4313563998 modified "2023-10-06" @default.
- W4313563998 title "Random Forest Regression for Battery State-of-Health Estimation Based on Unsupervised Transfer Component Analysis Domain Adaptation" @default.
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- W4313563998 doi "https://doi.org/10.1109/icit48603.2022.10002811" @default.
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