Transfer learning for battery smarter state estimation and ageing prognostics: Recent progress, challenges, and prospects

Liu, Kailong; Pan, Qiao; Che, Yunhong; Zheng, Yusheng; Li, Kang; Teodorescu, Remus; Widanage, Widanalage Dhammika; Barai, Anup

doi:10.1016/j.adapen.2022.100117

Cited by 70 publications

(14 citation statements)

References 87 publications

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“…Power generation and vehicular use are two of the main contributors to air pollution. One opportunity that can help to reduce air pollution is to increase the use of renewable energy sources in these industries, drawing on targeted measures to replace the use of fossil fuels with renewable energy such as wind, solar and tidal [30][31][32]. The replacement of fossil fuel powered vehicles with more sustainable electric vehicles is seen as one of the most promising solutions to control urban air pollution, resulting in improved environmental management and low-carbon mobility [33][34][35].…”

Section: Literature Reviewmentioning

confidence: 99%

Generation planning for power companies with hybrid production technologies under multiple renewable energy policies

Pan¹,

Liu²,

Zhang³

et al. 2023

Renewable and Sustainable Energy Reviews

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Section: Literature Reviewmentioning

confidence: 99%

Generation planning for power companies with hybrid production technologies under multiple renewable energy policies

Pan¹,

Liu²,

Zhang³

et al. 2023

Renewable and Sustainable Energy Reviews

Self Cite

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“…In this case, transfer learning (TL) technology is utilized for such problems [39]. Two strategies, namely, the model parameter fine-tuning and domain adaption methods, are used in recent works for SOH estimation [40].…”

Section: Sourcesmentioning

confidence: 99%

A Novel Method for State of Health Estimation of Lithium-Ion Batteries Based on Deep Learning Neural Network and Transfer Learning

Ren,

Du,

Zhao

2023

Batteries

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Accurate state of health (SOH) estimation of lithium-ion batteries is critical for maintaining reliable and safe working conditions for electric vehicles (EVs). The machine learning-based method with health features (HFs) is encouraging for health prognostics. However, the machine learning method assumes that the training and testing data have the same distribution, which restricts its application for different types of batteries. Thus, in this paper, a deep learning neural network and fine-tuning-based transfer learning strategy are proposed for accurate and robust SOH estimation toward different types of batteries. First, a universal HF extraction strategy is proposed to obtain four highly related HFs. Second, a deep learning neural network consisting of long short-term memory (LSTM) and fully connected layers is established to model the relationship between the HFs and SOH. Third, the fine-tuning-based transfer learning strategy is exploited for SOH estimation of various types of batteries. The proposed methods are comprehensively verified using three open-source datasets. Experimental results show that the proposed deep learning neural network with the HFs can estimate the SOH accurately in a single dataset without using the transfer learning strategy where the mean absolute error (MAE) and root mean square error (RMSE) are constrained to 1.21% and 1.83%. For the transfer learning between different aging datasets, the overall MAE and RMSE are limited to 1.09% and 1.41%, demonstrating the reliability of the fine-tuning strategy.

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“…The success of fine-tuning has been witnessed in predicting the SOH and RUL of aged batteries that are subject to a variety of discharge protocols 11 . However, the drawback of fine-tuning 32 the fact that sufficiently large amount of labeled data from the target domain should be supplied to avoid over-fitting. As for the domain adaptation, it minimizes the domain shift between the source and target domains by improving the extracted features 33,34 or learned feature spaces [35][36][37] .…”

Section: Introductionmentioning

confidence: 99%

Forecasting battery degradation trajectory under domain shift with domain generalization

Zhang,

Tan,

et al. 2024

Preprint

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Rechargeable batteries play a pivotal role in the transition towards a carbon-neutral future by electrifying transportation and mitigating the intermittency of renewable energies. Forecasting the degradation of batteries is crucial for maximizing their usage. However, predicting battery degradation is not trivial due to complex failure mechanisms and diverse working conditions and chemistries. To tackle this challenge, we develop a deep learning model by leveraging meta-learning-based and task-driven domain generalization techniques. Our model delivers superior performance in forecasting battery degradation trajectories on 182 cells of different chemistries and conditions with few or no target-domain training data. Moreover, we explore the interpretability of the model and find that the model might gain generalization capability by learning domain gaps that are similar to the differences of degradation modes behind various operational conditions. Collectively, our work not only showcases the promise of the high-reliability data-driven model in diverse conditions and chemistries, but also spotlights the potential interplay between artificial intelligence and domain knowledge.

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Transfer learning for battery smarter state estimation and ageing prognostics: Recent progress, challenges, and prospects

Cited by 70 publications

References 87 publications

Generation planning for power companies with hybrid production technologies under multiple renewable energy policies

Generation planning for power companies with hybrid production technologies under multiple renewable energy policies

A Novel Method for State of Health Estimation of Lithium-Ion Batteries Based on Deep Learning Neural Network and Transfer Learning

Forecasting battery degradation trajectory under domain shift with domain generalization

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