A Transfer Learning-Based Method for Personalized State of Health Estimation of Lithium-Ion Batteries

Ma, Guijun; Xu, Songpei; Yang, Tao; Du, Zhaohui; Zhu, Limin; Ding, Han; Yuan, Ye

doi:10.1109/tnnls.2022.3176925

Cited by 40 publications

(10 citation statements)

References 43 publications

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“…A general method for battery health prognostics using CNN-based deep learning was proposed by Ruan et al , 176 where the internal electrochemical parameter degradation can also be diagnosed. The domain adaptation method was used by Han et al 177 and Ma et al 178 to improve the estimation accuracy under different battery applications using long-short-term memory (LSTM) and CNN bottleneck, respectively. The main idea was to reduce the domain discrepancy between the hidden layers which represent the information vector for the source domain and target domain.…”

Section: Battery Health Prognosticsmentioning

confidence: 99%

Health prognostics for lithium-ion batteries: mechanisms, methods, and prospects

Che

Lin

et al. 2023

Energy Environ. Sci.

140

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Section: Battery Health Prognosticsmentioning

confidence: 99%

Health prognostics for lithium-ion batteries: mechanisms, methods, and prospects

Che

Lin

et al. 2023

Energy Environ. Sci.

140

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“…In Ref. [81] the MMD loss was integrated with the CNN framework to estimate the battery SoH. The data of the first 100 cycles was used for the domain adaptative CNN training while the rest was used for validation.…”

Section: Soh Estimation With Transfer Learningmentioning

confidence: 99%

“…[80] • I, V, T LSTM Domain adaptation ≤ 2.79% Ref. [81] • Voltage curve 2D CNN Domain adaptation ≤ 1.263% Ref. [82] • I, V, T , time BiGRU Domain adaptation ≤ 2.15% Ref.…”

Section: Soh Estimation With Transfer Learningmentioning

confidence: 99%

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

Liu

Pan

Che

et al. 2023

Advances in Applied Energy

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“…Kim et al [25] proposed a VarLSTM-TL to facilitate accurate SOH forecasting and RUL prediction for different LIB types to predict RUL values at a single point in time and forecast capacity-degradation patterns with credible intervals. Ma et al [26] introduced a transfer learning-based method by combining a convolutional neural network (CNN) with an improved domain adaptation method that is used to construct an SOH estimation model.…”

Section: Introductionmentioning

confidence: 99%

Transfer-Learning-Based State-of-Health Estimation for Lithium-Ion Battery With Cycle Synchronization

Zhou

Yan

Yuen

2023

IEEE/ASME Trans. Mechatron.

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Accurately estimating a battery's state of health (SOH) helps prevent battery-powered applications from failing unexpectedly. With the superiority of reducing the data requirement of model training for new batteries, transfer learning (TL) emerges as a promising machine learning approach that applies knowledge learned from a source battery, which has a large amount of data. However, the determination of whether the source battery model is reasonable and which part of information can be transferred for SOH estimation are rarely discussed, despite these being critical components of a successful TL. To address these challenges, this paper proposes an interpretable TL-based SOH estimation method by exploiting the temporal dynamic to assist transfer learning, which consists of three parts. First, with the help of dynamic time warping, the temporal data from the discharge time series are synchronized, yielding the warping path of the cycle-synchronized time series responsible for capacity degradation over cycles. Second, the canonical variates retrieved from the spatial path of the cycle-synchronized time series are used for distribution similarity analysis between the source and target batteries. Third, when the distribution similarity is within the predefined threshold, a comprehensive target SOH estimation model is constructed by transferring the common temporal dynamics from the source SOH estimation model and compensating the errors with a residual model from the target battery. Through a widely-used open-source benchmark dataset, the estimation error of the proposed method evaluated by the root mean squared error is as low as 0.0034 resulting in a 77% accuracy improvement compared with existing methods.

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A Transfer Learning-Based Method for Personalized State of Health Estimation of Lithium-Ion Batteries

Cited by 40 publications

References 43 publications

Health prognostics for lithium-ion batteries: mechanisms, methods, and prospects

Health prognostics for lithium-ion batteries: mechanisms, methods, and prospects

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

Transfer-Learning-Based State-of-Health Estimation for Lithium-Ion Battery With Cycle Synchronization

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