Cross-Domain Prognostic Method of Lithium-Ion Battery in New Energy Electric Aircraft With Domain Adaptation

Zhu, Yafei; Li, Xiang; Zhang, Yewei; Zhang, Wei

doi:10.1109/jsen.2023.3277131

Cited by 6 publications

(1 citation statement)

References 38 publications

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“…MMD is widely used in transfer learning algorithms to quantify the distribution difference between the source domain and the target domain [46,47]. When MMD = 0, it means that the distribution of the two domains is the same.…”

Section: Capacity Estimation Methods Of Single-source Domain Migratio...mentioning

confidence: 99%

Method for Evaluating Degradation of Battery Capacity Based on Partial Charging Segments for Multi-Type Batteries

Sun,

Tian,

et al. 2024

Batteries

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Accurately estimating the capacity degradation of lithium-ion batteries (LIBs) is crucial for evaluating the status of battery health. However, existing data-driven battery state estimation methods suffer from fixed input structures, high dependence on data quality, and limitations in scenarios where only early charge–discharge cycle data are available. To address these challenges, we propose a capacity degradation estimation method that utilizes shorter charging segments for multiple battery types. A learning-based model called GateCNN-BiLSTM is developed. To improve the accuracy of the basic model in small-sample scenarios, we integrate a single-source domain feature transfer learning framework based on maximum mean difference (MMD) and a multi-source domain framework using the meta-learning MAML algorithm. We validate the proposed algorithm using various LIB cell and battery pack datasets. Comparing the results with other models, we find that the GateCNN-BiLSTM algorithm achieves the lowest root mean square error (RMSE) and mean absolute error (MAE) for cell charging capacity estimation, and can accurately estimate battery capacity degradation based on actual charging data from electric vehicles. Moreover, the proposed method exhibits low dependence on the size of the dataset, improving the accuracy of capacity degradation estimation for multi-type batteries with limited data.

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Section: Capacity Estimation Methods Of Single-source Domain Migratio...mentioning

confidence: 99%

Method for Evaluating Degradation of Battery Capacity Based on Partial Charging Segments for Multi-Type Batteries

Sun,

Tian,

et al. 2024

Batteries

View full text Add to dashboard Cite

show abstract

A general multi-source ensemble transfer learning framework for health prognostic of lithium-ion batteries

Qiu,

Yan,

Wang

et al. 2024

Applied Energy

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Dynamic characteristics and vibration control of composite laminate wall panels in electric aircraft using NiTi shape memory alloys

Wang,

Zang,

Yang

et al. 2024

Aerospace Science and Technology

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Cross-Domain Prognostic Method of Lithium-Ion Battery in New Energy Electric Aircraft With Domain Adaptation

Cited by 6 publications

References 38 publications

Method for Evaluating Degradation of Battery Capacity Based on Partial Charging Segments for Multi-Type Batteries

Method for Evaluating Degradation of Battery Capacity Based on Partial Charging Segments for Multi-Type Batteries

A general multi-source ensemble transfer learning framework for health prognostic of lithium-ion batteries

Dynamic characteristics and vibration control of composite laminate wall panels in electric aircraft using NiTi shape memory alloys

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