Data-Driven In-Orbit Current and Voltage Prediction Using Bi-LSTM for LEO Satellite Lithium-Ion Battery SOC Estimation

Yun, Seok-Teak; Kong, Seung-Hyun

doi:10.1109/taes.2022.3167624

Cited by 26 publications

(5 citation statements)

References 38 publications

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“…Ref. [46] proposes a groundbased LIB state estimation technique for Low Earth Orbit (LEO) satellite systems. This technique utilizes an Unscented Kalman Filter (UKF)-based model that leverages battery current and voltage predictions made by the Bi-directional LSTM (Bi-LSTM) network.…”

Section: ) Bidirectional Recurrent Neural Networkmentioning

confidence: 99%

Advancing Lithium-Ion Battery Health Prognostics With Deep Learning: A Review and Case Study

Massaoudi,

Abu-Rub,

Ghrayeb

2024

IEEE Open J. Ind. Applicat.

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Lithium-ion battery prognostics and health management (BPHM) systems are vital to the longevity, economy, and environmental friendliness of electric vehicles and energy storage systems. Recent advancements in deep learning (DL) techniques have shown promising results in addressing the challenges faced by the battery research and innovation community. This review paper analyzes the mainstream developments in BPHM using DL techniques. The fundamental concepts of BPHM are discussed, followed by a detailed examination of the emerging DL techniques. A case study using a data-driven DLinear model for state of health estimation is introduced, achieving accurate forecasts with minimal data and high computational efficiency. Finally, the potential future pathways for research and development in BPHM are explored. This review offers a holistic understanding of emerging DL techniques in BPHM and provides valuable insights and guidance for future research endeavors.INDEX TERMS Deep learning, health and life-cycle analysis, Lithium-ion battery management system, prognostics and health management, remaining useful life prediction, state of charge estimation. Nomenclature

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Section: ) Bidirectional Recurrent Neural Networkmentioning

confidence: 99%

Advancing Lithium-Ion Battery Health Prognostics With Deep Learning: A Review and Case Study

Massaoudi,

Abu-Rub,

Ghrayeb

2024

IEEE Open J. Ind. Applicat.

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“…(15) The LSTM network has the characteristics of transmitting the current and memory states. (16) Compared with the static neural network, a cyclic structure containing LSTM nodes is more helpful in extracting the characteristics of the battery time series. Figure 4 shows the architecture of the SOC estimation algorithm for power LIBs in the LSTM window network.…”

Section: Soc Prediction Network Based On Window Lstmmentioning

confidence: 99%

State of Charge Estimation of Electric Vehicle Power Batteries Enabled by Fusion Algorithm Considering Extreme Temperatures

Xu¹,

Yong²

2023

Sensors and Materials

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When using the extended Kalman filter (EKF) to estimate the state of charge (SOC) of lithium-ion batteries (LIBs), the noise covariance matrices of system and observation noises for energy harvesters are mostly given randomly, which makes it impossible to optimize the noise problem. This results in the low accuracy and stability of SOC estimation. To address these problems, a method of estimating the SOC of power LIBs based on long short-term memoryadaptive unscented Kalman filter (LSTM-AUKF) fusion is proposed to improve the accuracy and stability of estimating the SOC of LIBs. First, the offline parameters of the Thevenin model are identified from the hybrid pulse power characterization (HPPC) experimental data. Then, the LSTM structure of the SOC estimation window is constructed for power LIBs, and the power battery SOC training network is predicted in real time from the power battery current, voltage, temperature, and historical data. Finally, the AUKF algorithm for estimating the SOC of power LIBs is designed, then a fusion strategy is proposed. The experimental validation shows that the root mean squared error (RMSE), maximum (MAX), and mean absolute error (MAE), used to estimate the SOC of the LSTM-AUKF hybrid power lithium battery in the research window, are 1.13, 1.74, and 0.39%, respectively. Compared with the window LSTM network, the fusion algorithm improves the accuracy and stability of SOC estimation for power LIBs.

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“…Typical DL algorithms include Recurrent Neural Network (RNN), LSTM, Transformer, and so on. Yun et al used Bi-LSTM to predict and monitor the battery data for all times [7]. DL has high degree of freedom and can realize end-to-end training, but it has certain requirements for data quantity.…”

Section: Introductionmentioning

confidence: 99%

CF-LSTM-based post-maneuver position prediction for GEO spacecraft

Long,

Yang,

Huang

et al. 2024

J. Phys.: Conf. Ser.

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Prediction of the position of Geosynchronous (GEO) spacecraft after a maneuver is crucial for space domain awareness (SDA), as it can enhance the flexibility of the space surveillance network (SSN). The longitude is a unique parameter that can be freely assigned to GEO spacecraft. In this paper, we propose a prediction model for the longitude of a GEO spacecraft based on Causal Feature Long Short-Term Memory networks (CF-LSTM). Initially, we analyze the historical data of GEO spacecraft in a time-series geographic position and identify the causal parameters of longitude using the Gaussian perturbation equation. Subsequently, we present the CF-LSTM to predict the longitude. Our experimental analysis shows that the proposed method reduces the mean absolute error (MAE) and mean square error (MSE) by 60.92% and 18.75%, respectively, compared to conventional LSTM.

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Data-Driven In-Orbit Current and Voltage Prediction Using Bi-LSTM for LEO Satellite Lithium-Ion Battery SOC Estimation

Cited by 26 publications

References 38 publications

Advancing Lithium-Ion Battery Health Prognostics With Deep Learning: A Review and Case Study

Advancing Lithium-Ion Battery Health Prognostics With Deep Learning: A Review and Case Study

State of Charge Estimation of Electric Vehicle Power Batteries Enabled by Fusion Algorithm Considering Extreme Temperatures

CF-LSTM-based post-maneuver position prediction for GEO spacecraft

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