4.8 Article

Deep Learning Framework for Lithium-ion Battery State of Charge Estimation: Recent Advances and Future Perspectives

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Summary: In this study, a deep fully convolutional network model was proposed to accurately estimate the state-of-charge (SOC) of lithium-ion batteries, achieving impressive performance under different temperature conditions.

IEEE TRANSACTIONS ON POWER ELECTRONICS (2021)

Article Chemistry, Physical

Physics-informed neural networks for electrode-level state estimation in lithium-ion batteries

Weihan Li et al.

Summary: The study introduces a hybrid state estimation method that combines physics-based and machine learning models to accurately estimate the internal states of lithium-ion batteries, demonstrating high reliability and generalization ability.

JOURNAL OF POWER SOURCES (2021)

Article Engineering, Electrical & Electronic

Cross-Domain State-of-Charge Estimation of Li-Ion Batteries Based on Deep Transfer Neural Network With Multiscale Distribution Adaptation

Chong Bian et al.

Summary: The research introduces a deep transfer neural network (DTNN) with multiscale distribution adaptation (MDA) for cross-domain SOC estimation, which learns dynamic features from battery measurements in different domains and minimizes distribution discrepancy through constraint terms. DTNN shows improved performance under specific battery conditions with limited training data, compared to existing transfer learning methods, enhancing generalizability and robustness.

IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION (2021)

Review Chemistry, Physical

The challenge and opportunity of battery lifetime prediction from field data

Valentin Sulzer et al.

Summary: Accurate battery life prediction is crucial for various applications, but existing methods based on lab data need to incorporate field data for a complete understanding of cell aging. Challenges arise due to uncontrolled operating conditions, less accurate sensors, and infrequent validation checks in real-world applications. Combining machine learning with physical models shows promise in estimating battery life, assessing second-life condition, and predicting future usage conditions.

JOULE (2021)

Article Chemistry, Physical

Deep neural network battery charging curve prediction using 30 points collected in 10 min

Jinpeng Tian et al.

Summary: The study introduces a method to accurately estimate the entire constant-current charging curves using a deep neural network, which can capture the curves accurately in a short amount of time and demonstrate effectiveness through validation, as well as the advantage of transfer learning.

JOULE (2021)

Review Energy & Fuels

A review of modeling, acquisition, and application of lithium-ion battery impedance for onboard battery management

Xueyuan Wang et al.

Summary: Impedance is closely related to the internal physical and chemical processes of lithium-ion batteries, providing detailed information. This paper reviews over 170 papers and discusses the possibility and value of impedance in onboard battery management, as well as the challenges faced. More significant work is needed to realize a more smart battery management system.

ETRANSPORTATION (2021)

Article Computer Science, Information Systems

An Improved Bidirectional Gated Recurrent Unit Method for Accurate State-of-Charge Estimation

Zhaowei Zhang et al.

Summary: The proposed NAG-based Bi-GRU method leverages deep learning to estimate SOC in lithium-ion batteries, addressing issues with traditional gradient descent algorithms by optimizing the gradient updates and capturing temporal information in both forward and backward directions. Experimental results demonstrate improved precision in SOC estimation across various ambient temperatures compared to previous methods.

IEEE ACCESS (2021)

Article Automation & Control Systems

From Battery Cell to Electrodes: Real-Time Estimation of Charge and Health of Individual Battery Electrodes

Satadru Dey et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2020)

Article Multidisciplinary Sciences

Closed-loop optimization of fast-charging protocols for batteries with machine learning

Peter M. Attia et al.

NATURE (2020)

Review Computer Science, Artificial Intelligence

A review on the long short-term memory model

Greg Van Houdt et al.

ARTIFICIAL INTELLIGENCE REVIEW (2020)

Review Engineering, Mechanical

Applications of machine learning to machine fault diagnosis: A review and roadmap

Yaguo Lei et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2020)

Article Chemistry, Physical

Battery life estimation based on cloud data for electric vehicles

Kai Li et al.

JOURNAL OF POWER SOURCES (2020)

Article Engineering, Multidisciplinary

State-of-Charge Estimation of Li-Ion Battery in Electric Vehicles: A Deep Neural Network Approach

Dickshon N. T. How et al.

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS (2020)

Article Chemistry, Physical

Big data training data for artificial intelligence-based Li-ion diagnosis and prognosis

Matthieu Dubarry et al.

JOURNAL OF POWER SOURCES (2020)

Review Computer Science, Artificial Intelligence

Predicting the state of charge and health of batteries using data-driven machine learning

Man-Fai Ng et al.

NATURE MACHINE INTELLIGENCE (2020)

Article Automation & Control Systems

Multiple Wavelet Coefficients Fusion in Deep Residual Networks for Fault Diagnosis

Minghang Zhao et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2019)

Article Engineering, Electrical & Electronic

A Novel Fractional Order Model for State of Charge Estimation in Lithium Ion Batteries

Rui Xiong et al.

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2019)

Article Energy & Fuels

A Novel Multiple Correction Approach for Fast Open Circuit Voltage Prediction of Lithium-Ion Battery

Jinhao Meng et al.

IEEE TRANSACTIONS ON ENERGY CONVERSION (2019)

Article Computer Science, Information Systems

State-of-Charge Estimation of Lithium-Ion Batteries via Long Short-Term Memory Network

Fangfang Yang et al.

IEEE ACCESS (2019)

Article Computer Science, Information Systems

Combined CNN-LSTM Network for State-of-Charge Estimation of Lithium-Ion Batteries

Xiangbao Song et al.

IEEE ACCESS (2019)

Article Automation & Control Systems

Long Short-Term Memory Networks for Accurate State-of-Charge Estimation of Li-ion Batteries

Ephrem Chemali et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2018)

Article Automation & Control Systems

Online Model Identification and State-of-Charge Estimate for Lithium-Ion Battery With a Recursive Total Least Squares-Based Observer

Zhongbao Wei et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2018)

Article Engineering, Electrical & Electronic

Long Short-Term Memory Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-Ion Batteries

Yongzhi Zhang et al.

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2018)

Article Computer Science, Information Systems

Neural Network Approach for Estimating State of Charge of lithium-Ion Battery Using Backtracking Search Algorithm

Mahammad A. Hannan et al.

IEEE ACCESS (2018)

Review Computer Science, Information Systems

Critical Review on the Battery State of Charge Estimation Methods for Electric Vehicles

Rui Xiong et al.

IEEE ACCESS (2018)

Article Chemistry, Physical

State-of-charge estimation of Li-ion batteries using deep neural networks: A machine learning approach

Ephrem Chemali et al.

JOURNAL OF POWER SOURCES (2018)

Review Green & Sustainable Science & Technology

A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations

M. A. Hannan et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2017)

Review Multidisciplinary Sciences

Deep learning

Yann LeCun et al.

NATURE (2015)

Article Chemistry, Physical

A comparative study of equivalent circuit models for Li-ion batteries

Xiaosong Hu et al.

JOURNAL OF POWER SOURCES (2012)

Article Computer Science, Artificial Intelligence

A Survey on Transfer Learning

Sinno Jialin Pan et al.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2010)

Article Biochemical Research Methods

Integrating structured biological data by Kernel Maximum Mean Discrepancy

Karsten M. Borgwardt et al.

BIOINFORMATICS (2006)

Article Chemistry, Physical

The challenge to fulfill electrical power requirements of advanced vehicles

M Anderman

JOURNAL OF POWER SOURCES (2004)