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Journal Articles

State-of-Charge Estimation of Lithium-Ion Batteries Using Convolutional Neural Network With Self-Attention Mechanism

Jianlong Chen, Chenghao Zhang, Cong Chen, Chenlei Lu, Dongji Xuan

J. Electrochem. En. Conv. Stor. August 2023, 20(3): 031010.

Paper No: JEECS-22-1048

DOI: https://doi.org/10.1115/1.4055985

Published Online: November 11, 2022

View Article titled, State-of-Charge Estimation of Lithium-Ion Batteries Using Convolutional Neural Network With Self-Attention Mechanism

Topics: Artificial neural networks, Batteries, Errors, Lithium-ion batteries, Temperature, Robustness, Cycles

Journal Articles

Developing Potential Energy Surfaces for Graphene-Based 2D–3D Interfaces From Modified High-Dimensional Neural Networks for Applications in Energy Storage

Vidushi Sharma, Dibakar Datta

J. Electrochem. En. Conv. Stor. November 2022, 19(4): 041006.

Paper No: JEECS-22-1032

DOI: https://doi.org/10.1115/1.4054781

Published Online: July 1, 2022

View Article titled, Developing Potential Energy Surfaces for Graphene-Based 2D–3D Interfaces From Modified High-Dimensional Neural Networks for Applications in Energy Storage

Topics: Artificial neural networks, Atoms, Energy storage, Graphene, Interface structure, Potential energy, Simulation, Tin, Electrodes, Density functional theory

Includes: Supplementary data

Journal Articles

Lithium-Ion Battery Strain Gauge Monitoring and Depth of Discharge Estimation

Christopher Hendricks, Bhanu Sood, Michael Pecht

J. Electrochem. En. Conv. Stor. February 2023, 20(1): 011008.

Paper No: JEECS-21-1224

DOI: https://doi.org/10.1115/1.4054340

Published Online: May 12, 2022

View Article titled, Lithium-Ion Battery Strain Gauge Monitoring and Depth of Discharge Estimation

Topics: Artificial neural networks, Batteries, Lithium-ion batteries, Strain gages, Lithium, Electrodes, Stress, US Department of Defense

Journal Articles

Lithium-Ion Battery State of Health Estimation Based on Electrochemical Impedance Spectroscopy and Cuckoo Search Algorithm Optimized Elman Neural Network

Chun Chang, Shaojin Wang, Jiuchun Jiang, Yang Gao, Yan Jiang, Li Liao

J. Electrochem. En. Conv. Stor. August 2022, 19(3): 030912.

Paper No: JEECS-21-1277

DOI: https://doi.org/10.1115/1.4054128

Published Online: April 8, 2022

View Article titled, Lithium-Ion Battery State of Health Estimation Based on Electrochemical Impedance Spectroscopy and Cuckoo Search Algorithm Optimized Elman Neural Network

Topics: Algorithms, Artificial neural networks, Batteries, Electrochemical impedance spectroscopy, Lithium-ion batteries, Cycles, Temperature, Battery life, Errors, Principal component analysis

Journal Articles

Short-Term Prediction of Remaining Life for Lithium-Ion Battery Based on Adaptive Hybrid Model With Long Short-Term Memory Neural Network and Optimized Particle Filter

Ning He, Cheng Qian, Lile He

J. Electrochem. En. Conv. Stor. August 2022, 19(3): 031004.

Paper No: JEECS-21-1144

DOI: https://doi.org/10.1115/1.4053141

Published Online: February 4, 2022

View Article titled, Short-Term Prediction of Remaining Life for Lithium-Ion Battery Based on Adaptive Hybrid Model With Long Short-Term Memory Neural Network and Optimized Particle Filter

Topics: Artificial neural networks, Batteries, Filters, Lithium-ion batteries, Particulate matter, Service life (Equipment), Errors, Neural network models

Journal Articles

A Hybrid Method for Lithium-Ion Batteries State-of-Charge Estimation Based on Gated Recurrent Unit Neural Network and an Adaptive Unscented Kalman Filter

Shuai Xu, Fei Zhou, Yuchen Liu

J. Electrochem. En. Conv. Stor. August 2022, 19(3): 031005.

Paper No: JEECS-21-1172

DOI: https://doi.org/10.1115/1.4053361

Published Online: February 4, 2022

View Article titled, A Hybrid Method for Lithium-Ion Batteries State-of-Charge Estimation Based on Gated Recurrent Unit Neural Network and an Adaptive Unscented Kalman Filter

Topics: Artificial neural networks, Batteries, Cycles, Errors, Kalman filters, Temperature, Lithium-ion batteries, Signals, Algorithms, Testing

Journal Articles

State of Health Estimation Method for Lithium-Ion Batteries Based on Nonlinear Autoregressive Neural Network Model With Exogenous Input

Yanbo Che, Yibin Cai, Hongfeng Li, Yushu Liu, Mingda Jiang, Peijun Qin

J. Electrochem. En. Conv. Stor. May 2022, 19(2): 021014.

Paper No: JEECS-21-1104

DOI: https://doi.org/10.1115/1.4052274

Published Online: November 9, 2021

View Article titled, State of Health Estimation Method for Lithium-Ion Batteries Based on Nonlinear Autoregressive Neural Network Model With Exogenous Input

Topics: Artificial neural networks, Batteries, Circuits, Cycles, Lithium-ion batteries, Neural network models, Temperature, Errors, Algorithms, Fittings

Journal Articles

A Multi-Source Data Feature Fusion and Expert Knowledge Integration Approach on Lithium-Ion Battery Anomaly Detection

Yudong Wang, Xiwei Bai, Chengbao Liu, Jie Tan

J. Electrochem. En. Conv. Stor. May 2022, 19(2): 021003.

Paper No: JEECS-21-1079

DOI: https://doi.org/10.1115/1.4051716

Published Online: August 4, 2021

View Article titled, A Multi-Source Data Feature Fusion and Expert Knowledge Integration Approach on Lithium-Ion Battery Anomaly Detection

Topics: Algorithms, Batteries, Filters, Lithium-ion batteries, Manufacturing, Artificial neural networks, Lithium

Journal Articles

Measurement and Prediction of Decomposed Energy Efficiencies of Lithium Ion Batteries With Two Charge Models

Yuhao Huang, Yan Su, Akhil Garg

J. Electrochem. En. Conv. Stor. August 2021, 18(3): 030901.

Paper No: JEECS-20-1145

DOI: https://doi.org/10.1115/1.4049576

Published Online: February 3, 2021

View Article titled, Measurement and Prediction of Decomposed Energy Efficiencies of Lithium Ion Batteries With Two Charge Models

Topics: Artificial neural networks, Cycles, Lithium-ion batteries, Stress, Batteries, Transients (Dynamics)

Journal Articles

A Novel Approach for Enhancing Thermal Performance of Battery Modules Based on Finite Element Modeling and Predictive Modeling Mechanism

Akhil Garg, C. Ruhatiya, Xujian Cui, Xiongbin Peng, Yogesh Bhalerao, Liang Gao

J. Electrochem. En. Conv. Stor. May 2020, 17(2): 021103.

Paper No: JEECS-19-1061

DOI: https://doi.org/10.1115/1.4045194

Published Online: November 12, 2019

View Article titled, A Novel Approach for Enhancing Thermal Performance of Battery Modules Based on Finite Element Modeling and Predictive Modeling Mechanism

Topics: Artificial neural networks, Batteries, Design, Finite element analysis, Finite element methods, Modeling, Optimization, Thermal management, Temperature, Cooling

Journal Articles

Prediction of Direct Methanol Fuel Cell Stack Performance Using Artificial Neural Network

M. A. Rafe Biswas, Melvin D. Robinson

J. Electrochem. En. Conv. Stor. August 2017, 14(3): 031008.

Paper No: JEECS-16-1162

DOI: https://doi.org/10.1115/1.4036811

Published Online: June 21, 2017

View Article titled, Prediction of Direct Methanol Fuel Cell Stack Performance Using Artificial Neural Network

Topics: Artificial neural networks, Direct methanol fuel cells, Methanol, Algorithms, Temperature, Flow (Dynamics), Anodes

Journal Articles

Artificial Neural Network-Based Model for Calculating the Flow Composition Influence of Solid Oxide Fuel Cell

Jarosław Milewski, Konrad Świrski

J. Electrochem. En. Conv. Stor. April 2014, 11(2): 021001.

Paper No: FC-12-1117

DOI: https://doi.org/10.1115/1.4025922

Published Online: December 4, 2013

View Article titled, Artificial Neural Network-Based Model for Calculating the Flow Composition Influence of Solid Oxide Fuel Cell

Topics: Artificial neural networks, Flow (Dynamics), Solid oxide fuel cells, Fuels

Journal Articles

Modeling of Direct Methanol Fuel Cell Using the Artificial Neural Network

Mehdi Tafazoli, Hamid Baseri, Ebrahim Alizadeh, Mohsen Shakeri

J. Electrochem. En. Conv. Stor. August 2013, 10(4): 041007.

Paper No: FC-12-1024

DOI: https://doi.org/10.1115/1.4024859

Published Online: July 5, 2013

View Article titled, Modeling of Direct Methanol Fuel Cell Using the Artificial Neural Network

Topics: Artificial neural networks, Direct methanol fuel cells, Flow (Dynamics), Methanol, Modeling, Oxygen, Pressure, Temperature, Testing, Fuel cells

Journal Articles

A Predictive Control Based on Neural Network for Dynamic Model of Proton Exchange Membrane Fuel Cell

M. Rezaei, M. Mohseni

J. Electrochem. En. Conv. Stor. June 2013, 10(3): 035001.

Paper No: FC-12-1089

DOI: https://doi.org/10.1115/1.4023838

Published Online: May 7, 2013

View Article titled, A Predictive Control Based on Neural Network for Dynamic Model of Proton Exchange Membrane Fuel Cell

Topics: Artificial neural networks, Dynamic models, Fuel cells, Predictive control, Proton exchange membrane fuel cells, Control equipment, Temperature

Journal Articles

An Empirical Stationary Fuel Cell Model Using Limited Experimental Data for Identification

M. Meiler, E. P. Hofer, A. Nuhic, O. Schmid

J. Electrochem. En. Conv. Stor. December 2012, 9(6): 061001.

DOI: https://doi.org/10.1115/1.4007195

Published Online: October 17, 2012

View Article titled, An Empirical Stationary Fuel Cell Model Using Limited Experimental Data for Identification

Topics: Artificial neural networks, Fuel cells, Modeling, Parameter estimation, Anodes, Pressure

Journal Articles

Temperature Control of a SOFC and MGT Hybrid System

Xiao-Juan Wu, Qi Huang, Xin-Jian Zhu, Chang-Hua Zhang

J. Electrochem. En. Conv. Stor. October 2011, 8(5): 051009.

DOI: https://doi.org/10.1115/1.4004174

Published Online: June 17, 2011

View Article titled, Temperature Control of a SOFC and MGT Hybrid System

Topics: Solid oxide fuel cells, Temperature control, Artificial neural networks, Temperature, Micro gas turbines

Journal Articles

Dynamic Test and Real-time Control Platform of Anode Recirculation for PEM Fuel Cell Systems

Cheng Bao, Kexun Zhang, Minggao Ouyang, Baolian Yi, Pingwen Ming

J. Electrochem. En. Conv. Stor. August 2006, 3(3): 333–345.

DOI: https://doi.org/10.1115/1.2217956

Published Online: February 21, 2006

View Article titled, Dynamic Test and Real-time Control Platform of Anode Recirculation for PEM Fuel Cell Systems

Topics: Anodes, Control equipment, Flow (Dynamics), Pressure, Proton exchange membrane fuel cells, Pumps, Fuzzy neural nets, Flowmeters, Fuel cells, Artificial neural networks

Journal Articles

Artificial Neural Network Modeling of PEM Fuel Cells

Shaoduan Ou, Luke E. K. Achenie

J. Electrochem. En. Conv. Stor. November 2005, 2(4): 226–233.

DOI: https://doi.org/10.1115/1.2039951

Published Online: May 23, 2005

View Article titled, Artificial Neural Network Modeling of PEM Fuel Cells

Topics: Artificial neural networks, Optimization, Proton exchange membrane fuel cells

Journal citation

NARROW

Supplemental Material

State-of-Charge Estimation of Lithium-Ion Batteries Using Convolutional Neural Network With Self-Attention Mechanism

Developing Potential Energy Surfaces for Graphene-Based 2D–3D Interfaces From Modified High-Dimensional Neural Networks for Applications in Energy Storage

Lithium-Ion Battery Strain Gauge Monitoring and Depth of Discharge Estimation

Lithium-Ion Battery State of Health Estimation Based on Electrochemical Impedance Spectroscopy and Cuckoo Search Algorithm Optimized Elman Neural Network

Short-Term Prediction of Remaining Life for Lithium-Ion Battery Based on Adaptive Hybrid Model With Long Short-Term Memory Neural Network and Optimized Particle Filter

A Hybrid Method for Lithium-Ion Batteries State-of-Charge Estimation Based on Gated Recurrent Unit Neural Network and an Adaptive Unscented Kalman Filter

State of Health Estimation Method for Lithium-Ion Batteries Based on Nonlinear Autoregressive Neural Network Model With Exogenous Input

A Multi-Source Data Feature Fusion and Expert Knowledge Integration Approach on Lithium-Ion Battery Anomaly Detection

Measurement and Prediction of Decomposed Energy Efficiencies of Lithium Ion Batteries With Two Charge Models

A Novel Approach for Enhancing Thermal Performance of Battery Modules Based on Finite Element Modeling and Predictive Modeling Mechanism

Prediction of Direct Methanol Fuel Cell Stack Performance Using Artificial Neural Network

Artificial Neural Network-Based Model for Calculating the Flow Composition Influence of Solid Oxide Fuel Cell

Modeling of Direct Methanol Fuel Cell Using the Artificial Neural Network

A Predictive Control Based on Neural Network for Dynamic Model of Proton Exchange Membrane Fuel Cell

An Empirical Stationary Fuel Cell Model Using Limited Experimental Data for Identification

Temperature Control of a SOFC and MGT Hybrid System

Dynamic Test and Real-time Control Platform of Anode Recirculation for PEM Fuel Cell Systems

Artificial Neural Network Modeling of PEM Fuel Cells

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