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Journal Articles

A Hybrid Data-Driven Method Based on Data Preprocessing to Predict the Remaining Useful Life of Lithium-Ion Batteries

Weiwei Huo, Aobo Wang, Bing Lu, Yunxu Jia, Chen Li

Publisher: ASME

J. Electrochem. En. Conv. Stor. August 2025, 22(3): 031001.

Paper No: JEECS-24-1035

https://doi.org/10.1115/1.4065862

Published Online: July 26, 2024

View Articletitled, A Hybrid Data-Driven Method Based on Data Preprocessing to Predict the Remaining Useful Life of Lithium-Ion Batteries

Topics: Algorithms, Batteries, Lithium-ion batteries, Service life (Equipment), Artificial neural networks, Errors, Principal component analysis

Journal Articles

Neural Network-Based Modeling of Diffusion-Induced Stress in a Hollow Cylindrical Nano-Electrode of Lithium-Ion Battery

Yong Li, Yunhao Wu, He Huang, Kai Zhang, Fuqian Yang

Publisher: ASME

J. Electrochem. En. Conv. Stor. February 2025, 22(1): 011011.

Paper No: JEECS-24-1046

https://doi.org/10.1115/1.4065536

Published Online: June 7, 2024

View Articletitled, Neural Network-Based Modeling of Diffusion-Induced Stress in a Hollow Cylindrical Nano-Electrode of Lithium-Ion Battery

Topics: Diffusion (Physics), Electrodes, Stress, Partial differential equations, Artificial neural networks, Lithium, Deep learning, Modeling, Boundary-value problems

Includes: Supplementary data

Journal Articles

Lithium-Ion Battery Capacity Prediction Method Based on Improved Extreme Learning Machine

Zhengyu Liu, Zaijun Huang, Liandong Tang, Hao Wang

Publisher: ASME

J. Electrochem. En. Conv. Stor. February 2025, 22(1): 011002.

Paper No: JEECS-23-1182

https://doi.org/10.1115/1.4065095

Published Online: April 8, 2024

View Articletitled, Lithium-Ion Battery Capacity Prediction Method Based on Improved Extreme Learning Machine

Topics: Batteries, Errors, Lithium-ion batteries, Machinery, Manufacturing, Optimization, Feature extraction, Artificial neural networks

Journal Articles

State of Charge Estimation of Lithium-Ion Battery Based on IDRSN and BiGRU

Jiahao Zhang, Jiadui Chen, Ling He, Dan Liu, Kai Yang, Qinghua Liu

Publisher: ASME

J. Electrochem. En. Conv. Stor. August 2024, 21(3): 031001.

Paper No: JEECS-23-1029

https://doi.org/10.1115/1.4063173

Published Online: October 5, 2023

View Articletitled, State of Charge Estimation of Lithium-Ion Battery Based on IDRSN and BiGRU

Topics: Algorithms, Artificial neural networks, Batteries, Cycles, Electric vehicles, Errors, Lithium, Lithium-ion batteries, Network models, Shrinkage (Materials)

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

Publisher: ASME

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

Paper No: JEECS-22-1048

https://doi.org/10.1115/1.4055985

Published Online: November 11, 2022

View Articletitled, 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

Publisher: ASME

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

Paper No: JEECS-22-1032

https://doi.org/10.1115/1.4054781

Published Online: July 1, 2022

View Articletitled, 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

Publisher: ASME

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

Paper No: JEECS-21-1224

https://doi.org/10.1115/1.4054340

Published Online: May 12, 2022

View Articletitled, 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

Publisher: ASME

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

Paper No: JEECS-21-1277

https://doi.org/10.1115/1.4054128

Published Online: April 8, 2022

View Articletitled, 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

Publisher: ASME

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

Paper No: JEECS-21-1144

https://doi.org/10.1115/1.4053141

Published Online: February 4, 2022

View Articletitled, 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

Publisher: ASME

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

Paper No: JEECS-21-1172

https://doi.org/10.1115/1.4053361

Published Online: February 4, 2022

View Articletitled, 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

Publisher: ASME

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

Paper No: JEECS-21-1104

https://doi.org/10.1115/1.4052274

Published Online: November 9, 2021

View Articletitled, 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

Publisher: ASME

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

Paper No: JEECS-21-1079

https://doi.org/10.1115/1.4051716

Published Online: August 4, 2021

View Articletitled, 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

Publisher: ASME

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

Paper No: JEECS-20-1145

https://doi.org/10.1115/1.4049576

Published Online: February 3, 2021

View Articletitled, 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

Publisher: ASME

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

Paper No: JEECS-19-1061

https://doi.org/10.1115/1.4045194

Published Online: November 12, 2019

View Articletitled, 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

Publisher: ASME

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

Paper No: JEECS-16-1162

https://doi.org/10.1115/1.4036811

Published Online: June 21, 2017

View Articletitled, 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

Publisher: ASME

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

Paper No: FC-12-1117

https://doi.org/10.1115/1.4025922

Published Online: December 4, 2013

View Articletitled, 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

Publisher: ASME

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

Paper No: FC-12-1024

https://doi.org/10.1115/1.4024859

Published Online: July 5, 2013

View Articletitled, 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

Publisher: ASME

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

Paper No: FC-12-1089

https://doi.org/10.1115/1.4023838

Published Online: May 7, 2013

View Articletitled, 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

Publisher: ASME

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

https://doi.org/10.1115/1.4007195

Published Online: October 17, 2012

View Articletitled, 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

Publisher: ASME

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

https://doi.org/10.1115/1.4004174

Published Online: June 17, 2011

View Articletitled, Temperature Control of a SOFC and MGT Hybrid System

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

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A Hybrid Data-Driven Method Based on Data Preprocessing to Predict the Remaining Useful Life of Lithium-Ion Batteries

Neural Network-Based Modeling of Diffusion-Induced Stress in a Hollow Cylindrical Nano-Electrode of Lithium-Ion Battery

Lithium-Ion Battery Capacity Prediction Method Based on Improved Extreme Learning Machine

State of Charge Estimation of Lithium-Ion Battery Based on IDRSN and BiGRU

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

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