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Experiment and Simulation of Energy Storage Systems and Renewable Energy...
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Experiment and Simulation of Energy Storage Systems and Renewable Energy Materials

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "D2: Electrochem: Batteries, Fuel Cells, Capacitors".

Deadline for manuscript submissions: closed (28 September 2023) | Viewed by 12386

Special Issue Editors

Dr. Lin Liu

E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of Kansas, Lawrence, KS 66045, USA
Interests: energy storage; energy conversion; multiscale and multiphysics modeling; mechanics of manufacturing processes; advanced manufacturing
Dr. Kai Sun

E-Mail Website
Guest Editor
Department of Materials Science and Engineering, University of Michigan, Ann Arbor, MI 48109, USA
Interests: characterization of materials by analytical electron microscopy; scanning electron microscopy; atomic force microscopy; X-ray photoelectron spectroscopy techniques; synthesis of nanostructured materials by electron and focused ion beams; wet chemical methods
Dr. James J. Wu

E-Mail Website
Guest Editor
Photovoltaic and Electrochemical Systems Branch, NASA Glenn Research Center, Cleveland, OH, USA
Interests: energy storage materials; solid-state/nonflammable safe electrolyte; high capacity and safe energy storage devices/battery technologies development

Special Issue Information

Dear Colleagues,

Due to the significant progress on emerging experimental techniques and high computing power over the past decades, we can design physical chemistry experiments, utilizing experiment-enhanced simulations to capture the complex multiscale and multiphysics phenomena in advanced energy systems with unprecedented sophistication and details at discrete temporal and spatial scales. Successful cases and examples are demonstrated in diverse technologies ranging from energy storage to conversion, catalysis, and optoelectronics. Although promising, it is urgent to address the dilemma faced by researchers to compensate for the trade-off between experimental complexity and computational cost, with current energy systems becoming more integrated and complicated by finer-scale effects essential to be experimentally/computationally captured so as to enable a comprehensive understanding of the above systems. To exploit and achieve the goal of accurate predictive capabilities, the innovation of mathematical and computational modeling is essential, as well as experimentation in electrochemical systems.

This Special Issue aims to investigate multiscale and multiphysics phenomena in advanced energy systems and collect major advances in experimental and modeling techniques, potential topics including, but not limited to:

  • In situ and in operando investigation of battery degradation;
  • Synthesis of materials for interfacial layers or coatings;
  • Mechanical–electrochemical–thermal simulation of fuel cells;
  • Mesoscale phase-field modeling of MEMS and NEMS;
  • Modeling for electrochemistry of semiconductor;
  • Synthesis of two-dimensional nanomaterials for supercapacitors.

Original research papers, as well as review articles, are welcome.

Dr. Lin Liu
Dr. Kai Sun
Dr. James J. Wu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • advanced manufacturing
  • numerical analysis
  • multiscale and multiphysics
  • systems engineering
  • data science
  • market analysis

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Published Papers (6 papers)

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Research

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16 pages, 897 KiB  
Article
Remaining Useful Life Prediction of Lithium-Ion Batteries by Using a Denoising Transformer-Based Neural Network
by Yunlong Han, Conghui Li, Linfeng Zheng, Gang Lei and Li Li
Energies 2023, 16(17), 6328; https://doi.org/10.3390/en16176328 - 31 Aug 2023
Cited by 14 | Viewed by 2355
Abstract
In this study, we introduce a novel denoising transformer-based neural network (DTNN) model for predicting the remaining useful life (RUL) of lithium-ion batteries. The proposed DTNN model significantly outperforms traditional machine learning models and other deep learning architectures in terms of accuracy and [...] Read more.
In this study, we introduce a novel denoising transformer-based neural network (DTNN) model for predicting the remaining useful life (RUL) of lithium-ion batteries. The proposed DTNN model significantly outperforms traditional machine learning models and other deep learning architectures in terms of accuracy and reliability. Specifically, the DTNN achieved an R2 value of 0.991, a mean absolute percentage error (MAPE) of 0.632%, and an absolute RUL error of 3.2, which are superior to other models such as Random Forest (RF), Decision Trees (DT), Multilayer Perceptron (MLP), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Dual-LSTM, and DeTransformer. These results highlight the efficacy of the DTNN model in providing precise and reliable predictions for battery RUL, making it a promising tool for battery management systems in various applications. Full article
(This article belongs to the Special Issue Experiment and Simulation of Energy Storage Systems and Renewable Energy Materials)
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18 pages, 4061 KiB  
Article
Modeling of Specific Energy in the Gear Honing Process
by Fuwei Wang, Yuanlong Chen, Yang Gao, Yuan Liang, Ruimin Wang and Defang Zhao
Energies 2023, 16(15), 5744; https://doi.org/10.3390/en16155744 - 1 Aug 2023
Viewed by 1048
Abstract
Gear honing is a cost-efficient method for the finishing of hardened gears in which material removal is realized through honing wheel and workpiece gear interactions. There are a number of indicators used for evaluating the degrees of these interactions, among which specific energy [...] Read more.
Gear honing is a cost-efficient method for the finishing of hardened gears in which material removal is realized through honing wheel and workpiece gear interactions. There are a number of indicators used for evaluating the degrees of these interactions, among which specific energy is a more appropriate one than the others since it is capable of quantifying the amount of energy consumption during the material removal process. Nevertheless, models for the prediction of specific energy in gear honing have not been thoroughly investigated. This work presents a theoretical model of specific energy to quantitatively evaluate the material removal efficiency in the external gear honing process. To develop the model, an analytical material removal rate and a honing force model are proposed, and the feasibility of the proposed model is validated against external gear honing experiments. The correlations of specific energy with processing parameters are investigated and the material removal efficiency scores of external gear honing and grinding are compared and discussed. The present approach enables an in-depth understanding of the abrasive–material interactions in the gear honing process and the effects of processing parameters on material removal efficiency. Full article
(This article belongs to the Special Issue Experiment and Simulation of Energy Storage Systems and Renewable Energy Materials)
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20 pages, 4213 KiB  
Article
Simulation for the Effect of Singlet Fission Mechanism of Tetracene on Perovskite Solar Cell
by Toan Ngoc Le and Lin Liu
Energies 2023, 16(5), 2428; https://doi.org/10.3390/en16052428 - 3 Mar 2023
Viewed by 1818
Abstract
The perovskite solar cell has recently gained momentum within the renewable energy industry due to its unique advantages such as high efficiency and cost-effectiveness. However, its instability remains a challenge to its commercialization. In this study, a singlet fission material, namely tetracene, is [...] Read more.
The perovskite solar cell has recently gained momentum within the renewable energy industry due to its unique advantages such as high efficiency and cost-effectiveness. However, its instability remains a challenge to its commercialization. In this study, a singlet fission material, namely tetracene, is coupled with the perovskite solar cell to simulate its effect on the solar cell. The amount of thermalization loss and the temperature of the perovskite layer are simulated and analyzed to indicate the mechanism’s effectiveness. We found that coupling the tetracene layer resulted in a drastic reduction in thermalization loss and a slower slope in perovskite layer temperature. This indicates that tetracene would stabilize the perovskite solar cell and minimize its potential losses. The thickness of the solar cell layers is also analyzed as a factor of the overall effectiveness of singlet fission on solar cells. Full article
(This article belongs to the Special Issue Experiment and Simulation of Energy Storage Systems and Renewable Energy Materials)
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13 pages, 7185 KiB  
Article
Magnetization Changes Induced by Stress Noncoaxial with the Magnetic Field in a Low-Carbon Steel
by Bin Yang, Zhifeng Liu, Yang Gao, Ruimin Wang, Yaru Feng and Xinyue Liu
Energies 2023, 16(3), 1103; https://doi.org/10.3390/en16031103 - 19 Jan 2023
Viewed by 1545
Abstract
Ferromagnetic materials are widely used in the manufacturing of key parts of energy equipment, due to their good mechanical properties, such as in nuclear power and pipes. Mechanical stress exists inside of these key parts during operation. Stress can be estimated indirectly by [...] Read more.
Ferromagnetic materials are widely used in the manufacturing of key parts of energy equipment, due to their good mechanical properties, such as in nuclear power and pipes. Mechanical stress exists inside of these key parts during operation. Stress can be estimated indirectly by nondestructive testing methods that measure the magnetic flux leakage signals on the surface of the structure, which is of great importance for ensuring the safety of the equipment. However, the physical mechanism of the stress and magnetic field in the magnetization of ferromagnetic materials is still unclear, leading to limited applications of the technique in practice. In this paper, magnetization tests were carried out to investigate magnetization changes under the coupling effect of stress and a noncoaxial magnetic field. Two identical Q195 low-carbon steel specimens were tested. Specimen 1 was subjected to magnetic field values successively increasing from 0 A/m to 6000 A/m under constant uniaxial tension at different angles θ between the field and stress axis. Specimen 2 was subjected to the same magnetic field under different levels of stress at an angle of 0°. The surface magnetic induction B of the specimens was measured and analyzed at each angle of stress–field orientation and at different levels of stress. It was found that there was a difference in the direction between the B and the magnetic field H at different angles θ. The magnetization curves correlated to the angle θ and the stress levels. The behavior of the derived maximum differential permeability and maximum magnetic induction could be used for the nondestructive evaluation of stress magnitude and direction in materials already in service. Full article
(This article belongs to the Special Issue Experiment and Simulation of Energy Storage Systems and Renewable Energy Materials)
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12 pages, 1934 KiB  
Article
Minimizing Energy Consumption and Powertrain Cost of Fuel Cell Hybrid Vehicles with Consideration of Different Driving Cycles and SOC Ranges
by Yang Gao, Changhong Liu, Yuan Liang, Sadegh Kouhestani Hamed, Fuwei Wang and Bo Bi
Energies 2022, 15(17), 6167; https://doi.org/10.3390/en15176167 - 25 Aug 2022
Cited by 3 | Viewed by 1471
Abstract
Hydrogen consumption is an important performance indicator of fuel cell hybrid vehicles (FCHVs). Previous studies have investigated fuel consumption minimization both under different driving cycles and using various power management strategies. However, different constrains on battery state of charge (SOC) ranges can also [...] Read more.
Hydrogen consumption is an important performance indicator of fuel cell hybrid vehicles (FCHVs). Previous studies have investigated fuel consumption minimization both under different driving cycles and using various power management strategies. However, different constrains on battery state of charge (SOC) ranges can also affect fuel consumption dramatically. In this study, we develop a power-source sizing model based on the Pontryagin’s Minimum Principle (PMP) to minimize the fuel consumption of FCHVs, considering different driving cycles (i.e., FTP-72 and US06) and SOC ranges (conservative 50–60% and aggressive 20–80%). The different driving cycles and SOC ranges present the real-world circumstances of driving FCHVs to some extent. Fuel consumptions are compared both under different driving cycles and using different SOC ranges. The simulation results show an effective power size map, with outlines of an ineffective sizing zone and an inefficient sizing zone based on vehicle performance requirements (e.g., maximum speed and acceleration) and fuel consumption, respectively. Based on the developed model, an optimal power-source size map can be determined while minimizing both fuel consumption and powertrain cost as well as considering different driving cycles and SOC ranges. Full article
(This article belongs to the Special Issue Experiment and Simulation of Energy Storage Systems and Renewable Energy Materials)
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Review

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26 pages, 3073 KiB  
Review
Prognosis and Health Management (PHM) of Solid-State Batteries: Perspectives, Challenges, and Opportunities
by Hamed Sadegh Kouhestani, Xiaoping Yi, Guoqing Qi, Xunliang Liu, Ruimin Wang, Yang Gao, Xiao Yu and Lin Liu
Energies 2022, 15(18), 6599; https://doi.org/10.3390/en15186599 - 9 Sep 2022
Cited by 12 | Viewed by 2916
Abstract
Solid-state batteries (SSBs) have proven to have the potential to be a proper substitute for conventional lithium-ion batteries due to their promising features. In order for the SSBs to be market-ready, the prognostics and health management (PHM) of battery systems plays a critical [...] Read more.
Solid-state batteries (SSBs) have proven to have the potential to be a proper substitute for conventional lithium-ion batteries due to their promising features. In order for the SSBs to be market-ready, the prognostics and health management (PHM) of battery systems plays a critical role in achieving such a goal. PHM ensures the reliability and availability of batteries during their operational time with acceptable safety margin. In the past two decades, much of the focus has been directed towards the PHM of lithium-ion batteries, while little attention has been given to PHM of solid-state batteries. Hence, this report presents a holistic review of the recent advances and current trends in PHM techniques of solid-state batteries and the associated challenges. For this purpose, notable commonly employed physics-based, data-driven, and hybrid methods are discussed in this report. The goal of this study is to bridge the gap between liquid state and SSBs and present the crucial aspects of SSBs that should be considered in order to have an accurate PHM model. The primary focus is given to the ML-based data-driven methods and the requirements that are needed to be included in the models, including anode, cathode, and electrolyte materials. Full article
(This article belongs to the Special Issue Experiment and Simulation of Energy Storage Systems and Renewable Energy Materials)
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