In the past 10 years, perovskite solar cells (PSCs) have undergone extremely rapid development, with a record certified power conversion efficiency (PCE) of 26.7%, which is very close to the limit efficiency. However, the inherent instability caused by ion migration impedes the realization of long-term operationally stable PSCs. In this review, the types and mechanisms of ion migration occurring in various functional layers of negative-intrinsic-positive (n-i-p) PSCs are summarized. Additionally, methods of suppressing ion migration are systematically discussed. Finally, the prospects of current challenges and future development directions are proposed to advance the achievement of high-performance regular PSCs with high stability and PCE.
{"title":"Methods of Suppressing Ion Migration in n-i-p Perovskite Solar Cells","authors":"Dongmei He;Yue Yu;Xinxing Liu;Xuxia Shai;Jiangzhao Chen","doi":"10.23919/IEN.2024.0029","DOIUrl":"https://doi.org/10.23919/IEN.2024.0029","url":null,"abstract":"In the past 10 years, perovskite solar cells (PSCs) have undergone extremely rapid development, with a record certified power conversion efficiency (PCE) of 26.7%, which is very close to the limit efficiency. However, the inherent instability caused by ion migration impedes the realization of long-term operationally stable PSCs. In this review, the types and mechanisms of ion migration occurring in various functional layers of negative-intrinsic-positive (n-i-p) PSCs are summarized. Additionally, methods of suppressing ion migration are systematically discussed. Finally, the prospects of current challenges and future development directions are proposed to advance the achievement of high-performance regular PSCs with high stability and PCE.","PeriodicalId":100648,"journal":{"name":"iEnergy","volume":"3 4","pages":"242-251"},"PeriodicalIF":0.0,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10818562","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study introduces a novel approach to realizing compact high-field superconducting magnets by enabling a closed-loop high temperature superconducting (HTS) coil through magnetization. A circular closed-loop HTS coil is fabricated with a low resistive joint for field cooling magnetization. The HTS coil achieved a trapped field with only a 0.0087% decay in central field over 30 minutes. More interestingly, the central trapped field of 4.59 T exceeds the initial applied field of 4.5 T, while a peak trapped field of 6 T near the inner edge of the HTS coil, is identified through further numerical investigation. This phenomenon differs from the trapped field distributions observed in HTS bulks and stacks, where the trapped cannot exceed the applied one. Unique distributions of current density and magnetic field are identified as the reason for the trapped field exceeding the applied field. This study offers a new way to develop compact HTS magnets for a range of high-field applications such as superconducting magnetic energy storage (SMES) systems, superconducting machines, Maglev and proposes a viable method for amplifying the field strength beyond that of existing magnetic field source devices.
{"title":"A Novel Trapped Field Magnet Enabled by a Quasi-Operational HTS Coil","authors":"Hengpei Liao;Aleksandr Shchukin;Roshan Parajuli;Xavier Chaud;Jung-Bin Song;Min Zhang;Weijia Yuan","doi":"10.23919/IEN.2024.0030","DOIUrl":"https://doi.org/10.23919/IEN.2024.0030","url":null,"abstract":"This study introduces a novel approach to realizing compact high-field superconducting magnets by enabling a closed-loop high temperature superconducting (HTS) coil through magnetization. A circular closed-loop HTS coil is fabricated with a low resistive joint for field cooling magnetization. The HTS coil achieved a trapped field with only a 0.0087% decay in central field over 30 minutes. More interestingly, the central trapped field of 4.59 T exceeds the initial applied field of 4.5 T, while a peak trapped field of 6 T near the inner edge of the HTS coil, is identified through further numerical investigation. This phenomenon differs from the trapped field distributions observed in HTS bulks and stacks, where the trapped cannot exceed the applied one. Unique distributions of current density and magnetic field are identified as the reason for the trapped field exceeding the applied field. This study offers a new way to develop compact HTS magnets for a range of high-field applications such as superconducting magnetic energy storage (SMES) systems, superconducting machines, Maglev and proposes a viable method for amplifying the field strength beyond that of existing magnetic field source devices.","PeriodicalId":100648,"journal":{"name":"iEnergy","volume":"3 4","pages":"261-267"},"PeriodicalIF":0.0,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10818558","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
One of the primary barriers to the advancement of high-efficiency energy conversion technologies is the Shockley-Queisser limit, which imposes a fundamental efficiency constraint on single-junction solar cells. The advent of multi-junction solar cells provides a formidable alternative to this obstacle. Among these, organic-inorganic perovskite solar cells (PSCs) have captured substantial interest due to their outstanding optoelectronic properties, including tunable bandgaps and high-power conversion efficiencies, positioning them as prime candidates for multi-junction photovoltaic systems. We give a review of the latest advancements in four-terminal (4T) perovskite tandem solar cells (TSCs), emphasizing four pertinent configurations: perovskite-silicon (PVK/Si), perovskite-perovskite (PVK/PVK), perovskite-Cu(In,Ga)Se 2 (PVK/CIGS), and perovskite-organic (PVK/organic), as well as other emerging 4T perovskite TSCs. Further, it also emphasizes the advancement of semitransparent wide-bandgap PSCs for TSC applications, tackling important issues and outlining potential future directions for optimizing 4T tandem design performance.
高效能量转换技术发展的主要障碍之一是Shockley-Queisser极限，它对单结太阳能电池的效率施加了基本的限制。多结太阳能电池的出现为这一障碍提供了一个强大的替代方案。其中，有机-无机钙钛矿太阳能电池（PSCs）由于其出色的光电特性（包括可调谐的带隙和高功率转换效率）而引起了极大的兴趣，使其成为多结光伏系统的主要候选者。本文综述了四端（4T）钙钛矿串联太阳能电池（tsc）的最新进展，重点介绍了四种相关结构：钙钛矿-硅（PVK/Si）、钙钛矿-钙钛矿（PVK/PVK）、钙钛矿- cu (in,Ga)Se2 （PVK/CIGS）和钙钛矿-有机（PVK/有机），以及其他新兴的4T钙钛矿串联太阳能电池。此外，它还强调了用于TSC应用的半透明宽带隙psc的进展，解决了重要问题并概述了优化4T串联设计性能的潜在未来方向。
{"title":"Four-Terminal Perovskite Tandem Solar Cells","authors":"Muhammad Rafiq;Hengyue Li;Junliang Yang","doi":"10.23919/IEN.2024.0025","DOIUrl":"https://doi.org/10.23919/IEN.2024.0025","url":null,"abstract":"One of the primary barriers to the advancement of high-efficiency energy conversion technologies is the Shockley-Queisser limit, which imposes a fundamental efficiency constraint on single-junction solar cells. The advent of multi-junction solar cells provides a formidable alternative to this obstacle. Among these, organic-inorganic perovskite solar cells (PSCs) have captured substantial interest due to their outstanding optoelectronic properties, including tunable bandgaps and high-power conversion efficiencies, positioning them as prime candidates for multi-junction photovoltaic systems. We give a review of the latest advancements in four-terminal (4T) perovskite tandem solar cells (TSCs), emphasizing four pertinent configurations: perovskite-silicon (PVK/Si), perovskite-perovskite (PVK/PVK), perovskite-Cu(In,Ga)Se\u0000<inf>2</inf>\u0000 (PVK/CIGS), and perovskite-organic (PVK/organic), as well as other emerging 4T perovskite TSCs. Further, it also emphasizes the advancement of semitransparent wide-bandgap PSCs for TSC applications, tackling important issues and outlining potential future directions for optimizing 4T tandem design performance.","PeriodicalId":100648,"journal":{"name":"iEnergy","volume":"3 4","pages":"216-241"},"PeriodicalIF":0.0,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10818560","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Power flow adjustment is a sequential decision problem. The operator makes decisions to ensure that the power flow meets the system's operational constraints, thereby obtaining a typical operating mode power flow. However, this decision-making method relies heavily on human experience, which is inefficient when the system is complex. In addition, the results given by the current evaluation system are difficult to directly guide the intelligent power flow adjustment. In order to improve the efficiency and intelligence of power flow adjustment, this paper proposes a power flow adjustment method based on deep reinforcement learning. Combining deep reinforcement learning theory with traditional power system operation mode analysis, the concept of region mapping is proposed to describe the adjustment process, so as to analyze the process of power flow calculation and manual adjustment. Considering the characteristics of power flow adjustment, a Markov decision process model suitable for power flow adjustment is constructed. On this basis, a double Q network learning method suitable for power flow adjustment is proposed. This method can adjust the power flow according to the set adjustment route, thus improving the intelligent level of power flow adjustment. The method in this paper is tested on China Electric Power Research Institute (CEPRI) test system.
{"title":"Intelligent Adjustment for Power System Operation Mode Based on Deep Reinforcement Learning","authors":"Wei Hu;Ning Mi;Shuang Wu;Huiling Zhang;Zhewen Hu;Lei Zhang","doi":"10.23919/IEN.2024.0028","DOIUrl":"https://doi.org/10.23919/IEN.2024.0028","url":null,"abstract":"Power flow adjustment is a sequential decision problem. The operator makes decisions to ensure that the power flow meets the system's operational constraints, thereby obtaining a typical operating mode power flow. However, this decision-making method relies heavily on human experience, which is inefficient when the system is complex. In addition, the results given by the current evaluation system are difficult to directly guide the intelligent power flow adjustment. In order to improve the efficiency and intelligence of power flow adjustment, this paper proposes a power flow adjustment method based on deep reinforcement learning. Combining deep reinforcement learning theory with traditional power system operation mode analysis, the concept of region mapping is proposed to describe the adjustment process, so as to analyze the process of power flow calculation and manual adjustment. Considering the characteristics of power flow adjustment, a Markov decision process model suitable for power flow adjustment is constructed. On this basis, a double Q network learning method suitable for power flow adjustment is proposed. This method can adjust the power flow according to the set adjustment route, thus improving the intelligent level of power flow adjustment. The method in this paper is tested on China Electric Power Research Institute (CEPRI) test system.","PeriodicalId":100648,"journal":{"name":"iEnergy","volume":"3 4","pages":"252-260"},"PeriodicalIF":0.0,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10818561","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The accurate forecast of wind power is crucial for the stable operation and economic dispatch of renewable energy power systems. To improve the accuracy of ultra-short-term wind-power forecast, we propose an improved model combining a convolutional neural network (CNN), bidirectional long short-term memory, and an attention mechanism network. First, the basic principle of the proposed model is introduced along with its merits in ultra-short-term wind-power forecast. Then, relevant data are processed based on the Pearson similarity criterion, and relevant feature parameters for wind-power forecast are optimized. Finally, the proposed model is analyzed based on the public dataset of the Baidu KDD Cup 2022 wind-power forecast competition and actual data from a wind farm in Shandong. Results show that the proposed model can effectively overcome the shortcomings of traditional forecast methods in terms of overfitting, feature extraction, and parameter tuning. Furthermore, the model exhibits higher forecast accuracy and stability.
{"title":"Ultra-Short-Term Wind-Power Forecasting Based on an Optimized CNN-BILSTM-Attention Model","authors":"Weilong Yu;Shuaibing Li;Hao Zhang;Yongqiang Kang;Hongwei Li;Haiying Dong","doi":"10.23919/IEN.2024.0026","DOIUrl":"https://doi.org/10.23919/IEN.2024.0026","url":null,"abstract":"The accurate forecast of wind power is crucial for the stable operation and economic dispatch of renewable energy power systems. To improve the accuracy of ultra-short-term wind-power forecast, we propose an improved model combining a convolutional neural network (CNN), bidirectional long short-term memory, and an attention mechanism network. First, the basic principle of the proposed model is introduced along with its merits in ultra-short-term wind-power forecast. Then, relevant data are processed based on the Pearson similarity criterion, and relevant feature parameters for wind-power forecast are optimized. Finally, the proposed model is analyzed based on the public dataset of the Baidu KDD Cup 2022 wind-power forecast competition and actual data from a wind farm in Shandong. Results show that the proposed model can effectively overcome the shortcomings of traditional forecast methods in terms of overfitting, feature extraction, and parameter tuning. Furthermore, the model exhibits higher forecast accuracy and stability.","PeriodicalId":100648,"journal":{"name":"iEnergy","volume":"3 4","pages":"268-282"},"PeriodicalIF":0.0,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10818559","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Since its launch in March 2022, iEnergy has published 12 issues. iEnergy strives to promote innovation and development in the field of power and energy and to provide a high-quality academic exchange platform for global scholars. We know that the academic level of a journal is an important criterion for first-class influence; therefore, we have continued to optimize the review process and improve publication usage standards to ensure that every published paper has a high level of academic value and practical significance. In addition, we strictly control the quality of published papers and ensure that every published article, review and letter is peer-reviewed and recognized by experts.
{"title":"Toward Clean, Efficient, and Intelligent Power and Energy Systems","authors":"","doi":"10.23919/IEN.2024.0031","DOIUrl":"https://doi.org/10.23919/IEN.2024.0031","url":null,"abstract":"Since its launch in March 2022, iEnergy has published 12 issues. iEnergy strives to promote innovation and development in the field of power and energy and to provide a high-quality academic exchange platform for global scholars. We know that the academic level of a journal is an important criterion for first-class influence; therefore, we have continued to optimize the review process and improve publication usage standards to ensure that every published paper has a high level of academic value and practical significance. In addition, we strictly control the quality of published papers and ensure that every published article, review and letter is peer-reviewed and recognized by experts.","PeriodicalId":100648,"journal":{"name":"iEnergy","volume":"3 4","pages":"185-186"},"PeriodicalIF":0.0,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10818557","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Effective stability analysis is essential for the secure operation of modern power systems. As smart grids evolve with increased interconnection, renewable energy integration, and electrification, the large-scale deployment of ultra-high voltage AC/DC networks introduces various operational modes and potential fault points, posing significant challenges to maintaining stability. Traditional analysis and control methods fall short under these conditions. In contrast, emerging artificial intelligence (AI) techniques, combined with real-time data collection, provide powerful tools for enhancing stability analysis in smart grids. This paper comprehensively explores AI techniques in stability analysis, discussing the necessity and rationale for integrating AI into stability analysis through the lenses of knowledge fusion, discovery, and adaptation. It provides a thorough review of current studies on AI applications in stability analysis, addresses key challenges, and outlines future prospects for AI integration, highlighting its potential to improve analytical capabilities in complex power systems.
{"title":"Artificial Intelligence Techniques for Stability Analysis in Modern Power Systems","authors":"Jiashu Fang;Chongru Liu","doi":"10.23919/IEN.2024.0027","DOIUrl":"https://doi.org/10.23919/IEN.2024.0027","url":null,"abstract":"Effective stability analysis is essential for the secure operation of modern power systems. As smart grids evolve with increased interconnection, renewable energy integration, and electrification, the large-scale deployment of ultra-high voltage AC/DC networks introduces various operational modes and potential fault points, posing significant challenges to maintaining stability. Traditional analysis and control methods fall short under these conditions. In contrast, emerging artificial intelligence (AI) techniques, combined with real-time data collection, provide powerful tools for enhancing stability analysis in smart grids. This paper comprehensively explores AI techniques in stability analysis, discussing the necessity and rationale for integrating AI into stability analysis through the lenses of knowledge fusion, discovery, and adaptation. It provides a thorough review of current studies on AI applications in stability analysis, addresses key challenges, and outlines future prospects for AI integration, highlighting its potential to improve analytical capabilities in complex power systems.","PeriodicalId":100648,"journal":{"name":"iEnergy","volume":"3 4","pages":"194-215"},"PeriodicalIF":0.0,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10818563","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this era of deep decarbonization, when the new mantra is green energy everywhere, can we find ourselves in a situation where we have too much green energy? Believe it or not, this is the energy paradox faced by Australia on October 3, 2024. The proliferation of photovoltaic panels on roofs is causing an over-production of electricity, threatening the grid's stability. On that day, the peak of solar energy reached a record level, far exceeding the expected consumption level. As a result, the electric load vanished, and the total demand seen by the dispatch center crossed the dangerous low limit set to ensure network stability. In Victoria, one of the wealthiest states in Australia, the electricity system is designed for demand ranging from 1,865 to 10,000 megawatts, with a typical average of 5,000 megawatts. But on Saturday, 3 October, the market fell to a record low of 1,352 megawatts. This unprecedented situation has put the electricity grid under immense pressure. While not resulting in a widespread blackout, it demonstrates the urgent need to adapt energy infrastructure and policies. Solutions such as cost-effective large-scale battery storage or virtual power plants improving the capacity to manage excess solar energy are urgently needed. Other countries, notably California, have experienced similar challenges, illustrated by the “Duck curve” (see Figure 1). The most straightforward mitigation means to “dump” the excess PV energy by capping their production, which amounts to increasing their total cost of ownership and lost opportunity for deeper decarbonization.
{"title":"EV and PV are Booming, but is the Grid Ready to Coordinate them?","authors":"Innocent Kamwa;Hajar Abdolahinia","doi":"10.23919/IEN.2024.0023","DOIUrl":"https://doi.org/10.23919/IEN.2024.0023","url":null,"abstract":"In this era of deep decarbonization, when the new mantra is green energy everywhere, can we find ourselves in a situation where we have too much green energy? Believe it or not, this is the energy paradox faced by Australia on October 3, 2024. The proliferation of photovoltaic panels on roofs is causing an over-production of electricity, threatening the grid's stability. On that day, the peak of solar energy reached a record level, far exceeding the expected consumption level. As a result, the electric load vanished, and the total demand seen by the dispatch center crossed the dangerous low limit set to ensure network stability. In Victoria, one of the wealthiest states in Australia, the electricity system is designed for demand ranging from 1,865 to 10,000 megawatts, with a typical average of 5,000 megawatts. But on Saturday, 3 October, the market fell to a record low of 1,352 megawatts. This unprecedented situation has put the electricity grid under immense pressure. While not resulting in a widespread blackout, it demonstrates the urgent need to adapt energy infrastructure and policies. Solutions such as cost-effective large-scale battery storage or virtual power plants improving the capacity to manage excess solar energy are urgently needed. Other countries, notably California, have experienced similar challenges, illustrated by the “Duck curve” (see Figure 1). The most straightforward mitigation means to “dump” the excess PV energy by capping their production, which amounts to increasing their total cost of ownership and lost opportunity for deeper decarbonization.","PeriodicalId":100648,"journal":{"name":"iEnergy","volume":"3 4","pages":"187-188"},"PeriodicalIF":0.0,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10787156","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Effective perovskite crystallization control strategies for flexible substrates with scalable processing techniques have rarely been reported and remain an important challenge. In this study, 3-mercaptobenzoic acid (3-MBA) was introduced into the perovskite precursor to modulate the crystallization dynamics, facilitating rapid nucleation while slowing down crystal growth. This approach enabled the formation of uniform, dense large-area perovskite films on flexible substrates. Consequently, a $12 text{cm}^{2}$ flexible perovskite solar module achieved a power conversion efficiency (PCE) of 16.43%. Additionally, the module exhibited enhanced mechanical stability under various bending radii and improved light stability, marking a substantial advance toward the practical application of flexible perovskite solar modules.
{"title":"Crystallization Regulation for Stable Blade-Coated Flexible Perovskite Solar Modules","authors":"Hua Zhong;Fei Zhang","doi":"10.23919/IEN.2024.0024","DOIUrl":"https://doi.org/10.23919/IEN.2024.0024","url":null,"abstract":"Effective perovskite crystallization control strategies for flexible substrates with scalable processing techniques have rarely been reported and remain an important challenge. In this study, 3-mercaptobenzoic acid (3-MBA) was introduced into the perovskite precursor to modulate the crystallization dynamics, facilitating rapid nucleation while slowing down crystal growth. This approach enabled the formation of uniform, dense large-area perovskite films on flexible substrates. Consequently, a \u0000<tex>$12 text{cm}^{2}$</tex>\u0000 flexible perovskite solar module achieved a power conversion efficiency (PCE) of 16.43%. Additionally, the module exhibited enhanced mechanical stability under various bending radii and improved light stability, marking a substantial advance toward the practical application of flexible perovskite solar modules.","PeriodicalId":100648,"journal":{"name":"iEnergy","volume":"3 4","pages":"189-193"},"PeriodicalIF":0.0,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10787155","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The accurate state-of-charge (SOC) estimation of sodium-ion batteries is the basis for their efficient application. In this paper, a new SOC estimation method suitable for sodium-ion batteries and their application conditions is proposed, which considers the combination of open circuit voltage (OCV) and internal resistance correction. First, the optimal order of equivalent circuit model is analyzed and selected, and the monotonic and stable mapping relationships between OCV and SOC, as well as between ohmic internal resistance and SOC are determined. Then, a joint estimation algorithm for battery model parameters and SOC is established, and a joint SOC correction strategy based on OCV and ohmic internal resistance is established. The test results show that OCV correction is reliable when polarization is small, that the ohmic internal resistance correction is reliable when the current fluctuation is large, and that the maximum absolute error of SOC estimation of the proposed method is not more than 2.6%.
{"title":"An Improved State-of-Charge Estimation Method for Sodium-Ion Battery Based on Combined Correction of Voltage and Internal Resistance","authors":"Yongqi Li;Cheng Chen;Youwei Wen;Qikai Lei;Kaixuan Zhang;Yifei Chen;Rui Xiong","doi":"10.23919/IEN.2024.0017","DOIUrl":"https://doi.org/10.23919/IEN.2024.0017","url":null,"abstract":"The accurate state-of-charge (SOC) estimation of sodium-ion batteries is the basis for their efficient application. In this paper, a new SOC estimation method suitable for sodium-ion batteries and their application conditions is proposed, which considers the combination of open circuit voltage (OCV) and internal resistance correction. First, the optimal order of equivalent circuit model is analyzed and selected, and the monotonic and stable mapping relationships between OCV and SOC, as well as between ohmic internal resistance and SOC are determined. Then, a joint estimation algorithm for battery model parameters and SOC is established, and a joint SOC correction strategy based on OCV and ohmic internal resistance is established. The test results show that OCV correction is reliable when polarization is small, that the ohmic internal resistance correction is reliable when the current fluctuation is large, and that the maximum absolute error of SOC estimation of the proposed method is not more than 2.6%.","PeriodicalId":100648,"journal":{"name":"iEnergy","volume":"3 3","pages":"128-134"},"PeriodicalIF":0.0,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10689486","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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