Pub Date : 2025-02-12DOI: 10.1016/j.geits.2025.100280
Malolan Sundararaman , Balasubramanian Sambasivam
This study explores the intersection of two pivotal interventions aimed at achieving carbon neutrality: the electric vehicles (EVs) adoption and the renewable energy (RE) electricity generation. Focusing on a Renewable Energy-Dominated (RED) electricity system, the research examines the interdependence between these interventions and their collective impact on economic dispatch. The study's objective is to determine optimal economic dispatch strategies that meet hourly electricity demand, considering two distinct supply scenarios across eight supply options. The first scenario assesses the maximum possible supply, while the second contemplates the minimum possible supply from each option. Additionally, the study delves into the influence of social cost of emissions on these economic dispatches. Employing an experimental design, the study generates representative load curves that incorporate EV charging demands for varied levels of EV penetration, alongside regular electricity demand. Data from Karnataka's RED electricity system provides a basis for the supply-side analysis. The economic dispatch for each supply scenario is formulated as a Mixed Integer Linear Program (MILP), aiming to minimize both costs for generation and social costs of emissions, while adhering to operational constraints of the supply options. Key findings from this approach, highlight several critical insights: the significant role of incorporating social costs in economic dispatch decisions, the tangible impact of EV demand on supply shortages, and the importance of maintaining supply capacity to minimize these shortages.
{"title":"The road to net zero in a renewable energy-dominated electricity system: Impact of EV charging and social cost of emission on the optimal economic dispatch","authors":"Malolan Sundararaman , Balasubramanian Sambasivam","doi":"10.1016/j.geits.2025.100280","DOIUrl":"10.1016/j.geits.2025.100280","url":null,"abstract":"<div><div>This study explores the intersection of two pivotal interventions aimed at achieving carbon neutrality: the electric vehicles (EVs) adoption and the renewable energy (RE) electricity generation. Focusing on a Renewable Energy-Dominated (RED) electricity system, the research examines the interdependence between these interventions and their collective impact on economic dispatch. The study's objective is to determine optimal economic dispatch strategies that meet hourly electricity demand, considering two distinct supply scenarios across eight supply options. The first scenario assesses the maximum possible supply, while the second contemplates the minimum possible supply from each option. Additionally, the study delves into the influence of social cost of emissions on these economic dispatches. Employing an experimental design, the study generates representative load curves that incorporate EV charging demands for varied levels of EV penetration, alongside regular electricity demand. Data from Karnataka's RED electricity system provides a basis for the supply-side analysis. The economic dispatch for each supply scenario is formulated as a Mixed Integer Linear Program (MILP), aiming to minimize both costs for generation and social costs of emissions, while adhering to operational constraints of the supply options. Key findings from this approach, highlight several critical insights: the significant role of incorporating social costs in economic dispatch decisions, the tangible impact of EV demand on supply shortages, and the importance of maintaining supply capacity to minimize these shortages.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 3","pages":"Article 100280"},"PeriodicalIF":0.0,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143924604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-07DOI: 10.1016/j.geits.2025.100266
Yang Wu , Udaya K. Madawala , Lei Zhao , Xin Dai
With the expansion of the application field of wireless power transmission (WPT) technology, there is an urgent need for bidirectional wireless power transmission technology (BWPT) to realize the energy interaction between wireless charging devices. This paper first analyses the development status of bidirectional capacitive power transfer (BCPT) system and the key issues that need to be addressed, briefly describes the basic operating principle of BWPT system, specifically discuss the main research achievements in terms of typical bidirectional conversion topologies, resonant networks, power control strategies and application scenarios of BWPT system. Finally, prospects for research directions worthy of attention in the future are presented.
{"title":"Research status of bidirectional wireless power transfer technology","authors":"Yang Wu , Udaya K. Madawala , Lei Zhao , Xin Dai","doi":"10.1016/j.geits.2025.100266","DOIUrl":"10.1016/j.geits.2025.100266","url":null,"abstract":"<div><div>With the expansion of the application field of wireless power transmission (WPT) technology, there is an urgent need for bidirectional wireless power transmission technology (BWPT) to realize the energy interaction between wireless charging devices. This paper first analyses the development status of bidirectional capacitive power transfer (BCPT) system and the key issues that need to be addressed, briefly describes the basic operating principle of BWPT system, specifically discuss the main research achievements in terms of typical bidirectional conversion topologies, resonant networks, power control strategies and application scenarios of BWPT system. Finally, prospects for research directions worthy of attention in the future are presented.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"5 2","pages":"Article 100266"},"PeriodicalIF":16.4,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.geits.2024.100246
Hui Zhang , Yiyue Luo , Naikan Ding , Toshiyuki Yamamoto , Chenming Fan , Chunhui Yang , Wei Xu , Chaozhong Wu
Electric vehicles are widely embraced as a promising solution to reduce energy consumption and emission to achieve the Carbon Peak and Carbon Neutrality vision, especially in developing countries. Specifically, it’s vital important to understand the ecological performance of electric vehicles and its association with driving behaviors under varying road and environmental conditions. However, current researches on ecological driving behavior mostly use structured data to reflect the characteristics of ecological driving behavior, and it is difficult to accurately reveal the recessive relationship between driving behavior and energy consumption. One promising and prevalent method for comprehensively and in-depth characterizing driving behaviors is “graph spectrums”, which allows for an effective and illustrative representation of complex driving behavior characteristics. This study presented an assessment method of ecological driving for electric vehicles based on the graph. Firstly, a multi-source refined data set was constructed through naturalistic driving experiments (NDE). Four typical traffic state (CCCF: congested close car-following; CSSF: constrained slow free-flow; CSCF: constrained slow car-following; UFFF: unconstrained fast free-flow) were classified through longitudinal acceleration data, and driving behavior graph was constructed to realize the visual representation of driving behavior. Then, the energy consumption graph was constructed using the energy loss of 100 km (EL) index. After the six drivers with the highest and lowest ecological assessment of driving behavior using the behavior graph and energy consumption graph, proposing the quantitative analysis of fifteen drivers' ecology driving behavior. The results show that: 1) The graphical method can describe the individual features of a driver’s ecological driving behavior; 2) Rapid acceleration of driving behavior leads to high energy consumption; 3) In the comparison among the six eco-drivers and energy-intensive drivers, founding that the energy-intensive drivers accelerate and decelerate significantly more in CCCF traffic state; 4) The driving behavior was more complex and unecological in CCCF traffic state; 5) Fifteen drivers had lower ecological scores in start-up driving. This study proposes a method for visualizing ecology driving behavior that not only help understand the individual characteristics of ecological driving behaviors, but also offers substantial application value for the subsequent construction of Ecological driving behavior regulation models.
{"title":"Evaluation of eco-driving performance of electric vehicles using driving behavior-enabled graph spectrums: A naturalistic driving study in China","authors":"Hui Zhang , Yiyue Luo , Naikan Ding , Toshiyuki Yamamoto , Chenming Fan , Chunhui Yang , Wei Xu , Chaozhong Wu","doi":"10.1016/j.geits.2024.100246","DOIUrl":"10.1016/j.geits.2024.100246","url":null,"abstract":"<div><div>Electric vehicles are widely embraced as a promising solution to reduce energy consumption and emission to achieve the Carbon Peak and Carbon Neutrality vision, especially in developing countries. Specifically, it’s vital important to understand the ecological performance of electric vehicles and its association with driving behaviors under varying road and environmental conditions. However, current researches on ecological driving behavior mostly use structured data to reflect the characteristics of ecological driving behavior, and it is difficult to accurately reveal the recessive relationship between driving behavior and energy consumption. One promising and prevalent method for comprehensively and in-depth characterizing driving behaviors is “graph spectrums”, which allows for an effective and illustrative representation of complex driving behavior characteristics. This study presented an assessment method of ecological driving for electric vehicles based on the graph. Firstly, a multi-source refined data set was constructed through naturalistic driving experiments (NDE). Four typical traffic state (CCCF: congested close car-following; CSSF: constrained slow free-flow; CSCF: constrained slow car-following; UFFF: unconstrained fast free-flow) were classified through longitudinal acceleration data, and driving behavior graph was constructed to realize the visual representation of driving behavior. Then, the energy consumption graph was constructed using the energy loss of 100 km (EL) index. After the six drivers with the highest and lowest ecological assessment of driving behavior using the behavior graph and energy consumption graph, proposing the quantitative analysis of fifteen drivers' ecology driving behavior. The results show that: 1) The graphical method can describe the individual features of a driver’s ecological driving behavior; 2) Rapid acceleration of driving behavior leads to high energy consumption; 3) In the comparison among the six eco-drivers and energy-intensive drivers, founding that the energy-intensive drivers accelerate and decelerate significantly more in CCCF traffic state; 4) The driving behavior was more complex and unecological in CCCF traffic state; 5) Fifteen drivers had lower ecological scores in start-up driving. This study proposes a method for visualizing ecology driving behavior that not only help understand the individual characteristics of ecological driving behaviors, but also offers substantial application value for the subsequent construction of Ecological driving behavior regulation models.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 1","pages":"Article 100246"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143168958","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}
Pub Date : 2025-02-01DOI: 10.1016/j.geits.2024.100248
Fei Chen , Tianxin Chen , Zhenxuan Wu , Zihan Zhou , Kunjie Lu , Jinyao Su , Yihua Wang , Jianfeng Hua , Xin Lai , Xuebin Han , Minggao Ouyang , Yuejiu Zheng
The advancement of lithium-ion batteries (LIBs) towards larger structures is considered the most efficient approach to enhance energy density in clean energy storage systems. However, this advancement poses significant challenges in terms of the filling and wetting processes of battery electrolytes. The intricate interplay between electrode microstructure and electrolyte wetting process still requires further investigation. This study aims to systematically investigate the primary mechanisms influencing electrolyte wetting on porous electrode structures produced through different manufacturing processes. Using advanced X-ray computed tomography, three-dimensional electrode structures are reconstructed, and permeability and capillary action are evaluated as key parameters. It is observed that increasing calendering pressure and active material content reduces electrode porosity, thereby decreasing permeability and penetration rate; however, it simultaneously enhances capillary action. The interplay between these indicators contributes to the complexity of wetting behavior. Incomplete wetting of electrolytes arises from two primary factors elucidated by further simulations: partial closure of pores induced by the calendering process impedes complete wetting, while non-wetting phase gases become trapped within the electrolyte during the wetting process hindering their release and inhibiting full penetration of the electrolyte. These findings have significant implications for designing and optimizing LIBs while offering profound insights for future advancements in battery technology.
{"title":"Unraveling mechanisms of electrolyte wetting process in three-dimensional electrode structures: Insights from realistic architectures","authors":"Fei Chen , Tianxin Chen , Zhenxuan Wu , Zihan Zhou , Kunjie Lu , Jinyao Su , Yihua Wang , Jianfeng Hua , Xin Lai , Xuebin Han , Minggao Ouyang , Yuejiu Zheng","doi":"10.1016/j.geits.2024.100248","DOIUrl":"10.1016/j.geits.2024.100248","url":null,"abstract":"<div><div>The advancement of lithium-ion batteries (LIBs) towards larger structures is considered the most efficient approach to enhance energy density in clean energy storage systems. However, this advancement poses significant challenges in terms of the filling and wetting processes of battery electrolytes. The intricate interplay between electrode microstructure and electrolyte wetting process still requires further investigation. This study aims to systematically investigate the primary mechanisms influencing electrolyte wetting on porous electrode structures produced through different manufacturing processes. Using advanced X-ray computed tomography, three-dimensional electrode structures are reconstructed, and permeability and capillary action are evaluated as key parameters. It is observed that increasing calendering pressure and active material content reduces electrode porosity, thereby decreasing permeability and penetration rate; however, it simultaneously enhances capillary action. The interplay between these indicators contributes to the complexity of wetting behavior. Incomplete wetting of electrolytes arises from two primary factors elucidated by further simulations: partial closure of pores induced by the calendering process impedes complete wetting, while non-wetting phase gases become trapped within the electrolyte during the wetting process hindering their release and inhibiting full penetration of the electrolyte. These findings have significant implications for designing and optimizing LIBs while offering profound insights for future advancements in battery technology.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 1","pages":"Article 100248"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143167980","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}
Pub Date : 2025-02-01DOI: 10.1016/j.geits.2024.100226
Chaoran Li , Sichen Zhu , Liuli Zhang , Xinjian Liu , Menghan Li , Haiqin Zhou , Qiang Zhang , Zhonghao Rao
State of charge (SOC) plays a vital role in the safe, efficient, and stable operation of lithium-ion batteries. Since the difference between the surface temperature and core temperature of batteries under severe conditions can reach 5–10 °C, using the surface temperature as input feature of SOC estimation is unreasonable. Due to the high requirement for storage space, SOC estimation methods based on deep learning methods are limited to implement in embedded devices. In this paper, to achieve reasonable and high accuracy SOC estimation and provide support for battery thermal management, SOC estimation based on state of temperature (SOT) is implemented. And weight clustered-convolutional neural network-long short-term memory (WC-CNN-LSTM) is proposed to achieve high accuracy SOT and SOC estimation with small model sizes. A self-established dataset is used to verify the effectiveness of the proposed method and model. The WC-CNN-LSTM model with the number of clusters of 400 could achieve comparative accuracy with the baseline model with a 52.98% smaller model size and 25.08% more time consumption for model training on SOT estimation. And it could also achieve consistent and even better accuracy on SOC estimation with the baseline model with a small model size.
{"title":"State of charge estimation of lithium-ion battery based on state of temperature estimation using weight clustered-convolutional neural network-long short-term memory","authors":"Chaoran Li , Sichen Zhu , Liuli Zhang , Xinjian Liu , Menghan Li , Haiqin Zhou , Qiang Zhang , Zhonghao Rao","doi":"10.1016/j.geits.2024.100226","DOIUrl":"10.1016/j.geits.2024.100226","url":null,"abstract":"<div><div>State of charge (SOC) plays a vital role in the safe, efficient, and stable operation of lithium-ion batteries. Since the difference between the surface temperature and core temperature of batteries under severe conditions can reach 5–10 °C, using the surface temperature as input feature of SOC estimation is unreasonable. Due to the high requirement for storage space, SOC estimation methods based on deep learning methods are limited to implement in embedded devices. In this paper, to achieve reasonable and high accuracy SOC estimation and provide support for battery thermal management, SOC estimation based on state of temperature (SOT) is implemented. And weight clustered-convolutional neural network-long short-term memory (WC-CNN-LSTM) is proposed to achieve high accuracy SOT and SOC estimation with small model sizes. A self-established dataset is used to verify the effectiveness of the proposed method and model. The WC-CNN-LSTM model with the number of clusters of 400 could achieve comparative accuracy with the baseline model with a 52.98% smaller model size and 25.08% more time consumption for model training on SOT estimation. And it could also achieve consistent and even better accuracy on SOC estimation with the baseline model with a small model size.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 1","pages":"Article 100226"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143167979","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}
Pub Date : 2025-02-01DOI: 10.1016/j.geits.2024.100245
Muthukumaran Thulasingam, Ajay D Vimal Raj Periyanayagam
This research focuses on restructuring medium-level voltage (MLV) distribution systems by integrating distributed renewable energy resources (DER) at multiple feed points. It examines the impact of incorporating renewable energy and evaluates system performance metrics such as robustness, static voltage stability, line carrying capacity, utility grid effectiveness, and losses within the conventional radial distribution framework commonly used in educational institutions. The contingency ranking of the real-time radial distribution system (RTRDS) for a typical educational institution consisting of N buses was conducted. Parameters such as the Voltage Performance Index (PIV) and Flow Performance Index (PIF) were evaluated. The results support the integration of distributed renewable energy sources within the existing radial distribution grid infrastructure. This research proposes enhanced contingency analyses through a straightforward reconfiguration process involving an additional tie line (N + 1) for the existing N bus radial distribution system (RDS). Load flow analysis of the RDS with distributed renewable energy resources (DER) for both N bus and N + 1 bus systems was conducted using the Gauss-Seidel and Newton–Raphson methods. Simulation results indicate that baseline loading is consistently maintained by grid sources and DER sources connected at multiple feed points. The proposed configuration of the N + 1 bus system for the existing RTRDS was evaluated for voltage performance and compared with the Grey Wolf Optimization (GWO) algorithm. The results indicate that the N + 1 bus configuration modeled using the MiPower tool performed comparably to the GWO results. Additionally, the contingency ranking for the proposed N + 1 configuration was validated using the IEEE 10 and 30 bus system.
{"title":"Radial distribution systems performance enhancement through RE (Renewable Energy) integration and comprehensive contingency ranking analysis","authors":"Muthukumaran Thulasingam, Ajay D Vimal Raj Periyanayagam","doi":"10.1016/j.geits.2024.100245","DOIUrl":"10.1016/j.geits.2024.100245","url":null,"abstract":"<div><div>This research focuses on restructuring medium-level voltage (MLV) distribution systems by integrating distributed renewable energy resources (DER) at multiple feed points. It examines the impact of incorporating renewable energy and evaluates system performance metrics such as robustness, static voltage stability, line carrying capacity, utility grid effectiveness, and losses within the conventional radial distribution framework commonly used in educational institutions. The contingency ranking of the real-time radial distribution system (RTRDS) for a typical educational institution consisting of <em>N</em> buses was conducted. Parameters such as the Voltage Performance Index (PIV) and Flow Performance Index (PIF) were evaluated. The results support the integration of distributed renewable energy sources within the existing radial distribution grid infrastructure. This research proposes enhanced contingency analyses through a straightforward reconfiguration process involving an additional tie line (<em>N</em> + 1) for the existing <em>N</em> bus radial distribution system (RDS). Load flow analysis of the RDS with distributed renewable energy resources (DER) for both <em>N</em> bus and <em>N</em> + 1 bus systems was conducted using the Gauss-Seidel and Newton–Raphson methods. Simulation results indicate that baseline loading is consistently maintained by grid sources and DER sources connected at multiple feed points. The proposed configuration of the <em>N</em> + 1 bus system for the existing RTRDS was evaluated for voltage performance and compared with the Grey Wolf Optimization (GWO) algorithm. The results indicate that the <em>N</em> + 1 bus configuration modeled using the MiPower tool performed comparably to the GWO results. Additionally, the contingency ranking for the proposed <em>N</em> + 1 configuration was validated using the IEEE 10 and 30 bus system.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 1","pages":"Article 100245"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143167978","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}
Pub Date : 2025-02-01DOI: 10.1016/j.geits.2024.100247
Craig McIntyre, Silvia Konaklieva, Artur Benedito Nunes, Richard A. McMahon
Wireless Power Transfer (WPT) is an alternative method of Electric Vehicle (EV) battery charging, particularly for fleet vehicles and people with mobility issues. The safe operation of WPT systems should therefore be of interest and importance to system designers, installers, and end-users. One aspect of safe operation is the potential exposure to high-power electromagnetic fields. There are international guidelines with recommended exposure limits that system designers can design and test to. Simulations can be used to predict magnetic field levels, but these should be developed in conjunction with physical measurements to improve the accuracy of such simulations.
1 Several factors can influence the WPT generated electromagnetic field, in regions where end users could be located during charging operation. These factors were studied for an in-house designed WPT system retrofitted to an electric vehicle. The magnetic field was physically measured around the vehicle for different operating conditions (alignment, power transfer level and probe position) to assess performance against recommended exposure levels, observe any trends in measurements and study the impact of the probe position.
Coil currents were measured and used within an initial simulation to predict magnetic field for comparison to physical values. The initial simulation predicted the trend of the magnetic field with reasonable accuracy. Where there was a difference in magnitude, the physical measurements highlighted that a High Frequency (HF cable) used within the vehicle assembly (not included in initial simulation) contributed to the magnetic field intensity. Overall, magnetic fields were within permitted exposure limits at 10 kW power and good alignment, and with misaligned coils, the system showed only minor exceedance of the most stringent limits, and DC–DC system efficiency was only slightly reduced.
{"title":"A study of the magnetic field emissions from a vehicle-mounted wireless power transfer system for safe operation when charging EV batteries","authors":"Craig McIntyre, Silvia Konaklieva, Artur Benedito Nunes, Richard A. McMahon","doi":"10.1016/j.geits.2024.100247","DOIUrl":"10.1016/j.geits.2024.100247","url":null,"abstract":"<div><div>Wireless Power Transfer (WPT) is an alternative method of Electric Vehicle (EV) battery charging, particularly for fleet vehicles and people with mobility issues. The safe operation of WPT systems should therefore be of interest and importance to system designers, installers, and end-users. One aspect of safe operation is the potential exposure to high-power electromagnetic fields. There are international guidelines with recommended exposure limits that system designers can design and test to. Simulations can be used to predict magnetic field levels, but these should be developed in conjunction with physical measurements to improve the accuracy of such simulations.</div><div>1 Several factors can influence the WPT generated electromagnetic field, in regions where end users could be located during charging operation. These factors were studied for an in-house designed WPT system retrofitted to an electric vehicle. The magnetic field was physically measured around the vehicle for different operating conditions (alignment, power transfer level and probe position) to assess performance against recommended exposure levels, observe any trends in measurements and study the impact of the probe position.</div><div>Coil currents were measured and used within an initial simulation to predict magnetic field for comparison to physical values. The initial simulation predicted the trend of the magnetic field with reasonable accuracy. Where there was a difference in magnitude, the physical measurements highlighted that a High Frequency (HF cable) used within the vehicle assembly (not included in initial simulation) contributed to the magnetic field intensity. Overall, magnetic fields were within permitted exposure limits at 10 kW power and good alignment, and with misaligned coils, the system showed only minor exceedance of the most stringent limits, and DC–DC system efficiency was only slightly reduced.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 1","pages":"Article 100247"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143167981","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}
Pub Date : 2025-02-01DOI: 10.1016/j.geits.2024.100167
Guangying Zhu , Jianguo Chen , Xuyang Liu , Tao Sun , Xin Lai , Yuejiu Zheng , Yue Guo , Rohit Bhagat
Lithium plating in lithium-ion batteries (LIBs) is one of the main causes of safety accidents in electric vehicles (EVs). The study of intelligent machine learning-based lithium plating detection and warning algorithms for LIBs is of great importance. Therefore, this paper proposes an intelligent lithium plating detection and early warning method for LIBs based on the random forest model. This method can accurately detect lithium plating during the charging process of LIBs, and play an early warning role according to the detection results. First, pulse charging experiments of LIBs, including normal and lithium plating charging tests, were completed and validated using in situ characterization methods. Second, the normalized internal resistance from the pulse charging test is used to detect lithium plating in LIBs. Third, a lithium plating feature extraction method is proposed to address the lack of useful lithium plating information for LIBs during the charging process. Finally, the Random Forest machine learning technique is used to classify and predict the lithium plating of LIBs. The model validation results show that the detection accuracy of lithium plating is greater than 97.2%. This is of significance for the study of intelligent lithium plating detection algorithms for LIBs.
{"title":"Intelligent lithium plating detection and prediction method for Li-ion batteries based on random forest model","authors":"Guangying Zhu , Jianguo Chen , Xuyang Liu , Tao Sun , Xin Lai , Yuejiu Zheng , Yue Guo , Rohit Bhagat","doi":"10.1016/j.geits.2024.100167","DOIUrl":"10.1016/j.geits.2024.100167","url":null,"abstract":"<div><div>Lithium plating in lithium-ion batteries (LIBs) is one of the main causes of safety accidents in electric vehicles (EVs). The study of intelligent machine learning-based lithium plating detection and warning algorithms for LIBs is of great importance. Therefore, this paper proposes an intelligent lithium plating detection and early warning method for LIBs based on the random forest model. This method can accurately detect lithium plating during the charging process of LIBs, and play an early warning role according to the detection results. First, pulse charging experiments of LIBs, including normal and lithium plating charging tests, were completed and validated using in situ characterization methods. Second, the normalized internal resistance from the pulse charging test is used to detect lithium plating in LIBs. Third, a lithium plating feature extraction method is proposed to address the lack of useful lithium plating information for LIBs during the charging process. Finally, the Random Forest machine learning technique is used to classify and predict the lithium plating of LIBs. The model validation results show that the detection accuracy of lithium plating is greater than 97.2%. This is of significance for the study of intelligent lithium plating detection algorithms for LIBs.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 1","pages":"Article 100167"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139457557","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}
Pub Date : 2025-02-01DOI: 10.1016/j.geits.2024.100179
Anran Cheng , Pei Gao , Ruxing Wang , Kangli Wang , Kai Jiang
Although showing huge potential in prospering the marketplace of all-solid-state lithium metal batteries (ASSLMBs), garnet-type solid electrolytes (Li6.5La3Zr1.5Ta0.6O12, LLZTO) are critically plagued by interface instability with Li anode and the vulnerability to Li dendrite, which are attributed to poor Li diffusion kinetic in bulk Li metal. Herein, a LixAg solid solution alloy with high Li diffusion kinetic is reported as a mixed ion-electron conductor (MIEC) alloy anode. The high Li diffusion kinetic stemming from a low eutectic point and a high mutual solubility of LixAg could reduce the Li concentration gradient in the anode, regulate Li electrochemical potential, and change the relative local overpotential for Li stripping/plating in the anode. Notably, Li stripping/plating prefers energetically at the LixAg/current collector interface rather than the LLZTO/LixAg interface. Therefore, the contact loss is avoided at the LLZTO/LixAg interface. As a result, excellent cycling stability (∼1,200 h at 0.2 mA/cm2), and dendrites tolerance (critical current density of 1.2 mA/cm2) are demonstrated by using LixAg as anode. Further research has elucidated that those alloys with low eutectic temperature and high mutual solubility with lithium should be focused on, as they would provide and maintain a soft lattice and a high lithium diffusion rate during composition change. This provides a basis for the selection of alloy phases in negative electrode materials, as well as their application in garnet-based ASSLMBs.
{"title":"Mixed ion-electron conducting LixAg alloy anode enabling stable Li plating/stripping in solid-state batteries via enhanced Li diffusion kinetic","authors":"Anran Cheng , Pei Gao , Ruxing Wang , Kangli Wang , Kai Jiang","doi":"10.1016/j.geits.2024.100179","DOIUrl":"10.1016/j.geits.2024.100179","url":null,"abstract":"<div><div>Although showing huge potential in prospering the marketplace of all-solid-state lithium metal batteries (ASSLMBs), garnet-type solid electrolytes (Li<sub>6.5</sub>La<sub>3</sub>Zr<sub>1.5</sub>Ta<sub>0.6</sub>O<sub>12</sub>, LLZTO) are critically plagued by interface instability with Li anode and the vulnerability to Li dendrite, which are attributed to poor Li diffusion kinetic in bulk Li metal. Herein, a Li<sub><em>x</em></sub>Ag solid solution alloy with high Li diffusion kinetic is reported as a mixed ion-electron conductor (MIEC) alloy anode. The high Li diffusion kinetic stemming from a low eutectic point and a high mutual solubility of Li<sub><em>x</em></sub>Ag could reduce the Li concentration gradient in the anode, regulate Li electrochemical potential, and change the relative local overpotential for Li stripping/plating in the anode. Notably, Li stripping/plating prefers energetically at the Li<sub><em>x</em></sub>Ag/current collector interface rather than the LLZTO/Li<sub><em>x</em></sub>Ag interface. Therefore, the contact loss is avoided at the LLZTO/Li<sub><em>x</em></sub>Ag interface. As a result, excellent cycling stability (∼1,200 h at 0.2 mA/cm<sup>2</sup>), and dendrites tolerance (critical current density of 1.2 mA/cm<sup>2</sup>) are demonstrated by using Li<sub><em>x</em></sub>Ag as anode. Further research has elucidated that those alloys with low eutectic temperature and high mutual solubility with lithium should be focused on, as they would provide and maintain a soft lattice and a high lithium diffusion rate during composition change. This provides a basis for the selection of alloy phases in negative electrode materials, as well as their application in garnet-based ASSLMBs.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 1","pages":"Article 100179"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139635946","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}
Pub Date : 2025-01-23DOI: 10.1016/j.geits.2025.100269
Baolu Li , Ping Liu , Lan Fu , Jinlong Li , Jianwu Fang , Zhigang Xu , Hongkai Yu
Vehicle Re-identification (Re-ID) has drawn extensive exploration recently; nevertheless, the issue of accurately distinguishing features in latent space across varying vehicle poses, remains a challenging hurdle for real-world application of Vehicle Re-ID. To address this challenge, we supply a novel idea which projects the various-pose vehicle images into a unified target pose so as to promote the discriminative capability of vehicle Re-ID model. Acknowledging the labor and cost of paired data for the same vehicle images across different traffic surveillance cameras in practical scenarios, we propose the pioneering Pair-flexible Pose Guided Image Synthesis for vehicle Re-ID, denominated as VehicleGAN. Our method is adept at both supervised (paired images of same vehicle) and unsupervised (unpaired images of any vehicle) settings, and bypasses the need of geometric 3D model information. Furthermore, we propose a novel Joint Metric Learning (JML) method to facilitate the effective fusion of both real and synthetic data. Comprehensive experimental analyses conducted on the public VeRi-776 and VehicleID datasets substantiate the precision and efficacy of our proposed VehicleGAN and JML.
{"title":"Enhancing vehicle Re-identification by pair-flexible pose guided vehicle image synthesis","authors":"Baolu Li , Ping Liu , Lan Fu , Jinlong Li , Jianwu Fang , Zhigang Xu , Hongkai Yu","doi":"10.1016/j.geits.2025.100269","DOIUrl":"10.1016/j.geits.2025.100269","url":null,"abstract":"<div><div>Vehicle Re-identification (Re-ID) has drawn extensive exploration recently; nevertheless, the issue of accurately distinguishing features in latent space across varying vehicle poses, remains a challenging hurdle for real-world application of Vehicle Re-ID. To address this challenge, we supply a novel idea which projects the various-pose vehicle images into a unified target pose so as to promote the discriminative capability of vehicle Re-ID model. Acknowledging the labor and cost of paired data for the same vehicle images across different traffic surveillance cameras in practical scenarios, we propose the pioneering Pair-flexible Pose Guided Image Synthesis for vehicle Re-ID, denominated as VehicleGAN. Our method is adept at both supervised (paired images of same vehicle) and unsupervised (unpaired images of any vehicle) settings, and bypasses the need of geometric 3D model information. Furthermore, we propose a novel Joint Metric Learning (JML) method to facilitate the effective fusion of both real and synthetic data. Comprehensive experimental analyses conducted on the public VeRi-776 and VehicleID datasets substantiate the precision and efficacy of our proposed VehicleGAN and JML.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 5","pages":"Article 100269"},"PeriodicalIF":16.4,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145010151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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