Pub Date : 2025-01-23DOI: 10.1016/j.geits.2025.100268
Shasha Zhao , Xianzhong Sun , Yabin An , Zhang Guo , Chen Li , Yanan Xu , Yi Li , Zhao Li , Xiong Zhang , Kai Wang , Yanwei Ma
Lithium-ion capacitors (LICs) offer higher power density and longer cycle life compared to lithium-ion batteries, and greater energy density than supercapacitors, making them ideal for applications requiring both high energy and power density. However, during high-rate charging, LIC anodes may suffer from lithium plating, a critical issue that remains unaddressed. To date, no direct analytical technique exists to study lithium plating behavior on LIC anodes. This study is the first to employ a 3-electrode pouch-type LICs, using differential analysis of the anode potential rather than the traditional terminal voltage approach, to accurately detect the charging rates at which lithium plating begins. We employed differential charging voltage (DCV), Coulombic efficiency (CE), and voltage relaxation profile (VRP) methods to comprehensively analyze lithium plating behavior. The feasibility of indirectly detecting lithium plating was validated by applying the CE and VRP methods to high-capacity 1,100 F LICs. The study found that lithium plating in LICs begins at a charging current of 20 C. The lithium deposited at currents below 50 C is reversible, while at currents above 50 C, irreversible dead lithium is formed. Furthermore, the study identified two reverse reactions following lithium deposition on the anode: lithium stripping and lithium intercalation. For soft carbon anodes, the potential difference between lithium stripping and intercalation was approximately 20 mV under relaxation conditions, and about 45 mV under constant voltage conditions. This research provides critical theoretical insights and practical guidance for optimizing LIC charging strategies.
{"title":"Lithium plating accurate detection of lithium-ion capacitors upon high-rate charging","authors":"Shasha Zhao , Xianzhong Sun , Yabin An , Zhang Guo , Chen Li , Yanan Xu , Yi Li , Zhao Li , Xiong Zhang , Kai Wang , Yanwei Ma","doi":"10.1016/j.geits.2025.100268","DOIUrl":"10.1016/j.geits.2025.100268","url":null,"abstract":"<div><div>Lithium-ion capacitors (LICs) offer higher power density and longer cycle life compared to lithium-ion batteries, and greater energy density than supercapacitors, making them ideal for applications requiring both high energy and power density. However, during high-rate charging, LIC anodes may suffer from lithium plating, a critical issue that remains unaddressed. To date, no direct analytical technique exists to study lithium plating behavior on LIC anodes. This study is the first to employ a 3-electrode pouch-type LICs, using differential analysis of the anode potential rather than the traditional terminal voltage approach, to accurately detect the charging rates at which lithium plating begins. We employed differential charging voltage (DCV), Coulombic efficiency (CE), and voltage relaxation profile (VRP) methods to comprehensively analyze lithium plating behavior. The feasibility of indirectly detecting lithium plating was validated by applying the CE and VRP methods to high-capacity 1,100 F LICs. The study found that lithium plating in LICs begins at a charging current of 20 C. The lithium deposited at currents below 50 C is reversible, while at currents above 50 C, irreversible dead lithium is formed. Furthermore, the study identified two reverse reactions following lithium deposition on the anode: lithium stripping and lithium intercalation. For soft carbon anodes, the potential difference between lithium stripping and intercalation was approximately 20 mV under relaxation conditions, and about 45 mV under constant voltage conditions. This research provides critical theoretical insights and practical guidance for optimizing LIC charging strategies.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 5","pages":"Article 100268"},"PeriodicalIF":16.4,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144903153","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 : 2025-01-11DOI: 10.1016/j.geits.2025.100251
Junfei Yan , Jian Song , Bengang Yi , Yi Quan , Cheng Xu , Wenyuan Gong , Zhaojun Du , Tengyong Liu , Changchun Xie , Darong Liang , Zihao Pu , Zhexuan Dong
In high-performance electric sports vehicles, the application of woven composite materials with the purpose of lightweight has become an inevitable choice. It is considerably difference between traditional metal materials and composites for the lightweight design strategy of electric vehicle structures, due to the multi-scale and anisotropic characteristics of fiber reinforced composites. Nevertheless, most of scholars are focus on the meso-scale mechanical responses of woven composites, and few studies are involved in their multi-scale mechanical behaviors and hierarchical design strategy of composite structures in electric vehicles. In this work, a multi-scale analysis strategy was proposed to investigate mechanical behaviors of composite front firewall. Subsequently, a hierarchical optimization strategy with the objective of lightweight design of composite front firewall was carried out. Finally, a reasonable layout scheme of composite front firewall was quantitatively obtained. The maximum errors between the predicted and theoretical/experimental results in terms of equivalent engineering constants of fiber yarns and 2D twill woven composites (2DTWCs) were 8.8 GPa and 7%, respectively. It indicates that the multi-scale models can be used to evaluate the mechanical properties of 2DTWCs. Additionally, the total weight of optimized composite front firewall was reduced by 36% in comparison with the reference, and simultaneously the total stiffness was improved by 26%. Hence, it is an effective strategy to design lightweight composite structures of electric vehicles. We hope the proposed multi-scale and hierarchical design strategy could promote the further development of composite structures in high-performance electric sports vehicles.
{"title":"Multi-scale analysis and hierarchical optimization design of a 2D twill woven composite front firewall for electric vehicles","authors":"Junfei Yan , Jian Song , Bengang Yi , Yi Quan , Cheng Xu , Wenyuan Gong , Zhaojun Du , Tengyong Liu , Changchun Xie , Darong Liang , Zihao Pu , Zhexuan Dong","doi":"10.1016/j.geits.2025.100251","DOIUrl":"10.1016/j.geits.2025.100251","url":null,"abstract":"<div><div>In high-performance electric sports vehicles, the application of woven composite materials with the purpose of lightweight has become an inevitable choice. It is considerably difference between traditional metal materials and composites for the lightweight design strategy of electric vehicle structures, due to the multi-scale and anisotropic characteristics of fiber reinforced composites. Nevertheless, most of scholars are focus on the meso-scale mechanical responses of woven composites, and few studies are involved in their multi-scale mechanical behaviors and hierarchical design strategy of composite structures in electric vehicles. In this work, a multi-scale analysis strategy was proposed to investigate mechanical behaviors of composite front firewall. Subsequently, a hierarchical optimization strategy with the objective of lightweight design of composite front firewall was carried out. Finally, a reasonable layout scheme of composite front firewall was quantitatively obtained. The maximum errors between the predicted and theoretical/experimental results in terms of equivalent engineering constants of fiber yarns and 2D twill woven composites (2DTWCs) were 8.8 GPa and 7%, respectively. It indicates that the multi-scale models can be used to evaluate the mechanical properties of 2DTWCs. Additionally, the total weight of optimized composite front firewall was reduced by 36% in comparison with the reference, and simultaneously the total stiffness was improved by 26%. Hence, it is an effective strategy to design lightweight composite structures of electric vehicles. We hope the proposed multi-scale and hierarchical design strategy could promote the further development of composite structures in high-performance electric sports vehicles.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 2","pages":"Article 100251"},"PeriodicalIF":0.0,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143508507","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 : 2025-01-10DOI: 10.1016/j.geits.2025.100256
Da Yu , Xiangyu Han , Wenrui Wang , He Zhang , Peixun Xiong , Keren Dai
Wide-area battlefields, smart ammunition, and precision damage are the new directions of modern warfare, while munition-borne electric systems serve as “decision-makers” for smart ammunition. As the primary energy supplier for the entire system, munition-borne power sources hold a veto power position. The complexity of the application environment for munition-borne power sources involves enduring high overloads, high centrifugal forces, ballistic aerothermal effects, variations in ballistic airflow fields, central blast impacts, complex disturbances in indefinite postures, and even the influence of complex ionized media. These factors represent weak links in research on the entire munition-borne electric system. Therefore, nations around the world attach great importance to developing munition-borne power sources and conducting research on various related aspects, such as technological innovation, digital simulation, and testing techniques. This paper elaborates on the existing technologies and scientific issues facing munition-borne power sources, comparing and analyzing the advantages and disadvantages of liquid reserve batteries, solid-state thermoelectric batteries, and supercapacitors as energy sources for modern warfare systems. It also discusses current technological developments and future challenges. To address the insufficient environmental and spatial adaptability of munition-borne power sources, this paper proposes a design approach that couples excitation with integrated packaging. Specifically, although the diversity of ammunition platforms leads to differences in power source requirements, common problems faced by munition-borne electric systems in modern battlefield environments include extreme impact mechanics, low-temperature rapid activation requirements, and structural size limitations. This paper comprehensively discusses the extreme mechanical environments of ammunition platforms, failure mechanisms and protection methods under high-impact conditions for munition-borne power sources, low-temperature rapid activation, and miniaturization design and proposes protective design concepts such as elastic skeleton structures and high-pressure sealed secondary packaging. Additionally, these findings suggest the use of capillary microarray structures with electrode membranes to increase infiltration rates and further improve the activation rate of munition-borne power sources. Lastly, this paper outlines future directions for the development of munition-borne electrical system power sources, primarily from the perspectives of non-reserve primary batteries, non-bottle-breaking reserve batteries, new system batteries, and the advantages of battery-supercapacitor composite energy, providing a reference for the design of munition-borne electrical system power sources used in diversified weapon system platforms.
{"title":"Power supply for the projectile-borne electromechanical system: A review","authors":"Da Yu , Xiangyu Han , Wenrui Wang , He Zhang , Peixun Xiong , Keren Dai","doi":"10.1016/j.geits.2025.100256","DOIUrl":"10.1016/j.geits.2025.100256","url":null,"abstract":"<div><div>Wide-area battlefields, smart ammunition, and precision damage are the new directions of modern warfare, while munition-borne electric systems serve as “decision-makers” for smart ammunition. As the primary energy supplier for the entire system, munition-borne power sources hold a veto power position. The complexity of the application environment for munition-borne power sources involves enduring high overloads, high centrifugal forces, ballistic aerothermal effects, variations in ballistic airflow fields, central blast impacts, complex disturbances in indefinite postures, and even the influence of complex ionized media. These factors represent weak links in research on the entire munition-borne electric system. Therefore, nations around the world attach great importance to developing munition-borne power sources and conducting research on various related aspects, such as technological innovation, digital simulation, and testing techniques. This paper elaborates on the existing technologies and scientific issues facing munition-borne power sources, comparing and analyzing the advantages and disadvantages of liquid reserve batteries, solid-state thermoelectric batteries, and supercapacitors as energy sources for modern warfare systems. It also discusses current technological developments and future challenges. To address the insufficient environmental and spatial adaptability of munition-borne power sources, this paper proposes a design approach that couples excitation with integrated packaging. Specifically, although the diversity of ammunition platforms leads to differences in power source requirements, common problems faced by munition-borne electric systems in modern battlefield environments include extreme impact mechanics, low-temperature rapid activation requirements, and structural size limitations. This paper comprehensively discusses the extreme mechanical environments of ammunition platforms, failure mechanisms and protection methods under high-impact conditions for munition-borne power sources, low-temperature rapid activation, and miniaturization design and proposes protective design concepts such as elastic skeleton structures and high-pressure sealed secondary packaging. Additionally, these findings suggest the use of capillary microarray structures with electrode membranes to increase infiltration rates and further improve the activation rate of munition-borne power sources. Lastly, this paper outlines future directions for the development of munition-borne electrical system power sources, primarily from the perspectives of non-reserve primary batteries, non-bottle-breaking reserve batteries, new system batteries, and the advantages of battery-supercapacitor composite energy, providing a reference for the design of munition-borne electrical system power sources used in diversified weapon system platforms.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 5","pages":"Article 100256"},"PeriodicalIF":16.4,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145010154","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}
The development of microgrid systems forces to integration of various distributed generators (DG) and battery energy storage (BES) systems. The integration of a BES system in MG provides several benefits such as fast response, short-term power supply, improved power quality, ancillary service, and arbitrage. The system constraints as power balance and the assets constraints as power limit of different DGs, energy, and charge/discharge power limit of BES increase the complexity of the original problem. Therefore, to tackle such a problem an efficient, robust, and strong optimization algorithm is required. In this paper, a recently developed optimization method known as the wild geese algorithm (WGA) has been applied to solve the problem. The WGA is a population-based metaheuristic approach inspired by the different aspects of the living behavior of wild geese. This algorithm has developed with the inspiration of different phases of wild geese's lives, such as their evolution, well-organized and coordinated long-distance group migration, and fatality. The WGA has tested on the MG problem and the obtained simulation results are validated by comparison of results obtained from the other methods. The result shows the WGA is efficiently able to handle the MG operational problem with numerous constraints and shows the potential to produce a high-quality solution in terms of cost reduction. The incorporation of BES reduces operating costs for MG's off-grid and on-grid operational modes by 5.91% and 8.62%, respectively. Further, the analysis for off-grid mode under different seasonality, reduction in the operational cost by 4.47%, 9.28%, 6.37%, and 7.22% was measured in the summer, autumn, winter, and spring seasons, respectively, with the integration of BES. Additionally, the integration of BES in on-grid mode results in a decrease in operating costs by 7.15%, 12.54%, 7.56%, and 11.07% in the summer, autumn, winter, and spring, respectively.
{"title":"Economic dispatch in microgrid with battery storage system using wild geese algorithm","authors":"Vimal Tiwari , Hari Mohan Dubey , Manjaree Pandit , Surender Reddy Salkuti","doi":"10.1016/j.geits.2025.100263","DOIUrl":"10.1016/j.geits.2025.100263","url":null,"abstract":"<div><div>The development of microgrid systems forces to integration of various distributed generators (DG) and battery energy storage (BES) systems. The integration of a BES system in MG provides several benefits such as fast response, short-term power supply, improved power quality, ancillary service, and arbitrage. The system constraints as power balance and the assets constraints as power limit of different DGs, energy, and charge/discharge power limit of BES increase the complexity of the original problem. Therefore, to tackle such a problem an efficient, robust, and strong optimization algorithm is required. In this paper, a recently developed optimization method known as the wild geese algorithm (WGA) has been applied to solve the problem. The WGA is a population-based metaheuristic approach inspired by the different aspects of the living behavior of wild geese. This algorithm has developed with the inspiration of different phases of wild geese's lives, such as their evolution, well-organized and coordinated long-distance group migration, and fatality. The WGA has tested on the MG problem and the obtained simulation results are validated by comparison of results obtained from the other methods. The result shows the WGA is efficiently able to handle the MG operational problem with numerous constraints and shows the potential to produce a high-quality solution in terms of cost reduction. The incorporation of BES reduces operating costs for MG's off-grid and on-grid operational modes by 5.91% and 8.62%, respectively. Further, the analysis for off-grid mode under different seasonality, reduction in the operational cost by 4.47%, 9.28%, 6.37%, and 7.22% was measured in the summer, autumn, winter, and spring seasons, respectively, with the integration of BES. Additionally, the integration of BES in on-grid mode results in a decrease in operating costs by 7.15%, 12.54%, 7.56%, and 11.07% in the summer, autumn, winter, and spring, respectively.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 5","pages":"Article 100263"},"PeriodicalIF":16.4,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144913804","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 : 2025-01-07DOI: 10.1016/j.geits.2025.100259
Zhiqiang Han , Zeyu Chen , Zhou Yang , Zilu Zhang , Bo Zhang
Precisely estimating the remaining mileage of electric vehicles is highly important for vehicle control and battery recharging determinations. Remaining mileage estimation (RME) is a technique difficulty in practice since it is impacted by many factors, including the battery state of charge (SOC), state of health (SOH), ambient temperature, and traffic condition, etc. In this study, an online RME method is proposed based on dual extended Kalman filter (DEKF) and extreme gradient boosting (XGB) algorithms. Firstly, the battery SOC and SOH are co-estimated based on DEKF with considering the impacts of ambient temperature. Secondly, the current traffic condition are analyzed by using a historical data segement, and then the energy consumpation rate is predicted by XGB algorithm. The XGB algorithm's accuracy under the varying length of data segment is analyzed for determining the proper algorithm parameters. The presented method is evaluated by a simulation study. The results under several typical driving cycles indicate that the precise RME can be achieved with the maximum error less than 1.2%. The method is expected to be useful in providing credible mileage estimation in electric vehiecle applications.
{"title":"Remaining mileage estimation for electric vehicles based on dual extended Kalman filter and eXtreme gradient boosting","authors":"Zhiqiang Han , Zeyu Chen , Zhou Yang , Zilu Zhang , Bo Zhang","doi":"10.1016/j.geits.2025.100259","DOIUrl":"10.1016/j.geits.2025.100259","url":null,"abstract":"<div><div>Precisely estimating the remaining mileage of electric vehicles is highly important for vehicle control and battery recharging determinations. Remaining mileage estimation (RME) is a technique difficulty in practice since it is impacted by many factors, including the battery state of charge (SOC), state of health (SOH), ambient temperature, and traffic condition, etc. In this study, an online RME method is proposed based on dual extended Kalman filter (DEKF) and extreme gradient boosting (XGB) algorithms. Firstly, the battery SOC and SOH are co-estimated based on DEKF with considering the impacts of ambient temperature. Secondly, the current traffic condition are analyzed by using a historical data segement, and then the energy consumpation rate is predicted by XGB algorithm. The XGB algorithm's accuracy under the varying length of data segment is analyzed for determining the proper algorithm parameters. The presented method is evaluated by a simulation study. The results under several typical driving cycles indicate that the precise RME can be achieved with the maximum error less than 1.2%. The method is expected to be useful in providing credible mileage estimation in electric vehiecle applications.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 6","pages":"Article 100259"},"PeriodicalIF":16.4,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145519801","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 : 2025-01-07DOI: 10.1016/j.geits.2025.100265
Lidia Zewde, Ioannis A. Raptis
The effective integration of Urban Air Mobility (UAM) hinges upon the adoption of a comprehensive approach that harmoniously melds various network components, placing paramount importance on the pillars of safety, sustainability, and efficiency. Existing technologies are currently undergoing a transformative evolution to cater to the distinctive requirements of UAM, with an unwavering commitment to enhancing safety, sustainability, and efficiency. This paper meticulously elucidates the extant technologies and methodologies that pertain to the safe and efficient realm of air transportation while delving into the perspective of key UAM network components: (1) Aircraft classification, range, and operational technology; (2) Airspace typology and structural intricacies; and (3) Air Traffic Management (ATM) services. In conclusion, this paper culminates by offering insights into prospective research directions in this burgeoning field.
{"title":"Conceptualizing UAM: Technologies and methods for safe and efficient urban air transportation","authors":"Lidia Zewde, Ioannis A. Raptis","doi":"10.1016/j.geits.2025.100265","DOIUrl":"10.1016/j.geits.2025.100265","url":null,"abstract":"<div><div>The effective integration of Urban Air Mobility (UAM) hinges upon the adoption of a comprehensive approach that harmoniously melds various network components, placing paramount importance on the pillars of safety, sustainability, and efficiency. Existing technologies are currently undergoing a transformative evolution to cater to the distinctive requirements of UAM, with an unwavering commitment to enhancing safety, sustainability, and efficiency. This paper meticulously elucidates the extant technologies and methodologies that pertain to the safe and efficient realm of air transportation while delving into the perspective of key UAM network components: (1) Aircraft classification, range, and operational technology; (2) Airspace typology and structural intricacies; and (3) Air Traffic Management (ATM) services. In conclusion, this paper culminates by offering insights into prospective research directions in this burgeoning field.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"5 1","pages":"Article 100265"},"PeriodicalIF":16.4,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145618582","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}
Electric vehicles (EVs) are gaining popularity across the globe. Various initiatives are being implemented to ensure that most of the operating vehicles on public roadways are EVs by 2050. Such initiatives include the construction of charging stations to improve EV charging accessibility. The utilization of the charging stations has not been explored to a great extent, despite its importance in future installations in various cities. This study evaluated the EV station utilization across eleven cities in three countries: the United States, Canada, and Scotland. The Negative Binomial (NB) regression model was applied to understand the influence of the spatial–temporal factors on the daily utilization of EV charging stations. In addition to the overall analysis, country-specific analyses were also performed. It was revealed that there is a great variation in daily EV utilization across the cities in different countries and within the country. In fact, only stations in Crieff, Scotland, showed lower predicted daily utilization, while cities in the United States had over two times predicted daily utilization compared to stations in Aberfeldy, Scotland. Furthermore, the longer the station has been in service, the higher the daily utilization, although there was significant variation across cities. Further, the day of the week and months of the year depicted consistent utilization patterns for Scotland and the United States but showed mixed findings for Canada. The study findings can help planners and policymakers improve the allocation of EV charging stations.
{"title":"Understanding spatial–temporal attributes influencing electric vehicle's charging stations utilization: A multi-city study","authors":"Boniphace Kutela , Abdallah Kinero , Hellen Shita , Subasish Das , Cuthbert Ruseruka , Tumlumbe Juliana Chengula , Norris Novat","doi":"10.1016/j.geits.2025.100255","DOIUrl":"10.1016/j.geits.2025.100255","url":null,"abstract":"<div><div>Electric vehicles (EVs) are gaining popularity across the globe. Various initiatives are being implemented to ensure that most of the operating vehicles on public roadways are EVs by 2050. Such initiatives include the construction of charging stations to improve EV charging accessibility. The utilization of the charging stations has not been explored to a great extent, despite its importance in future installations in various cities. This study evaluated the EV station utilization across eleven cities in three countries: the United States, Canada, and Scotland. The Negative Binomial (NB) regression model was applied to understand the influence of the spatial–temporal factors on the daily utilization of EV charging stations. In addition to the overall analysis, country-specific analyses were also performed. It was revealed that there is a great variation in daily EV utilization across the cities in different countries and within the country. In fact, only stations in Crieff, Scotland, showed lower predicted daily utilization, while cities in the United States had over two times predicted daily utilization compared to stations in Aberfeldy, Scotland. Furthermore, the longer the station has been in service, the higher the daily utilization, although there was significant variation across cities. Further, the day of the week and months of the year depicted consistent utilization patterns for Scotland and the United States but showed mixed findings for Canada. The study findings can help planners and policymakers improve the allocation of EV charging stations.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 5","pages":"Article 100255"},"PeriodicalIF":16.4,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144902445","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 : 2025-01-04DOI: 10.1016/j.geits.2025.100262
Ross Porter , Morteza Biglari-Abhari , Benjamin Tan , Duleepa Thrimawithana
Electric Vehicle (EV) ‘DC Fast Charging’ systems directly connect an EV's battery to an external charger. A compromised EV charger may damage the EV or be used as part of a demand-side power grid attack. We show that the newest charging standard ISO 15118–20 is not sufficient to prevent charging attacks, as it provides no mechanism to verify charger integrity. We present system and threat models for the attack, before defining an extension to ISO 15118–20 that adds support for firmware integrity verification through remote attestation, while remaining interoperable with non-supporting devices. A proof of concept implementation demonstrates the security improvement by protecting against the specified attack while requiring only 85 bytes of secure storage, 8 kB of working memory, and adding less than 0.5 s to the length of a charging session. Backwards compatibility with an implementation of the original standard is also demonstrated.
{"title":"Enhancing security in the ISO 15118–20 EV charging system","authors":"Ross Porter , Morteza Biglari-Abhari , Benjamin Tan , Duleepa Thrimawithana","doi":"10.1016/j.geits.2025.100262","DOIUrl":"10.1016/j.geits.2025.100262","url":null,"abstract":"<div><div>Electric Vehicle (EV) ‘DC Fast Charging’ systems directly connect an EV's battery to an external charger. A compromised EV charger may damage the EV or be used as part of a demand-side power grid attack. We show that the newest charging standard ISO 15118–20 is not sufficient to prevent charging attacks, as it provides no mechanism to verify charger integrity. We present system and threat models for the attack, before defining an extension to ISO 15118–20 that adds support for firmware integrity verification through remote attestation, while remaining interoperable with non-supporting devices. A proof of concept implementation demonstrates the security improvement by protecting against the specified attack while requiring only 85 bytes of secure storage, 8 kB of working memory, and adding less than 0.5 s to the length of a charging session. Backwards compatibility with an implementation of the original standard is also demonstrated.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 6","pages":"Article 100262"},"PeriodicalIF":16.4,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145417330","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 : 2025-01-04DOI: 10.1016/j.geits.2025.100261
Xuemei Chen, Jia Wu, Jiachen Hao, Yixuan Yang
The task of executing left turns at signal-free T-shaped intersections without protective signals poses a critical challenge in the realm of autonomous driving. Conventional rule-based approaches tend to be excessively cautious, rendering them inadequate for effectively managing driving tasks within unpredictable T-shaped intersection environments. In the case of complex traffic scenarios, a single model is less effective in convergence and has a lower pass rate and poorer safety. Thus, this study introduces a multi-layer reinforcement learning model, employing D3QN (Dueling Double DQN) and TD3 (Twin Delayed Deep Deterministic policy gradient algorithm) for advanced behavioral decision-making and vertical acceleration planning, respectively. In our experimental investigation, we designed four simulation scenarios based on the driving behavior of the Carla simulator to replicate real-world driving conditions. Verification and test simulation outcomes substantiate that, in comparison to other single-trained reinforcement learning models, the multi-layer reinforcement learning model proposed in this study attains the highest success rate. Specifically, the pass rate in the verification scenario, consistent with the training conditions, achieves an impressive 99.5%. Furthermore, the pass rate in the comprehensive test scenario reaches 89.6%. These experiments unequivocally demonstrate the considerable enhancement in T-shaped intersections pass rates achieved by the proposed method while ensuring both traffic efficiency and safety.
{"title":"Research on vertical strategy for left turn at signal-free T-shaped intersections based on multi-layer reinforcement learning methods","authors":"Xuemei Chen, Jia Wu, Jiachen Hao, Yixuan Yang","doi":"10.1016/j.geits.2025.100261","DOIUrl":"10.1016/j.geits.2025.100261","url":null,"abstract":"<div><div>The task of executing left turns at signal-free T-shaped intersections without protective signals poses a critical challenge in the realm of autonomous driving. Conventional rule-based approaches tend to be excessively cautious, rendering them inadequate for effectively managing driving tasks within unpredictable T-shaped intersection environments. In the case of complex traffic scenarios, a single model is less effective in convergence and has a lower pass rate and poorer safety. Thus, this study introduces a multi-layer reinforcement learning model, employing D3QN (Dueling Double DQN) and TD3 (Twin Delayed Deep Deterministic policy gradient algorithm) for advanced behavioral decision-making and vertical acceleration planning, respectively. In our experimental investigation, we designed four simulation scenarios based on the driving behavior of the Carla simulator to replicate real-world driving conditions. Verification and test simulation outcomes substantiate that, in comparison to other single-trained reinforcement learning models, the multi-layer reinforcement learning model proposed in this study attains the highest success rate. Specifically, the pass rate in the verification scenario, consistent with the training conditions, achieves an impressive 99.5%. Furthermore, the pass rate in the comprehensive test scenario reaches 89.6%. These experiments unequivocally demonstrate the considerable enhancement in T-shaped intersections pass rates achieved by the proposed method while ensuring both traffic efficiency and safety.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 5","pages":"Article 100261"},"PeriodicalIF":16.4,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144913805","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 : 2025-01-04DOI: 10.1016/j.geits.2025.100267
Chunling Wu , Chenfeng Xu , Liding Wang , Juncheng Fu , Jinhao Meng
To improve the accuracy and stability of battery remaining useful life (RUL) prediction for lithium-ion batteries, this paper proposes a new convolutional neural network-gated recurrent unit-particle filter (CNN-GRU-PF) fusion prediction model. First, the battery capacity series is decomposed and reconstructed by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm and Pearson correlation coefficient method, which reduces the influence of noise on RUL prediction. Then, the capacity is predicted by CNN-GRU, and the CNN-GRU prediction value is used as the observation value of PF, and the prediction error of CNN-GRU is corrected by the state prediction ability of PF. A moving window is used to iteratively update the training set, and the PF optimization value is added to the CNN-GRU training set, forming an iterative training and dynamic updating between them, which improves the long-term prediction performance of CNN-GRU. To verify the effectiveness of proposed method, CNN-GRU-PF model is applied to predict the battery's RUL. The experiments show that CNN-GRU-PF improves the prediction accuracy of battery B5 by 87.27%, 82.88%, and 55.43% respectively compared with GRU, PF and GRU-PF, and also achieves significant improvement for other batteries. The new model is an effective RUL prediction method with good accuracy and robustness.
{"title":"Lithium-ion battery remaining useful life prediction based on data-driven and particle filter fusion model","authors":"Chunling Wu , Chenfeng Xu , Liding Wang , Juncheng Fu , Jinhao Meng","doi":"10.1016/j.geits.2025.100267","DOIUrl":"10.1016/j.geits.2025.100267","url":null,"abstract":"<div><div>To improve the accuracy and stability of battery remaining useful life (RUL) prediction for lithium-ion batteries, this paper proposes a new convolutional neural network-gated recurrent unit-particle filter (CNN-GRU-PF) fusion prediction model. First, the battery capacity series is decomposed and reconstructed by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm and Pearson correlation coefficient method, which reduces the influence of noise on RUL prediction. Then, the capacity is predicted by CNN-GRU, and the CNN-GRU prediction value is used as the observation value of PF, and the prediction error of CNN-GRU is corrected by the state prediction ability of PF. A moving window is used to iteratively update the training set, and the PF optimization value is added to the CNN-GRU training set, forming an iterative training and dynamic updating between them, which improves the long-term prediction performance of CNN-GRU. To verify the effectiveness of proposed method, CNN-GRU-PF model is applied to predict the battery's RUL. The experiments show that CNN-GRU-PF improves the prediction accuracy of battery B5 by 87.27%, 82.88%, and 55.43% respectively compared with GRU, PF and GRU-PF, and also achieves significant improvement for other batteries. The new model is an effective RUL prediction method with good accuracy and robustness.</div></div>","PeriodicalId":100596,"journal":{"name":"Green Energy and Intelligent Transportation","volume":"4 5","pages":"Article 100267"},"PeriodicalIF":16.4,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145010152","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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