Wenfa Kang;Jianquan Liao;Minyou Chen;Josep M. Guerrero;Kai Sun
This paper introduces a novel fully distributed economic power dispatch (EPD) strategy for distribution networks, integrating dynamic tariffs. A two-layer model is proposed: the first layer comprises the physical power distribution network, including photovoltaic (PV) sources, wind turbine (WT) generators, energy storage systems (ESS), flexible loads (FLs), and other inflexible loads. The upper layer consists of agents dedicated to communication, calculation, and control tasks. Unlike previous EPD strategies, this approach incorporates dynamic tariffs derived from voltage constraints to ensure compliance with nodal voltage constraints. Additionally, a fast distributed optimization algorithm with an event-triggered communication protocol has been developed to address the EPD problem effectively. Through mathematical and simulation analyses, the proposed algorithm's efficiency and rapid convergence capability are demonstrated.
{"title":"Distributed Event-Triggered Optimal Power Management of Distribution Networks Considering Dynamic Tariff","authors":"Wenfa Kang;Jianquan Liao;Minyou Chen;Josep M. Guerrero;Kai Sun","doi":"10.23919/IEN.2024.0016","DOIUrl":"https://doi.org/10.23919/IEN.2024.0016","url":null,"abstract":"This paper introduces a novel fully distributed economic power dispatch (EPD) strategy for distribution networks, integrating dynamic tariffs. A two-layer model is proposed: the first layer comprises the physical power distribution network, including photovoltaic (PV) sources, wind turbine (WT) generators, energy storage systems (ESS), flexible loads (FLs), and other inflexible loads. The upper layer consists of agents dedicated to communication, calculation, and control tasks. Unlike previous EPD strategies, this approach incorporates dynamic tariffs derived from voltage constraints to ensure compliance with nodal voltage constraints. Additionally, a fast distributed optimization algorithm with an event-triggered communication protocol has been developed to address the EPD problem effectively. Through mathematical and simulation analyses, the proposed algorithm's efficiency and rapid convergence capability are demonstrated.","PeriodicalId":100648,"journal":{"name":"iEnergy","volume":"3 3","pages":"142-151"},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10682540","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368483","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 November 2018, during the 26th General Conference on Weights and Measures (CGPM) in Versailles, France, a landmark resolution was adopted to redefine four base units of the International System of Units (SI)(https://www.bipm.org/en/committees/cg/cgpm/26-2018/resolution-1). The kilogram, mole, ampere, and kelvin were redefined in terms of fundamental physical constants: the Planck constant $(h)$ , the Avogadro constant $(N_{mathrm{A}})$ , the elementary charge $(e)$ , and the Boltzmann constant $(k)$ , respectively. As illustrated in Figure 1(a), the new SI framework defines all seven base units based on fundamental constants, marking a complete transition of metrology into the quantum era. This system has been in effect globally since May 20, 2019, on World Metrology Day.
{"title":"Advancing Quantum Metrology in China's Power Grids","authors":"Shisong Li","doi":"10.23919/IEN.2024.0018","DOIUrl":"https://doi.org/10.23919/IEN.2024.0018","url":null,"abstract":"In November 2018, during the 26th General Conference on Weights and Measures (CGPM) in Versailles, France, a landmark resolution was adopted to redefine four base units of the International System of Units (SI)(https://www.bipm.org/en/committees/cg/cgpm/26-2018/resolution-1). The kilogram, mole, ampere, and kelvin were redefined in terms of fundamental physical constants: the Planck constant \u0000<tex>$(h)$</tex>\u0000, the Avogadro constant \u0000<tex>$(N_{mathrm{A}})$</tex>\u0000, the elementary charge \u0000<tex>$(e)$</tex>\u0000, and the Boltzmann constant \u0000<tex>$(k)$</tex>\u0000, respectively. As illustrated in Figure 1(a), the new SI framework defines all seven base units based on fundamental constants, marking a complete transition of metrology into the quantum era. This system has been in effect globally since May 20, 2019, on World Metrology Day.","PeriodicalId":100648,"journal":{"name":"iEnergy","volume":"3 3","pages":"125-127"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10703140","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368482","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}
Electric vehicles and battery energy storage are effective technical paths to achieve carbon neutrality, and lithium-ion batteries (LiBs) are very critical energy storage devices, which is of great significance to the goal. However, the battery's characteristics of instant degradation seriously affect its long life and high safety applications. The aging mechanisms of LiBs are complex and multi-faceted, strongly influenced by numerous interacting factors. Currently, the degree of capacity fading is commonly used to describe the aging of the battery, and the ratio of the maximum available capacity to the rated capacity of the battery is defined as the state of health (SOH). However, the aging or health of the battery should be multifaceted. to realize the multi-dimensional comprehensive evaluation of battery health status, a novel SOH estimation method driven by multidimensional aging characteristics is proposed through the improved single-particle model. The parameter identification and sensitivity analysis of the model were carried out during the whole cycle of life in a wide temperature environment. Nine aging characteristic parameters were obtained to describe the SOH. Combined with aging mechanisms, the current health status was evaluated from four aspects: capacity level, lithium-ion diffusion, electrochemical reaction, and power capacity. The proposed method can more comprehensively evaluate the aging characteristics of batteries, and the SOH estimation error is within 2%.
{"title":"A New Multi-Dimensional State of Health Evaluation Method for Lithium-Ion Batteries","authors":"Peng Peng;Yue Sun;Man Chen;Yuxuan Li;Zhenkai Hu;Rui Xiong","doi":"10.23919/IEN.2024.0020","DOIUrl":"https://doi.org/10.23919/IEN.2024.0020","url":null,"abstract":"Electric vehicles and battery energy storage are effective technical paths to achieve carbon neutrality, and lithium-ion batteries (LiBs) are very critical energy storage devices, which is of great significance to the goal. However, the battery's characteristics of instant degradation seriously affect its long life and high safety applications. The aging mechanisms of LiBs are complex and multi-faceted, strongly influenced by numerous interacting factors. Currently, the degree of capacity fading is commonly used to describe the aging of the battery, and the ratio of the maximum available capacity to the rated capacity of the battery is defined as the state of health (SOH). However, the aging or health of the battery should be multifaceted. to realize the multi-dimensional comprehensive evaluation of battery health status, a novel SOH estimation method driven by multidimensional aging characteristics is proposed through the improved single-particle model. The parameter identification and sensitivity analysis of the model were carried out during the whole cycle of life in a wide temperature environment. Nine aging characteristic parameters were obtained to describe the SOH. Combined with aging mechanisms, the current health status was evaluated from four aspects: capacity level, lithium-ion diffusion, electrochemical reaction, and power capacity. The proposed method can more comprehensively evaluate the aging characteristics of batteries, and the SOH estimation error is within 2%.","PeriodicalId":100648,"journal":{"name":"iEnergy","volume":"3 3","pages":"175-184"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10703143","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368256","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 paper presents an optimization model for the location and capacity of electric vehicle (EV) charging stations. The model takes the multiple factors of the “vehicle-station-grid” system into account. Then, ArcScene is used to couple the road and power grid models and ensure that the coupling system is strictly under the goal of minimizing the total social cost, which includes the operator cost, user charging cost, and power grid loss. An immune particle swarm optimization algorithm (IPSOA) is proposed in this paper to obtain the optimal coupling strategy. The simulation results show that the algorithm has good convergence and performs well in solving multi-modal problems. It also balances the interests of users, operators, and the power grid. Compared with other schemes, the grid loss cost is reduced by 11.1% and 17.8%, and the total social cost decreases by 9.96% and 3.22%.
{"title":"Optimal Urban EV Charging Station Site Selection and Capacity Determination Considering Comprehensive Benefits of Vehicle-Station-Grid","authors":"Hongwei Li;Yufeng Song;Jiuding Tan;Yi Cui;Shuaibing Li;Yongqiang Kang;Haiying Dong","doi":"10.23919/IEN.2024.0021","DOIUrl":"https://doi.org/10.23919/IEN.2024.0021","url":null,"abstract":"This paper presents an optimization model for the location and capacity of electric vehicle (EV) charging stations. The model takes the multiple factors of the “vehicle-station-grid” system into account. Then, ArcScene is used to couple the road and power grid models and ensure that the coupling system is strictly under the goal of minimizing the total social cost, which includes the operator cost, user charging cost, and power grid loss. An immune particle swarm optimization algorithm (IPSOA) is proposed in this paper to obtain the optimal coupling strategy. The simulation results show that the algorithm has good convergence and performs well in solving multi-modal problems. It also balances the interests of users, operators, and the power grid. Compared with other schemes, the grid loss cost is reduced by 11.1% and 17.8%, and the total social cost decreases by 9.96% and 3.22%.","PeriodicalId":100648,"journal":{"name":"iEnergy","volume":"3 3","pages":"162-174"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10703177","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368481","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}
Quantum power system state estimation (QPSSE) offers an inspiring direction for tackling the challenge of state estimation through quantum computing. Nevertheless, the current bottlenecks originate from the scarcity of practical and scalable QPSSE methodologies in the noisy intermediate-scale quantum (NISQ) era. This paper devises a NISQ-QPSSE algorithm that facilitates state estimation on real NISQ devices. Our new contributions include: (1) A variational quantum circuit (VQC)-based QPSSE formulation that empowers QPSSE analysis utilizing shallow-depth quantum circuits; (2) A variational quantum linear solver (VQLS)-based QPSSE solver integrating QPSSE iterations with VQC optimization; (3) An advanced NISQ-compatible QPSSE methodology for tackling the measurement and coefficient matrix issues on real quantum computers; (4) A noise-resilient method to alleviate the detrimental effects of noise disturbances. The encouraging test results on the simulator and real-scale systems affirm the precision, universality, and scalability of our QPSSE algorithm and demonstrate the vast potential of QPSSE in the thriving NISQ era.
{"title":"Noisy-Intermediate-Scale Quantum Power System State Estimation","authors":"Fei Feng;Peng Zhang;Yifan Zhou;Yacov A. Shamash","doi":"10.23919/IEN.2024.0019","DOIUrl":"https://doi.org/10.23919/IEN.2024.0019","url":null,"abstract":"Quantum power system state estimation (QPSSE) offers an inspiring direction for tackling the challenge of state estimation through quantum computing. Nevertheless, the current bottlenecks originate from the scarcity of practical and scalable QPSSE methodologies in the noisy intermediate-scale quantum (NISQ) era. This paper devises a NISQ-QPSSE algorithm that facilitates state estimation on real NISQ devices. Our new contributions include: (1) A variational quantum circuit (VQC)-based QPSSE formulation that empowers QPSSE analysis utilizing shallow-depth quantum circuits; (2) A variational quantum linear solver (VQLS)-based QPSSE solver integrating QPSSE iterations with VQC optimization; (3) An advanced NISQ-compatible QPSSE methodology for tackling the measurement and coefficient matrix issues on real quantum computers; (4) A noise-resilient method to alleviate the detrimental effects of noise disturbances. The encouraging test results on the simulator and real-scale systems affirm the precision, universality, and scalability of our QPSSE algorithm and demonstrate the vast potential of QPSSE in the thriving NISQ era.","PeriodicalId":100648,"journal":{"name":"iEnergy","volume":"3 3","pages":"135-141"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10703139","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368210","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}
Ning Guo;Tuo Ji;Xiaolong Xiao;Tiankui Sun;Jinming Chen;Xiaoxing Lu;Xinyi Zheng;Shufeng Dong
To adress the problems of insufficient consideration of charging pile resource limitations, discrete-time scheduling methods that do not meet the actual demand and insufficient descriptions of peak-shaving response capability in current electric vehicle (EV) optimization scheduling, edge intelligence-oriented electric vehicle optimization scheduling and charging station peak-shaving response capability assessment methods are proposed on the basis of the consideration of electric vehicle and charging pile matching. First, an edge-intelligence-oriented electric vehicle regulation frame for charging stations is proposed. Second, continuous time variables are used to represent the available charging periods, establish the charging station controllable EV load model and the future available charging pile mathematical model, and establish the EV and charging pile matching matrix and constraints. Then, with the goal of maximizing the user charging demand and reducing the charging cost, the charging station EV optimal scheduling model is established, and the EV peak response capacity assessment model is further established by considering the EV load shifting constraints under different peak response capacities. Finally, a typical scenario of a real charging station is taken as an example for the analysis of optimal EV scheduling and peak shaving response capacity, and the proposed method is compared with the traditional method to verify the effectiveness and practicality of the proposed method.
{"title":"Electric Vehicle Optimal Scheduling Method Considering Charging Piles Matching Based on Edge Intelligence","authors":"Ning Guo;Tuo Ji;Xiaolong Xiao;Tiankui Sun;Jinming Chen;Xiaoxing Lu;Xinyi Zheng;Shufeng Dong","doi":"10.23919/IEN.2024.0022","DOIUrl":"https://doi.org/10.23919/IEN.2024.0022","url":null,"abstract":"To adress the problems of insufficient consideration of charging pile resource limitations, discrete-time scheduling methods that do not meet the actual demand and insufficient descriptions of peak-shaving response capability in current electric vehicle (EV) optimization scheduling, edge intelligence-oriented electric vehicle optimization scheduling and charging station peak-shaving response capability assessment methods are proposed on the basis of the consideration of electric vehicle and charging pile matching. First, an edge-intelligence-oriented electric vehicle regulation frame for charging stations is proposed. Second, continuous time variables are used to represent the available charging periods, establish the charging station controllable EV load model and the future available charging pile mathematical model, and establish the EV and charging pile matching matrix and constraints. Then, with the goal of maximizing the user charging demand and reducing the charging cost, the charging station EV optimal scheduling model is established, and the EV peak response capacity assessment model is further established by considering the EV load shifting constraints under different peak response capacities. Finally, a typical scenario of a real charging station is taken as an example for the analysis of optimal EV scheduling and peak shaving response capacity, and the proposed method is compared with the traditional method to verify the effectiveness and practicality of the proposed method.","PeriodicalId":100648,"journal":{"name":"iEnergy","volume":"3 3","pages":"152-161"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10703176","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368484","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}
Producing renewable e-methanol from e-hydrogen and diverse carbon sources is an essential way for clean methanol preparation. Despite this, the technical and economic feasibility of different e-methanols has yet to be thoroughly compared, leaving the most promising pathway to achieve commercialization yet evident. This paper reports a preliminary analysis of the lifecycle greenhouse gas (GHG) emissions and costs of four renewable e-methanols with different carbon sources: bio-carbon, direct air capture (DAC), fossil fuel carbon capture (FFCC), and fossil. The results indicate that renewable e-methanol costs (4167–10250 CNY/tonne) 2–4 times the market rate of grey methanol. However, with the carbon tax and the projected decline in e-H 2 costs, blue e-methanol may initially replace diesel in inland navigation, followed by a shift from heavy fuel oil (HFO) to green e-methanol in ocean shipping. Furthermore, the e-H 2 cost and the availability of green carbon are vital factors affecting cost-effectiveness. A reduction in e-H 2 cost from 2.1 CNY/Nm 3 to 1.1 CNY/Nm 3 resulting from a transition from an annual to a daily scheduling period, could lower e-methanol costs by 1200 to 2100 CNY. This paper also provides an in-depth discussion on the challenges and opportunities associated with the various green carbon sources.
{"title":"Feasibility Study of Renewable e-Methanol Production: A Substitution Pathway from Blue to Green","authors":"Peiyang Li;Jin Lin;Zhipeng Yu;Yingtian Chi;Kai Zhao","doi":"10.23919/IEN.2024.0013","DOIUrl":"https://doi.org/10.23919/IEN.2024.0013","url":null,"abstract":"Producing renewable e-methanol from e-hydrogen and diverse carbon sources is an essential way for clean methanol preparation. Despite this, the technical and economic feasibility of different e-methanols has yet to be thoroughly compared, leaving the most promising pathway to achieve commercialization yet evident. This paper reports a preliminary analysis of the lifecycle greenhouse gas (GHG) emissions and costs of four renewable e-methanols with different carbon sources: bio-carbon, direct air capture (DAC), fossil fuel carbon capture (FFCC), and fossil. The results indicate that renewable e-methanol costs (4167–10250 CNY/tonne) 2–4 times the market rate of grey methanol. However, with the carbon tax and the projected decline in e-H\u0000<inf>2</inf>\u0000 costs, blue e-methanol may initially replace diesel in inland navigation, followed by a shift from heavy fuel oil (HFO) to green e-methanol in ocean shipping. Furthermore, the e-H\u0000<inf>2</inf>\u0000 cost and the availability of green carbon are vital factors affecting cost-effectiveness. A reduction in e-H\u0000<inf>2</inf>\u0000 cost from 2.1 CNY/Nm\u0000<sup>3</sup>\u0000 to 1.1 CNY/Nm\u0000<sup>3</sup>\u0000 resulting from a transition from an annual to a daily scheduling period, could lower e-methanol costs by 1200 to 2100 CNY. This paper also provides an in-depth discussion on the challenges and opportunities associated with the various green carbon sources.","PeriodicalId":100648,"journal":{"name":"iEnergy","volume":"3 2","pages":"108-114"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10587141","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141543842","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}
Chaofan Lin;Peng Zhang;Yacov A. Shamash;Zongli Lin;Xiaonan Lu
Green hydrogen has shown great potential to power microgrids as a primary source, whereas the resilient operation methodology under extreme events remains an open area. To fill this gap, this letter establishes an operational optimization strategy towards resilient hydrogen-powered microgrids. The frequency and voltage regulation characteristics of primary hydrogen sources under droop control and their electrical-chemical conversion process with nonlinear stack efficiency are accurately modeled by piecewise linear constraints. A resilience-oriented multi-time-slot stochastic optimization model is then formulated for an economic and robust operation under changing uncertainties. Test results show that the new formulation can leverage the primary hydrogen sources to achieve a resilience and safety-assured operation plan, supplying maximum critical loads while significantly reducing the frequency and voltage variations.
{"title":"Resilience-Assuring Hydrogen-Powered Microgrids","authors":"Chaofan Lin;Peng Zhang;Yacov A. Shamash;Zongli Lin;Xiaonan Lu","doi":"10.23919/IEN.2024.0015","DOIUrl":"https://doi.org/10.23919/IEN.2024.0015","url":null,"abstract":"Green hydrogen has shown great potential to power microgrids as a primary source, whereas the resilient operation methodology under extreme events remains an open area. To fill this gap, this letter establishes an operational optimization strategy towards resilient hydrogen-powered microgrids. The frequency and voltage regulation characteristics of primary hydrogen sources under droop control and their electrical-chemical conversion process with nonlinear stack efficiency are accurately modeled by piecewise linear constraints. A resilience-oriented multi-time-slot stochastic optimization model is then formulated for an economic and robust operation under changing uncertainties. Test results show that the new formulation can leverage the primary hydrogen sources to achieve a resilience and safety-assured operation plan, supplying maximum critical loads while significantly reducing the frequency and voltage variations.","PeriodicalId":100648,"journal":{"name":"iEnergy","volume":"3 2","pages":"77-81"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10587145","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141543843","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}
With the increasing demand for high power density, and to meet extreme working conditions, research has been focused on investigating the performance of power electronics devices at cryogenic temperatures. The aim of this paper is to review the performance of power semiconductor devices, passive components, gate drivers, sensors, and eventually power electronics converters at cryogenic temperatures. By comparing the physical properties of semiconductor materials and the electrical performance of commercial power semiconductor devices, silicon carbide switches show obvious disadvantages due to the increased on-resistance and switching time at cryogenic temperature. In contrast, silicon and gallium nitride devices exhibit improved performance when temperature is decreased. The performance ceiling of power semiconductor devices can be influenced by gate drivers, within which the commercial alternatives show deteriorated performance at cryogenic temperature compared to room temperature. Moreover, options for voltage and current sense in cryogenic environments are justified. Based on the cryogenic performance of the various components afore-discussed, this paper ends by presenting an overview of the published converter, which are either partially or fully tested in a cryogenic environment.
{"title":"Review of Semiconductor Devices and Other Power Electronics Components at Cryogenic Temperature","authors":"Yuchuan Liao;Abdelrahman Elwakeel;Yudi Xiao;Rafael Peña Alzola;Min Zhang;Weijia Yuan;Alfonso J. Cruz Feliciano;Lukas Graber","doi":"10.23919/IEN.2024.0014","DOIUrl":"https://doi.org/10.23919/IEN.2024.0014","url":null,"abstract":"With the increasing demand for high power density, and to meet extreme working conditions, research has been focused on investigating the performance of power electronics devices at cryogenic temperatures. The aim of this paper is to review the performance of power semiconductor devices, passive components, gate drivers, sensors, and eventually power electronics converters at cryogenic temperatures. By comparing the physical properties of semiconductor materials and the electrical performance of commercial power semiconductor devices, silicon carbide switches show obvious disadvantages due to the increased on-resistance and switching time at cryogenic temperature. In contrast, silicon and gallium nitride devices exhibit improved performance when temperature is decreased. The performance ceiling of power semiconductor devices can be influenced by gate drivers, within which the commercial alternatives show deteriorated performance at cryogenic temperature compared to room temperature. Moreover, options for voltage and current sense in cryogenic environments are justified. Based on the cryogenic performance of the various components afore-discussed, this paper ends by presenting an overview of the published converter, which are either partially or fully tested in a cryogenic environment.","PeriodicalId":100648,"journal":{"name":"iEnergy","volume":"3 2","pages":"95-107"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10587144","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141543872","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}
A number of research papers including several review papers have reported that autonomous vehicles (AVs) consume a significant amount of power to run the onboard computers that do all the calculations needed to process and analyze the significant amount of data. In addition, there is a substantial amount of power consumption by onboard sensors including radars, cameras, Lidars, etc. [1] The resulting power consumption results in range reduction in electric autonomous vehicles. This in-turn increases the emissions based on how the electricity is obtained for charging these vehicle batteries. As the degree of automation moves up the ladder of AVs, the complexity of the overall control, management, and the associated tasks grow exponentially, and hence increasing the power consumption. The Society of Automotive Engineers (SAE) defines 6 levels of driving automation ranging from Level 0 (fully manual) to Level 5 (fully autonomous). Level 0 is no driving automation and the driver is responsible for full control of the vehicle. Level 1 is the driver assisted by a support system like adaptive cruise control or lane-changing assistance, but the driver must remain engaged. In Level 2, the vehicle can perform multiple automated functions, such as braking, accelerating, and steering, through advanced driving assistance systems (ADAS), but the driver must actively supervise the vehicle's operation. The Level 3 vehicle can manage all aspects of driving, but the driver still needs to be present in case of an emergency or system failure to override the automation. In Level 4, the vehicle operates autonomously but is limited by speed or to a certain location. The human override is still an option. Level 5 is full driving automation, which can operate autonomously in all driving scenarios and under any conditions, without geographic or speed limitations. At this level, human interaction is reduced to merely setting the destination. The levels of automated vehicles currently in market mostly are Levels 1 and 2. Mercedes-Benz offers Level 3 autonomous driving in its S-Class and EQS models. Few other companies including Audi, BMW, and Tesla are planning to soon market the Level 3 vehicles. Levels 3 and 4 are currently being deployed for testing in a few cities in limited areas. Level 5 will hopefully be someday in the future.
{"title":"Energy Impacts of Autonomous Vehicles - Present and the Future","authors":"Kaushik Rajashekara","doi":"10.23919/IEN.2024.0012","DOIUrl":"https://doi.org/10.23919/IEN.2024.0012","url":null,"abstract":"A number of research papers including several review papers have reported that autonomous vehicles (AVs) consume a significant amount of power to run the onboard computers that do all the calculations needed to process and analyze the significant amount of data. In addition, there is a substantial amount of power consumption by onboard sensors including radars, cameras, Lidars, etc.\u0000<sup>[1]</sup>\u0000 The resulting power consumption results in range reduction in electric autonomous vehicles. This in-turn increases the emissions based on how the electricity is obtained for charging these vehicle batteries. As the degree of automation moves up the ladder of AVs, the complexity of the overall control, management, and the associated tasks grow exponentially, and hence increasing the power consumption. The Society of Automotive Engineers (SAE) defines 6 levels of driving automation ranging from Level 0 (fully manual) to Level 5 (fully autonomous). Level 0 is no driving automation and the driver is responsible for full control of the vehicle. Level 1 is the driver assisted by a support system like adaptive cruise control or lane-changing assistance, but the driver must remain engaged. In Level 2, the vehicle can perform multiple automated functions, such as braking, accelerating, and steering, through advanced driving assistance systems (ADAS), but the driver must actively supervise the vehicle's operation. The Level 3 vehicle can manage all aspects of driving, but the driver still needs to be present in case of an emergency or system failure to override the automation. In Level 4, the vehicle operates autonomously but is limited by speed or to a certain location. The human override is still an option. Level 5 is full driving automation, which can operate autonomously in all driving scenarios and under any conditions, without geographic or speed limitations. At this level, human interaction is reduced to merely setting the destination. The levels of automated vehicles currently in market mostly are Levels 1 and 2. Mercedes-Benz offers Level 3 autonomous driving in its S-Class and EQS models. Few other companies including Audi, BMW, and Tesla are planning to soon market the Level 3 vehicles. Levels 3 and 4 are currently being deployed for testing in a few cities in limited areas. Level 5 will hopefully be someday in the future.","PeriodicalId":100648,"journal":{"name":"iEnergy","volume":"3 2","pages":"73-74"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10587143","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141544048","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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