The development of a battery management algorithm is highly dependent on high-quality battery operation data,especially the data in extreme conditions such as low temperatures.The data in faults are also essential for...The development of a battery management algorithm is highly dependent on high-quality battery operation data,especially the data in extreme conditions such as low temperatures.The data in faults are also essential for failure and safety management research.This study developed a battery big data platform to realize vehicle operation,energy interaction and data management.First,we developed an electric vehicle with vehicle navigation and position detection and designed an environmental cabin that allows the vehicle to operate autonomously.Second,charging and heating systems based on wireless energy transfer were developed and equipped on the vehicle to investigate optimal charging and heating methods of the batteries in the vehicle.Third,the data transmission network was designed,a real-time monitoring interface was developed,and the self-developed battery management system was used to measure,collect,upload,and store battery operation data in real time.Finally,experimental validation was performed on the platform.Results demonstrate the efficiency and reliability of the platform.Battery state of charge estimation is used as an example to illustrate the availability of battery operation data.展开更多
Most researches focus on the regenerative braking system design in vehicle components control and braking torque distribution,few combine the connected vehicle technologies into braking velocity planning.If the brakin...Most researches focus on the regenerative braking system design in vehicle components control and braking torque distribution,few combine the connected vehicle technologies into braking velocity planning.If the braking intention is accessed by the vehicle-to-everything communication,the electric vehicles(EVs)could plan the braking velocity for recovering more vehicle kinetic energy.Therefore,this paper presents an energy-optimal braking strategy(EOBS)to improve the energy efficiency of EVs with the consideration of shared braking intention.First,a double-layer control scheme is formulated.In the upper-layer,an energy-optimal braking problem with accessed braking intention is formulated and solved by the distance-based dynamic programming algorithm,which could derive the energy-optimal braking trajectory.In the lower-layer,the nonlinear time-varying vehicle longitudinal dynamics is transformed to the linear time-varying system,then an efficient model predictive controller is designed and solved by quadratic programming algorithm to track the original energy-optimal braking trajectory while ensuring braking comfort and safety.Several simulations are conducted by jointing MATLAB and CarSim,the results demonstrated the proposed EOBS achieves prominent regeneration energy improvement than the regular constant deceleration braking strategy.Finally,the energy-optimal braking mechanism of EVs is investigated based on the analysis of braking deceleration,battery charging power,and motor efficiency,which could be a guide to real-time control.展开更多
There is a paradigm shift happening in automotive industry towards electric vehicles as environment and sustainability issues gainedmomentum in the recent years among potential users.Connected and Autonomous Electric ...There is a paradigm shift happening in automotive industry towards electric vehicles as environment and sustainability issues gainedmomentum in the recent years among potential users.Connected and Autonomous Electric Vehicle(CAEV)technologies are fascinating the automakers and inducing them to manufacture connected autonomous vehicles with self-driving features such as autopilot and self-parking.Therefore,Traffic Flow Prediction(TFP)is identified as a major issue in CAEV technologies which needs to be addressed with the help of Deep Learning(DL)techniques.In this view,the current research paper presents an artificial intelligence-based parallel autoencoder for TFP,abbreviated as AIPAE-TFP model in CAEV.The presented model involves two major processes namely,feature engineering and TFP.In feature engineering process,there are multiple stages involved such as feature construction,feature selection,and feature extraction.In addition to the above,a Support Vector Data Description(SVDD)model is also used in the filtration of anomaly points and smoothen the raw data.Finally,AIPAE model is applied to determine the predictive values of traffic flow.In order to illustrate the proficiency of the model’s predictive outcomes,a set of simulations was performed and the results were investigated under distinct aspects.The experimentation outcomes verified the effectual performance of the proposed AIPAE-TFP model over other methods.展开更多
Vehicle to grid technology allows bidirectional energy exchange between electric vehicles and the power grid for achieving many known benefits. However, V2G networks suffer from certain security threats, such as EV’s...Vehicle to grid technology allows bidirectional energy exchange between electric vehicles and the power grid for achieving many known benefits. However, V2G networks suffer from certain security threats, such as EV’s privacy and authentication problem. In this paper, we propose an anonymous group authentication scheme for V2G communications. This scheme realizes dynamic joining and revocation of EVs, and greatly reduces the overhead of EV revocation. Through the theoretical analysis, this scheme can ensure identity privacy of EV user and security of data transmission in the process of charging and discharging.展开更多
With the shortages of resources,environmental pollution,climate change,and other issues becoming more and more serious,it is extremely urgent to vigorously develop new energy vehicles.As the cost of batteries decrease...With the shortages of resources,environmental pollution,climate change,and other issues becoming more and more serious,it is extremely urgent to vigorously develop new energy vehicles.As the cost of batteries decrease year by year,the production and quantity of sales of electric vehicles(EVs)in the world,especially in China,increased substantially.In order to make vehicles to grid(V2G)technology better developed and applied in China.The brief introduction to V2G is given at first.Then the development status and specific cases of V2G at home and abroad are summarized.Finally,the problems that V2G may encounter during promotion and application in China are analyzed.Based on the development of the United States and Japan,specific policy recommendations are given in line with the basic national conditions of China.展开更多
With the problem of global energy shortage and people’s awareness of energy saving, electric vehicles receive world-wide attention from government to business. Then the load of the power grid will rapidly increase in...With the problem of global energy shortage and people’s awareness of energy saving, electric vehicles receive world-wide attention from government to business. Then the load of the power grid will rapidly increase in a short term, and a series of effects will bring to the power grid operation, management, production and planning. With the large-scale penetration of electric vehicles and distributed energy gradually increased, if they can be effectively controlled and regulated, they can play the roles of load shifting, stabling intermittent renewable energy sources, providing emergency power supply and so on. Otherwise they may have a negative impact, which calls for a good interaction of electric vehicles and power grid. Analyzed the status of the current study on the interaction between the electric vehicles and the power grid, this paper builds the material basis, information architecture and the corresponding control method for the interaction from the aspect of the energy and information exchanging, and then discusses the key issues, which makes a useful exploration for the further research.展开更多
The charging of electric vehicles(EVs) impacts the distribution grid, and its cost depends on the price of electricity when charging. An aggregator that is responsible for a large fleet of EVs can use a market-based c...The charging of electric vehicles(EVs) impacts the distribution grid, and its cost depends on the price of electricity when charging. An aggregator that is responsible for a large fleet of EVs can use a market-based control algorithm to coordinate the charging of these vehicles, in order to minimize the costs. In such an optimization, the operational parameters of the distribution grid, to which the EVs are connected, are not considered. This can lead to violations of the technical constraints of the grid(e.g., undervoltage, phase unbalances); for example, because many vehicles start charging simultaneously when the price is low. An optimization that simultaneously takes the economic and technical aspects into account is complex, because it has to combine time-driven control at the market level with eventdriven control at the operational level. Diff erent case studies investigate under which circumstances the market-based control, which coordinates EV charging, conflicts with the operational constraints of the distribution grid. Especially in weak grids, phase unbalance and voltage issues arise with a high share of EVs. A low-level voltage droop controller at the charging point of the EV can be used to avoid many grid constraint violations, by reducing the charge power if the local voltage is too low. While this action implies a deviation from the cost-optimal operating point, it is shown that this has a very limited impact on the business case of an aggregator, and is able to comply with the technical distribution grid constraints, even in weak distribution grids with many EVs.展开更多
For the negative impact of large-scale electric vehicles (EVs) disorderly charging on the power grid, a multi-objective optimization strategy for coordinated charging and discharging of EVs based on Stackelberg game i...For the negative impact of large-scale electric vehicles (EVs) disorderly charging on the power grid, a multi-objective optimization strategy for coordinated charging and discharging of EVs based on Stackelberg game is proposed. As the leader, the grid company aims to stabilize load fluctuations and formulate a reasonable electricity price strategy to guide EVs to participate in vehicle-to-grid (V2G);As followers, EV users optimize their charging plans based on electricity price information with the objective of reducing costs and obtaining good comfort. This paper uses the MOPSO algorithm to solve the proposed multi-objective Stackelberg problem, and calculates the optimization results under various preferences, which proves the effectiveness of the proposed model and method.展开更多
This paper assesses 4 years of operation of a 1.75 kW roof top solar PV system installed in a Sydney suburban house. The system consists of 10 PV panels, a DC/AC inverter, and a grid connected gross meter. Solar elect...This paper assesses 4 years of operation of a 1.75 kW roof top solar PV system installed in a Sydney suburban house. The system consists of 10 PV panels, a DC/AC inverter, and a grid connected gross meter. Solar electricity delivered to grid is verified with the results from a computer simulation package (PVSYST) by adopting the installed component specifications, operation conditions, and weather data of the site. The results show high consistency between the values of energy delivered to the grid measured by the energy company and the energy estimated by system simulation. New system performance indicator is developed and called the optimum performance compliance ratio (PCR). It is a measure of the compliance of the output of the designed PV system with the output which would be produced by the same system with a solar tracker. This indicator provides system designers, contractors and energy providers with the actual capacity of the system that they can offer the end-users.展开更多
Removal of the electrical shielding from a type of Fourier transform seismometer overlays seismic information with Extremely Low Frequency-range (ELF) electromagnetic signals between about 0.3 Hz and 36 Hz (the ITU-de...Removal of the electrical shielding from a type of Fourier transform seismometer overlays seismic information with Extremely Low Frequency-range (ELF) electromagnetic signals between about 0.3 Hz and 36 Hz (the ITU-designated range of ELF is 3 to 30 Hz). The observed signals originate in the electric power grid, shown clearly by the fact that they are sum and difference heterodyne products with the power grid’s higher harmonics of 60 Hz, typically the 36th and 37th, because the seismometer’s chosen frequency modulation (FM) carrier frequency is roughly 2200 Hz. It is especially interesting that on 2017-03-19, prior to 14:25:12 UTC, the instrument recorded an 11 minute sequence of 20.3 Hz ELF outbursts that culminated intimately with a 3.2 magnitude earthquake located a few miles west of Bardwell KY. These ~20.3 Hz ELF signals, very near the third Schumann resonance frequency, have been recorded numerous times. They are distinctive and fairly strong, ranging 15 to 30 db or more above the noise floor, but definitely not an every-day event;months can pass without them. So far most of these ELF signals do not have an intimately associated earthquake, with the event of 2017-03-19 being one of only two exceptions recorded thus far. That quake’s location was more than one hundred miles from the instrument, in the New Madrid Seismic Zone (NMSZ). The second case, a quake in Kansas, was about three times farther from the instrument, and its ELF signals were correspondingly weaker. Those other, unassociated electromagnetic events might come from quakes too weak to detect, but it should be noted that stronger, easily detected quakes also rarely exhibit any ELF/seismic “connectivity”. This paper describes an instrument that overlays ELF, electric field and seismic signals. The instrument’s two-dimensional (2D) output has a time axis (horizontal) resolution of ~3 seconds and an ELF frequency (vertical) resolution of ~0.3 Hz.展开更多
This paper explores the movement of connected vehicles in Indiana for vehicles classified by the NHTSA Product Information Catalog Vehicle listing as being either electric (EV) or hybrid electric (HV). Analysis of tra...This paper explores the movement of connected vehicles in Indiana for vehicles classified by the NHTSA Product Information Catalog Vehicle listing as being either electric (EV) or hybrid electric (HV). Analysis of trajectories from July 12-18, 2021 for the state of Indiana observed nearly 33,300 trips and 267,000 vehicle miles travelled (VMT) for the combination of EV and HV. Approximately 53% of the VMT occurred in just 10 counties. For just EVs, there were 9814 unique trips and 64,700 Electric Vehicle Miles Traveled (EVMTs) in total. A further categorization of this revealed that 18% of these EVMTs were on Interstate roadways and 82% on non-interstate roads. <span style="font-family:Verdana;"> </span><span style="font-family:Verdana;">Proximity analysis of existing DC Fast charging stations in relation to interstate roadways revealed multiple charging deserts that would be most benefited by additional charging capacity. Eleven roadway sections among the 9 interstates were found to have a gap in available DC fast chargers of 50 miles or more. Although the connected vehicle data set analyzed did not include all EV’s the methodology presented in this paper provides a technique that can be scaled as additional EV connected vehicle data becomes available to agencies. Furthermore, it emphasizes the need for transportation agencies and automotive vendors to strengthen their data sharing partnerships to help accelerate </span><span style="font-family:Verdana;">the </span><span style="font-family:Verdana;">adoption of EV and reduce consumer range anxiety with EV. Graphics are included that illustrate examples of counties that are both overserved and underserved by charging infrastructure.</span>展开更多
Applications of electric vehicles need to build a large number of charging stations. The electric vehicle charging stations communicate with the grid. In V2G (vehicle to grid) mode, electric vehicles can be used as ...Applications of electric vehicles need to build a large number of charging stations. The electric vehicle charging stations communicate with the grid. In V2G (vehicle to grid) mode, electric vehicles can be used as energy storage units and transfer power to the grid. The electric vehicles charge at night to reduce the cost and the grid load, simultaneously to fill the valley. When grid load increases, electric vehicles' batteries discharge to the grid to improve the stability of the grid. As distributed storage units, electric vehicles are important components of the smart grid. In this paper, the three-phase PWM (pulse width modulation) rectifier used for smart charging and discharging system of electric vehicles are analyzed and designed. This paper includes the principle of PWM rectifier-inverter and direct current control strategy. Also, the SVPWM (space vector pulse width modulation) and system design of three-phase PWM rectifiers are analyzed. A 10 kW prototype is developed. Simulation and experiment results show that the three-phase PWM rectifiers reach the unit power factor. From the experimental results, PWM rectifier implements the sinusoidal grid current and achieves the unit power factor.展开更多
促使风电、光伏等分布式能源和电动汽车保有量快速增长。考虑电动汽车到电网(vehicle to grid,V2G)能量互动对多元化能源发电出力随机性及波动性的平抑作用,以及提升风/光电的消纳水平,采用虚拟电厂(virtual power plant,VPP)技术实现...促使风电、光伏等分布式能源和电动汽车保有量快速增长。考虑电动汽车到电网(vehicle to grid,V2G)能量互动对多元化能源发电出力随机性及波动性的平抑作用,以及提升风/光电的消纳水平,采用虚拟电厂(virtual power plant,VPP)技术实现对二者的统一协调管理,进而结合电动汽车全生命周期碳排放数量和分布式能源运行时碳排放数量,构建电动汽车参与的虚拟电厂整体多目标优化模型,采用粒子群优化算法对该模型进行求解,从而优化系统运行成本及碳排放成本。在结合真实数据配置的算例模型上进行实验分析,实验结果表明,提出的优化模型可以有效调度虚拟电厂各要素,充分发挥电动汽车V2G入网充放电带来的运行和碳排放收益,可以为低碳目标背景下电网系统的安全稳定运行提供技术参考。展开更多
基金Supported by National Key R&D Program of China (Grant No.2021YFB2402002)Beijing Natural Science Foundation of China (Grant No.L223013)。
文摘The development of a battery management algorithm is highly dependent on high-quality battery operation data,especially the data in extreme conditions such as low temperatures.The data in faults are also essential for failure and safety management research.This study developed a battery big data platform to realize vehicle operation,energy interaction and data management.First,we developed an electric vehicle with vehicle navigation and position detection and designed an environmental cabin that allows the vehicle to operate autonomously.Second,charging and heating systems based on wireless energy transfer were developed and equipped on the vehicle to investigate optimal charging and heating methods of the batteries in the vehicle.Third,the data transmission network was designed,a real-time monitoring interface was developed,and the self-developed battery management system was used to measure,collect,upload,and store battery operation data in real time.Finally,experimental validation was performed on the platform.Results demonstrate the efficiency and reliability of the platform.Battery state of charge estimation is used as an example to illustrate the availability of battery operation data.
基金Supported by Jiangsu Provincial Key R&D Program(Grant No.BE2019004)National Natural Science Funds for Distinguished Young Scholar of China(Grant No.52025121)+1 种基金National Nature Science Foundation of China(Grant Nos.51805081,51975118,52002066)Jiangsu Provincial Achievement Transformation Project(Grant No.BA2018023).
文摘Most researches focus on the regenerative braking system design in vehicle components control and braking torque distribution,few combine the connected vehicle technologies into braking velocity planning.If the braking intention is accessed by the vehicle-to-everything communication,the electric vehicles(EVs)could plan the braking velocity for recovering more vehicle kinetic energy.Therefore,this paper presents an energy-optimal braking strategy(EOBS)to improve the energy efficiency of EVs with the consideration of shared braking intention.First,a double-layer control scheme is formulated.In the upper-layer,an energy-optimal braking problem with accessed braking intention is formulated and solved by the distance-based dynamic programming algorithm,which could derive the energy-optimal braking trajectory.In the lower-layer,the nonlinear time-varying vehicle longitudinal dynamics is transformed to the linear time-varying system,then an efficient model predictive controller is designed and solved by quadratic programming algorithm to track the original energy-optimal braking trajectory while ensuring braking comfort and safety.Several simulations are conducted by jointing MATLAB and CarSim,the results demonstrated the proposed EOBS achieves prominent regeneration energy improvement than the regular constant deceleration braking strategy.Finally,the energy-optimal braking mechanism of EVs is investigated based on the analysis of braking deceleration,battery charging power,and motor efficiency,which could be a guide to real-time control.
文摘There is a paradigm shift happening in automotive industry towards electric vehicles as environment and sustainability issues gainedmomentum in the recent years among potential users.Connected and Autonomous Electric Vehicle(CAEV)technologies are fascinating the automakers and inducing them to manufacture connected autonomous vehicles with self-driving features such as autopilot and self-parking.Therefore,Traffic Flow Prediction(TFP)is identified as a major issue in CAEV technologies which needs to be addressed with the help of Deep Learning(DL)techniques.In this view,the current research paper presents an artificial intelligence-based parallel autoencoder for TFP,abbreviated as AIPAE-TFP model in CAEV.The presented model involves two major processes namely,feature engineering and TFP.In feature engineering process,there are multiple stages involved such as feature construction,feature selection,and feature extraction.In addition to the above,a Support Vector Data Description(SVDD)model is also used in the filtration of anomaly points and smoothen the raw data.Finally,AIPAE model is applied to determine the predictive values of traffic flow.In order to illustrate the proficiency of the model’s predictive outcomes,a set of simulations was performed and the results were investigated under distinct aspects.The experimentation outcomes verified the effectual performance of the proposed AIPAE-TFP model over other methods.
文摘Vehicle to grid technology allows bidirectional energy exchange between electric vehicles and the power grid for achieving many known benefits. However, V2G networks suffer from certain security threats, such as EV’s privacy and authentication problem. In this paper, we propose an anonymous group authentication scheme for V2G communications. This scheme realizes dynamic joining and revocation of EVs, and greatly reduces the overhead of EV revocation. Through the theoretical analysis, this scheme can ensure identity privacy of EV user and security of data transmission in the process of charging and discharging.
基金Natural Science Foundation of Shanghai,China(No.17ZR1411200)Shanghai International Automobile City(Group)Co.,Ltd.,China,(No.H2017-032)
文摘With the shortages of resources,environmental pollution,climate change,and other issues becoming more and more serious,it is extremely urgent to vigorously develop new energy vehicles.As the cost of batteries decrease year by year,the production and quantity of sales of electric vehicles(EVs)in the world,especially in China,increased substantially.In order to make vehicles to grid(V2G)technology better developed and applied in China.The brief introduction to V2G is given at first.Then the development status and specific cases of V2G at home and abroad are summarized.Finally,the problems that V2G may encounter during promotion and application in China are analyzed.Based on the development of the United States and Japan,specific policy recommendations are given in line with the basic national conditions of China.
文摘With the problem of global energy shortage and people’s awareness of energy saving, electric vehicles receive world-wide attention from government to business. Then the load of the power grid will rapidly increase in a short term, and a series of effects will bring to the power grid operation, management, production and planning. With the large-scale penetration of electric vehicles and distributed energy gradually increased, if they can be effectively controlled and regulated, they can play the roles of load shifting, stabling intermittent renewable energy sources, providing emergency power supply and so on. Otherwise they may have a negative impact, which calls for a good interaction of electric vehicles and power grid. Analyzed the status of the current study on the interaction between the electric vehicles and the power grid, this paper builds the material basis, information architecture and the corresponding control method for the interaction from the aspect of the energy and information exchanging, and then discusses the key issues, which makes a useful exploration for the further research.
基金supported in part by the European Commission through the project P2P-Smartest:Peer to Peer Smart Energy Distribution Networks (H2020-LCE-2014-3,project 646469)
文摘The charging of electric vehicles(EVs) impacts the distribution grid, and its cost depends on the price of electricity when charging. An aggregator that is responsible for a large fleet of EVs can use a market-based control algorithm to coordinate the charging of these vehicles, in order to minimize the costs. In such an optimization, the operational parameters of the distribution grid, to which the EVs are connected, are not considered. This can lead to violations of the technical constraints of the grid(e.g., undervoltage, phase unbalances); for example, because many vehicles start charging simultaneously when the price is low. An optimization that simultaneously takes the economic and technical aspects into account is complex, because it has to combine time-driven control at the market level with eventdriven control at the operational level. Diff erent case studies investigate under which circumstances the market-based control, which coordinates EV charging, conflicts with the operational constraints of the distribution grid. Especially in weak grids, phase unbalance and voltage issues arise with a high share of EVs. A low-level voltage droop controller at the charging point of the EV can be used to avoid many grid constraint violations, by reducing the charge power if the local voltage is too low. While this action implies a deviation from the cost-optimal operating point, it is shown that this has a very limited impact on the business case of an aggregator, and is able to comply with the technical distribution grid constraints, even in weak distribution grids with many EVs.
文摘For the negative impact of large-scale electric vehicles (EVs) disorderly charging on the power grid, a multi-objective optimization strategy for coordinated charging and discharging of EVs based on Stackelberg game is proposed. As the leader, the grid company aims to stabilize load fluctuations and formulate a reasonable electricity price strategy to guide EVs to participate in vehicle-to-grid (V2G);As followers, EV users optimize their charging plans based on electricity price information with the objective of reducing costs and obtaining good comfort. This paper uses the MOPSO algorithm to solve the proposed multi-objective Stackelberg problem, and calculates the optimization results under various preferences, which proves the effectiveness of the proposed model and method.
文摘This paper assesses 4 years of operation of a 1.75 kW roof top solar PV system installed in a Sydney suburban house. The system consists of 10 PV panels, a DC/AC inverter, and a grid connected gross meter. Solar electricity delivered to grid is verified with the results from a computer simulation package (PVSYST) by adopting the installed component specifications, operation conditions, and weather data of the site. The results show high consistency between the values of energy delivered to the grid measured by the energy company and the energy estimated by system simulation. New system performance indicator is developed and called the optimum performance compliance ratio (PCR). It is a measure of the compliance of the output of the designed PV system with the output which would be produced by the same system with a solar tracker. This indicator provides system designers, contractors and energy providers with the actual capacity of the system that they can offer the end-users.
文摘Removal of the electrical shielding from a type of Fourier transform seismometer overlays seismic information with Extremely Low Frequency-range (ELF) electromagnetic signals between about 0.3 Hz and 36 Hz (the ITU-designated range of ELF is 3 to 30 Hz). The observed signals originate in the electric power grid, shown clearly by the fact that they are sum and difference heterodyne products with the power grid’s higher harmonics of 60 Hz, typically the 36th and 37th, because the seismometer’s chosen frequency modulation (FM) carrier frequency is roughly 2200 Hz. It is especially interesting that on 2017-03-19, prior to 14:25:12 UTC, the instrument recorded an 11 minute sequence of 20.3 Hz ELF outbursts that culminated intimately with a 3.2 magnitude earthquake located a few miles west of Bardwell KY. These ~20.3 Hz ELF signals, very near the third Schumann resonance frequency, have been recorded numerous times. They are distinctive and fairly strong, ranging 15 to 30 db or more above the noise floor, but definitely not an every-day event;months can pass without them. So far most of these ELF signals do not have an intimately associated earthquake, with the event of 2017-03-19 being one of only two exceptions recorded thus far. That quake’s location was more than one hundred miles from the instrument, in the New Madrid Seismic Zone (NMSZ). The second case, a quake in Kansas, was about three times farther from the instrument, and its ELF signals were correspondingly weaker. Those other, unassociated electromagnetic events might come from quakes too weak to detect, but it should be noted that stronger, easily detected quakes also rarely exhibit any ELF/seismic “connectivity”. This paper describes an instrument that overlays ELF, electric field and seismic signals. The instrument’s two-dimensional (2D) output has a time axis (horizontal) resolution of ~3 seconds and an ELF frequency (vertical) resolution of ~0.3 Hz.
文摘This paper explores the movement of connected vehicles in Indiana for vehicles classified by the NHTSA Product Information Catalog Vehicle listing as being either electric (EV) or hybrid electric (HV). Analysis of trajectories from July 12-18, 2021 for the state of Indiana observed nearly 33,300 trips and 267,000 vehicle miles travelled (VMT) for the combination of EV and HV. Approximately 53% of the VMT occurred in just 10 counties. For just EVs, there were 9814 unique trips and 64,700 Electric Vehicle Miles Traveled (EVMTs) in total. A further categorization of this revealed that 18% of these EVMTs were on Interstate roadways and 82% on non-interstate roads. <span style="font-family:Verdana;"> </span><span style="font-family:Verdana;">Proximity analysis of existing DC Fast charging stations in relation to interstate roadways revealed multiple charging deserts that would be most benefited by additional charging capacity. Eleven roadway sections among the 9 interstates were found to have a gap in available DC fast chargers of 50 miles or more. Although the connected vehicle data set analyzed did not include all EV’s the methodology presented in this paper provides a technique that can be scaled as additional EV connected vehicle data becomes available to agencies. Furthermore, it emphasizes the need for transportation agencies and automotive vendors to strengthen their data sharing partnerships to help accelerate </span><span style="font-family:Verdana;">the </span><span style="font-family:Verdana;">adoption of EV and reduce consumer range anxiety with EV. Graphics are included that illustrate examples of counties that are both overserved and underserved by charging infrastructure.</span>
文摘Applications of electric vehicles need to build a large number of charging stations. The electric vehicle charging stations communicate with the grid. In V2G (vehicle to grid) mode, electric vehicles can be used as energy storage units and transfer power to the grid. The electric vehicles charge at night to reduce the cost and the grid load, simultaneously to fill the valley. When grid load increases, electric vehicles' batteries discharge to the grid to improve the stability of the grid. As distributed storage units, electric vehicles are important components of the smart grid. In this paper, the three-phase PWM (pulse width modulation) rectifier used for smart charging and discharging system of electric vehicles are analyzed and designed. This paper includes the principle of PWM rectifier-inverter and direct current control strategy. Also, the SVPWM (space vector pulse width modulation) and system design of three-phase PWM rectifiers are analyzed. A 10 kW prototype is developed. Simulation and experiment results show that the three-phase PWM rectifiers reach the unit power factor. From the experimental results, PWM rectifier implements the sinusoidal grid current and achieves the unit power factor.
文摘促使风电、光伏等分布式能源和电动汽车保有量快速增长。考虑电动汽车到电网(vehicle to grid,V2G)能量互动对多元化能源发电出力随机性及波动性的平抑作用,以及提升风/光电的消纳水平,采用虚拟电厂(virtual power plant,VPP)技术实现对二者的统一协调管理,进而结合电动汽车全生命周期碳排放数量和分布式能源运行时碳排放数量,构建电动汽车参与的虚拟电厂整体多目标优化模型,采用粒子群优化算法对该模型进行求解,从而优化系统运行成本及碳排放成本。在结合真实数据配置的算例模型上进行实验分析,实验结果表明,提出的优化模型可以有效调度虚拟电厂各要素,充分发挥电动汽车V2G入网充放电带来的运行和碳排放收益,可以为低碳目标背景下电网系统的安全稳定运行提供技术参考。