Dewi Anggraeni, Budi Sudiarto, Ery Fitrianingsih, Purnomo Sidi Priambodo
The primary indicator of battery level in a battery management system (BMS) is the state of charge, which plays a crucial role in enhancing safety in terms of energy transfer. Accurate measurement of SoC is essential to guaranteeing battery safety, avoiding hazardous scenarios, and enhancing the performance of the battery. To improve SoC accuracy, first-order and second-order adaptive extended Kalman filtering (AEKF) are the best choices, as they have less computational cost and are more robust in uncertain circumstances. The impact on SoC estimation accuracy of increasing the cycle and its interaction with the size of the tuning window was evaluated using both models. The research results show that tuning the window size (M) greatly affects the accuracy of SoC estimation in both methods. M provides a quick response detection measurement and adjusts the estimation’s character with the actual value. The results indicate that the precision of SoC improves as the value of M decreases. In addition, the application of first-order AEKF has practical advantages because it does not require pre-processing steps to determine polarization resistance and polarization capacity, while second-order AEKF has better capabilities in terms of SoC estimation. The robustness of the two techniques was also evaluated by administering various initial SoCs. The examination findings demonstrate that the estimated trajectory can approximate the actual trajectory of the SoC.
{"title":"Tuning Window Size to Improve the Accuracy of Battery State-of-Charge Estimations Due to Battery Cycle Addition","authors":"Dewi Anggraeni, Budi Sudiarto, Ery Fitrianingsih, Purnomo Sidi Priambodo","doi":"10.3390/wevj14110307","DOIUrl":"https://doi.org/10.3390/wevj14110307","url":null,"abstract":"The primary indicator of battery level in a battery management system (BMS) is the state of charge, which plays a crucial role in enhancing safety in terms of energy transfer. Accurate measurement of SoC is essential to guaranteeing battery safety, avoiding hazardous scenarios, and enhancing the performance of the battery. To improve SoC accuracy, first-order and second-order adaptive extended Kalman filtering (AEKF) are the best choices, as they have less computational cost and are more robust in uncertain circumstances. The impact on SoC estimation accuracy of increasing the cycle and its interaction with the size of the tuning window was evaluated using both models. The research results show that tuning the window size (M) greatly affects the accuracy of SoC estimation in both methods. M provides a quick response detection measurement and adjusts the estimation’s character with the actual value. The results indicate that the precision of SoC improves as the value of M decreases. In addition, the application of first-order AEKF has practical advantages because it does not require pre-processing steps to determine polarization resistance and polarization capacity, while second-order AEKF has better capabilities in terms of SoC estimation. The robustness of the two techniques was also evaluated by administering various initial SoCs. The examination findings demonstrate that the estimated trajectory can approximate the actual trajectory of the SoC.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":"55 s191","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135341706","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}
Justin Anthony Mahlberg, Jairaj Desai, Darcy M. Bullock
As the country moves toward electric vehicles (EV), the United States is in the process of investing over USD 7.5 billion in EV charging stations, and Indiana has been allocated $100 million to invest in their EV charging network. In contrast to traditional “gas stations”, EV charging times, depending on the charger power delivery rating, can require considerably longer dwell times. As a result, drivers tend to pair charging with other activities. This study looks at two EV public charging locations and monitors driver activity while charging, charge time, and station utilization over a 2-month period in Lafayette, Indiana. Over 4000 charging sessions at stations with varying power levels (350 kW, 150 kW, and 50 kW) were monitored, and the median charge time was between 28 and 36 min. A large variation in station utilization was observed at Electrify America charging stations that had a range of stations with 350 kW, 150 kW, and 50 kW available. The highest utilization rates by hour of day on average were observed at 25% at the 150 kW Tesla station. Driver activity during charging influenced dwell times, with the average dwell time of drivers who waited in their vehicles to charge being 10 min shorter than those who would travel to the shops. Rain in the forecast also impacted the number of users per day. Although there are no published metrics for EV utilization and associated driver activities, we believe examining this relationship will produce best practices for planning future investments in EV charging infrastructure as public and private sector partners develop a nationwide charging network.
{"title":"Evaluation of Electric Vehicle Charging Usage and Driver Activity","authors":"Justin Anthony Mahlberg, Jairaj Desai, Darcy M. Bullock","doi":"10.3390/wevj14110308","DOIUrl":"https://doi.org/10.3390/wevj14110308","url":null,"abstract":"As the country moves toward electric vehicles (EV), the United States is in the process of investing over USD 7.5 billion in EV charging stations, and Indiana has been allocated $100 million to invest in their EV charging network. In contrast to traditional “gas stations”, EV charging times, depending on the charger power delivery rating, can require considerably longer dwell times. As a result, drivers tend to pair charging with other activities. This study looks at two EV public charging locations and monitors driver activity while charging, charge time, and station utilization over a 2-month period in Lafayette, Indiana. Over 4000 charging sessions at stations with varying power levels (350 kW, 150 kW, and 50 kW) were monitored, and the median charge time was between 28 and 36 min. A large variation in station utilization was observed at Electrify America charging stations that had a range of stations with 350 kW, 150 kW, and 50 kW available. The highest utilization rates by hour of day on average were observed at 25% at the 150 kW Tesla station. Driver activity during charging influenced dwell times, with the average dwell time of drivers who waited in their vehicles to charge being 10 min shorter than those who would travel to the shops. Rain in the forecast also impacted the number of users per day. Although there are no published metrics for EV utilization and associated driver activities, we believe examining this relationship will produce best practices for planning future investments in EV charging infrastructure as public and private sector partners develop a nationwide charging network.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":"55 s190","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135341707","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}
Research on battery thermal management systems (BTMSs) is particularly significant since the electric vehicle sector is growing in importance and because the batteries that power them have high operating temperature requirements. Among them, heat pipe (HP)-based battery thermal management systems have very high heat transfer performance but fall short in maintaining uniform temperature distribution. This study presented forced air cooling by an axial fan as a method of improving the cooling performance of flat heat pipes coupled with aluminum fins (FHPAFs) and investigated the impact of air velocity on the battery pack’s maximum temperature differential (ΔTmax). All experiments were conducted on lithium nickel manganese cobalt oxide (NMC) pouch battery cells with a 20 Ah capacity in seven series connections at room temperature, under forced and natural convection, at various air velocity values (12.7 m/s, 9.5 m/s, and 6.3 m/s), and with 1C, 2C, 3C, and 4C discharge rates. The results indicated that at the same air velocity, increasing the discharge rate increases the ΔTmax significantly. Forced convection has a higher ΔTmax than natural convection. The ΔTmax was reduced when the air velocity was increased during forced convection.
{"title":"Experimental Investigation on Affecting Air Flow against the Maximum Temperature Difference of a Lithium-Ion Battery with Heat Pipe Cooling","authors":"Chokchai Anamtawach, Soontorn Odngam, Chaiyut Sumpavakup","doi":"10.3390/wevj14110306","DOIUrl":"https://doi.org/10.3390/wevj14110306","url":null,"abstract":"Research on battery thermal management systems (BTMSs) is particularly significant since the electric vehicle sector is growing in importance and because the batteries that power them have high operating temperature requirements. Among them, heat pipe (HP)-based battery thermal management systems have very high heat transfer performance but fall short in maintaining uniform temperature distribution. This study presented forced air cooling by an axial fan as a method of improving the cooling performance of flat heat pipes coupled with aluminum fins (FHPAFs) and investigated the impact of air velocity on the battery pack’s maximum temperature differential (ΔTmax). All experiments were conducted on lithium nickel manganese cobalt oxide (NMC) pouch battery cells with a 20 Ah capacity in seven series connections at room temperature, under forced and natural convection, at various air velocity values (12.7 m/s, 9.5 m/s, and 6.3 m/s), and with 1C, 2C, 3C, and 4C discharge rates. The results indicated that at the same air velocity, increasing the discharge rate increases the ΔTmax significantly. Forced convection has a higher ΔTmax than natural convection. The ΔTmax was reduced when the air velocity was increased during forced convection.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":"77 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135433114","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}
Dana Abudayyeh, Malek Almomani, Omar Almomani, Douha Jaber, Eman Alhelo
Recently, technologies for electric mobility have developed rapidly. Since the introduction and spread of Electric Vehicles (EVs), several studies have attempted to investigate the benefits and risks that impact on the growth of the EV market by evaluating data gathered from various drivers. However, some variables were disregarded such as: Public Involvement, Knowledge of EVs, Perceived Risk, Behavioural Intention, and EV acceptance. These variables are considered vital when analysing the intention to use EVs. Therefore, this study compiles the above mentioned variables to evaluate their effect on the intention to use EVs in Jordan. 501 collected responses were examined using the Smart PLS-Structural Equation Model algorithm. In general, the analysis revealed high levels of EV acceptance. The study proposed twelve direct relationship hypotheses. Out of these hypotheses, ten hypotheses were supported and two were rejected. The final conclusions are that an increase in public involvement is associated with an increase in knowledge of EVs, and an increase in their perceived risk. Moreover, the knowledge of EVs has positively and significantly influenced the perceived usefulness and perceived ease of use, along with EV acceptance. However, no relationships were found between the following: 1. the knowledge of EVs and perceived risk; and 2. perceived risk and behavioural intention.
{"title":"Examining the Determinants of Electric Vehicle Acceptance in Jordan: A PLS-SEM Approach","authors":"Dana Abudayyeh, Malek Almomani, Omar Almomani, Douha Jaber, Eman Alhelo","doi":"10.3390/wevj14110304","DOIUrl":"https://doi.org/10.3390/wevj14110304","url":null,"abstract":"Recently, technologies for electric mobility have developed rapidly. Since the introduction and spread of Electric Vehicles (EVs), several studies have attempted to investigate the benefits and risks that impact on the growth of the EV market by evaluating data gathered from various drivers. However, some variables were disregarded such as: Public Involvement, Knowledge of EVs, Perceived Risk, Behavioural Intention, and EV acceptance. These variables are considered vital when analysing the intention to use EVs. Therefore, this study compiles the above mentioned variables to evaluate their effect on the intention to use EVs in Jordan. 501 collected responses were examined using the Smart PLS-Structural Equation Model algorithm. In general, the analysis revealed high levels of EV acceptance. The study proposed twelve direct relationship hypotheses. Out of these hypotheses, ten hypotheses were supported and two were rejected. The final conclusions are that an increase in public involvement is associated with an increase in knowledge of EVs, and an increase in their perceived risk. Moreover, the knowledge of EVs has positively and significantly influenced the perceived usefulness and perceived ease of use, along with EV acceptance. However, no relationships were found between the following: 1. the knowledge of EVs and perceived risk; and 2. perceived risk and behavioural intention.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":"12 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135818339","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}
Ximena Carolina Acaro Chacón, Stefano Laureti, Marco Ricci, Gregorio Cappuccino
Lithium-ion batteries are considered the most suitable option for powering electric vehicles in modern transportation systems due to their high energy density, high energy efficiency, long cycle life, and low weight. Nonetheless, several safety concerns and their tendency to lose charge over time demand methods capable of determining their state of health accurately, as well as estimating a range of relevant parameters in order to ensure their safe and efficient use. In this framework, non-destructive inspection methods play a fundamental role in assessing the condition of lithium-ion batteries, allowing for their thorough examination without causing any damage. This aspect is particularly crucial when batteries are exploited in critical applications and when evaluating the potential second life usage of the cells. This review explores various non-destructive methods for evaluating lithium batteries, i.e., electrochemical impedance spectroscopy, infrared thermography, X-ray computed tomography and ultrasonic testing, considers and compares several aspects such as sensitivity, flexibility, accuracy, complexity, industrial applicability, and cost. Hence, this work aims at providing academic and industrial professionals with a tool for choosing the most appropriate methodology for a given application.
{"title":"A Review of Non-Destructive Techniques for Lithium-Ion Battery Performance Analysis","authors":"Ximena Carolina Acaro Chacón, Stefano Laureti, Marco Ricci, Gregorio Cappuccino","doi":"10.3390/wevj14110305","DOIUrl":"https://doi.org/10.3390/wevj14110305","url":null,"abstract":"Lithium-ion batteries are considered the most suitable option for powering electric vehicles in modern transportation systems due to their high energy density, high energy efficiency, long cycle life, and low weight. Nonetheless, several safety concerns and their tendency to lose charge over time demand methods capable of determining their state of health accurately, as well as estimating a range of relevant parameters in order to ensure their safe and efficient use. In this framework, non-destructive inspection methods play a fundamental role in assessing the condition of lithium-ion batteries, allowing for their thorough examination without causing any damage. This aspect is particularly crucial when batteries are exploited in critical applications and when evaluating the potential second life usage of the cells. This review explores various non-destructive methods for evaluating lithium batteries, i.e., electrochemical impedance spectroscopy, infrared thermography, X-ray computed tomography and ultrasonic testing, considers and compares several aspects such as sensitivity, flexibility, accuracy, complexity, industrial applicability, and cost. Hence, this work aims at providing academic and industrial professionals with a tool for choosing the most appropriate methodology for a given application.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":"21 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135868538","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}
Ruifeng Shi, Shuaikang Peng, Tai Chang, Kwang Y. Lee
Vehicle-to-grid (V2G) technology has received a lot of attention as a smart interconnection solution between electric vehicles and the grid. This paper analyzes the relevant research progress and hotpots of V2G by using CiteSpace 6.1.R6 software to construct a visualization graph, which includes keyword co-occurrence, clustering, and burstiness, and further systematically summarizes the main trends and key results of V2G research. First, the connection between electric vehicles and the grid is outlined and the potential advantages of V2G technology are emphasized, such as energy management, load balancing, and environmental sustainability. The important topics of V2G, including renewable energy consumption, power dispatch, regulation and optimization of the grid, and the smart grid, are discussed. This paper also emphasizes the positive impacts of V2G technologies on the grid, including reduced carbon emissions, improved grid reliability, and the support for renewable energy integration. Current and future challenges for V2G research, such as standardization, policy support, and business models, are also considered. This review provides a comprehensive perspective for scholars and practitioners in V2G research and contributes to a better understanding of the current status and future trends of V2G technology.
{"title":"Annotated Survey on the Research Progress within Vehicle-to-Grid Techniques Based on CiteSpace Statistical Result","authors":"Ruifeng Shi, Shuaikang Peng, Tai Chang, Kwang Y. Lee","doi":"10.3390/wevj14110303","DOIUrl":"https://doi.org/10.3390/wevj14110303","url":null,"abstract":"Vehicle-to-grid (V2G) technology has received a lot of attention as a smart interconnection solution between electric vehicles and the grid. This paper analyzes the relevant research progress and hotpots of V2G by using CiteSpace 6.1.R6 software to construct a visualization graph, which includes keyword co-occurrence, clustering, and burstiness, and further systematically summarizes the main trends and key results of V2G research. First, the connection between electric vehicles and the grid is outlined and the potential advantages of V2G technology are emphasized, such as energy management, load balancing, and environmental sustainability. The important topics of V2G, including renewable energy consumption, power dispatch, regulation and optimization of the grid, and the smart grid, are discussed. This paper also emphasizes the positive impacts of V2G technologies on the grid, including reduced carbon emissions, improved grid reliability, and the support for renewable energy integration. Current and future challenges for V2G research, such as standardization, policy support, and business models, are also considered. This review provides a comprehensive perspective for scholars and practitioners in V2G research and contributes to a better understanding of the current status and future trends of V2G technology.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":"10 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135972885","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 utility factor (UF) of a plug-in hybrid electric vehicle (PHEV) refers to the ratio of miles traveled in electric mode to the total miles traveled. Standard UF curves provide a prediction of the expected achievable UF by a PHEV given its all-electric range (AER), but such predictions entail assumptions about both the driving patterns (distance traveled and energy intensity) and charging behavior. Studies have attempted to compare the real-world UF achieved by PHEVs to their standard values, but deviations can stem from deviations in assumptions about: (i) achievable electric range, (ii) travel distance and (iii) charging frequency. In this paper, we derive analytical models for modified utility factor curves as a function of both AER and charging behavior. We show that average charging frequency is insufficient to exactly predict UF but can still estimate bounds. Our generalized model can also provide insights into the efficacy of PHEVs in reducing carbon emissions.
{"title":"Utility Factor Curves for Plug-in Hybrid Electric Vehicles: Beyond the Standard Assumptions","authors":"Karim Hamza, Kenneth P. Laberteaux","doi":"10.3390/wevj14110301","DOIUrl":"https://doi.org/10.3390/wevj14110301","url":null,"abstract":"The utility factor (UF) of a plug-in hybrid electric vehicle (PHEV) refers to the ratio of miles traveled in electric mode to the total miles traveled. Standard UF curves provide a prediction of the expected achievable UF by a PHEV given its all-electric range (AER), but such predictions entail assumptions about both the driving patterns (distance traveled and energy intensity) and charging behavior. Studies have attempted to compare the real-world UF achieved by PHEVs to their standard values, but deviations can stem from deviations in assumptions about: (i) achievable electric range, (ii) travel distance and (iii) charging frequency. In this paper, we derive analytical models for modified utility factor curves as a function of both AER and charging behavior. We show that average charging frequency is insufficient to exactly predict UF but can still estimate bounds. Our generalized model can also provide insights into the efficacy of PHEVs in reducing carbon emissions.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":"44 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135808569","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}
Battery electric vehicles (BEV) are suitable alternatives for achieving energy independence and meeting the criteria for reducing greenhouse emissions in the transportation sector. Evaluating their performance and energy consumption in the real-data driving cycle (DC) is important. The purpose of this work is to develop a BEV DC for the interlinked urban and suburban route of Addis Ababa (AA) in Ethiopia. In this study, a new approach of micro-trip random selection-to-rebuild with behaviour split (RSBS) was implemented, and its effectiveness was compared via the k-means clustering method. When comparing the statistical distribution of velocity and acceleration with measured real data, the RSBS cycle shows a minimum error of 2% and 2.3%, respectively. By splitting driving behaviour, aggressive drivers were found to consume more energy because of frequent panic stops and subsequent acceleration. In braking mode, coast drivers were found to improve the regenerative braking possibility and efficiency, which can extend the range by 10.8%, whereas aggressive drivers could only achieve 3.9%. Also, resynthesised RSBS with the percentage of behaviour split and its energy and power consumption were compared with standard cycles. A significant reduction of 14.57% from UDDS and 8.9% from WLTC-2 in energy consumption was achieved for the AA and its suburbs DC, indicating that this DC could be useful for both the city and suburbs.
{"title":"Assessment of an Electric Vehicle Drive Cycle in Relation to Minimised Energy Consumption with Driving Behaviour: The Case of Addis Ababa, Ethiopia, and Its Suburbs","authors":"Tatek Mamo, Girma Gebresenbet, Rajendran Gopal, Bisrat Yoseph","doi":"10.3390/wevj14110302","DOIUrl":"https://doi.org/10.3390/wevj14110302","url":null,"abstract":"Battery electric vehicles (BEV) are suitable alternatives for achieving energy independence and meeting the criteria for reducing greenhouse emissions in the transportation sector. Evaluating their performance and energy consumption in the real-data driving cycle (DC) is important. The purpose of this work is to develop a BEV DC for the interlinked urban and suburban route of Addis Ababa (AA) in Ethiopia. In this study, a new approach of micro-trip random selection-to-rebuild with behaviour split (RSBS) was implemented, and its effectiveness was compared via the k-means clustering method. When comparing the statistical distribution of velocity and acceleration with measured real data, the RSBS cycle shows a minimum error of 2% and 2.3%, respectively. By splitting driving behaviour, aggressive drivers were found to consume more energy because of frequent panic stops and subsequent acceleration. In braking mode, coast drivers were found to improve the regenerative braking possibility and efficiency, which can extend the range by 10.8%, whereas aggressive drivers could only achieve 3.9%. Also, resynthesised RSBS with the percentage of behaviour split and its energy and power consumption were compared with standard cycles. A significant reduction of 14.57% from UDDS and 8.9% from WLTC-2 in energy consumption was achieved for the AA and its suburbs DC, indicating that this DC could be useful for both the city and suburbs.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":"32 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135808694","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}
Arief Nurdini, Rahmat Nurcahyo, Anton Satria Prabuwono
The introduction of electric vehicles (EVs) represents a promising solution for addressing urban air pollution, particularly CO2 emissions in the transportation sector. Numerous countries are actively promoting EV adoption and the electrification of transportation systems, leading to a surge in research on EV-related topics. This study employs bibliometrics as a valuable tool to investigate the research landscape in electric vehicle waste management. Drawing from a dataset of 593 documents retrieved from SCOPUS from 1995 to 20 September 2023, this research employs descriptive analysis and bibliometric mapping techniques. Notably, China stands out as the leading contributor to publications, with Tsinghua University being a prominent research institution in this field. An examination of keyword trends reveals dynamic shifts in research focus. In 2023, the most frequently occurring topic is “closed loop”. “Recycling” is the dominant keyword, appearing 681 times. Additionally, TreeMaps and VOSviewer results indicate that the most commonly used keywords are “electronic waste” and “recycling”. Projections suggest that “recycling materials” will gain prominence in mid-2023, further highlighting the evolving nature of this research field. Researchers in recycling materials disciplines can leverage these insights to explore new research avenues and contribute to sustainable waste management practices in the context of electric vehicles.
{"title":"Waste from Electric Vehicle: A Bibliometric Analysis from 1995 to 2023","authors":"Arief Nurdini, Rahmat Nurcahyo, Anton Satria Prabuwono","doi":"10.3390/wevj14110300","DOIUrl":"https://doi.org/10.3390/wevj14110300","url":null,"abstract":"The introduction of electric vehicles (EVs) represents a promising solution for addressing urban air pollution, particularly CO2 emissions in the transportation sector. Numerous countries are actively promoting EV adoption and the electrification of transportation systems, leading to a surge in research on EV-related topics. This study employs bibliometrics as a valuable tool to investigate the research landscape in electric vehicle waste management. Drawing from a dataset of 593 documents retrieved from SCOPUS from 1995 to 20 September 2023, this research employs descriptive analysis and bibliometric mapping techniques. Notably, China stands out as the leading contributor to publications, with Tsinghua University being a prominent research institution in this field. An examination of keyword trends reveals dynamic shifts in research focus. In 2023, the most frequently occurring topic is “closed loop”. “Recycling” is the dominant keyword, appearing 681 times. Additionally, TreeMaps and VOSviewer results indicate that the most commonly used keywords are “electronic waste” and “recycling”. Projections suggest that “recycling materials” will gain prominence in mid-2023, further highlighting the evolving nature of this research field. Researchers in recycling materials disciplines can leverage these insights to explore new research avenues and contribute to sustainable waste management practices in the context of electric vehicles.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136264145","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}
Static frequency converters (SFCs) are very important for starting the connection of synchronous capacitors to the power grid, which is beneficial for ensuring the impact of electric vehicle connection on the inertia of the power grid. In the traditional sensorless initial rotor position detection method, the signal-to-noise ratio of the induced voltage at the machine terminal is small, making it difficult to accurately extract the rotor position. In this study, a reliable initial position detection method for a sensorless-controlled synchronous machine drive is proposed. A step excitation voltage was applied to the excitation circuit before the motor was started, and the three-phase induction voltage at the terminals was sampled in real time. The sampling signal was processed in two ways: digital filter processing and stator flux calculation. The accuracy of the initial rotor position is determined by comparing the differences between the two results. This algorithm does not depend on additional hardware circuits and has fewer setting parameters; therefore, it is easy to apply in engineering applications. Finally, a comparative experiment was conducted using a real-time digital system (RTDS) to verify the feasibility and effectiveness of the proposed method. The proposed rotor position detection method can effectively improve the detection reliability and ensure the start-up reliability of SFCS.
{"title":"High-Reliability Rotor Position Detection Method for Sensorless Control of Synchronous Condenser","authors":"Xiangjian Shi, Teng Liu, Wei Mu, Jianfeng Zhao","doi":"10.3390/wevj14100299","DOIUrl":"https://doi.org/10.3390/wevj14100299","url":null,"abstract":"Static frequency converters (SFCs) are very important for starting the connection of synchronous capacitors to the power grid, which is beneficial for ensuring the impact of electric vehicle connection on the inertia of the power grid. In the traditional sensorless initial rotor position detection method, the signal-to-noise ratio of the induced voltage at the machine terminal is small, making it difficult to accurately extract the rotor position. In this study, a reliable initial position detection method for a sensorless-controlled synchronous machine drive is proposed. A step excitation voltage was applied to the excitation circuit before the motor was started, and the three-phase induction voltage at the terminals was sampled in real time. The sampling signal was processed in two ways: digital filter processing and stator flux calculation. The accuracy of the initial rotor position is determined by comparing the differences between the two results. This algorithm does not depend on additional hardware circuits and has fewer setting parameters; therefore, it is easy to apply in engineering applications. Finally, a comparative experiment was conducted using a real-time digital system (RTDS) to verify the feasibility and effectiveness of the proposed method. The proposed rotor position detection method can effectively improve the detection reliability and ensure the start-up reliability of SFCS.","PeriodicalId":38979,"journal":{"name":"World Electric Vehicle Journal","volume":"13 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135511186","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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