Hui Hou; Junyi Tang; Bo Zhao; Leiqi Zhang; Yifan Wang; Changjun Xie; Changjun Xie
Harvested from ORCID Public Data FileChangjun Xie in OpenAIREA reasonable plan for charging stations is critical to the widespread use of electric vehicles. In this paper, we propose an optimal planning method for electric vehicle charging stations. First of all, we put forward a forecasting method for the distribution of electric vehicle fast charging demand in urban areas. Next, a new mathematical model that considers the mutual benefit of electric vehicle users and the power grid is set up, aiming to minimize the social cost of charging stations. Then, the model is solved by the Voronoi diagram combined with improved particle swarm optimization. In the end, the proposed method is applied to an urban area, simulation results demonstrate that the proposed method can yield optimal location and capacity of each charging station. A contrasting case is carried out to verify that improved particle swarm optimization is more effective in finding the global optimal solution than particle swarm optimization.
Access Routesgold 16 citations 16 popularity Top 10% influence Top 10% impulse Top 10% 
Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/wevj12040244&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu- Hui Hou; Junyi Tang; Bo Zhao; Leiqi Zhang; Yifan Wang; Changjun Xie;
Changjun Xie
Harvested from ORCID Public Data FileChangjun Xie in OpenAIREA reasonable plan for charging stations is critical to the widespread use of electric vehicles. In this paper, we propose an optimal planning method for electric vehicle charging stations. First of all, we put forward a forecasting method for the distribution of electric vehicle fast charging demand in urban areas. Next, a new mathematical model that considers the mutual benefit of electric vehicle users and the power grid is set up, aiming to minimize the social cost of charging stations. Then, the model is solved by the Voronoi diagram combined with improved particle swarm optimization. In the end, the proposed method is applied to an urban area, simulation results demonstrate that the proposed method can yield optimal location and capacity of each charging station. A contrasting case is carried out to verify that improved particle swarm optimization is more effective in finding the global optimal solution than particle swarm optimization.
Access Routesgold 16 citations 16 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/wevj12040244&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu The combination of car-sharing and electric vehicles can increase the acceptance of electric vehicles and facilitate car-sharing to be a more sustainable means of transport. However, this also poses more challenges for the good planning of electric car-sharing systems. To assist car-sharing companies in improving the planning decisions, this paper developed an evaluation framework from a comprehensive view. In the first step, four evaluation criteria were identified according to the planning process: construction of stations; routine inspection; vehicle usability and relocation management; and the maintenance and replacement of stations. Then, a combinatorial method based on analytic hierarchy process (AHP), cost-benefit analysis (CBA), and Voronoi diagram (VD) is developed to determine the relative weight of the four criteria and evaluate the alternative. Finally, the evaluation framework was applied in a realistic case of EVCARD, which is the most influential electric car-sharing company in China. The performance of two different operational districts of EVCARD—Jingan and Changning—were compared. The results showed that vehicle usability and relocation management is the greatest criterion influencing the planning performance of the electric car-sharing system in China, and that routine inspection is a negligible but important factor. According to the relative scores, Jiagan District performed better than Changning district.
Access Routesgold 8 citations 8 popularity Top 10% influence Average impulse Top 10% Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/su11205627&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.euThe combination of car-sharing and electric vehicles can increase the acceptance of electric vehicles and facilitate car-sharing to be a more sustainable means of transport. However, this also poses more challenges for the good planning of electric car-sharing systems. To assist car-sharing companies in improving the planning decisions, this paper developed an evaluation framework from a comprehensive view. In the first step, four evaluation criteria were identified according to the planning process: construction of stations; routine inspection; vehicle usability and relocation management; and the maintenance and replacement of stations. Then, a combinatorial method based on analytic hierarchy process (AHP), cost-benefit analysis (CBA), and Voronoi diagram (VD) is developed to determine the relative weight of the four criteria and evaluate the alternative. Finally, the evaluation framework was applied in a realistic case of EVCARD, which is the most influential electric car-sharing company in China. The performance of two different operational districts of EVCARD—Jingan and Changning—were compared. The results showed that vehicle usability and relocation management is the greatest criterion influencing the planning performance of the electric car-sharing system in China, and that routine inspection is a negligible but important factor. According to the relative scores, Jiagan District performed better than Changning district.
Access Routesgold 8 citations 8 popularity Top 10% influence Average impulse Top 10% Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/su11205627&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu- Access Routes
gold 3 citations 3 popularity Average influence Average impulse Average Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/jmse10101470&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - Access Routes
gold 3 citations 3 popularity Average influence Average impulse Average Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/jmse10101470&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu With the popularization of electric vehicles (EVs), the out-of-order charging behaviors of large numbers of EVs will bring new challenges to the safe and economic operation of power systems. This paper studies an optimal charging strategy for EVs. For that a typical urban zone is divided into four regions, a regional time-of-use (RTOU) electricity price model is proposed to guide EVs when and where to charge considering spatial and temporal characteristics. In light of the elastic coefficient, the user response to the RTOU electricity price is analyzed, and also a bilayer optimization charging strategy including regional-layer and node-layer models is suggested to schedule the EVs. On the one hand, the regional layer model is designed to coordinate the EVs located in different time and space. On the other hand, the node layer model is built to schedule the EVs to charge in certain nodes. According to the simulations of an IEEE 33-bus distribution network, the performance of the proposed optimal charging strategy is verified. The results demonstrate that the proposed bilayer optimization strategy can effectively decrease the charging cost of users, mitigate the peak-valley load difference and the network loss. Besides, the RTOU electricity price shows better performance than the time-of-use (TOU) electricity price.
Access Routesgold 10 citations 10 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/en9090670&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.euWith the popularization of electric vehicles (EVs), the out-of-order charging behaviors of large numbers of EVs will bring new challenges to the safe and economic operation of power systems. This paper studies an optimal charging strategy for EVs. For that a typical urban zone is divided into four regions, a regional time-of-use (RTOU) electricity price model is proposed to guide EVs when and where to charge considering spatial and temporal characteristics. In light of the elastic coefficient, the user response to the RTOU electricity price is analyzed, and also a bilayer optimization charging strategy including regional-layer and node-layer models is suggested to schedule the EVs. On the one hand, the regional layer model is designed to coordinate the EVs located in different time and space. On the other hand, the node layer model is built to schedule the EVs to charge in certain nodes. According to the simulations of an IEEE 33-bus distribution network, the performance of the proposed optimal charging strategy is verified. The results demonstrate that the proposed bilayer optimization strategy can effectively decrease the charging cost of users, mitigate the peak-valley load difference and the network loss. Besides, the RTOU electricity price shows better performance than the time-of-use (TOU) electricity price.
Access Routesgold 10 citations 10 popularity Top 10% influence Top 10% impulse Top 10% Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/en9090670&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu- M. S. Hossain;
M. S. Hossain
Harvested from ORCID Public Data FileM. S. Hossain in OpenAIRE
Laveet Kumar; Laveet Kumar
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesLaveet Kumar in OpenAIRE M. M. Islam; Jeyraj Selvaraj;M. M. Islam
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesM. M. Islam in OpenAIREIn this article, the concept of an electric vehicle (EV) as a sustainable development (SD) is discussed, and the viability of the development of electric vehicles is assessed. This study broadens the conventional definition of sustainable development by incorporating and prioritizing crucial areas of technology, environment, and policy performance. The proposed review studies have summarized the elements that can promote the integration of electric vehicle technology. The innovation of the EV has just become a modern innovation. At the same time, some obstacles, such as policy and lower adoption, are resisting its goals. To overcome this situation, electric cars have to adopt some innovative approaches that can be another path to success. The review result shows that the proposal discusses the technological advancements of electric vehicles worldwide and paves the way for further improvements. The results also mentioned technological development to reduce emissions and help us understand the impact on the environment and health benefits. However, the summary would be advantageous to both scholars and policymakers, as there is a lack of integrative reviews that assess the global demand and development of EVs simultaneously and collectively. This review would provide insight for investors and policymakers to envisage electric mobility.
Access Routesgold 52 citations 52 popularity Top 10% influence Top 10% impulse Top 1% Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1155/2022/3868388&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - M. S. Hossain;
M. S. Hossain
Harvested from ORCID Public Data FileM. S. Hossain in OpenAIRE Laveet Kumar; Laveet Kumar
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesLaveet Kumar in OpenAIRE M. M. Islam; Jeyraj Selvaraj;M. M. Islam
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesM. M. Islam in OpenAIREIn this article, the concept of an electric vehicle (EV) as a sustainable development (SD) is discussed, and the viability of the development of electric vehicles is assessed. This study broadens the conventional definition of sustainable development by incorporating and prioritizing crucial areas of technology, environment, and policy performance. The proposed review studies have summarized the elements that can promote the integration of electric vehicle technology. The innovation of the EV has just become a modern innovation. At the same time, some obstacles, such as policy and lower adoption, are resisting its goals. To overcome this situation, electric cars have to adopt some innovative approaches that can be another path to success. The review result shows that the proposal discusses the technological advancements of electric vehicles worldwide and paves the way for further improvements. The results also mentioned technological development to reduce emissions and help us understand the impact on the environment and health benefits. However, the summary would be advantageous to both scholars and policymakers, as there is a lack of integrative reviews that assess the global demand and development of EVs simultaneously and collectively. This review would provide insight for investors and policymakers to envisage electric mobility.
Access Routesgold 52 citations 52 popularity Top 10% influence Top 10% impulse Top 1% Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1155/2022/3868388&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - Jinhao Meng; Guangzhao Luo;
Jinhao Meng
Harvested from ORCID Public Data FileJinhao Meng in OpenAIRE Mattia Ricco; Maciej Swierczynski; +2 AuthorsMattia Ricco
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesMattia Ricco in OpenAIRE Jinhao Meng; Guangzhao Luo;Jinhao Meng
Harvested from ORCID Public Data FileJinhao Meng in OpenAIRE Mattia Ricco; Maciej Swierczynski;Mattia Ricco
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesMattia Ricco in OpenAIRE Daniel-Ioan Stroe; Daniel-Ioan Stroe
Harvested from ORCID Public Data FileDaniel-Ioan Stroe in OpenAIRE Remus Teodorescu; Remus Teodorescu
Harvested from ORCID Public Data FileRemus Teodorescu in OpenAIREAs a critical indictor in the Battery Management System (BMS), State of Charge (SOC) is closely related to the reliable and safe operation of lithium-ion (Li-ion) batteries. Model-based methods are an effective solution for accurate and robust SOC estimation, the performance of which heavily relies on the battery model. This paper mainly focuses on battery modeling methods, which have the potential to be used in a model-based SOC estimation structure. Battery modeling methods are classified into four categories on the basis of their theoretical foundations, and their expressions and features are detailed. Furthermore, the four battery modeling methods are compared in terms of their pros and cons. Future research directions are also presented. In addition, after optimizing the parameters of the battery models by a Genetic Algorithm (GA), four typical battery models including a combined model, two RC Equivalent Circuit Model (ECM), a Single Particle Model (SPM), and a Support Vector Machine (SVM) battery model are compared in terms of their accuracy and execution time.
Access RoutesGreen gold 272 citations 272 popularity Top 0.1% influence Top 1% impulse Top 1% Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/app8050659&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - Jinhao Meng; Guangzhao Luo;
Jinhao Meng
Harvested from ORCID Public Data FileJinhao Meng in OpenAIRE Mattia Ricco; Maciej Swierczynski; +2 AuthorsMattia Ricco
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesMattia Ricco in OpenAIRE Jinhao Meng; Guangzhao Luo;Jinhao Meng
Harvested from ORCID Public Data FileJinhao Meng in OpenAIRE Mattia Ricco; Maciej Swierczynski;Mattia Ricco
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesMattia Ricco in OpenAIRE Daniel-Ioan Stroe; Daniel-Ioan Stroe
Harvested from ORCID Public Data FileDaniel-Ioan Stroe in OpenAIRE Remus Teodorescu; Remus Teodorescu
Harvested from ORCID Public Data FileRemus Teodorescu in OpenAIREAs a critical indictor in the Battery Management System (BMS), State of Charge (SOC) is closely related to the reliable and safe operation of lithium-ion (Li-ion) batteries. Model-based methods are an effective solution for accurate and robust SOC estimation, the performance of which heavily relies on the battery model. This paper mainly focuses on battery modeling methods, which have the potential to be used in a model-based SOC estimation structure. Battery modeling methods are classified into four categories on the basis of their theoretical foundations, and their expressions and features are detailed. Furthermore, the four battery modeling methods are compared in terms of their pros and cons. Future research directions are also presented. In addition, after optimizing the parameters of the battery models by a Genetic Algorithm (GA), four typical battery models including a combined model, two RC Equivalent Circuit Model (ECM), a Single Particle Model (SPM), and a Support Vector Machine (SVM) battery model are compared in terms of their accuracy and execution time.
Access RoutesGreen gold 272 citations 272 popularity Top 0.1% influence Top 1% impulse Top 1% Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/app8050659&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - Yazhu Zou; Moyan Li; Jiabo Guo;
Yazhu Zou
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesYazhu Zou in OpenAIRE Enjian Yao; +1 AuthorsEnjian Yao
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesEnjian Yao in OpenAIRE Yazhu Zou; Moyan Li; Jiabo Guo;Yazhu Zou
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesYazhu Zou in OpenAIRE Enjian Yao; Rongsheng Chen;Enjian Yao
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesEnjian Yao in OpenAIREThis study proposes a two-level optimization model system for vehicle control and signal timing at isolated signal intersections under the mixed traffic flow environment composed of intelligent connected autonomous vehicles (CAVs) and connected human-driven vehicles (CHVs), to minimize the energy consumption and vehicle delay at intersections. The proposed two-layer optimization model is composed of a two-layer vehicle trajectory control model and a fuzzy control signal timing optimization model. The two-layer vehicle trajectory control model includes a signal-oriented vehicle trajectory control model and a car-following oriented vehicle trajectory control model. The former calculates expected acceleration and speed commands at each time step according to the coming signal information, to help vehicle pass the closest signal intersection without stopping during the green light interval; the latter uses the variable headway (VTH) strategy to follow the preceding vehicle by maintaining a safe distance. A microscopic simulator based on SUMO is developed to test the performance of the proposed optimization algorithm. In the simulation experiment, with the driving characteristics of CHV drivers considered, the results show that our model performs well under a CAV penetration rate of 30%–60% and under small or moderate levels of traffic flow. The average waiting time of vehicles is reduced by about 25% compared with the uncontrolled scheme. Under the condition of penetration rate of 60%, the average energy consumption of vehicles in the proposed model is 17.56% lower than that of the uncontrolled scheme. In addition, the proposed model reduces by 21.94% compared with the scheme of only controlling vehicles. When the traffic flow is at a low or medium level, the average energy consumption and waiting time of vehicles are reduced by nearly 35% with the proposed model.
Access Routesgold 4 citations 4 popularity Average influence Average impulse Average Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1155/2023/9744737&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - Yazhu Zou; Moyan Li; Jiabo Guo;
Yazhu Zou
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesYazhu Zou in OpenAIRE Enjian Yao; +1 AuthorsEnjian Yao
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesEnjian Yao in OpenAIRE Yazhu Zou; Moyan Li; Jiabo Guo;Yazhu Zou
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesYazhu Zou in OpenAIRE Enjian Yao; Rongsheng Chen;Enjian Yao
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesEnjian Yao in OpenAIREThis study proposes a two-level optimization model system for vehicle control and signal timing at isolated signal intersections under the mixed traffic flow environment composed of intelligent connected autonomous vehicles (CAVs) and connected human-driven vehicles (CHVs), to minimize the energy consumption and vehicle delay at intersections. The proposed two-layer optimization model is composed of a two-layer vehicle trajectory control model and a fuzzy control signal timing optimization model. The two-layer vehicle trajectory control model includes a signal-oriented vehicle trajectory control model and a car-following oriented vehicle trajectory control model. The former calculates expected acceleration and speed commands at each time step according to the coming signal information, to help vehicle pass the closest signal intersection without stopping during the green light interval; the latter uses the variable headway (VTH) strategy to follow the preceding vehicle by maintaining a safe distance. A microscopic simulator based on SUMO is developed to test the performance of the proposed optimization algorithm. In the simulation experiment, with the driving characteristics of CHV drivers considered, the results show that our model performs well under a CAV penetration rate of 30%–60% and under small or moderate levels of traffic flow. The average waiting time of vehicles is reduced by about 25% compared with the uncontrolled scheme. Under the condition of penetration rate of 60%, the average energy consumption of vehicles in the proposed model is 17.56% lower than that of the uncontrolled scheme. In addition, the proposed model reduces by 21.94% compared with the scheme of only controlling vehicles. When the traffic flow is at a low or medium level, the average energy consumption and waiting time of vehicles are reduced by nearly 35% with the proposed model.
Access Routesgold 4 citations 4 popularity Average influence Average impulse Average Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1155/2023/9744737&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu Since the implementation of the sulfur cap legislation in 2020, marine very-low-sulfur fuel oil, often known as VLSFO, has become a crucial source of fuel for the contemporary shipping industry. However, both the production and utilization processes of VLSFO are plagued by the poor miscibility of the cutter fraction and the residual fraction, which can result in the precipitation of asphaltene. In this study, biodiesel was chosen as a cutter fraction to improve the stability and compatibility of asphaltene in VLSFO because of its environmental benefit and strong solubility. The average chemical structure of asphaltene derived from the marine low-sulfur fuel oil sample was analyzed using element analysis, FTIR, 1HNMR, and time-flight spectroscopy. The composition of biodiesel was analyzed using GC-MS. It was found that the asphaltene had a feature of a short side chain, low H/C ratio, high aromaticity, and a high proportion of heteroatoms. Both laboratory experiments and molecular dynamic simulations were applied to investigate the dispersion effect and mechanism compared with other dispersants. The dispersion effect of biodiesel was studied using measurements of the initial precipitation point (IPP), dispersion improvement rate, and morphology of asphaltene in the model oil. Experimental results revealed that biodiesel was fully compatible with heavy fuel oil and that it can postpone the IPP from 46% to 54% and increase the dispersion improvement rate to 35%. Molecular dynamics (MDs) simulation results show that biodiesel can form strong interactions with the fused aromatics structures and heteroatoms in the asphaltene; such interactions can increase the solubility of asphaltene and acts as a “connection bridge” to promote the dispersion effect of asphaltene molecules.
Access Routesgold 6 citations 6 popularity Top 10% influence Average impulse Top 10% Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/jmse11020315&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.euSince the implementation of the sulfur cap legislation in 2020, marine very-low-sulfur fuel oil, often known as VLSFO, has become a crucial source of fuel for the contemporary shipping industry. However, both the production and utilization processes of VLSFO are plagued by the poor miscibility of the cutter fraction and the residual fraction, which can result in the precipitation of asphaltene. In this study, biodiesel was chosen as a cutter fraction to improve the stability and compatibility of asphaltene in VLSFO because of its environmental benefit and strong solubility. The average chemical structure of asphaltene derived from the marine low-sulfur fuel oil sample was analyzed using element analysis, FTIR, 1HNMR, and time-flight spectroscopy. The composition of biodiesel was analyzed using GC-MS. It was found that the asphaltene had a feature of a short side chain, low H/C ratio, high aromaticity, and a high proportion of heteroatoms. Both laboratory experiments and molecular dynamic simulations were applied to investigate the dispersion effect and mechanism compared with other dispersants. The dispersion effect of biodiesel was studied using measurements of the initial precipitation point (IPP), dispersion improvement rate, and morphology of asphaltene in the model oil. Experimental results revealed that biodiesel was fully compatible with heavy fuel oil and that it can postpone the IPP from 46% to 54% and increase the dispersion improvement rate to 35%. Molecular dynamics (MDs) simulation results show that biodiesel can form strong interactions with the fused aromatics structures and heteroatoms in the asphaltene; such interactions can increase the solubility of asphaltene and acts as a “connection bridge” to promote the dispersion effect of asphaltene molecules.
Access Routesgold 6 citations 6 popularity Top 10% influence Average impulse Top 10% Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/jmse11020315&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu- Yupeng Yuan; Xiaoyu Wang; Liang Tong; Chengqing Yuan; +2 Authors
Yupeng Yuan
Harvested from ORCID Public Data FileYupeng Yuan in OpenAIRE Yupeng Yuan; Xiaoyu Wang; Liang Tong; Chengqing Yuan;Yupeng Yuan
Harvested from ORCID Public Data FileYupeng Yuan in OpenAIRE Boyang Shen; Boyang Shen
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesBoyang Shen in OpenAIRE Teng Long; AbstractHybrid ships have recently garnered increasing attention as a promising solution for energy shortages and environmental pollution. However, changes in the navigation environment can greatly affect the energy efficiency of these vessels. Therefore, improving the energy efficiency of hybrid ships continues to be a key issue. Our study examined the energy efficiency of a diesel‐electric series hybrid ship operating on an inland river, and a method to optimise series hybrid ship energy efficiency was proposed. The energy consumption model, power demand model, and decision function with minimum fuel consumption as the evaluation criteria were established according to the ship's characteristics and hybrid system. By analysing the data collected from an actual ship, the operating conditions of the navigation environment were identified using the k‐means algorithm, and the total distance travelled by the ship was divided into several sub‐trajectories. Afterwards, the optimised speed of the ship and the optimised output power of each power source were determined using the grasshopper optimization algorithm. The ship's energy efficiency levels before and after using the energy efficiency enhancement method were compared and analysed, and the effects of this optimization method on the ship's energy efficiency in several typical voyages were analysed. Our findings demonstrated that this optimization method can distribute the output of the power source in a better way, thereby optimising the speed of the vessel and maintaining stable sailing. More importantly, the proposed method can reduce fuel consumption by 17.04%.
Access RoutesGreen gold 2 citations 2 popularity Top 10% influence Average impulse Average Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1049/els2.12085&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - Yupeng Yuan; Xiaoyu Wang; Liang Tong; Chengqing Yuan; +2 Authors
Yupeng Yuan
Harvested from ORCID Public Data FileYupeng Yuan in OpenAIRE Yupeng Yuan; Xiaoyu Wang; Liang Tong; Chengqing Yuan;Yupeng Yuan
Harvested from ORCID Public Data FileYupeng Yuan in OpenAIRE Boyang Shen; Boyang Shen
Derived by OpenAIRE algorithms or harvested from 3rd party repositoriesBoyang Shen in OpenAIRE Teng Long; AbstractHybrid ships have recently garnered increasing attention as a promising solution for energy shortages and environmental pollution. However, changes in the navigation environment can greatly affect the energy efficiency of these vessels. Therefore, improving the energy efficiency of hybrid ships continues to be a key issue. Our study examined the energy efficiency of a diesel‐electric series hybrid ship operating on an inland river, and a method to optimise series hybrid ship energy efficiency was proposed. The energy consumption model, power demand model, and decision function with minimum fuel consumption as the evaluation criteria were established according to the ship's characteristics and hybrid system. By analysing the data collected from an actual ship, the operating conditions of the navigation environment were identified using the k‐means algorithm, and the total distance travelled by the ship was divided into several sub‐trajectories. Afterwards, the optimised speed of the ship and the optimised output power of each power source were determined using the grasshopper optimization algorithm. The ship's energy efficiency levels before and after using the energy efficiency enhancement method were compared and analysed, and the effects of this optimization method on the ship's energy efficiency in several typical voyages were analysed. Our findings demonstrated that this optimization method can distribute the output of the power source in a better way, thereby optimising the speed of the vessel and maintaining stable sailing. More importantly, the proposed method can reduce fuel consumption by 17.04%.
Access RoutesGreen gold 2 citations 2 popularity Top 10% influence Average impulse Average Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1049/els2.12085&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - Laijin Luo; Chaolong Zhang; Youhui Tian;
Chaolong Zhang
Harvested from ORCID Public Data FileChaolong Zhang in OpenAIRE Huihan Liu; Huihan Liu
Harvested from ORCID Public Data FileHuihan Liu in OpenAIREAn accurate state-of-health (SOH) estimation is vital to guarantee the safety and reliability of a lithium-ion battery management system. In application, the electrical vehicles generally start charging when the battery is at a non-zero state of charge (SOC), which will influence the charging current, voltage and duration, greatly hindering many traditional health features to estimate the SOH. However, the constant voltage charging phase is not limited by the previous non-zero SOC starting charge. In order to overcome the difficulty, a method of estimating the battery SOH based on the information entropy of battery currents of the constant voltage charging phase and charging duration is proposed. Firstly, the time series of charging current data from the constant voltage phase are measured, and then the information entropy of battery currents and charging time are calculated as new indicators. The penalty coefficient and width factor of a support vector machine (SVM) improved by the sparrow search algorithm is utilized to establish the underlying mapping relationships between the current entropy, charging duration and battery SOH. Additionally, the results indicate the adaptability and effectiveness of the proposed approach for a battery pack and cell SOH estimation.
Access Routesgold 7 citations 7 popularity Top 10% influence Average impulse Top 10% Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/wevj13080148&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu - Laijin Luo; Chaolong Zhang; Youhui Tian;
Chaolong Zhang
Harvested from ORCID Public Data FileChaolong Zhang in OpenAIRE Huihan Liu; Huihan Liu
Harvested from ORCID Public Data FileHuihan Liu in OpenAIREAn accurate state-of-health (SOH) estimation is vital to guarantee the safety and reliability of a lithium-ion battery management system. In application, the electrical vehicles generally start charging when the battery is at a non-zero state of charge (SOC), which will influence the charging current, voltage and duration, greatly hindering many traditional health features to estimate the SOH. However, the constant voltage charging phase is not limited by the previous non-zero SOC starting charge. In order to overcome the difficulty, a method of estimating the battery SOH based on the information entropy of battery currents of the constant voltage charging phase and charging duration is proposed. Firstly, the time series of charging current data from the constant voltage phase are measured, and then the information entropy of battery currents and charging time are calculated as new indicators. The penalty coefficient and width factor of a support vector machine (SVM) improved by the sparrow search algorithm is utilized to establish the underlying mapping relationships between the current entropy, charging duration and battery SOH. Additionally, the results indicate the adaptability and effectiveness of the proposed approach for a battery pack and cell SOH estimation.
Access Routesgold 7 citations 7 popularity Top 10% influence Average impulse Top 10% Powered by BIP!add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.<script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/wevj13080148&type=result"></script>'); --> </script>For further information contact us at helpdesk@openaire.eu
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