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1
Shi, Jian, Bin Liu, Yong He Huang, and Hua Liang Hou. "Forecast on China's New Energy Vehicle Market Demand." Applied Mechanics and Materials 496-500 (January 2014): 2822–26. http://dx.doi.org/10.4028/www.scientific.net/amm.496-500.2822.
Повний текст джерелаАнотація:
With the rapid development of new energy vehicle in China, the volume has been the hot topic in the fields of automotive industry. A series of subsidy and financial policies has been released by the government. Peoples in this industry care about the effective of the policies especially the new energy vehicles volume and market share in China. In this paper, we analysis the development experience of developed countries such as the US and Japan, and calculate the new energy vehicles volume and market share in China from 2015 to 2020 by model. Its more effective to the government department to draw a plan of new energy vehicle development blue print.
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Jardin, Philippe, Arved Esser, Stefano Givone, Tobias Eichenlaub, Jean-Eric Schleiffer, and Stephan Rinderknecht. "The Sensitivity in Consumption of Different Vehicle Drivetrain Concepts Under Varying Operating Conditions: A Simulative Data Driven Approach." Vehicles 1, no. 1 (March 14, 2019): 69–87. http://dx.doi.org/10.3390/vehicles1010005.
Повний текст джерелаАнотація:
As an important aspect of today’s efforts to reduce greenhouse gas emissions, the energy demand of passenger cars is a subject of research. Different drivetrain concepts like plug-in hybrid electric vehicles (PHEV) and battery electric vehicles (BEV) are introduced into the market in addition to conventional internal combustion engine vehicles (ICEV) to address this issue. However, the consumption highly depends on individual usage profiles and external operating conditions, especially when considering secondary energy demands like heating, ventilation and air conditioning (HVAC). The approach presented in this work aims to estimate vehicle consumptions based on real world driving profiles and weather data under consideration of secondary demands. For this purpose, a primary and a secondary consumption model are developed that interact with each other to estimate realistic vehicle consumptions for different drivetrain concepts. The models are parametrized by referring to state of the art contributions and the results are made plausible by comparison to literature. The sensitivities of the consumptions are then analysed as a function of trip distance and ambient temperature to assess the influence of the operating conditions on the consumption. The results show that especially in the case of the BEV and PHEV, the trip distance and the ambient temperature are a first-order influencing factor on the total vehicle energy demand. Thus, it is not sufficient to evaluate new vehicle concepts solely on one-dimensional driving cycles to assess their energy demand. Instead, the external conditions must be taken into account for a proper assessment of the vehicle’s real world consumption.
3
Chen, Yuche, Ruixiao Sun, and Xuanke Wu. "Estimating Bounds of Aerodynamic, Mass, and Auxiliary Load Impacts on Autonomous Vehicles: A Powertrain Simulation Approach." Sustainability 13, no. 22 (November 10, 2021): 12405. http://dx.doi.org/10.3390/su132212405.
Повний текст джерелаАнотація:
Vehicle automation requires new onboard sensors, communication equipment, and/or data processing units, and may encourage modifications to existing onboard components (such as the steering wheel). These changes impact the vehicle’s mass, auxiliary load, coefficient of drag, and frontal area, which then change vehicle performance. This paper uses the powertrain simulation model FASTSim to quantify the impact of autonomy-related design changes on a vehicle’s fuel consumption. Levels 0, 2, and 5 autonomous vehicles are modeled for two battery-electric vehicles (2017 Chevrolet Bolt and 2017 Nissan Leaf) and a gasoline powered vehicle (2017 Toyota Corolla). Additionally, a level 5 vehicle is divided into pessimistic and optimistic scenarios which assume different electronic equipment integration format. The results show that 4–8% reductions in energy economy can be achieved in a L5 optimistic scenario and an 10–15% increase in energy economy will be the result in a L5 pessimistic scenario. When looking at impacts on different power demand sources, inertial power is the major power demand in urban driving conditions and aerodynamic power demand is the major demand in highway driving conditions.
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Pan, Xiaoming, Yong Wu, and Gao Chong. "Multipoint Distribution Vehicle Routing Optimization Problem considering Random Demand and Changing Load." Security and Communication Networks 2022 (July 8, 2022): 1–10. http://dx.doi.org/10.1155/2022/8199991.
Повний текст джерелаАнотація:
In the distribution scenario, the using cost of vehicles is closely related to energy consumption, and the energy consumption rate of a vehicle is closely related to the size of its load. The traditional vehicle routing optimization model takes the shortest distance as the optimization goal when the customer demand is determined, while the influence of the random demand and the changing load on the energy consumption and cost of vehicles in the process of distribution is ignored. Therefore, in this paper, load varying vehicle routing problem with stochastic demands (LVGVRPSD) model is proposed with the goal of minimizing transportation energy consumption and considering the load variability and the randomness of customer demand. K-means clustering algorithm is combined with ant colony optimization (ACO) to solve the problem, and the constraint of risk probability is introduced to describe the vehicle overload problem. Examples in the standard vehicle routing problem test data set are provided and analyzed. LVGVRPSD is also compared with the traditional capacitated vehicle routing problem (CVRP) model. The case study results show that the vehicle energy consumption can be reduced by 2% in the model that considers changing load compared to the model that does not consider changing load. The results illustrate that the method of path optimization is more advantageous and reasonable in the pursuit of reducing energy consumption, when the changing load and the random demand of customer are considered.
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Waldron, Julie, Lucelia Rodrigues, Mark Gillott, Sophie Naylor, and Rob Shipman. "The Role of Electric Vehicle Charging Technologies in the Decarbonisation of the Energy Grid." Energies 15, no. 7 (March 26, 2022): 2447. http://dx.doi.org/10.3390/en15072447.
Повний текст джерелаАнотація:
Vehicle-to-grid (V2G) has been identified as a key technology to help reduce carbon emissions from the transport and energy sectors. However, the benefits of this technology are best achieved when multiple variables are considered in the process of charging and discharging an electric vehicle. These variables include vehicle behaviour, building energy demand, renewable energy generation, and grid carbon intensity. It is expected that the transition to electric mobility will add pressure to the energy grid. Using the batteries of electric vehicles as energy storage to send energy back to the grid during high-demand, carbon-intensive periods will help to reduce the impact of introducing electric vehicles and minimise carbon emissions of the system. In this paper, the authors present a method and propose a V2G control scheme integrating one year of historical vehicle and energy datasets, aiming towards carbon emissions reduction through increased local consumption of renewable energy, offset of vehicle charging demand to low carbon intensity periods, and offset of local building demand from peak and carbon-intensive periods through storage in the vehicle battery. The study included assessment of strategic location and the number of chargers to support a fleet of five vehicles to make the transition to electric mobility and integrate vehicle-to-grid without impacting current service provision. The authors found that the proposed V2G scheme helped to reduce the average carbon intensity per kilowatt (gCO2/kWh) in simulation scenarios, despite the increased energy demand from electric vehicles charging. For instance, in one of the tested scenarios V2G reduced the average carbon intensity per kilowatt from 223.8 gCO2/kWh with unmanaged charging to 218.9 gCO2/kWh using V2G.
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Feng, Ziru, Tian Cai, Kangli Xiang, Chenxi Xiang, and Lei Hou. "Evaluating the Impact of Fossil Fuel Vehicle Exit on the Oil Demand in China." Energies 12, no. 14 (July 19, 2019): 2771. http://dx.doi.org/10.3390/en12142771.
Повний текст джерелаАнотація:
Vehicle ownership is one of the most important factors affecting fuel demand. Based on the forecast of China’s vehicle ownership, this paper estimates China’s fuel demand in 2035 and explores the impact of new energy vehicles replacing fossil fuel vehicles. The paper contributes to the existing literature by taking into account the heterogeneity of provinces when using the Gompertz model to forecast future vehicle ownership. On that basis, the fuel demand of each province in 2035 is calculated. The results show that: (1) The vehicle ownership rate of each province conforms to the S-shape trend with the growth of real GDP per capita. At present, most provinces are at a stage of accelerating growth. However, the time for the vehicle ownership rate of each province to reach the inflection point is quite different. (2) Without considering the replacement of new energy vehicles, China’s auto fuel demand is expected to be 746.69 million tonnes (Mt) in 2035. Guangdong, Henan, and Shandong are the top three provinces with the highest fuel demand due to economic and demographic factors. The fuel demand is expected to be 76.76, 64.91, and 63.95 Mt, respectively. (3) Considering the replacement of new energy vehicles, China’s fuel demand in 2035 will be 709.35, 634.68, and 560.02 Mt, respectively, under the scenarios of slow, medium, and fast substitution—and the replacement levels are 37.34, 112.01, and 186.67 Mt, respectively. Under the scenario of rapid substitution, the reduction in fuel demand will reach 52.2% of China’s net oil imports in 2016. Therefore, the withdrawal of fuel vehicles will greatly reduce the oil demand and the dependence on foreign oil of China. Faced with the dual pressure of environmental crisis and energy crisis, the forecast results of this paper provide practical reference for policy makers to rationally design the future fuel vehicle exit plan and solve related environmental issues.
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Wang, Junmin. "Energy Consumption and Tailpipe Emission Reductions by Personalized Control of Connected Vehicles." Mechanical Engineering 139, no. 09 (September 1, 2017): S5—S11. http://dx.doi.org/10.1115/1.2017-sep-4.
Повний текст джерелаАнотація:
This article demonstrates several approaches to the vehicle energy consumption and tailpipe emission reduction opportunities. The article leverages the vehicle storage dynamics through smart and personalized optimization and control approaches in the context of connected vehicles. Recent advances in vehicle connectivity and automation have brought unprecedented information richness and new degrees of freedom that can be synergized with insightful understanding of vehicle powertrain and aftertreatment physical systems. Vehicle automation also provides new degrees of freedom that can be further leveraged by the vehicle control systems to improve vehicle energy efficiency and reduce tailpipe emissions. While vehicle automation levels probably will keep increasing, humans will still be involved in vehicle operations at various levels for the foreseeable future. The prediction of future vehicle’s power demand based on vehicle connectivity can significantly benefit tailpipe emission reductions and fuel economy.
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Khan Ankur, Atiquzzaman, Stefan Kraus, Thomas Grube, Rui Castro, and Detlef Stolten. "A Versatile Model for Estimating the Fuel Consumption of a Wide Range of Transport Modes." Energies 15, no. 6 (March 18, 2022): 2232. http://dx.doi.org/10.3390/en15062232.
Повний текст джерелаАнотація:
The importance of a flexible and comprehensive vehicle fuel consumption model cannot be understated for understanding the implications of the modal changes currently occurring in the transportation sector. In this study, a model is developed to determine the tank-to-wheel energy demand for passenger and freight transportation within Germany for different modes of transport. These modes include light-duty vehicles (LDVs), heavy-duty vehicles (HDVs), airplanes, trains, ships, and unmanned aviation. The model further estimates future development through 2050. Utilizing standard driving cycles, backward-looking longitudinal vehicle models are employed to determine the energy demand for all on-road vehicle modes. For non-road vehicle modes, energy demand from the literature is drawn upon to develop the model. It is found that various vehicle parameters exert different effects on vehicle energy demand, depending on the driving scenario. Public transportation offers the most energy-efficient means of travel in the forms of battery electric buses (33.9 MJ/100 pkm), battery electric coaches (21.3 MJ/100 pkm), fuel cell electric coaches (32.9 MJ/100 pkm), trams (43.3 MJ/100 pkm), and long-distance electric trains (31.8 MJ/100 pkm). International shipping (9.9 MJ/100 tkm) is the most energy-efficient means of freight transport. The electrification of drivetrains and the implementation of regenerative braking show large potential for fuel consumption reduction, especially in urban areas. Occupancy and loading rates for vehicles play a critical role in determining the energy demand per passenger-kilometer for passenger modes, and tonne-kilometer for freight modes.
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Kubendran, V., Y. Mohamed Shuaib, and J. Preetha Roselyn. "Modelling of Vehicle Dynamics and Determination of Energy Demand for Electric Vehicle." Journal of Physics: Conference Series 2335, no. 1 (September 1, 2022): 012049. http://dx.doi.org/10.1088/1742-6596/2335/1/012049.
Повний текст джерелаАнотація:
Abstract The WLTP Class 3 driving cycle is used in this article for the design of a battery and super-capacitor for electric vehicles. The energy demand for electric vehicle is calculated using WLTP drive cycle and the total power required for electric vehicle is calculated by calculating tractive force. A hybrid energy storage system (HESS) overcomes numerous shortcomings of a battery energy storage system (BESS), including reduced battery life, limited power density, etc. In a proposed system, a Li-Ion battery is coupled with a super-capacitor/Ultra-capacitor as a bidirectional converter, where the Li-Ion battery is the primary energy source, while the Super Capacitor/Ultra-capacitor is an auxiliary energy source. The range of battery and Super Capacitor/ultra-capacitor sizes for Tata Nixon 2020 are calculated and simulated using the MATLAB/SIMULINK environment to verify the efficiency and effectiveness of the proposed model.
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Qu, Lu, and Yanwei Li. "Research on Industrial Policy from the Perspective of Demand-Side Open Innovation—A Case Study of Shenzhen New Energy Vehicle Industry." Journal of Open Innovation: Technology, Market, and Complexity 5, no. 2 (May 28, 2019): 31. http://dx.doi.org/10.3390/joitmc5020031.
Повний текст джерелаАнотація:
Nowadays, new energy vehicles play an important role in the transformation and upgrade of China’s energy security, energy conservation and other industries. At present, there are 26 pilot cities for the demonstration of new energy vehicles in China; however, the operation effect and experience of the pilot cities have been summarized less. This paper takes Shenzhen’s new energy vehicle industry policy as the object of research, in order to explore the impact of demand innovation on the development of new energy vehicles. This paper summarizes the three stages of Shenzhen’s new energy vehicle industry promotion, and further analyzes the policy and market environments of each stage by using the demand-side innovation policy theory. By reflecting on the concept of policy design, this paper proposes that decision makers need to cultivate open innovative thinking, and transform their production-oriented policy design into a demand-oriented policy design. This conclusion is helpful for pilot cities in order to adjust their policies over time according to the different stages of industrial development, and further improve the innovation and competitiveness of China’s new energy vehicle industry.
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Gu, Jinhui, and Chunlin Guo. "New energy vehicles taking into account user needs participate in the FM model." Journal of Physics: Conference Series 2247, no. 1 (April 1, 2022): 012010. http://dx.doi.org/10.1088/1742-6596/2247/1/012010.
Повний текст джерелаАнотація:
Abstract In this paper, a new energy vehicle participation FM model considering user needs is established and verified by simulation. It has been proved that the model can guarantee user travel needs. The new energy vehicle participation FM model is divided into three parts: energy storage calculation, power constraint and energy constraint. Energy storage calculation is to model and analyse the available energy of new energy vehicles participating in FM, and get the variation of participating FM capacity in a day considering the user SOC demand. Power constraint is the charge and discharge power constraint of new energy vehicles, which is the necessary condition to ensure that new energy vehicles do not violate the technical conditions of new energy vehicles and ensure that the energy constraint of user demand participates in frequency modulation.
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Li, Yong, Fuyong Liu, and Ruimin Hao. "Scenario demand-based design of new energy vehicles from the inside out and its practices." Journal of Physics: Conference Series 2235, no. 1 (May 1, 2022): 012081. http://dx.doi.org/10.1088/1742-6596/2235/1/012081.
Повний текст джерелаАнотація:
Abstract Based on the related theories of scenario study and pure electric vehicle interior design, this paper constructs scenario demand-based interior design process framework and method for pure electric vehicles. After completing construction of the design process framework and method, corresponding design practice is carried out to complete theoretical verification. The demands are sorted through KANO model. Then, a design framework is built according to the demand sorting result. Finally, specific program is designed based on the design framework. This paper perfects the theoretical system for interior design of pure electric vehicles. The interior design program for future pure electric vehicles will provide design references for interior designers.
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Sumanasena, Vidura, Lakshitha Gunasekara, Sachin Kahawala, Nishan Mills, Daswin De Silva, Mahdi Jalili, Seppo Sierla, and Andrew Jennings. "Artificial Intelligence for Electric Vehicle Infrastructure: Demand Profiling, Data Augmentation, Demand Forecasting, Demand Explainability and Charge Optimisation." Energies 16, no. 5 (February 26, 2023): 2245. http://dx.doi.org/10.3390/en16052245.
Повний текст джерелаАнотація:
Electric vehicles (EVs) are advancing the transport sector towards a robust and reliable carbon-neutral future. Given this increasing uptake of EVs, electrical grids and power networks are faced with the challenges of distributed energy resources, specifically the charge and discharge requirements of the electric vehicle infrastructure (EVI). Simultaneously, the rapid digitalisation of electrical grids and EVs has led to the generation of large volumes of data on the supply, distribution and consumption of energy. Artificial intelligence (AI) algorithms can be leveraged to draw insights and decisions from these datasets. Despite several recent work in this space, a comprehensive study of the practical value of AI in charge-demand profiling, data augmentation, demand forecasting, demand explainability and charge optimisation of the EVI has not been formally investigated. The objective of this study was to design, develop and evaluate a comprehensive AI framework that addresses this gap in EVI. Results from the empirical evaluation of this AI framework on a real-world EVI case study confirm its contribution towards addressing the emerging challenges of distributed energy resources in EV adoption.
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Oyedeji, Mojeed O., Mujahed AlDhaifallah, Hegazy Rezk, and Ahmed Ali A. Mohamed. "Computational Models for Forecasting Electric Vehicle Energy Demand." International Journal of Energy Research 2023 (February 3, 2023): 1–16. http://dx.doi.org/10.1155/2023/1934188.
Повний текст джерелаАнотація:
Electric vehicles (EV) are fast becoming an integral part of our evolving society. There is a growing movement in advanced countries to replace gas-driven vehicles with EVs towards cutting down pollution from emissions. When fully integrated into society, electric vehicles will share from energy available on the grid; therefore, it is important to understand consumption profiles for EVs. In this study, some computation models are developed from predicting day-ahead energy consumption for electric vehicles in the city of Barcelona. Five different machine learning algorithms namely support vector regression (SVR), Gaussian process regression (GPR), artificial neural networks (ANN), decision tree (DT), and ensemble learners were used to train the forecasting models. The hyperparameters for each of the ML algorithms were tuned by Bayesian optimization algorithm. In order to propose efficient features for modeling EV demand, two different model structures were investigated, named Type-I and Type-II model. In the instance of the Type-I model, seven regressors representing the consumption of the previous seven days were considered as input features. The Type-II models considered only the EV consumption on the previous day and on the same day in the previous week. Based on the results in this study, we find that the performance of the Type-II models was as good as the Type-I models across all the algorithms considered although less input features were considered. Overall, the all algorithms employed in this study gave about 75-80% model accuracy based on the R 2 performance criterion. The models formulated in this study may prove useful for planning and unit commitment functions in city energy management functions.
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Vani, Bakul, Devyani Chaturvedi, and Preeti Yadav. "Grid Management through Vehicle-To-Grid Technology." International Journal of Recent Technology and Engineering (IJRTE) 10, no. 2 (July 30, 2021): 5–9. http://dx.doi.org/10.35940/ijrte.b6036.0710221.
Повний текст джерелаАнотація:
This research paper is based on a project which is a prototype on a smaller level of integrating vehicle-to-grid technology at the residential and commercial levels which can be expanded in future with the help of bi-directional AC-DC power converters and Control systems. Vehicle-to-Grid is a technology that allows energy to be supplied back to the power grid from the battery of an electric car for fulfilling the excess demand on the grid. It is depicted in the prototype with the help of TP charging module and embedded system i.e. Arduino to manage the ever-increasing energy demand from the grid. With the increasing environmental problems, modern automobile technology is innovating in the field of Electric Vehicles (EV) with lesser pollution and better efficiency. This has attracted a lot of attention, but the major hindrance faced is the availability of energy required to maintain the grid is resonance. We can overcome this by vehicle-to-grid technology in smart parking systems. When the EV is parked, energy may be drawn out or supplied to the EV through the grid depending upon the requirements of the grid and the vehicle’s battery. The implementation of this technology enables the stored energy in the electric vehicle to be transferred to the power grid and vice-versa.
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Islam, Ehsan Sabri, Shabbir Ahmed, and Aymeric Rousseau. "Future Battery Material Demand Analysis Based on U.S. Department of Energy R&D Targets." World Electric Vehicle Journal 12, no. 3 (June 25, 2021): 90. http://dx.doi.org/10.3390/wevj12030090.
Повний текст джерелаАнотація:
The U.S. Department of Energy’s Vehicle Technologies Office (VTO) supports research, development, and deployment of efficient, sustainable transportation technologies that will improve energy efficiency and fuel economy, and enable America to use less petroleum. To accelerate the development and adoption of new technologies, VTO has developed specific targets for a wide range of powertrain components, including the energy storage system. In this study, we use Autonomie, Argonne National Laboratory’s (Argonne’s) vehicle system simulation tool to evaluate future energy storage requirements (power, energy, etc.) for different vehicle classes, powertrains, component technologies and timeframes. BatPac, Argonne’s tool dedicated to energy storage pack design and costs, is then used to quantify the materials required for each pack. Market penetrations are then used to estimate the overall material demand worldwide and in the United States, with or without recycling. The results demonstrate that the positive impact of VTO research and development will lead to significant reduction in material compared to business-as-usual due to new anode and cathode designs, along with acceleration in battery cell chemistry penetrations. In terms of material demands, it is observed that lithium demand reaches about 80,000 tons (by a factor of 42–45), nickel demand reaches about 500,000 tons (by a factor of 47–56), manganese demand reaches about 30,000–50,000 tons (by a factor of 20–34), and cobalt demand reaches about 30,000 tons (by a factor of 13–28) in the future by 2050. The individual material demand per unit energy, however, decreases significantly in the future due to advances in VTO research and development activities. The increase in battery material demands is mostly driven by increased electrified vehicle fleet penetration in the markets.
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Agrawal, Himanshi, Akash Talwariya, Amandeep Gill, Aman Singh, Hashem Alyami, Wael Alosaimi, and Arturo Ortega-Mansilla. "A Fuzzy-Genetic-Based Integration of Renewable Energy Sources and E-Vehicles." Energies 15, no. 9 (April 30, 2022): 3300. http://dx.doi.org/10.3390/en15093300.
Повний текст джерелаАнотація:
E-Vehicles are used for transportation and, with a vehicle-to-grid optimization approach, they may be used for supplying a backup source of energy for renewable energy sources. Renewable energy sources are integrated to maintain the demand of consumers, mitigate the active and reactive power losses, and maintain the voltage profile. Renewable energy sources are not supplied all day and, to meet the peak demand, extra electricity may be supplied through e-Vehicles. E-Vehicles with random integration may cause system unbalancing problems and need a solution. The objective of this paper is to integrate e-Vehicles with the grid as a backup source of energy through the grid-to-vehicle optimization approach by reducing active and reactive power losses and maintaining voltage profile. In this paper, three case studies are discussed: (i) integration of renewable energy sources alone; (ii) integration of e-Vehicles alone; (iii) integration of renewable energy sources and e-Vehicles in hybrid mode. The simulation results show the effectiveness of the integration and the active and reactive power losses are minimum when we used the third case.
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Zeng, Lin Hui, Guang Ming Li, and Song Li. "Modeling Energy Demand and Carbon Emissions from Transport in Shanghai." Advanced Materials Research 997 (August 2014): 736–39. http://dx.doi.org/10.4028/www.scientific.net/amr.997.736.
Повний текст джерелаАнотація:
Private vehicle traffic is among the main contributors to anthropogenic carbon emissions. Efforts have been made to control private vehicle in major cities around the world to mitigate carbon emissions from transport sector. The aim of the paper was to find policy implications for energy saving and carbon reduction in transportation in the future. Gompertz model and Spread Sheet model were applied in this paper to estimates energy consumption and carbon emissions from Shanghai’s vehicle transport in the future. Results showed that vehicle ownership of the city will reach 6.15 million in 2030. Under BAU scenario, CO2 emissions from Shanghai’s vehicle will reach 34.163 million t in 2030. While under the FEP scenario, gasoline demand will reduce by 30% compared with the baseline scenario. It shows that fuel economy policy is essential in energy saving and carbon reduction in vehicle sector.
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Hogeveen, Peter, Maarten Steinbuch, Geert Verbong, and Auke Hoekstra. "Quantifying the Fleet Composition at Full Adoption of Shared Autonomous Electric Vehicles: An Agent-based Approach." Open Transportation Journal 15, no. 1 (May 17, 2021): 47–60. http://dx.doi.org/10.2174/1874447802115010047.
Повний текст джерелаАнотація:
Aims: Exploring the impact of full adoption of fit-for-demand shared and autonomous electric vehicles on the passenger vehicle fleet of a society. Background: Shared Eutonomous Electric Vehicles (SAEVs) are expected to have a disruptive impact on the mobility sector. Reduced cost for mobility and increased accessibility will induce new mobility demand and the vehicles that provide it will be fit-for-demand vehicles. Both these aspects have been qualitatively covered in recent research, but there have not yet been attempts to quantify fleet compositions in scenarios where passenger transport is dominated by fit-for-demand, one-person autonomous vehicles. Objective: To quantify the composition of the future vehicle fleet when all passenger vehicles are autonomous, shared and fit-for-demand and where cheap and accessible mobility has significantly increased the mobility demand. Methods: An agent-based model is developed to model detailed travel dynamics of a large population. Numerical data is used to mimic actual driving motions in the Netherlands. Next, passenger vehicle trips are changed to trips with fit-for-demand vehicles, and new mobility demand is added in the form of longer tips, more frequent trips, modal shifts from public transport, redistribution of shared vehicles, and new user groups. Two scenarios are defined for the induced mobility demand from SAEVs, one scenario with limited increased mobility demand, and one scenario with more than double the current mobility demand. Three categories of fit-for-demand vehicles are stochastically mapped to all vehicle trips based on each trip's characteristics. The vehicle categories contain two one-person vehicle types and one multi-person vehicle type. Results: The simulations show that at full adoption of SAEVs, the maximum daily number of passenger vehicles on the road increases by 60% to 180%. However, the total fleet size could shrink by up to 90% if the increase in mobility demand is limited. An 80% reduction in fleet size is possible at more than doubling the current mobility demand. Additionally, about three-quarters of the SAEVs can be small one-person vehicles. Conclusion: Full adoption of fit-for-demand SAEVs is expected to induce new mobility demand. However, the results of this research indicate that there would be 80% to 90% less vehicles required in such a situation, and the vast majority would be one-person vehicles. Such vehicles are less resource-intense and, because of their size and electric drivetrains, are significantly more energy-efficient than the average current-day vehicle. This research indicates the massive potential of SAEVs to lower both the cost and the environmental impact of the mobility sector. Quantification of these environmental benefits and reduced mobility costs are proposed for further research.
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Drabek, Pavel, and Lubos Streit. "The Energy Storage System for Light Trails Applications Based on the Supercapacitors." Applied Mechanics and Materials 284-287 (January 2013): 1141–45. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.1141.
Повний текст джерелаАнотація:
This paper presents research motivated by industrial demand for energy storage system for city transport vehicles. The kinetic energy is accumulated into the supercapacitor during vehicle braking. This energy can be used to accelerating in next time. It is important to save the energy in the vehicles, which accelerate very often.
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Dvorak, Dominik, Daniele Basciotti, and Imre Gellai. "Demand-Based Control Design for Efficient Heat Pump Operation of Electric Vehicles." Energies 13, no. 20 (October 19, 2020): 5440. http://dx.doi.org/10.3390/en13205440.
Повний текст джерелаАнотація:
Thermal management systems of passenger vehicles are fundamental to provide adequate cabin thermal comfort. However, for battery electric vehicles they can use a significant amount of battery energy and thus reduce the real driving range. Indeed, when heating or cooling the vehicle cabin the thermal management system can consume up to 84% of the battery capacity. This study proposes a model-based approach to design an energy-efficient control strategy for heating electric vehicles, considering the entire climate control system at different ambient conditions. Specifically, the study aims at reducing the energy demand of the compressor and water pumps when operating in heat pump mode. At this scope, the climate control system of the reference vehicle is modelled and validated, enabling a system efficiency analysis in different operating points. Based on the system performance assessment, the optimized operating strategy for the compressor and the water pumps is elaborated and the results show that the demand-based control achieves up to 34% energy reduction when compared to the standard control.
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Çodur, Muhammed Yasin, and Ahmet Ünal. "An Estimation of Transport Energy Demand in Turkey via Artificial Neural Networks." PROMET - Traffic&Transportation 31, no. 2 (March 26, 2019): 151–61. http://dx.doi.org/10.7307/ptt.v31i2.3041.
Повний текст джерелаАнотація:
The transportation sector accounts for nearly 19% of total energy consumption in Turkey, where energy demand increases rapidly depending on the economic and human population growth and the increasing number of motor vehicles. Hence, the estimation of future energy demand is of great importance to design, plan and use the transportation systems more efficiently, for which a reliable quantitative estimation is of primary concern. However, the estimation of transport energy demand is a complex task, since various model parameters are interacting with each other. In this study, artificial neural networks were used to estimate the energy demand in transportation sector in Turkey. Gross domestic product, oil prices, population, vehicle-km, ton-km and passenger-km were selected as parameters by considering the data for the period from 1975 to 2016. Seven models in total were created and analyzed. The best yielding model with the parameters of oil price, population and motor vehicle-km was determined to have the lowest error and the highest R2 values. This model was selected to estimate transport energy demand for the years 2020, 2023, 2025 and 2030.
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Gnann, Till, Daniel Speth, Michael Krail, Martin Wietschel, and Stella Oberle. "Pathways to Carbon-Free Transport in Germany until 2050." World Electric Vehicle Journal 13, no. 8 (July 28, 2022): 136. http://dx.doi.org/10.3390/wevj13080136.
Повний текст джерелаАнотація:
The transport sector has to be widely decarbonized by 2050 to reach the targets of the Paris Agreement. This can be performed with different drive trains and energy carriers. This paper explored four pathways to a carbon-free transport sector in Germany in 2050 with foci on electricity, hydrogen, synthetic methane, or liquid synthetic fuels. We used a transport demand model for future vehicle use and a simulation model for the determination of alternative fuel vehicle market shares. We found a large share of electric vehicles in all scenarios, even in the scenarios with a focus on other fuels. In all scenarios, the final energy consumption decreased significantly, most strongly when the focus was on electricity and almost one-third lower in primary energy demand compared with the other scenarios. A further decrease of energy demand is possible with an even faster adoption of electric vehicles, yet fuel cost then has to be even higher or electricity prices lower.
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Wulff, Niklas, Felix Steck, Hans Christian Gils, Carsten Hoyer-Klick, Bent van den Adel, and John E. Anderson. "Comparing Power-System and User-Oriented Battery Electric Vehicle Charging Representation and Its Implications on Energy System Modeling." Energies 13, no. 5 (March 2, 2020): 1093. http://dx.doi.org/10.3390/en13051093.
Повний текст джерелаАнотація:
Battery electric vehicles (BEV) provide an opportunity to balance supply and demand in future power systems with high shares of fluctuating renewable energy. Compared to other storage systems such as pumped-storage hydroelectricity, electric vehicle energy demand is highly dependent on charging and connection choices of vehicle users. We present a model framework of a utility-based stock and flow model, a utility-based microsimulation of charging decisions, and an energy system model including respective interfaces to assess how the representation of battery electric vehicle charging affects energy system optimization results. We then apply the framework to a scenario study for controlled charging of nine million electric vehicles in Germany in 2030. Assuming a respective fleet power demand of 27 TWh, we analyze the difference between power-system-based and vehicle user-based charging decisions in two respective scenarios. Our results show that taking into account vehicle users’ charging and connection decisions significantly decreases the load shifting potential of controlled charging. The analysis of marginal values of equations and variables of the optimization problem yields valuable insights on the importance of specific constraints and optimization variables. Assumptions on fleet battery availability and a detailed representation of fast charging are found to have a strong impact on wind curtailment, renewable energy feed-in, and required gas power plant flexibility. A representation of fleet connection to the grid in high temporal detail is less important. Peak load can be reduced by 5% and 3% in both scenarios, respectively. Shifted load is robust across sensitivity analyses while other model results such as curtailment are more sensitive to factors such as underlying data years. Analyzing the importance of increased BEV fleet battery availability for power systems with different weather and electricity demand characteristics should be further scrutinized.
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Li, Shufeng, Qiang Yao, Zhankun Xu, Jianwei Gao, and Yu Yang. "Based on Prospect Theory Regional Integrated Energy Electric Vehicle Scheduling Model." E3S Web of Conferences 299 (2021): 01015. http://dx.doi.org/10.1051/e3sconf/202129901015.
Повний текст джерелаАнотація:
Regional comprehensive energy is the focus of current research, and electric vehicles are an important part of regional energy. The orderly participation of regional EV groups in demand response for optimal scheduling of charge and discharge can not only save the charging cost of EV owners, but also smooth the load fluctuation caused by EV charging. In this paper, an Integrated Energy Electric Vehicle Scheduling Model Based on Prospect Theory is proposed. Firstly, the optimal charging and discharging strategy of each Electric Vehicle is obtained Based on the price demand response Model. Secondly, a decision-making method of participation willingness based on the prospect theory is proposed to consider the risk bias of EV owners. Finally, a case study is provided to verify the effectiveness of the proposed method. Compared with electric vehicles participating in random charging, the optimization model proposed in this paper reduces the cost by 32% and the average hourly load by 67%.
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Stabile, Pietro, Federico Ballo, Gianpiero Mastinu, and Massimiliano Gobbi. "An Ultra-Efficient Lightweight Electric Vehicle—Power Demand Analysis to Enable Lightweight Construction." Energies 14, no. 3 (February 1, 2021): 766. http://dx.doi.org/10.3390/en14030766.
Повний текст джерелаАнотація:
A detailed analysis of the power demand of an ultraefficient lightweight-battery electric vehicle is performed. The aim is to overcome the problem of lightweight electric vehicles that may have a relatively bad environmental impact if their power demand is not extremely reduced. In particular, electric vehicles have a higher environmental impact during the production phase, which should be balanced by a lower impact during the service life by means of a lightweight design. As an example of an ultraefficient electric vehicle, a prototype for the Shell Eco-marathon competition is considered. A “tank-to-wheel” multiphysics model (thermo-electro-mechanical) of the vehicle was developed in “Matlab-Simscape”. The model includes the battery, the DC motors, the motor controller and the vehicle drag forces. A preliminary model validation was performed by considering experimental data acquisitions completed during the 2019 Shell Eco-marathon European competition at the Brooklands Circuit (UK). Numerical simulations are employed to assess the sharing of the energy consumption among the main dissipation sources. From the analysis, we found that the main sources of mechanical dissipation (i.e., rolling resistance, gravitational/inertial force and aerodynamic drag) have the same role in the defining the power consumption of such kind of vehicles. Moreover, the effect of the main vehicle parameters (i.e., mass, aerodynamic coefficient and tire rolling resistance coefficient) on the energy consumption was analyzed through a sensitivity analysis. Results showed a linear correlation between the variation of the parameters and the power demand, with mass exhibiting the highest influence. The results of this study provide fundamental information to address critical decisions for designing new and more efficient lightweight vehicles, as they allow the designer to clearly identify which are the main parameters to keep under control during the design phase and which are the most promising areas of action.
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Wang, Xiaoguang, Tao Lv, and Lei Fan. "New Energy Vehicle Consumer Demand Mining Research Based on Fusion Topic Model: A Case in China." Sustainability 14, no. 6 (March 11, 2022): 3316. http://dx.doi.org/10.3390/su14063316.
Повний текст джерелаАнотація:
This study extracted the demand preference topic words of new energy vehicle consumers with the help of the topic model, calculated the similarity between the word vectors and the topic keywords and expanded the topic keywords, analyzed and compared the demand topics and feature expansion words of different car models, and summarized the demand differences of other consumer groups. The analysis results show that consumers’ demands of different groups have the exact demand dimensions such as new energy features and brand features, and different demand dimensions such as application, services, and professional performance. The research findings help consumers filter valuable information from online review data and help car companies objectively and accurately obtain consumer demands, develop more reasonable marketing strategies, and achieve healthy and sustainable corporate development.
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Olmez, Sedar, Jason Thompson, Ellie Marfleet, Keiran Suchak, Alison Heppenstall, Ed Manley, Annabel Whipp, and Rajith Vidanaarachchi. "An Agent-Based Model of Heterogeneous Driver Behaviour and Its Impact on Energy Consumption and Costs in Urban Space." Energies 15, no. 11 (May 30, 2022): 4031. http://dx.doi.org/10.3390/en15114031.
Повний текст джерелаАнотація:
By 2020, over 100 countries had expanded electric and plug-in hybrid electric vehicle (EV/PHEV) technologies, with global sales surpassing 7 million units. Governments are adopting cleaner vehicle technologies due to the proven environmental and health implications of internal combustion engine vehicles (ICEVs), as evidenced by the recent COP26 meeting. This article proposes an agent-based model of vehicle activity as a tool for quantifying energy consumption by simulating a fleet of EV/PHEVs within an urban street network at various spatio-temporal resolutions. Driver behaviour plays a significant role in energy consumption; thus, simulating various levels of individual behaviour and enhancing heterogeneity should provide more accurate results of potential energy demand in cities. The study found that (1) energy consumption is lowest when speed limit adherence increases (low variance in behaviour) and is highest when acceleration/deceleration patterns vary (high variance in behaviour); (2) vehicles that travel for shorter distances while abiding by speed limit rules are more energy efficient compared to those that speed and travel for longer; and (3) on average, for tested vehicles, EV/PHEVs were £233.13 cheaper to run than ICEVs across all experiment conditions. The difference in the average fuel costs (electricity and petrol) shrinks at the vehicle level as driver behaviour is less varied (more homogeneous). This research should allow policymakers to quantify the demand for energy and subsequent fuel costs in cities.
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Vijay Kumar, K., and T. Bharath Kumar. "Optimal Scheduling of Micro Grid for Plug-In Electrical Vehicle." International Journal of Engineering & Technology 7, no. 2.7 (March 18, 2018): 558. http://dx.doi.org/10.14419/ijet.v7i2.7.10882.
Повний текст джерелаАнотація:
Owing to several advantages of Plug-in Electric Vehicles (PEVs) like less noise, emission less, good efficiency and the reduced cost has attention to the governments, researchers and manufactures in recent time. The Plug-in Electric Vehicle (PEV) plays a vital role in replacement of conventional vehicles in future, because of penetration of renewable energy resources in conventional generation. The modernized of micro grid is happening due to usage of clean energy for EV charging. The cost of electric vehicle charging is challenging issue in the development of plug-in electric vehicle. The coordination between renewable generation and conventional generation is very much needed in near future. The dynamic nature of renewable energy resources causes frequent interrupts in electric vehicle charging. The problem of nonlinear power generation with renewable resources is overcome by electric vehicle battery storage system which enables the EV battery to charge during low demand period and gets discharged into the micro grid during high demand periods. This paper developed an optimal schedule for stationary Plug-in Electric Vehicle charging in operation with micro grid. The obtained optimal schedule provides balance between active and reactive power in generation and load as well. The integration of renewable energy resources is achieved through solar, wind in Vehicle-2-Grid (V2G) approach which is used to safeguard to renewable energy resources by store additional energy produced during peak load period and feeding back to the micro grid during low load period. As a result the stable operation of the micro grid and EV charging with low cost is achieved in this paper.
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Topić, Jakov, Branimir Škugor, and Joško Deur. "Neural Network-Based Modeling of Electric Vehicle Energy Demand and All Electric Range." Energies 12, no. 7 (April 11, 2019): 1396. http://dx.doi.org/10.3390/en12071396.
Повний текст джерелаАнотація:
A deep neural network-based approach of energy demand modeling of electric vehicles (EV) is proposed in this paper. The model-based prediction of energy demand is based on driving cycle time series used as a model input, which is properly preprocessed and transformed into 1D or 2D static maps to serve as a static input to the neural network. Several deep feedforward neural network architectures are considered for this application along with different model input formats. Two energy demand models are derived, where the first one predicts the battery state-of-charge and fuel consumption at destination for an extended range electric vehicle, and the second one predicts the vehicle all-electric range. The models are validated based on a separate test dataset when compared to the one used in neural network training, and they are compared with the traditional response surface approach to illustrate effectiveness of the method proposed.
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Taqvi, Syed Taha, Ali Almansoori, Azadeh Maroufmashat, and Ali Elkamel. "Utilizing Rooftop Renewable Energy Potential for Electric Vehicle Charging Infrastructure Using Multi-Energy Hub Approach." Energies 15, no. 24 (December 16, 2022): 9572. http://dx.doi.org/10.3390/en15249572.
Повний текст джерелаАнотація:
Electric vehicles (EV) have the potential to significantly reduce carbon emissions. Yet, the current electric vehicle charging infrastructure utilizes electricity generated from non-renewable sources. In this study, the rooftop area of structures is analyzed to assess electricity that can be generated through solar- and wind-based technologies. Consequently, planning an electric vehicle charging infrastructure that is powered through ‘clean’ energy sources is presented. We developed an optimal modeling framework for the consideration of Renewable Energy Technologies (RET) along with EV infrastructure. After examining the level of technology, a MATLAB image segmentation technique was used to assess the available rooftop area. In this study, two competitive objectives including the economic cost of the system and CO2 emissions are considered. Three scenarios are examined to assess the potential of RET to meet the EV demand along with the Abu Dhabi city one while considering the life-cycle emission of RET and EV systems. When meeting only EV demand through Renewable Energy Technologies (RET), about 187 ktonnes CO2 was reduced annually. On the other hand, the best economic option was still to utilize grid-connected electricity, yielding about 2.24 Mt CO2 annually. In the scenario of meeting both 10% EV demand and all Abu Dhabi city electricity demand using RE, wind-based technology is only able to meet around 3%. Analysis carried out by studying EV penetration demonstrated the preference of using level 2 AC home chargers compared to other ones. When the EV penetration exceeds 25%, preference was observed for level 2 (AC public 3ϕ) chargers.
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Wasiak, Andrzej L. "Modeling the Effects of Implementation of Alternative Ways of Vehicle Powering." Fuels 2, no. 4 (November 26, 2021): 487–500. http://dx.doi.org/10.3390/fuels2040028.
Повний текст джерелаАнотація:
The trend to replace traditional fossil fuel vehicles is becoming increasingly apparent. The replacement concerns the use of pure biofuels or in blends with traditional fuels, the use of hydrogen as an alternative fuel and, above all, the introduction of electric propulsion. The introduction of new types of vehicle propulsion affects the demand for specific fuels, the needs for new infrastructure, or the nature of the emissions to the environment generated by fuel production and vehicle operation. The article presents a mathematical model using the difference of two logistic functions, the first of which describes the development of the production of a specific type of vehicle, and the second, the withdrawal of this type of vehicle from traffic after its use. The model makes it possible to forecast both the number of vehicles of each generation as a function of time, as well as changes in energy demand from various sources and changes in exhaust emissions. The results of the numerical simulation show replacing classic vehicles with alternative vehicles increases the total energy demand if the generation of the next generation occurs earlier than the decay of the previous generation of vehicles and may decrease in the case of overlapping or delays in the creation of new vehicles compared to the course of the decay function of the previous generation. For electric vehicles, carbon dioxide emissions are largely dependent on the emissions from electricity generation. The proposed model can be used to forecast technology development variants, as well as analyze the current situation based on the approximation of real data from Vehicle Registration Offices.
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Wulff, Niklas, Fabia Miorelli, Hans Christian Gils, and Patrick Jochem. "Vehicle Energy Consumption in Python (VencoPy): Presenting and Demonstrating an Open-Source Tool to Calculate Electric Vehicle Charging Flexibility." Energies 14, no. 14 (July 19, 2021): 4349. http://dx.doi.org/10.3390/en14144349.
Повний текст джерелаАнотація:
As electric vehicle fleets grow, rising electric loads necessitate energy systems models to incorporate their respective demand and potential flexibility. Recently, a small number of tools for electric vehicle demand and flexibility modeling have been released under open source licenses. These usually sample discrete trips based on aggregate mobility statistics. However, the full range of variables of travel surveys cannot be accessed in this way and sub-national mobility patterns cannot be modeled. Therefore, a tool is proposed to estimate future electric vehicle fleet charging flexibility while being able to directly access detailed survey results. The framework is applied in a case study involving two recent German national travel surveys (from the years 2008 and 2017) to exemplify the implications of different mobility patterns of motorized individual vehicles on load shifting potential of electric vehicle fleets. The results show that different mobility patterns, have a significant impact on the resulting load flexibilites. Most obviously, an increased daily mileage results in higher electricty demand. A reduced number of trips per day, on the other hand, leads to correspondingly higher grid connectivity of the vehicle fleet. VencoPy is an open source, well-documented and maintained tool, capable of assessing electric vehicle fleet scenarios based on national travel surveys. To scrutinize the tool, a validation of the simulated charging by empirically observed electric vehicle fleet charging is advised.
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Zhen, Yongcheng, Yong Bao, Zaimin Zhong, Stephan Rinderknecht, and Song Zhou. "Development of a PHEV Hybrid Transmission for Low-End MPVs Based on AMT." Vehicles 2, no. 2 (March 25, 2020): 236–48. http://dx.doi.org/10.3390/vehicles2020013.
Повний текст джерелаАнотація:
In order to improve the fuel economy of vehicles, based on the automated mechanical transmission (AMT), a plug-in hybrid electric vehicle (PHEV) hybrid transmission for low-end multi-purpose vehicles (MPVs) is developed. To obtain the statistics of the best-selling models, we took several best-selling models in the Chinese market as the research object to study the relationship between power demand, energy demand, weight, and cost. The power requirements and energy requirements of PHEVs are decoupled. According to the decoupled theory, a single-motor parallel scheme based on the AMT is adopted to develop a PHEV hybrid transmission. In the distribution of engine and motor power, the engine just needs to meet the vehicle’s constant driving power, and the backup power can be provided by the motor, which means we can use an engine with a smaller power rating. The energy of short-distance travel is mainly provided by the motor, which can make full use of the battery, reducing the fuel consumption. The energy of long-distance travel is mainly provided by the engine, which can reduce the need for battery capacity. The working modes of the electrified mechanical transmission (EMT) are proposed, using P3 as the basic working mode and setting the P2 mode at the same time, and the gear ratios are designed. Based on the above basic scheme, two rounds of prototype development and assembling prototype vehicles for testing are carried out for the front-engine-front-drive (FF) layout. The test results show that the vehicle’s economy has been improved compared to the unmodified vehicle, and the fuel-saving rate of 100 kilometers has been achieved at 35.18%. The prototype development and the vehicle matching verify the effectiveness of the new configuration based on AMT.
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Mirzaei, Shokoufeh, Krishna Krishnan, and Bayram Yildrim. "Energy-Efficient Location-Routing Problem with Time Windows with Dynamic Demand." Industrial and Systems Engineering Review 3, no. 1 (January 21, 2015): 17–36. http://dx.doi.org/10.37266/iser.2015v3i1.pp17-36.
Повний текст джерелаАнотація:
Sustainability and energy savings have attracted considerable attention in recent years. However, in the traditional location-routing problem (LRP), the objective function has yet to minimize the distance traveled regardless of the amount of energy consumed. Although, distance is one of the major factors determining the energy consumption of a distribution network, it is not the only factor. Therefore, this paper explains the development of a novel formulation of the LRP that considers energy minimization, which is called the energy-efficient location-routing problem (EELRP). The energy consumed by a vehicle to travel between two nodes in a system depends on many forces. Among those, rolling resistance (RR) and aerodynamic drag are considered in this paper to be the major contributing forces. The presented mixed-integer non-linear program (MINLP) finds the best location-allocation routing plan with the objective function of minimizing total costs, including energy, emissions, and depot establishment. The proposed model can also handle the vehicle-selection problem with respect to a vehicles’ capacity, source of energy, and aerodynamic characteristics. The formulation proposed can also solve the problems with hard and soft time window constraints. Also, the model is enhanced to handle the EELRP with dynamic customers’ demands. Some examples are presented to illustrate the formulations presented in this paper.
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Hu, Tengda, Yunwu Li, Zhi Zhang, Ying Zhao, and Dexiong Liu. "Energy Management Strategy of Hybrid Energy Storage System Based on Road Slope Information." Energies 14, no. 9 (April 21, 2021): 2358. http://dx.doi.org/10.3390/en14092358.
Повний текст джерелаАнотація:
To maximize the performance of power batteries and supercapacitors in a hybrid energy storage system (HESS) and to resolve the conflict between the high power demands of electric vehicles and the limitations of high-current charging and discharging of the power battery, a vehicle power demand model incorporating road slope information has been constructed. This paper takes a HESS composed of power battery and supercapacitor as the object, and a rule-based energy management strategy (EMS) based on road slope information is proposed to realize the reasonable distribution and management of energy under the slope condition. According to the slope information of the road ahead, the energy consumption in the next period was predicted, and the supercapacitor is charged and discharged in advance to meet the energy demand of uphill and the energy recovery capacity of downhill to avoid the high current charge and discharge of the battery. Subsequently, the improved EMS performance was simulated under the New York City Cycle (NYCC) driving conditions with additional slope driving conditions. The simulated results indicate that compared to the existing EMS, the proposed EMS based on slope information can effectively distribute the power demand between the power battery and the supercapacitor, can reduce the discharge current and the duration of high-power discharge, and has a 20.4% higher energy recovery efficiency, effectively increasing the cruising range.
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Vijayakumar, Vishnu, Alan Jenn, and Lewis Fulton. "Low Carbon Scenario Analysis of a Hydrogen-Based Energy Transition for On-Road Transportation in California." Energies 14, no. 21 (November 1, 2021): 7163. http://dx.doi.org/10.3390/en14217163.
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Fuel cell electric vehicles (FCEV) are emerging as one of the prominent zero emission vehicle technologies. This study follows a deterministic modeling approach to project two scenarios of FCEV adoption and the resulting hydrogen demand (low and high) up to 2050 in California, using a transportation transition model. The study then estimates the number of hydrogen production and refueling facilities required to meet demand. The impact of system scale-up and learning rates on hydrogen price is evaluated using standalone supply chain models: H2A, HDSAM, HRSAM and HDRSAM. A sensitivity analysis explores key factors that affect hydrogen prices. In the high scenario, light and heavy-duty fuel cell vehicle stocks reach 12.5 million and 1 million by 2050, respectively. The resulting annual hydrogen demand is 3.9 billion kg, making hydrogen the dominant transportation fuel. Satisfying such high future demands will require rapid increases in infrastructure investments starting now, but especially after 2030 when there is an exponential increase in the number of production plants and refueling stations. In the long term, electrolytic hydrogen delivered using dedicated hydrogen pipelines to larger stations offers substantial cost savings. Feedstock prices, size of the hydrogen market and station utilization are the prominent parameters that affect hydrogen price.
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Nazri, Gholam-Abbas. "Issues in Energy Storage for Electric-Based Transportation." MRS Bulletin 27, no. 8 (August 2002): 628–31. http://dx.doi.org/10.1557/mrs2002.200.
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AbstractThe key to market success for electric vehicles (EVs) has always been the energy-storage device, which limits driving range and vehicle acceleration. In many respects, rechargeable lithium batteries are the most attractive technology for storing energy and delivering it on demand in an automobile. Past and ongoing efforts to develop electric-based automotive propulsion systems are chronicled in this article.
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Gutsche, Jan, Łukasz Muślewski, Anna Dzioba, and Davor Kolar. "The development of electromobility in the aspect of the energy infrastructure condition assessment." MATEC Web of Conferences 338 (2021): 01009. http://dx.doi.org/10.1051/matecconf/202133801009.
Повний текст джерелаАнотація:
Electric vehicles are considered emission-free. However, having high-emission and a high degree of electricity demand coverage infrastructure, these vehicles should be considered as internal combustion cars. The purpose of the analysis carried out in this study is to present the state of energy infrastructure in Poland, to determine losses related to energy transmission and greenhouse gas emissions to the atmosphere in relation to unit consumption. Analyzing the data obtained, the emission of the electric vehicle was compared to that of a vehicle powered by diesel oil and fuel with the addition of a biocomponent.
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Bauer, Mariano, and Elizabeth Mar. "Transport and Energy Demand in the Developing World: The Urgent Alternatives." Energy & Environment 16, no. 5 (September 2005): 825–43. http://dx.doi.org/10.1260/095830505774478521.
Повний текст джерелаАнотація:
The appeal of individual mobility provided today by automobiles and light trucks with internal combustion engines, makes transportation the sector most resilient to a fuel substitution away from its dependence on oil. While the number of vehicles per capita and the distance traveled per vehicle are approaching saturation levels in the industrialized countries (IEA 2002), increases in population and income per capita, economic reforms and industry globalization can result in an off-trend accelerated growth of vehicles in the economies in transition (FSU and EE) and in the developing world (China, India and Latin America, mainly). The corresponding world road use energy consumption could reach a 200 percent increase from present levels by the year 2020, instead of an already worrisome “business as usual” projection of 75 percent (BAUER 2003, 2004). This paper analyses the mitigation effect on world oil demand and on its environmental impact that a policy of leapfrogging towards energy efficient internal combustion technologies and/or alternative vehicles – hybrid or fully electric – could have.
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Dik, Abdullah, Siddig Omer, and Rabah Boukhanouf. "Electric Vehicles: V2G for Rapid, Safe, and Green EV Penetration." Energies 15, no. 3 (January 22, 2022): 803. http://dx.doi.org/10.3390/en15030803.
Повний текст джерелаАнотація:
Low carbon and renewable energy sources (RESs) are fast becoming a key sustainable instrument in meeting the global growth of electricity demand while curbing carbon emissions. For example, the gradual displacement of fossil-fuelled vehicles with electrically driven counterparts will inevitably increase both the power grid baseload and peak demand. In many developed countries, the electrification process of the transport sector has already started in tandem with the installation of multi-GW renewable energy capacity, particularly wind and solar, huge investment in power storage technology, and end-user energy demand management. The expansion of the Electric Vehicle (EV) market presents a new opportunity to create a cleaner and transformative new energy carrier. For instance, a managed EV battery charging and discharging profile in conjunction with the national grid, known as the Vehicle-to-Grid system (V2G), is projected to be an important mechanism in reducing the impact of renewable energy intermittency. This paper presents an extensive literature review of the current status of EVs and allied interface technology with the power grid. The main findings and statistical details are drawn from up-to-date publications highlighting the latest technological advancements, limitations, and potential future market development. The authors believe that electric vehicle technology will bring huge technological innovation to the energy market where the vehicle will serve both as a means of transport and a dynamic energy vector interfacing with the grid (V2G), buildings (V2B), and others (V2X).
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Fuerst Pacheco, Victor, and Diego Alves de Miranda. "Aerodynamic Analysis of High Energy Efficiency Vehicles by Computational Fluid Dynamics Simulation." Advanced Engineering Forum 32 (April 2019): 41–51. http://dx.doi.org/10.4028/www.scientific.net/aef.32.41.
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The growing demand for energy efficiency gains in vehicles has led to several advances in more technological and efficient driving units, projects using lighter and more resistant materials and, in particular, a deeper study of aerodynamic studies in order to understand the fluid flow around the object of study. This work presents an aerodynamic study for a vehicle of high-energy efficiency, through computational fluid dynamics simulation in Ansys Fluent software. The main objective is to obtain the traction and drag force vectors acting on the vehicle at different speeds and to better understand the airflow before, during and after contact with the vehicle. With the possession of results, it was facilitated the implementation of improvements that enabled the vehicle to operate even more efficiently.
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Chen, Xu, Guangdi Hu, Feng Guo, Mengqi Ye, and Jingyuan Huang. "Switched Energy Management Strategy for Fuel Cell Hybrid Vehicle Based on Switch Network." Energies 13, no. 1 (January 3, 2020): 247. http://dx.doi.org/10.3390/en13010247.
Повний текст джерелаАнотація:
Environmentally friendly and pollution-free fuel cell/lithium battery hybrid vehicles have received the attention of the community in recent years. It is imperative for fuel cell/lithium battery hybrid vehicles to use the energy management strategy (EMS) to distribute the output power of each power source to improve fuel economy and system life. In practical application, inconsistency of battery pack will lead to security hazard and capacity degradation. However, few EMS take the inconsistency of battery pack into account. Also, the current battery equalization strategy rarely discusses how to perform the equilibrium process while meeting the power demand of vehicle. To solve these issues, a novel equalization energy management strategy (EEMS) based on the switch network is proposed at first. Then, a switched energy management strategy (SEMS) that switches between the EEMS and the equivalent consumption minimization strategy (ECMS) is proposed and implemented in the fuel cell/lithium battery hybrid system to validate its effectiveness. The results show that the proposed SEMS can ameliorate the inconsistency of series lithium battery pack while meeting the power demand of vehicle’s normal operation. It can improve the safety and durability of the system and reduce the equalization time. Besides, it has good expansibility and no energy waste.
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Lopez, Neil Stephen, Adrian Allana, and Jose Bienvenido Manuel Biona. "Modeling Electric Vehicle Charging Demand with the Effect of Increasing EVSEs: A Discrete Event Simulation-Based Model." Energies 14, no. 13 (June 22, 2021): 3734. http://dx.doi.org/10.3390/en14133734.
Повний текст джерелаАнотація:
Electric vehicle (EV) use is growing at a steady rate globally. Many countries are planning to ban internal combustion engines by 2030. One of the key issues needed to be addressed before the full-scale deployment of EVs is ensuring energy security. Various studies have developed models to simulate and study hourly electricity demand from EV charging. In this study, we present an improved model based on discrete event simulation, which allows for modeling characteristics of individual EV users, including the availability of electric vehicle supply equipment (EVSE) outside homes and the charging threshold of each EV user. The model is illustrated by simulating 1000 random electric vehicles generated using data from an actual survey. The results agree with previous studies that daily charging demands do not significantly vary. However, the results show a significant shift in charging schedule during weekends. Moreover, the simulation demonstrated that the charging peak demand can be reduced by as much as 11% if EVSEs are made more available outside homes. Interestingly, a behavioral solution, such as requiring users to fully utilize their EV’s battery capacity, is more effective in reducing the peak demand (14–17%). Finally, the study concludes by discussing a few potential implications on electric vehicle charging policy.
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Kene, Raymond, Thomas Olwal, and Barend J. van Wyk. "Sustainable Electric Vehicle Transportation." Sustainability 13, no. 22 (November 9, 2021): 12379. http://dx.doi.org/10.3390/su132212379.
Повний текст джерелаАнотація:
The future direction of electric vehicle (EV) transportation in relation to the energy demand for charging EVs needs a more sustainable roadmap, compared to the current reliance on the centralised electricity grid system. It is common knowledge that the current state of electricity grids in the biggest economies of the world today suffer a perennial problem of power losses; and were not designed for the uptake and integration of the growing number of large-scale EV charging power demands from the grids. To promote sustainable EV transportation, this study aims to review the current state of research and development around this field. This study is significant to the effect that it accomplishes four major objectives. (1) First, the implication of large-scale EV integration to the electricity grid is assessed by looking at the impact on the distribution network. (2) Secondly, it provides energy management strategies for optimizing plug-in EVs load demand on the electricity distribution network. (3) It provides a clear direction and an overview on sustainable EV charging infrastructure, which is highlighted as one of the key factors that enables the promotion and sustainability of the EV market and transportation sector, re-engineered to support the United Nations Climate Change Agenda. Finally, a conclusion is made with some policy recommendations provided for the promotion of the electric vehicle market and widespread adoption in any economy of the world.
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Hensher, D. A. "Dimensions of Automobile Demand: An Overview of an Australian Research Project." Environment and Planning A: Economy and Space 18, no. 10 (October 1986): 1339–74. http://dx.doi.org/10.1068/a181339.
Повний текст джерелаАнотація:
The major objective of the study of the dimensions of automobile demand (1981–1988) is to obtain reliable forecasts of the variables which drive the fundamental energy equation: energy consumed (litres) = efficiency of technology (litres per 100 kilometres) × utilisation rate (kilometres per period). Since the level of utilisation is unlikely to be independent of the state of technology, and since both dimensions are conditioned by the state of the economy and the nature of households as well as by the extent of corporate-sector support to the household sector, it is necessary to view the levels of vehicle usage and vehicle fuel efficiency as outputs of the broader household decision process. This broader context can be represented by a study of the household's choice of automobiles (by number and composition) and level of utilisation. This perspective enables us to view vehicle efficiency and utilisation as derivatives of a study of the household's demand for mobility services, which is derived from the demand for end activities (consumption of goods and leisure). Since we are especially interested in the role of fuel prices and vehicle technology in the household's decision on the level of vehicle utilisation, it is desirable to monitor the response path of a sample of households over a period of time. A single cross-section approach cannot identify the influence of changing fuel prices on vehicle use, nor can it adequately accommodate the temporal relationship between vehicle purchase/disposal decisions and the utilisation rate. To represent satisfactorily the role of policy variables (for example, fuel prices, taxes associated with vehicle possession, standards for vehicle technology) in the context of the wider set of influences on household automobile possession and usage, the study members have developed an econometric model system which jointly models the household's choice of vehicles and utilisation level over the period 1981–1985. This paper provides an overview of the theoretical, methodological, and empirical dimensions of the project and, where appropriate, introduces some preliminary findings. The project in its entirety is due for completion in late 1988.
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An, Xiaopan, Yu Liu, Hanzhengnan Yu, Zhichao Liu, Songbo Qi, and Yang Wang. "Application of shortening time test in battery electric range calculation of PEV based on CLTC-P." E3S Web of Conferences 268 (2021): 01045. http://dx.doi.org/10.1051/e3sconf/202126801045.
Повний текст джерелаАнотація:
In order to obtain the actual results of battery electric range test, and meet the demand of fiscal subsidy policy introduction of new energy vehicles, some modifications of the standard GB/T 18386.1 Test Method for Energy Consumption and Range of Electric Vehicles- part1: Light-duty Vehicles are revised by drafting group. Such as China Light-duty vehicle Test Cycle for passenger car (CLTC-P), Shortened time test method, etc. To prove the reasonability of Shortened time test to measure battery electric range (BER) based on CLTC-P, two sets of vehicle tests were carried out. Results show that shortened time test greatly reduce test time, and the weighting factor setting of DS1 and DS2 is reasonable. Although some deviation between Shortened time test and consecutive cycle test exist, but not regularly. It is related to the vehicle energy regulation strategy individually. Finally, it is recommended that the test vehicle should be forced to break 10 minutes after the end of CSSM.
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Serafini, Luca, Emanuele Principi, Susanna Spinsante, and Stefano Squartini. "Multi-Household Energy Management in a Smart Neighborhood in the Presence of Uncertainties and Electric Vehicles." Electronics 10, no. 24 (December 20, 2021): 3186. http://dx.doi.org/10.3390/electronics10243186.
Повний текст джерелаАнотація:
The pathway toward the reduction of greenhouse gas emissions is dependent upon increasing Renewable Energy Sources (RESs), demand response, and electrification of public and private transportation. Energy management techniques are necessary to coordinate the operation in this complex scenario, and in recent years several works have appeared in the literature on this topic. This paper presents a study on multi-household energy management for Smart Neighborhoods integrating RESs and electric vehicles participating in Vehicle-to-Home (V2H) and Vehicle-to-Neighborhood (V2N) programs. The Smart Neighborhood comprises multiple households, a parking lot with public charging stations, and an aggregator that coordinates energy transactions using a Multi-Household Energy Manager (MH-EM). The MH-EM jointly maximizes the profits of the aggregator and the households by using the augmented ϵ-constraint approach. The generated Pareto optimal solutions allow for different decision policies to balance the aggregator’s and households’ profits, prioritizing one of them or the RES energy usage within the Smart Neighborhood. The experiments have been conducted over an entire year considering uncertainties related to the energy price, electric vehicles usage, energy production of RESs, and energy demand of the households. The results show that the MH-EM optimizes the Smart Neighborhood operation and that the solution that maximizes the RES energy usage provides the greatest benefits also in terms of peak-shaving and valley-filling capability of the energy demand.
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Farag, Mohamed M. G., and Hesham A. Rakha. "Development and Evaluation of a Cellular Vehicle-to-Everything Enabled Energy-Efficient Dynamic Routing Application." Sensors 23, no. 4 (February 19, 2023): 2314. http://dx.doi.org/10.3390/s23042314.
Повний текст джерелаАнотація:
Cellular vehicle-to-everything (C-V2X) is a communication technology that supports various safety, mobility, and environmental applications, given its higher reliability properties compared to other communication technologies. The performance of these C-V2X-enabled intelligent transportation system (ITS) applications is affected by the performance of the C-V2X communication technology (mainly packet loss). Similarly, the performance of the C-V2X communication is dependent on the vehicular traffic density which is affected by the traffic mobility patterns and vehicle routing strategies. Consequently, it is critical to develop a tool that can simulate, analyze, and evaluate the mutual interactions of the transportation and communication systems at the application level to quantify the benefits of C-V2X-enabled ITS applications realistically. In this paper, we demonstrate the benefits gained when using C-V2X Vehicle-to-Infrastructure (V2I) communication technology in an energy-efficient dynamic routing application. Specifically, we develop a Connected Energy-Efficient Dynamic Routing (C-EEDR) application using C-V2X as a communication medium in an integrated vehicular traffic and communication simulator (INTEGRATION). The results demonstrate that the C-EEDR application achieves fuel savings of up to 16.6% and 14.7% in the IDEAL and C-V2X communication cases, respectively, for a peak hour demand on the downtown Los Angeles network considering a 50% level of market penetration of connected vehicles. The results demonstrate that the fuel savings increase with increasing levels of market penetration at lower traffic demand levels (25% and 50% the peak demand). At higher traffic demand levels (75% and 100%), the fuel savings increase with increasing levels of market penetration with maximum benefits at a 50% market penetration rate. Although the communication system is affected by the high density of vehicles at the high traffic demand levels (75% and 100% the peak demand), the C-EEDR application manages to perform reliably, producing system-wide fuel consumption savings.The C-EEDR application achieves fuel savings of 15.2% and 11.7% for the IDEAL communication and 14% and 9% for the C-V2X communication at the 75% and 100% market penetration rates, respectively. Finally, the paper demonstrates that the C-V2X communication constraints only affect the performance of the C-EEDR application at the full demand level when the market penetration of the connected vehicles exceeds 25%. This degradation, however, is minimal (less than a 2.5% reduction in fuel savings).
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Cieslik, Wojciech, and Weronika Antczak. "Research of Load Impact on Energy Consumption in an Electric Delivery Vehicle Based on Real Driving Conditions: Guidance for Electrification of Light-Duty Vehicle Fleet." Energies 16, no. 2 (January 9, 2023): 775. http://dx.doi.org/10.3390/en16020775.
Повний текст джерелаАнотація:
Electromobility is developing rapidly in all areas of transportation, starting with small personal vehicles and passenger cars through public transportation vehicles and ending with noticeable expansion in the area of urban transportation services. So far, however, there is a lack of research determining how the effect of load weight defines the energy intensity of a vehicle under real conditions, especially in the areas of urban, suburban and highway driving. Therefore, this paper presents an analysis of a representative delivery vehicle and its energy consumption in two transportation scenarios where cargo weight is a variable. A survey was also conducted to determine the actual demand and requirements placed on the electric vehicle by transportation companies.