Journal articles: 'Electric car range prediction'

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Abdul Karim, Kasrul, Md Nazri Othman, Wan Ahmas Redhauddin, Mohd Ismadi Bugis, Zulkifli Ramli, Abdul Rahim Abdullah, and Auzani Jidin. "Electric Vehicle Development and Prediction of Battery Consumption Based on a Journey Profile." Applied Mechanics and Materials 699 (November 2014): 794–99. http://dx.doi.org/10.4028/www.scientific.net/amm.699.794.

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The lack of internal combustion engine (ICE) for electric vehicle (EV) makes it solely dependable on battery supply. Due to this, the prediction of battery consumption becomes crucial to determine the accurate driving range before the car needs to be re-charged. The developed prediction model can also be applied as simulation tool and help in reducing the length and the cost of the EV design process. This paper presents the prediction of battery power requirement for a small re-engineered EV converted from a commercial ICE car by using vehicle dynamic mathematical equations. The prediction is performed based on multiple journey profile that gathered before the actual test. The development of re-engineered EV is also presented, along with the experimental result of an actual drive test on a racing circuit to validate the prediction model.

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Bucher, Dominik, Henry Martin, Jannik Hamper, Atefeh Jaleh, Henrik Becker, Pengxiang Zhao, and Martin Raubal. "Exploring Factors that Influence Individuals’ Choice Between Internal Combustion Engine Cars and Electric Vehicles." AGILE: GIScience Series 1 (July 15, 2020): 1–23. http://dx.doi.org/10.5194/agile-giss-1-2-2020.

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Abstract. The adoption of electric vehicles has the potential to help decarbonizing the transport sector if they are powered by renewable energy sources. Limitations commonly associated with e-cars are their comparatively short ranges and long recharging cycles, leading to anxiety when having to travel long distances. Other factors such as temperature, destination or weekday may influence people in choosing an e-car for a certain trip. Using a unique dataset of 129 people who own both an electric vehicle (EV) as well as one powered by an internal combustion engine (ICE), we analyze tracking data over a year in order to have an empirically verified choice model. Based on a wide range of predictors, this model tells us for an individual journey if the person would rather choose the EV or the ICE car. Our findings show that there are only weak relations between the predictor and target variables, indicating that for many people the switch to an e-car would not affect their lifestyle and the related range anxiety diminishes when actually owning an electric vehicle. In addition, we find that choice behavior does not generalize well over different users.

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Lee, Choong Hoon. "Transient Fuel Injection Rate and Fuel Economy Prediction for a Vehicle Driven with FTP-75 Mode Using an ECU HILS." Advanced Materials Research 772 (September 2013): 543–48. http://dx.doi.org/10.4028/www.scientific.net/amr.772.543.

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A fuel injection system which is operated with ECU HILS was used to evaluate the transient fuel injection rate and fuel economy. Several sensor signals from a car such as the engine speed, car speed, cam position, crank position, air mass flow, throttle position and several other sensor signals are measured and recorded as a data file with DAQ-system for a vehicle driven in FTP-75 mode in a chassis dynamometer. Electric signals that are equivalent to the sensor signals from the car are reconstructed from the recorded data file using DAQ boards and a computer. All the reconstructed sensor signals are provided to the ECU with synchronized timing. Injectors were connected to the ECU output pins and were driven by the system to measure the quantity of injected fuel. The ECU HILS show that the transient fuel injection rate and fuel economy for the car driving mode could be successfully predicted. Also, the repeatability of the generation of sensor signals can enhance the accuracy of a range of experiments related to cars fuel injection experiment.

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Philipsen, Ralf, Teresa Brell, Hannah Biermann, and Martina Ziefle. "Under Pressure—Users’ Perception of Range Stress in the Context of Charging and Traditional Refueling." World Electric Vehicle Journal 10, no. 3 (August 1, 2019): 50. http://dx.doi.org/10.3390/wevj10030050.

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Although electric drives can locally reduce the environmental impact of traffic, the penetration rates of battery electric vehicles (BEV) are far below expectations, not least because the charging infrastructure network is still considered insufficient by potential users. Therefore, the planning of charging infrastructure that considers both needs and user requirements is essential to remove an important barrier to widespread adaptation of e-vehicles, but it is also a challenge. A better understanding of the charging behavior and the underlying usage motivation is therefore needed. A frequently mentioned factor is the so-called range stress. While there are many studies on this subject with new BEV users, there is a lack of approaches that also include experienced e-vehicle users and at the same time allow a comparison with drivers of cars with internal combustion engines (ICE). In this paper, this is realized with the help of a questionnaire study ( n = 204 ). The results show that ICE and BEV users at different experience levels hardly differ regarding the perceived range stress; BEV users even perceive less stress. BEV users also showed more trust in the vehicle and in the tank/battery indicators, while this trust depends only marginally on the type of information provided by the car. Furthermore, there is a correlation between users’ technology commitment and risk-taking, on the one hand, and range stress, on the other. However, for the prediction of range stress, gender, experience with e-cars, and the question of whether cars are privately owned, or car-sharing is used, are more relevant.

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Gonsrang, S., and R. Kasper. "Optimisation-Based Power Management System for an Electric Vehicle with a Hybrid Energy Storage System." International Journal of Automotive and Mechanical Engineering 15, no. 4 (December 24, 2018): 5729–47. http://dx.doi.org/10.15282/ijame.15.4.2018.2.0439.

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Hybridisation of energy storage sources is necessary for extending mileage of electric vehicles. However, coordination of multiple devices with different characteristics is challenging. This paper presents a power management system (PMS) for an electric car equipped with a battery pack, supercapacitor bank, and range extender. The proposed PMS deals with vehicular load distribution by solving a power management problem, formulated as a constrained quadratic program (CQP). Then, the optimised variables, such as the desired speed and optimised operation points of the car’s components, are implemented by controllers at a component level. Complete knowledge about the trip is unwanted because the proposed PMS considers a power management problem only over a controlled horizon of one sampling period. Furthermore, this work varies weight factors to tackle various difficulties, for instance, regenerative power management. The simulation results revealed that the proposed system optimally allocated an electric power load to the car components, without violating any physical constraints. Also, the comparative study showed that the performance of the CQP in power management was comparable to that of the benchmark, based on a nonlinear model predictive control.

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Elsherbiny, Hanaa, Mohamed Kamal Ahmed, and Mahmoud Elwany. "Comparative Evaluation for Torque Control Strategies of Interior Permanent Magnet Synchronous Motor for Electric Vehicles." Periodica Polytechnica Electrical Engineering and Computer Science 65, no. 3 (July 6, 2021): 244–61. http://dx.doi.org/10.3311/ppee.16672.

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This paper presents a detailed analysis and comparative investigation for the torque control techniques of interior permanent magnet synchronous motor (IPMSM) for electric vehicles (EVs). The study involves the field-oriented control (FOC), direct torque control (DTC), and model predictive direct torque control (MPDTC) techniques. The control aims to achieve vehicle requirements that involve maximum torque per ampere (MTPA), minimum torque ripples, maximum efficiency, fast dynamics, and wide speed range. The MTPA is achieved by the direct calculation of reference flux-linkage as a function of commanded torque. The calculation of reference flux-linkage is done online by the solution of a quartic equation. Therefore, it is a more practical solution compared to look-up table methods that depend on machine parameters and require extensive offline calculations in advance. For realistic results, the IPMSM model is built considering iron losses. Besides, the IGBTs and diodes losses (conduction and switching losses) in power inverter are modeled and calculated to estimate properly total system efficiency. In addition, a bidirectional dc-dc boost converter is connected to the battery to improve the overall drive performance and achieve higher efﬁciency values. Also, instead of the conventional PI controller which suffers from parameter variation, the control scheme includes an adaptive fuzzy logic controller (FLC) to provide better speed tracking performance. It also provides a better robustness against disturbance and uncertainties. Finally, a series of simulation results with detailed analysis are executed for a 60 kW IPMSM. The electric vehicle (EV) parameters are equivalent to Nissan Leaf 2018 electric car.

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Xu, Qiwei, Chuan Zhou, Hong Huang, and Xuefeng Zhang. "Research on the Coordinated Control of Regenerative Braking System and ABS in Hybrid Electric Vehicle Based on Composite Structure Motor." Electronics 10, no. 3 (January 20, 2021): 223. http://dx.doi.org/10.3390/electronics10030223.

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An antilock braking system (ABS) can ensure that the wheels are not locked during the braking process which is an important system to ensure the safety of braking. Regenerative braking is also a crucial system for hybrid vehicles and helps to improve the cruising range of the car. As such, the coordinated control of a braking system and an ABS is an important research direction. This paper researches the coordinated control of the regenerative braking system and the ABS in the hybrid vehicle based on the composite structure motor (CSM-HEV). Firstly, two new braking modes which are engine-motor coordinated braking (EMCB) and dual-motor braking (DMB) are proposed and the coordinated control model of regenerative braking and ABS is established. Then, for the purpose of optimal operating efficiency and guaranteeing the vehicle brake slip rate, a braking force distribution strategy based on predictive control algorithm is proposed. Finally, the Simulink model is established to simulate the control strategy. Results show that the slip rate can well track the target and ensure the efficient operation of the system. Compared with the normal braking mode, the braking energy recovery rate of EMCB is similar, but it can reduce the fuel loss of the engine during the braking process by 30.1%, DMB can improve the braking energy recovery efficiency by 16.78%, and the response time to track target slip is increased by 12 ms.

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Conradi, Peter. "Range Prediction for Electric Vehicles." ATZelektronik worldwide 7, no. 3 (June 2012): 16–21. http://dx.doi.org/10.1365/s38314-012-0089-y.

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Conradi, Peter. "Range Prediction for Electric Vehicles." ATZautotechnology 12, no. 3 (June 2012): 54–59. http://dx.doi.org/10.1365/s35595-012-0122-z.

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Muslim, Supari, Tri Wrahatnolo, Sri Handayani, Erina Rahmadyanti, Nita Kusumawati, and Achmad Imam Agung. "Development of Electrical Car Learning Media as A Future Alternative Car." JETL (Journal of Education, Teaching and Learning) 5, no. 1 (March 31, 2020): 199. http://dx.doi.org/10.26737/jetl.v5i1.1822.

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This research aims to: (1) develop Electric Car Learning Media through research and development (R&D) ADDIE Model (Analysis, Design, Development, Implementation, and Evaluation); (2) knowing the level of feasibility of learning media for electric cars (LMEC) in a learning perspective; and (3) to determine the effectiveness of LMEC in the learning process. The study found that: (1) based on the validator's assessment, that the LMEC that had been developed was very suitable for use in learning Electric Cars; (2) based on student assessment, that LMEC is very suitable for use in learning Electric Cars in the Department of Electrical Engineering, State University of Surabaya; and (3) LMEC is very effective, proven as many as 24 (77.41%) of students get a score of learning outcomes in the range 71-100, with good and excellent categories.

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Viehl, Alexander, Emre Çakar, Maximilian Engler, and Stefan Köhler. "Weather Data in Range Prediction for Electric Vehicles." ATZ worldwide 118, no. 5 (April 22, 2016): 26–33. http://dx.doi.org/10.1007/s38311-016-0034-4.

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Li, Chun, Fan Yang, and Zhenchong Wang. "Experimental study on high-speed endurance of electric vehicle at normal temperature (25℃)." E3S Web of Conferences 268 (2021): 01032. http://dx.doi.org/10.1051/e3sconf/202126801032.

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Electric vehicle[1] endurance has always been a major concern for car buyers. Based on the six conventional electric vehicles selected from the market, the driving range of the chassis dynamometer with the environment warehouse is first carried out under the CLTC-P condition of normal temperature environment, and compared with the vehicle meter-display driving range. After testing the speed of 100 km/h of the driving range, the high-speed driving range at normal temperature is obtained, and then compared with the normal temperature driving range and the meter-display driving range, the drop rate of high-speed driving range is obtained. By analyzing and comparing the different test conditions of 6 vehicles, the influence trend of battery quantity, test quality, resistance and driving mode on high-speed driving range is obtained. Allowing consumers to anticipate their travel plans and also provides data for subsequent car companies to improve the quality of electric vehicles.

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Dixon, James, Sofia Koukoura, Christian Brand, Malcolm Morgan, and Keith Bell. "Spatially Disaggregated Car Ownership Prediction Using Deep Neural Networks." Future Transportation 1, no. 1 (June 20, 2021): 113–33. http://dx.doi.org/10.3390/futuretransp1010008.

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Predicting car ownership patterns at high spatial resolution is key to understanding pathways for decarbonisation—via electrification and demand reduction—of the private vehicle fleet. As the factors widely understood to influence car ownership are highly interdependent, linearised regression models, which dominate previous work on spatially explicit car ownership modelling in the UK, have shortcomings in accurately predicting the relationship. This paper presents predictions of spatially disaggregated car ownership—and change in car ownership over time—in Great Britain (GB) using deep neural networks (NNs) with hyperparameter tuning. The inputs to the models are demographic, socio-economic and geographic datasets compiled at the level of Census Lower Super Output Areas (LSOAs)—areas covering between 300 and 600 households. It was found that when optimal hyperparameters are selected, these neural networks can predict car ownership with a mean absolute error of up to 29% lower than when formulating the same problem as a linear regression; the results from NN regression are also shown to outperform three other artificial intelligence (AI)-based methods: random forest, stochastic gradient descent and support vector regression. The methods presented in this paper could enhance the capability of transport/energy modelling frameworks in predicting the spatial distribution of vehicle fleets, particularly as demographics, socio-economics and the built environment—such as public transport availability and the provision of local amenities—evolve over time. A particularly relevant contribution of this method is that by coupling it with a technology dissipation model, it could be used to explore the possible effects of changing policy, behaviour and socio-economics on uptake pathways for electric vehicles —cited as a vital technology for meeting Net Zero greenhouse gas emissions by 2050.

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Jiao, Hai Ning, Xi Ping Liu, and Jie Yang. "Design of Controller of Permanent Magnet Brushless DC Motor for Electric Vehicle." Advanced Materials Research 986-987 (July 2014): 1063–66. http://dx.doi.org/10.4028/www.scientific.net/amr.986-987.1063.

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In view of the defects of the electric car driver motor technology, considering actual needs of the car running, relaxing the electrical power density and the limitation of the moment of inertia etc, focusing on the reliability, speed range and high efficient operation area of the motor, based on the analysis of permanent magnet brushless dc motor operation principle, this paper designed a low cost, high intelligent electric car permanent magnet brushless dc motor controller. Through the analysis, simulation, experiment and comparison, the results show that the control system has good dynamic response and steady state properties.

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Lee, Jungho, Chanhee Park, Gyuneui Yang, Gangkoo Shim, and Chulmin Bae. "Development and Performance of BMS Modules for Urban Electric Car Using Life Prediction Method." Transactions of the Korean Society of Automotive Engineers 21, no. 6 (November 1, 2013): 147–54. http://dx.doi.org/10.7467/ksae.2013.21.6.147.

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Rahman, Ataur, Altab Hossain, Zahirul Alam A.H.M., and Mabubur Rashid. "Fuzzy knowledge-based model for prediction of traction force of an electric golf car." Journal of Terramechanics 49, no. 1 (February 2012): 13–25. http://dx.doi.org/10.1016/j.jterra.2011.08.001.

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Tang, Tie-Qiao, Liang Chen, Shi-Chun Yang, and Hua-Yan Shang. "An extended car-following model with consideration of the electric vehicle’s driving range." Physica A: Statistical Mechanics and its Applications 430 (July 2015): 148–55. http://dx.doi.org/10.1016/j.physa.2015.01.014.

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Riess, Christian, Michael Simon Josef Walter, Stefan Weiherer, Tiffany Haas, Sebastian Haas, and Alexandru Salceanu. "Heating an electric car with a biofuel operated heater during cold seasons – design, application and test." ACTA IMEKO 7, no. 4 (January 9, 2019): 48. http://dx.doi.org/10.21014/acta_imeko.v7i4.578.

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The automotive industry is currently undergoing far-reaching structural changes. Automobile manufacturers are pursuing intensive scientific research and technological development in the field of alternative drive systems, such as electric powertrains. If electric car batteries are charged with regenerative generated electricity, their emission output is zero (from a well-to-wheel view). Furthermore, electric drives have very high efficiency. At cold temperatures, however, the battery power drops due to energy-intensive loads, such as the heating of the passenger compartment, and this consequently reduces the range dramatically. Therefore, the focus of this research work is external energy supply for the required heat capacity. The auxiliary energy may be generated by renewable energy technologies in order to further improve the CO<sub>2</sub> balance of electric vehicles. The paper deals with the design, application, and testing of a biofuel-operated heater to heat the passenger compartment of a battery-powered electric car (a Renault ZOE R240). The practical use of the heating system is analyzed in several test drives, performed during winter 2018. The results as well as the range extension of the electric car that can be achieved by substituting the on-board heating system by the fuel-operated heater are quantified herein.

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Zhao, Kan, Cong Zhu, Hong Wen Xia, and Cheng Zeng. "Prediction and Analysis of the Driving Range of Electric Bus." Applied Mechanics and Materials 427-429 (September 2013): 787–92. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.787.

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In this paper, a method used to predict the driving range of electric bus based on electrochemical model of lithium ion battery was presented. Using a electric bus powered by lithium ion battery as an example, the driving ranges under three different driving cycles including American UDDS, European EUDC and Japanese 1015 were respectively predicted by the proposed method, and the effects of the temperature of battery pack and the number of battery module on the lowest state of charge SOCL required by the bus to travel a given distance were also analyzed.

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Simonis, Christoph. "Flexible Range Prediction for the Energy Management of Electric Vehicles." ATZ worldwide 121, no. 9 (August 30, 2019): 74–79. http://dx.doi.org/10.1007/s38311-019-0100-9.

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Kachmar, Roman. "Features of operation of the Tesla Model S90D electric car in the conditions of the cities of Ukraine." Transport technologies 2021, no. 1 (June 18, 2021): 75–83. http://dx.doi.org/10.23939/tt2021.01.075.

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Tendencies of sustainable development of transport, the use of alternative energy sources, and requirements to the reduction of the carbon footprint on all stages of life cycle on all levels of road transport have led to the increase of the share of the electric car both at the first registration and used vehicles. The appearance of a large amount of used modern electric cars on the market caused the necessity to develop the procedures of provision their technical operation both by requirements of rational use and provision of traffic safety. Research of the use of electric car Tesla Model S90D in conditions of Ukraine is carried out with certain indicators of environment and with different settings of car parameters. This provides an opportunity to understand how much the given model of the electric car is appropriate in exploitation in different conditions and also to determine which parameters of electric car settings fit the economic usage in different conditions and different seasons most of all. By results of road research on the change of accumulator battery capacity depending on the temperature of the environment, we established that the capacity reduces by 1% with every degree of negative temperature. A comfortable range of temperatures is determined for the use of accumulator battery of car (from 0 to 20 °С) is determined. By results of the research, the indicators of the effectiveness of the use of electric car battery power saving mode are determined – the reduction of the consumption is from 14 to 29 % depending on energy consumers. The significant impact of the pressure values in electric car tires on the energy consumption is established independently from turning on the energy consumers – the increase is from 10% to 40% depending on its speed of movement. Additionally, the determination of the indicators of electric car energy effectiveness for the speed of 140-150 km/h is carried out by which the energy use is 550-670 W h/km and provides the driving distance on the level of 160 km.

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Brandstätter, Georg, Markus Leitner, and Ivana Ljubić. "Location of Charging Stations in Electric Car Sharing Systems." Transportation Science 54, no. 5 (September 2020): 1408–38. http://dx.doi.org/10.1287/trsc.2019.0931.

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Electric vehicles are prime candidates for use within urban car sharing systems, both from economic and environmental perspectives. However, their relatively short range necessitates frequent and rather time-consuming recharging throughout the day. Thus, charging stations must be built throughout the system’s operational area where cars can be charged between uses. In this work, we introduce and study an optimization problem that models the task of finding optimal locations and sizes for charging stations, using the number of expected trips that can be accepted (or their resulting revenue) as a gauge of quality. Integer linear programming formulations and construction heuristics are introduced, and the resulting algorithms are tested on grid-graph-based instances, as well as on real-world instances from Vienna. The results of our computational study show that the best-performing exact algorithm solves most of the benchmark instances to optimality and usually provides small optimality gaps for the remaining ones, whereas our heuristics provide high-quality solutions very quickly. Our algorithms also provide better solutions than a sequential approach that considers strategic and operational decisions separately. A cross-validation study analyzes the algorithms’ performance in cases where demand is uncertain and shows the advantage of combining individual solutions into a single consensus solution, and a simulation study investigates their behavior in car sharing systems that provide their customers with more flexibility regarding vehicle selection.

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Chanaron, Jean-Jacques. "Challenges to radical innovation: A car for the future: Part one." Megatrend revija 17, no. 2 (2020): 1–30. http://dx.doi.org/10.5937/megrev2002001c.

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The car of the future is one of the most fashionable issues in the press, in political sketches and in academia. This first article is dealing with the various available options for automotive power train: gasoline, diesel, bio-fuels, natural gas, liquefied petroleum gas, hydrogen, hybrid electric, "plug-in" vehicle hybrid electric, extended range hybrid electric, and finally full battery electric vehicle and fuel cell electric vehicle. For each option, the paper analyzes the key characteristics in terms of political, social and cultural acceptability, degree of achievement, current Status and long term perspectives. The content for each characteristic is given by up-to-date literature and interviews with key experts. When available, market data have been collected and are analyzed and discussed. Key indicators as obstacles to or in favor of one given options are also investigated and critically discussed.

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Nagy, Andras. "Electric aircraft - present and future." Production Engineering Archives 23, no. 23 (June 1, 2019): 36–40. http://dx.doi.org/10.30657/pea.2019.23.06.

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Abstract In this paper, an outlook about the present of electrical aviation is given. The relatively small energy density of current battery technologies is adequate to build usable electric car, but not suitable for electric aircraft. Because of the very limited amount of energy available on-board, a couple of percent in efficiency can give significant increase in range and flight time, hence the development of more efficient propulsion system and E-motor is as important as the development of battery technologies. Current research results at the University of Dunaujvaros show, that building E-motors from amorphous materials is possible, and can easily increase the efficiency of high speed E-motors.

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Patidar, Lalit, and Sri Ramya Bhamidipati. "Parametric Study of Drag Force on a Formula Student Electric Race Car Using CFD." Applied Mechanics and Materials 575 (June 2014): 300–305. http://dx.doi.org/10.4028/www.scientific.net/amm.575.300.

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Aerodynamic drag plays an important role in fuel economy of the vehicle especially for electric cars directly affecting the range. The objective of Aerodynamics subsystem of IIT Bombay racing team is to predict and minimize drag force on the Formula student electric race car thereby improving the performance. A standard generic car body known as Ahmed body is taken to set up simulation parameters in FLUENT by validating a test case against the experimental data available in literature. Variation and dependence of drag force on parameters such as frontal area, distribution of pressure coefficient and pressure loss in wake region is studied numerically. Comparison is made between Formula Student 2013 car Evo2 and newly designed car Evo3 for coming season of Formula Student 2014. A substantial reduction in drag force of 18.8% is achieved which can be attributed to lower frontal area and streamlined bodyworks design. Energy consumption of the vehicle for endurance race is reduced by 11.5 % improving the fuel economy.

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Zhao, Liang, Wei Yao, Yu Wang, and Jie Hu. "Machine Learning-Based Method for Remaining Range Prediction of Electric Vehicles." IEEE Access 8 (2020): 212423–41. http://dx.doi.org/10.1109/access.2020.3039815.

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Wang, Xu Dong, Hai Xing Zhang, Shu Cai Yang, Yong Qin Zhou, and Jin Fa Liu. "Research on Torque Distribution Strategy for ISG Hybrid Electric Cars." Advanced Materials Research 546-547 (July 2012): 212–17. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.212.

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Based on the configuration and working state analysis of the ISG hybrid electric cars, the torque distribution strategy of a hybrid system is designed to delineate the maximum and minimum work torque curves of the engine, achieve optimization of engine’s range so as to make sure the target torque of the engine and ISG motor, and finally through the calibrated driving characteristics MAP and battery SOC state to achieve the calculation of total vehicle torque demand. Taking the Hafei Saibao ISG hybrid car as a test model, the test of fuel economy and emissions carried out under specific conditions showed that using the torque distribution strategy has increased by 12.8 % of the ISG hybrid car fuel economy and improved emissions performance to some extent compared to the traditional Hafei Saibao cars.

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Caunii, Vasile, and Adrian Sachelarie. "Aspects of Modeling and Optimizing Air Circulation Currents in a Car Cabin." Applied Mechanics and Materials 659 (October 2014): 163–70. http://dx.doi.org/10.4028/www.scientific.net/amm.659.163.

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Air conditioning system is one of the main components of modern cars, which defines the level of comfort, its performances directly affecting the car performances. In order to have a car with high level of thermal comfort, the air-conditioning system must be efficient in terms of energy, it has to perform many functions regarding the microclimate control (control of temperature, humidity, filtration), and in addition must fulfill safety and security functions in circulation (demisting and defrosting). Also vehicle air-conditioning system can significantly influence fuel economy and tailpipe emissions of conventional and hybrid electric vehicles (HEV) and reduce electric vehicle (EV) range.

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Barbu, Anca Giorgiana, and Marian Gheorghe. "Development of the Car Seat through a Wider Range of Active Elements Implementation." Advanced Engineering Forum 42 (September 7, 2021): 113–21. http://dx.doi.org/10.4028/www.scientific.net/aef.42.113.

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Car seats represent a constructive - functional system including a variety of elements, assembled on a specific frame, among those the main components are the seat pan, the seat backrest, and the seat headrest. In order to improve the comfort of the driver and passengers, as well vehicle general safety, the car seats are under of intensive research and implementation of advanced developments.Thus, through of variety of sensors integrated into the seat, some important parameters are analysed for keeping the drivers alert and comfortable while driving semi-autonomous or autonomous cars. For adjustment of the seat linear and angular positions, a special system has been implemented, comprising electric, electronic, and pneumatic elements. Also, seat micro-adjustments are possible through the action of specific actuators. The car seat design and manufacture take into consideration a diverse array of data, based on a large range of parameters to face the diversity of drivers requirements. The present paper is advancing a conceptual development of car complex seat, integrating a number of active elements, with multiple possible adjustments. The new seat model is designed to satisfy a larger diversity of people both in terms of comfort and safety.

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Sun, Shuai, Jun Zhang, Jun Bi, and Yongxing Wang. "A Machine Learning Method for Predicting Driving Range of Battery Electric Vehicles." Journal of Advanced Transportation 2019 (January 9, 2019): 1–14. http://dx.doi.org/10.1155/2019/4109148.

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It is of great significance to improve the driving range prediction accuracy to provide battery electric vehicle users with reliable information. A model built by the conventional multiple linear regression method is feasible to predict the driving range, but the residual errors between -3.6975 km and 3.3865 km are relatively unfaithful for real-world driving. The study is innovative in its application of machine learning method, the gradient boosting decision tree algorithm, on the driving range prediction which includes a very large number of factors that cannot be considered by conventional regression methods. The result of the machine learning method shows that the maximum prediction error is 1.58 km, the minimum prediction error is -1.41 km, and the average prediction error is about 0.7 km. The predictive accuracy of the gradient boosting decision tree is compared against that of the conventional approaches.

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Zou, Tianqi, Moein Khaloei, and Don MacKenzie. "Effects of Charging Infrastructure Characteristics on Electric Vehicle Preferences of New and Used Car Buyers in the United States." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 12 (October 3, 2020): 165–75. http://dx.doi.org/10.1177/0361198120952792.

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The used car market is a critical element for the mass adoption of electric vehicles (EVs). However, most previous studies on EV adoption have focused only on new car markets. This article examines and compares the effects of charging infrastructure characteristics on the preferences for EVs among both new and used car buyers. This study is based on an online stated preference choice experiment among private car owners in the U.S., and the results of comparable binomial logistic models show that new and used car buyers generally share similar patterns in preferences for EVs, with exceptions for sensitivity toward fast charging time, and home charging solutions. Respondents’ stated willingness to adopt an EV increases considerably with improvements in driving range, and the effects on new and used car buyers are similar. The study also finds that better availability of charging infrastructure largely increases preference for EVs. The results further reveal that slow and fast charging have complementary effects on encouraging EV adoption as the combination of public slow and fast charging can compensate for the unavailability of home charging.

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Foster, I., and J. R. Bumby. "A Hybrid Internal Combustion Engine/Battery Electric Passenger Car for Petroleum Displacement." Proceedings of the Institution of Mechanical Engineers, Part D: Transport Engineering 202, no. 1 (January 1988): 51–64. http://dx.doi.org/10.1243/pime_proc_1988_202_155_02.

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This paper examines the potential of the hybrid electric vehicle in substituting petroleum fuel by broad-based electrical energy. In particular a hybrid car is considered. The way in which the powertrain can be controlled and the effect component ratings have on achieving the petroleum substitution objective are described. It is shown that a hybrid vehicle can be designed that can achieve a petroleum substitution of between 20 and 70 per cent of the equivalent internal combustion engine vehicle, be capable of entering environmentally sensitive areas and yet be capable of a range at high and intermediate speeds that is limited only by the size of its fuel tank.

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Sun, Xiaobo, Weirong Liu, Mengfei Wen, Yue Wu, Heng Li, Jiahao Huang, Chao Hu, and Zhiwu Huang. "A Real-Time Optimal Car-Following Power Management Strategy for Hybrid Electric Vehicles with ACC Systems." Energies 14, no. 12 (June 10, 2021): 3438. http://dx.doi.org/10.3390/en14123438.

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This paper develops a model predictive multi-objective control framework based on an adaptive cruise control (ACC) system to solve the energy allocation and battery state of charge (SOC) maintenance problems of hybrid electric vehicles in the car-following scenario. The proposed control framework is composed of a car-following layer and an energy allocation layer. In the car-following layer, a multi-objective problem is solved to maintain safety and comfort, and the generated speed sequence in the prediction time domain is put forward to the energy allocation layer. In the energy allocation layer, an adaptive equivalent-factor-based consumption minimization strategy with the predicted velocity sequences is adopted to improve the engine efficiency and fuel economy. The equivalent factor reflects the extent of SOC variation, which is used to maintain the battery SOC level when optimizing the energy. The proposed controller is evaluated in the New York City Cycle (NYCC) driving cycle and the Urban Dynamometer Driving Schedule (UDDS) driving cycle, and the comparison results demonstrate the effectiveness of the proposed controller.

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Hong, Meenchee, and Nabsiah Abdul Wahid. "Modelling Malaysians willingness-to-pay for electric cars: evidence from discrete choice data." 11th GLOBAL CONFERENCE ON BUSINESS AND SOCIAL SCIENCES 11, no. 1 (December 9, 2020): 33. http://dx.doi.org/10.35609/gcbssproceeding.2020.11(33).

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Road transportation is the major contributing factor of increasing carbon emission to the environment that eventually leads to the severe air pollution. In recent decade, electric vehicles (EVs) have been introduced to replace fossil fuel cars as a way to reduce carbon emission from the road. The study examines Malaysians willingness-to-pay for electric cars by utilising the discrete choice modelling approach. Consumers in Kuala Lumpur and Penang were sampled and given sets of alternatives determined from a systemically review of past studies to make their choices. The choices are made up of three important attributes of EVs: the price, driving range and charging time of the vehicle. This paper also describes the relationship between socio-demographic factors of consumers and their buying decision. Results show that brand, design and safety features of the electric car significantly influence willingness-to-pay for electric cars. This study also reveals that Malaysians have low knowledge and willingness to purchase electric cars. The main reason was the costly battery replacement. Nonetheless, some respondents are willing to pay a higher price for a better performance electric car. The findings may be beneficial to policymakers and car producers in both profit gaining and environment protection. Keywords: Electric cars, willingness-to-pay, multinomial logit model, random utility theory, consumption, carbon emission

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Cieslik, Wojciech, Filip Szwajca, Wojciech Golimowski, and Andrew Berger. "Experimental Analysis of Residential Photovoltaic (PV) and Electric Vehicle (EV) Systems in Terms of Annual Energy Utilization." Energies 14, no. 4 (February 19, 2021): 1085. http://dx.doi.org/10.3390/en14041085.

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Electrification of powertrain systems offers numerous advantages in the global trend in vehicular applications. A wide range of energy sources and zero-emission propulsion in the tank to wheel significantly add to electric vehicles’ (EV) attractiveness. This paper presents analyses of the energy balance between micro-photovoltaic (PV) installation and small electric vehicle in real conditions. It is based on monitoring PV panel’s energy production and car electricity consumption. The methodology included energy data from real household PV installation (the most common renewable energy source in Poland), electric vehicle energy consumption during real driving conditions, and drivetrain operating parameters, all collected over a period of one year by indirect measuring. A correlation between energy produced by the micro-PV installation and small electric car energy consumption was described. In the Winter, small electric car energy consumption amounted to 14.9 kWh per 100 km and was 14% greater than summer, based on test requirements of real driving conditions. The 4.48 kW PV installation located in Poznań produced 4101 kWh energy in 258 days. The calculation indicated 1406 kWh energy was available for EV charging after household electricity consumption subtraction. The zero-emission daily distance analysis was done by the simplified method.

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Vaz, Warren, Arup K. R. Nandi, Robert G. Landers, and Umit O. Koylu. "Electric vehicle range prediction for constant speed trip using multi-objective optimization." Journal of Power Sources 275 (February 2015): 435–46. http://dx.doi.org/10.1016/j.jpowsour.2014.11.043.

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Sautermeister, Stefan, Max Falk, Bernard Baker, Frank Gauterin, and Moritz Vaillant. "Influence of Measurement and Prediction Uncertainties on Range Estimation for Electric Vehicles." IEEE Transactions on Intelligent Transportation Systems 19, no. 8 (August 2018): 2615–26. http://dx.doi.org/10.1109/tits.2017.2762829.

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Kneba, Zbigniew, and Jacek Kropiwnicki. "The Initial Selection of Parameters for Dynamic Brake for Car Engine Test Dynamometer." Key Engineering Materials 597 (December 2013): 179–84. http://dx.doi.org/10.4028/www.scientific.net/kem.597.179.

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The article presents an analysis of the car engine loads. The aim of the analysis was to determine the parameters of the brake / electric motor on an engine test bad. Analysis of loads on the driving tests concerned on chassis dynamo to simulate them on an engine dynamometer. To check the range of dynamometer shaft angular acceleration inertial mass of a car was reduced on the crankshaft axis. The calculations of maximum shaft acceleration were performed. Also the whole car acceleration when pulling away from the place was calculated. Calculations were verified by experiment on road. They both gave a good agreement.

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Xu, Peng, Rengkui Liu, Quanxin Sun, and Futian Wang. "A Novel Short-Range Prediction Model for Railway Track Irregularity." Discrete Dynamics in Nature and Society 2012 (2012): 1–12. http://dx.doi.org/10.1155/2012/591490.

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In recent years, with axle loads, train loads, transport volume, and travel speed constantly increasing and railway network steadily lengthening, shortcomings of current maintenance strategies are getting to be noticed from an economical and safety perspective. To overcome the shortcomings, permanent-of-way departments throughout the world have given a considerable attention to an ideal maintenance strategy which is to carry out appropriate maintenances just in time on track locations really requiring maintenance. This strategy is simplified as the condition-based maintenance (CBM) which has attracted attentions of engineers of many industries in the recent 70 years. To implement CBM for track irregularity, there are many issues which need to be addressed. One of them focuses on predicting track irregularity of each day in a future short period. In this paper, based on track irregularity evolution characteristics, a Short-Range Prediction Model was developed to this aim and is abbreviated to TI-SRPM. Performance analysis results for TI-SRPM illustrate that track irregularity amplitude predictions on sampling points by TI-SRPM are very close to their measurements by Track Geometry Car.

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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.

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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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Goo, Byeong Choon, and Jung Won Seo. "Probabilistic Estimation of the Fatigue Life of an Electric Car Bogie Frame." Key Engineering Materials 261-263 (April 2004): 1281–86. http://dx.doi.org/10.4028/www.scientific.net/kem.261-263.1281.

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Bogie frames of electric cars play an important role as a structural member for the support of vehicle loading. To guarantee more than 25 years' durability, a lot of study has been carried out for the prediction of the fatigue life of the bogie frame in experimental and theoretical domains. One of the new methods being applied in recent years for rolling stock structures is a reliability-based approach. In this paper, using one of these methods, we estimate fatigue life of the bogie frame of an electric car, which was developed by the Korea Railroad Research Institute. First of all, we have carried out tensile fatigue tests with several types of specimens: as-welded, ground on weld toe, reinforcement-removed and post weld heat treated specimens for each type aforementioned. In addition, we perform the probabilistic distribution tests of the S-N curves for the ground specimens. Normal, lognormal and Weibull distributions are used to model the distributions. Along with the obtained S-N relations, we use two approaches to evaluate the fatigue life of the bogie frame. In the first approach probabilistic distribution of the S-N curve is used. The S-N curves are obtained in function of failure percent from the specimen tests. Then the fatigue life is estimated by use of Miner-Palmgren hypothesis. In the second approach, load spectra measured by strain gauges are approximated by a two variable Weibull distribution. And a limit state function for the criterion of failure is derived by use of Miner-Palmgren hypothesis. The fatigue life is evaluated by a reliability engineering method.

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Mebarki, Brahim, Belkacem Draoui, Boumediène Allaou, Lakhdar Rahmani, and Elhadj Benachour. "Impact of the Air-Conditioning System on the Power Consumption of an Electric Vehicle Powered by Lithium-Ion Battery." Modelling and Simulation in Engineering 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/935784.

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The car occupies the daily universe of our society; however, noise pollution, global warming gas emissions, and increased fuel consumption are constantly increasing. The electric vehicle is one of the recommended solutions by the raison of its zero emission. Heating and air-conditioning (HVAC) system is a part of the power system of the vehicle when the purpose is to provide complete thermal comfort for its occupants, however it requires far more energy than any other car accessory. Electric vehicles have a low-energy storage capacity, and HVAC may consume a substantial amount of the total energy stored, considerably reducing the vehicle range, which is one of the most important parameters for EV acceptability. The basic goal of this paper is to simulate the air-conditioning system impact on the power energy source of an electric vehicle powered by a lithium-ion battery.

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Khurana, Anil, V. V. Ravi Kumar, and Manish Sidhpuria. "A Study on the Adoption of Electric Vehicles in India: The Mediating Role of Attitude." Vision: The Journal of Business Perspective 24, no. 1 (December 5, 2019): 23–34. http://dx.doi.org/10.1177/0972262919875548.

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Pollution of the environment is currently a global concern. Toxic emission from internal combustion engines is one of the primary air pollutants. In order to mitigate the effects of fossil fuel emission and address environmental concerns (ECs), electric vehicles (EVs) are being promoted aggressively all over the world. Various governments are encouraging people to switch to EVs by incentivizing the transition. Previous studies indicate that the high cost of the electric car, non-availability of charging infrastructure, time and range anxiety act as impediments to consumer adoption. The Government of India has given a call for ‘only Electric Vehicles’ on Road by 2030. This article is contemporary and examines the different factors that affect a consumer’s adoption of an EV. The respondents of the study are existing car owners in India. The data were analysed using Structured Equation Modelling (SEM). Attitude (ATT) emerged as a strong mediator, influencing the adoption of electric cars.

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Łebkowski, Andrzej. "Steam and Oxyhydrogen Addition Influence on Energy Usage by Range Extender—Battery Electric Vehicles." Energies 11, no. 9 (September 11, 2018): 2403. http://dx.doi.org/10.3390/en11092403.

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The objective of this paper is to illustrate the benefits of the influence of the steam and oxyhydrogen gas (HHO) on the composition of emitted exhaust gases and energy usage of operating the internal combustion engine (ICE) that drives a generator-powered battery electric vehicle (BEV). The employed internal combustion generating sets can be used as trailer mounted electric energy sources allowing one to increase the range of BEV vehicles, mainly during long distance travel between cities. The basic configurations of hybrid and electric propulsion systems used in a given Electric Vehicles (xEV) includes all types of Hybrid Electric Vehicles (xHEV) and Battery Electric Vehicles (xBEV), which are discussed. Using the data collected during traction tests in real road traffic (an electric car with a trailer range extender (RE) fitted with ICE generators (5 kW petrol, 6.5 kW diesel), a mathematical model was developed in the Modelica package. The elaborated mathematical model takes into account the dynamic loads acting on the set of vehicles in motion and the electric drive system assisted by the work of RE. Conducted tests with steam and HHO additives for ICE have shown reduced (5–10%) fuel consumption and emissions (3–19%) of harmful gases into the atmosphere.

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Grijalva, Edwin, José López Martínez, M. Flores, and Víctor del Pozo. "Design and Simulation of a Powertrain System for a Fuel Cell Extended Range Electric Golf Car." Energies 11, no. 7 (July 5, 2018): 1766. http://dx.doi.org/10.3390/en11071766.

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Du, Jiuyu, Xinying Mo, Yalun Li, Qun Zhang, Jianqiu Li, Xiaogang Wu, Languang Lu, and Minggao Ouyang. "Boundaries of high-power charging for long-range battery electric car from the heat generation perspective." Energy 182 (September 2019): 211–23. http://dx.doi.org/10.1016/j.energy.2019.05.222.

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Mamala, Jarosław, Michał Śmieja, and Krzysztof Prażnowski. "Analysis of the Total Unit Energy Consumption of a Car with a Hybrid Drive System in Real Operating Conditions." Energies 14, no. 13 (July 1, 2021): 3966. http://dx.doi.org/10.3390/en14133966.

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The market demand for vehicles with reduced energy consumption, as well as increasingly stringent standards limiting CO2 emissions, are the focus of a large number of research works undertaken in the analysis of the energy consumption of cars in real operating conditions. Taking into account the growing share of hybrid drive units on the automotive market, the aim of the article is to analyse the total unit energy consumption of a car operating in real road conditions, equipped with an advanced hybrid drive system of the PHEV (plug-in hybrid electric vehicles) type. In this paper, special attention has been paid to the total unit energy consumption of a car resulting from the cooperation of the two independent power units, internal combustion and electric. The results obtained for the individual drive units were presented in the form of a new unit index of the car, which allows us to compare the consumption of energy obtained from fuel with the use of electricity supported from the car’s batteries, during journeys in real road conditions. The presented research results indicate a several-fold increase in the total unit energy consumption of a car powered by an internal combustion engine compared to an electric car. The values of the total unit energy consumption of the car in real road conditions for the internal combustion drive are within the range 1.25–2.95 (J/(kg·m)) in relation to the electric drive 0.27–1.1 (J/(kg·m)) in terms of instantaneous values. In terms of average values, the appropriate values for only the combustion engine are 1.54 (J/(kg·m)) and for the electric drive only are 0.45 (J/(kg·m)) which results in the internal combustion engine values being 3.4 times higher than the electric values. It is the combustion of fuel that causes the greatest increase in energy supplied from the drive unit to the car’s propulsion system in the TTW (tank to wheels) system. At the same time this component is responsible for energy losses and CO2 emissions to the environment. The results were analysed to identify the differences between the actual life cycle energy consumption of the hybrid powertrain and the WLTP (Worldwide Harmonized Light-Duty Test Procedure) homologation cycle.

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Henao-Muñoz, Andrés Camilo, Paulo Pereirinha, and Alain Bouscayrol. "Regenerative Braking Strategy of a Formula SAE Electric Race Car Using Energetic Macroscopic Representation." World Electric Vehicle Journal 11, no. 2 (June 11, 2020): 45. http://dx.doi.org/10.3390/wevj11020045.

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This paper presents a braking strategy analysis for a Formula SAE electric race car. The proposed braking strategy aims to increase the recovery energy by a relevant distribution of the braking forces between the rear and front wheels. A mathematical model of the car is presented, and a simulation is performed in Matlab-Simulink. The model is organized using the energetic macroscopic representation graphical formalism. A real racetrack driving cycle is considered. Three braking strategies are compared considering the energy recovery and the vehicle stability. The simulation results show that the proposed strategy enables higher energy recovery while avoiding locking on both rear and front wheels. As in such a race the driving range is fixed, the reduction in energy consumption can be used to reduce the battery size. The battery weight can thus be decreased to improve the vehicle performance during competition.

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Varga, Bogdan, Arsen Sagoian, and Florin Mariasiu. "Prediction of Electric Vehicle Range: A Comprehensive Review of Current Issues and Challenges." Energies 12, no. 5 (March 12, 2019): 946. http://dx.doi.org/10.3390/en12050946.

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Electric vehicles (EV) are the immediate solution to drastically reducing pollutant emissions from the transport sector. There is a continuing increase in the number of EVs in use, but their widespread and massive acceptance by automotive consumers is related to the performance they can deliver. The most important feature here (a hot topic at present in EV research) is related to the possibility of providing a more accurate prediction of range. Range prediction is a complex problem because it depends on a lot of influence factors (internal, external, constant, variables) and the present paper aims to investigate the effect of these factors on the range of EVs. The results and aspects of current worldwide research on this theme are presented through the analysis of the main classes of influence factors: Vehicle design, the driver and the environment. Further, the weight and effect of each potential factor which influences EV range was analyzed by presenting current issues. An exhaustive and comprehensive analysis has made it possible to identify future research and development directions in the EV research field, resulting in massive future and immediate EV penetration in the automotive market.

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Dollinger, Manfred, and Gerhard Fischerauer. "Model-Based Range Prediction for Electric Cars and Trucks under Real-World Conditions." Energies 14, no. 18 (September 14, 2021): 5804. http://dx.doi.org/10.3390/en14185804.

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The further development of electric mobility requires major scientific efforts to obtain reliable data for vehicle and drive development. Practical experience has repeatedly shown that vehicle data sheets do not contain realistic consumption and range figures. Since the fear of low range is a significant obstacle to the acceptance of electric mobility, a reliable database can provide developers with additional insights and create confidence among vehicle users. This study presents a detailed, yet easy-to-implement and modular physical model for both passenger and commercial battery electric vehicles. The model takes consumption-relevant parameters, such as seasonal influences, terrain character, and driving behavior, into account. Without any a posteriori parameter adjustments, an excellent agreement with known field data and other experimental observations is achieved. This validation conveys much credibility to model predictions regarding the real-world impact on energy consumption and cruising range in standardized driving cycles. Some of the conclusions, almost impossible to obtain experimentally, are that winter conditions and a hilly terrain each reduce the range by 7–9%, and aggressive driving reduces the range by up to 20%. The quantitative results also reveal the important contributions of recuperation and rolling resistance towards the overall energy budget.

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Journal articles on the topic 'Electric car range prediction'

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