Journal articles: 'Driving range'

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Baldomero, Arianne K., Paula K. Skarda, and John J. Marini. "Driving Pressure: Defining the Range." Respiratory Care 64, no. 8 (May 14, 2019): 883–89. http://dx.doi.org/10.4187/respcare.06599.

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Aaron Peysack. "At the All-Night Driving Range." Antipodes 28, no. 2 (2014): 451. http://dx.doi.org/10.13110/antipodes.28.2.0451.

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Gruber. "Gefährdung durch Driving-Range eines Golfplatzes." Wirtschaftsrechtliche Blätter 22, no. 7 (July 2008): 351. http://dx.doi.org/10.1007/s00718-008-1190-0.

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Tian, Le, Lijian Wu, Xiaoyan Huang, and Youtong Fang. "Driving range parametric analysis of electric vehicles driven by interior permanent magnet motors considering driving cycles." CES Transactions on Electrical Machines and Systems 3, no. 4 (December 2019): 377–81. http://dx.doi.org/10.30941/cestems.2019.00049.

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Pai, Sravan, and M. R. Sindhu. "Intelligent driving range predictor for green transport." IOP Conference Series: Materials Science and Engineering 561 (November 12, 2019): 012110. http://dx.doi.org/10.1088/1757-899x/561/1/012110.

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Basu, Urna, Pierre de Buyl, Christian Maes, and Karel Netočný. "Driving-induced stability with long-range effects." EPL (Europhysics Letters) 115, no. 3 (August 1, 2016): 30007. http://dx.doi.org/10.1209/0295-5075/115/30007.

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Müller, Helfried, Axel-Oscar Bernt, Patrick Salman, and Alexander Trattner. "Fuel cell range extended electric vehicle fcreev long driving ranges without emissions." ATZ worldwide 119, no. 5 (April 28, 2017): 56–60. http://dx.doi.org/10.1007/s38311-017-0033-0.

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Zherdev, A. V. "Value range of solutions to the chordal Loewner equation with restriction on the driving function." Issues of Analysis 26, no. 2 (June 2019): 92–104. http://dx.doi.org/10.15393/j3.art.2019.6270.

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Steinstraeter, Matthias, Marcel Lewke, Johannes Buberger, Tobias Hentrich, and Markus Lienkamp. "Range Extension via Electrothermal Recuperation." World Electric Vehicle Journal 11, no. 2 (May 25, 2020): 41. http://dx.doi.org/10.3390/wevj11020041.

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One of the decisive reasons for the slow market penetration of electric vehicles is their short driving range, especially in cold temperatures. The goal of this paper was to increase the driving range in cold temperatures. Electric vehicles recover kinetic energy by recuperation and storage in the battery. However, if the battery is fully charged or cold, the option of recuperation is severely limited. Braking energy is dissipated into the environment via the mechanical brake, and the range thus decreases. Electrothermal recuperation (ETR) enables the braking power to be used in heater systems and thus saves energy in the overall system. In this paper, ETR was investigated with a highly responsive serial layer heater. An overall model consisting of the electric powertrain, the heating circuit, and the vehicle interior was developed and validated. The limitations of recuperation capability were determined from driving tests. The factors state of charge and battery temperature were varied in the conducted simulations in order to quantify the range increase through ETR. The results showed that the range could be increased via electrothermal recuperation by up to 8% at −10 °C in a real driving cycle, using a serial heater. A control strategy of the heating circuit enabled the coolant circuit to function as buffer storage. The interior temperature—and consequently user comfort—remained unchanged.

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Pan, Chaofeng, Wei Dai, Liao Chen, Long Chen, and Limei Wang. "Driving range estimation for electric vehicles based on driving condition identification and forecast." AIP Advances 7, no. 10 (October 2017): 105206. http://dx.doi.org/10.1063/1.4993945.

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Chen, Xiaoxi, Yichen Bai, Chen Chao, and Mao Ye. "Driving liquid crystal lens to extend focus range." Japanese Journal of Applied Physics 57, no. 7 (June 14, 2018): 072601. http://dx.doi.org/10.7567/jjap.57.072601.

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Kunii, Yasuharu, Kouhei Tada, Yoji Kuroda, and Takashi Kubota. "Tele-Driving Method for Long-Range Terrain Traversal." IFAC Proceedings Volumes 34, no. 19 (September 2001): 233–38. http://dx.doi.org/10.1016/s1474-6670(17)33142-7.

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Tseng, Yi-Hsiang, and Yee-Pien Yang. "Torque and Battery Distribution Strategy for Saving Energy of an Electric Vehicle with Three Traction Motors." Applied Sciences 10, no. 8 (April 11, 2020): 2653. http://dx.doi.org/10.3390/app10082653.

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A torque and battery distribution (TBD) strategy is proposed for saving energy for an electric vehicle (EV) that is driven by three traction motors. Each traction motor is driven by an independent inverter and a battery pack. When the vehicle is accelerating or cruising, its vehicle control unit determines the optimal torque distribution of the three motors by particle swarm optimization (PSO) theory to minimize energy consumption on the basis of their torque–speed–efficiency maps. Simultaneously, the states of charge (SOC) of the three battery packs are controlled in balance for improving the driving range and for avoiding unexpected battery depletion. The proposed TBD strategy can increase 7.7% driving range in the circular New European Driving Cycle (NEDC) of radius 100 m and 28% in the straight-line NEDC. All the battery energy can be effectively distributed and utilized for extending the driving range with an improved energy consumption efficiency.

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Lu, Zhenbo, Qi Zhang, Yu Yuan, and Weiping Tong. "Optimal Driving Range for Battery Electric Vehicles Based on Modeling Users’ Driving and Charging Behavior." Journal of Advanced Transportation 2020 (June 16, 2020): 1–10. http://dx.doi.org/10.1155/2020/8813137.

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This paper proposes a simulation approach for the optimal driving range of battery electric vehicles (BEVs) by modeling the driving and charging behavior. The driving and charging patterns of BEV users are characterized by reconstructing the daily travel chain based on the practical data collected from Shanghai, China. Meanwhile, interdependent behavioral variables for daily trips and each trip are defined in the daily trip chain. To meet the goal of the fitness of driving range, a stochastic simulation framework is established by the Monte Carlo method. Finally, with consideration of user heterogeneity, the optimal driving range under different charging scenarios is analyzed. The findings include the following. (1) The daily trip chain can be reconstructed through the behavioral variables for daily trips and each trip, and there is a correlation between the variables examined by the copula function. (2) Users with different daily travel demand have a different optimal driving range. When choosing a BEV, users are recommended to consider that the daily vehicle kilometers traveled are less than 34% of the battery driving range. (3) Increasing the charging opportunity and charging power is more beneficial to drivers who are characterized by high daily travel demand. (4) On the premise of meeting travel demand, the beneficial effects of increased fast-charging power will gradually decline.

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Gill, Jennifer A., José A. Alves, and Tómas G. Gunnarsson. "Mechanisms driving phenological and range change in migratory species." Philosophical Transactions of the Royal Society B: Biological Sciences 374, no. 1781 (July 29, 2019): 20180047. http://dx.doi.org/10.1098/rstb.2018.0047.

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Many migratory systems are changing rapidly in space and time, and these changes present challenges for conservation. Changes in local abundance and site occupancy across species' ranges have raised concerns over the efficacy of the existing protected area networks, while changes in phenology can potentially create mismatches in the timing of annual events with the availability of key resources. These changes could arise either through individuals shifting in space and time or through generational shifts in the frequency of individuals using different locations or on differing migratory schedules. Using a long-term study of a migratory shorebird in which individuals have been tracked through a period of range expansion and phenological change, we show that these changes occur through generational shifts in spatial and phenological distributions, and that individuals are highly consistent in space and time. Predictions of future rates of changes in range size and phenology, and their implications for species conservation, will require an understanding of the processes that can drive generational shifts. We therefore explore the developmental, demographic and environmental processes that could influence generational shifts in phenology and distribution, and the studies that will be needed to distinguish among these mechanisms of change. This article is part of the theme issue ‘Linking behaviour to dynamics of populations and communities: application of novel approaches in behavioural ecology to conservation’.

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Hadsell, Raia, Pierre Sermanet, Jan Ben, Ayse Erkan, Marco Scoffier, Koray Kavukcuoglu, Urs Muller, and Yann LeCun. "Learning long-range vision for autonomous off-road driving." Journal of Field Robotics 26, no. 2 (February 2009): 120–44. http://dx.doi.org/10.1002/rob.20276.

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Xie, Yunkun, Yangyang Li, Zhichao Zhao, Hao Dong, Shuqian Wang, Jingping Liu, Jinhuan Guan, and Xiongbo Duan. "Microsimulation of electric vehicle energy consumption and driving range." Applied Energy 267 (June 2020): 115081. http://dx.doi.org/10.1016/j.apenergy.2020.115081.

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Du, Chang Qing, Gang Du, Ke Cheng Tan, and Yong Shan Liu. "Research on Remaining Driving Range Estimation of Electric Vehicle Based on Dynamic Working Condition." Advanced Materials Research 945-949 (June 2014): 509–15. http://dx.doi.org/10.4028/www.scientific.net/amr.945-949.509.

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Aimed at estimating the remaining driving range of electric vehicle (EV) on dynamic working condition and exploring the influences on remaining driving range estimation, combined GT-Drive and Matlab, EV simulation model and remaining driving range estimation model have established. Based on UDDS, compared simulation results with estimation results of remaining driving range, the influences on remaining driving range estimation have been discovered, then a optimization scheme is put forward. With the method of corrected parameter, the error of initial estimation value is reduced; when braking energy recovery module is added, EV simulation model is optimized. The results of test indicates that the optimized model has realized to accurately estimate the remaining driving range, meets the requirements of the expected estimation accuracy.

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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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Phatak, Jyothi P., L. Venkatesha, and C. S. Raviprasad. "Driving cycle based battery rating selection and range analysis in EV applications." International Journal of Power Electronics and Drive Systems (IJPEDS) 12, no. 2 (June 1, 2021): 637. http://dx.doi.org/10.11591/ijpeds.v12.i2.pp637-649.

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<p>The energy consumption of electric vehicles (EVs)depends on traffic environment, terrain, resistive forces acting on vehicle, vehicle characteristics and driving habits of driver. The battery pack in EV is the main energy storage element and the energy capacity determines the range of vehicle. This paper discusses the behavior of battery when EV is subjected to different driving environments such as urban and highway. The battery rating is selected based on requirement of driving cycle. The MATLAB/Simulink model of battery energy storage system (BESS) consisting of battery, bidirectional DC/DCconverter and electric propulsion system is built. The simulation is carried out and the performance of BESS is tested for standard driving cycles which emulate actual driving situations. It has been shown that, the amount of the energy recovered by battery during deceleration depends on the amount of regenerative energy available in the driving cycle. If the battery recovers more energy during deceleration, the effective energy consumed by it reduces and the range of the vehicle increases.</p>

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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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Liu, Guang Ming, Hong Fu, Lan Guang Lu, Yan Jing Wang, Jian Feng Hua, Jian Qiu Li, Ming Gao Ouyang, Chao Feng, Shan Xue, and Ping Chen. "Remaining Driving Range Estimation for Electric Vehicles through Information Fusion Method." Applied Mechanics and Materials 668-669 (October 2014): 641–46. http://dx.doi.org/10.4028/www.scientific.net/amm.668-669.641.

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Electric vehicle (EV) is one of the current research focuses due to its environmental friendliness, but the inaccuracy in remaining driving range (RDR) estimation lowers the consumers’ confidence on EVs. This paper introduces a remaining driving range estimation method based on information fusion (RDRIF), in which the RDR is determined by the fusion of the calculated range (calculation of the present battery and vehicle consumption states) and the cumulated range (cumulation of the real-traveled distance) to provide a accuracy RDR result. The RDRIF algorithm was embedded in a type of pure electric vehicle, and a set of vehicle experiments was carried out on a dynamometer test system. Two urban driving cycles representing normal and aggressive driving conditions are implemented in the tests to validate the effectiveness of RDRIF. The results show that the RDRIF could provide a stable and convergent estimation result under both driving conditions compared with the traditional methods, and the estimation error could be rapidly limited to ±5%, which effectively reduces the passengers’ range anxiety.

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Farzaneh-Gord, Mahmood, Hamid Reza Rahbari, and Hossin Nikofard. "The effect of important parameters on the natural gas vehicles driving range." Polish Journal of Chemical Technology 14, no. 4 (December 1, 2012): 61–68. http://dx.doi.org/10.2478/v10026-012-0104-3.

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Abstract One of the most important issues regarding Natural Gas Vehicles (NGVs) is the Driving Range, which is defined as capability of a NGV to travel a certain distance after each refueling. The Driving Range is a serious obstacle in the development and growth of NGVs. Thus the necessity of studying the effects of various parameters on the Driving Range could be realized. It is found that the on-board storage capacity and the natural gas heating value have the greatest effect on the Driving Range. The charged mass of NGV cylinders is varied due to the natural gas composition and the final in-cylinder values (temperature and pressure). Underfilling of NGV cylinders, during charging operations, is a result of the elevated temperature which occurs in the NGV storage cylinder, due to compression and other processes could be overcome by applying extensive over-pressurization of the cylinder during the fuelling operation. Here, the effects of the most important parameters on the Driving Range have been investigated. The parameters are natural gas composition, engine efficiency and final NGV on-board in-cylinder temperature and pressure. It is found that, the composition has big effects on the Driving Range. The results also show that as final in-cylinder pressure decreases (or temperature increases), the Driving Range will be increased.

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Fan, Yu Cheng, and Jan Hung Shen. "Low Luminance Dynamic Range Converter for Vehicle Application." Applied Mechanics and Materials 284-287 (January 2013): 2171–75. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.2171.

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This paper presents a low luminance dynamic range converter that includes light recording and adjustment system for vehicle application under light insufficient environment. The development of vehicle electronic technology has improved traditional vehicle function. How to design a safety function system for vehicle become more and more important in vehicle industries. This work designs low luminance dynamic range converter circuit for vehicle application and provides a safety-driving environment. In the proposed method, we adopt video capture system to record light information from driving environment. An adaptive adjustment is adopted to re-arrange the histogram according to the distribution of luminance. Then, we use a series of test images and extract the characteristic value to train the system to reflect practical circumstances. Next, color calibrations and de-noise processing are performed to improve the visual quality. The presented approach considers the realistic driving situation and provides a bright and safe visual environment for driver under light insufficient environment.

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Tikhomirov, A. N., O. B. Tikhomirova, M. E. Gnenik, and V. Gropa. "Estimation of appropriate power range extender for battery electric vehicle." E3S Web of Conferences 124 (2019): 02001. http://dx.doi.org/10.1051/e3sconf/201912402001.

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The article presents the results of an experimental and theoretical study of the energy consumption of a vehicle under different conditions. The purpose was to determine the power of the auxiliary range extender on board of the electric battery vehicle. Driving is considered both for real road conditions of a large city, and a specific driving cycle. The high validity of the results is ensured by the use of the new driving cycle WLTC. It is shown that in urban traffic conditions 5 kW auxiliary power plant is sufficient for adequate compensation of electricity consumption of a vehicle with a curb weight 2500 kg.

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Yu, Yuanbin, Junyu Jiang, Zhaoxiang Min, Pengyu Wang, and Wangsheng Shen. "Research on Energy Management Strategies of Extended-Range Electric Vehicles Based on Driving Characteristics." World Electric Vehicle Journal 11, no. 3 (August 5, 2020): 54. http://dx.doi.org/10.3390/wevj11030054.

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The extended-range electric vehicle (E-REV) can solve the problems of short driving range and long charging time of pure electric vehicles, but it is necessary to control the engine working points and allocate the power of the energy sources reasonably. In order to improve the fuel economy of the vehicle, an energy management strategy (EMS) that can adapt to the daily driving characteristics of the driver and adjust the control parameters online is proposed in this paper. Firstly, through principal component analysis (PCA) and iterative self-organizing data analysis techniques algorithm (ISODATA) of historical driving data, a typical driving cycle which can describe driving characteristics of the driver is constructed. Then offline optimization of control parameters by adaptive simulated annealing under each typical driving cycle and online recognition of driving cycles by extreme learning machine (ELM) are applied to the adaptive multi-workpoints energy management strategy (A-MEMS) of E-REV. In the end, compared with traditional rule-based control strategies, A-MEMS achieves good fuel-saving and emission-reduction result by simulation verification, and it explores a new and feasible solution for the continuous upgrade of the EMS.

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Itoh, Makoto, Toshiyuki Inagaki, Yasuhiro Shiraishi, Takayuki Watanabe, and Yasuhiko Takae. "Contributing Factors for Mode Awareness of a Vehicle with a Low-Speed Range and a High-Speed Range Acc Systems." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 49, no. 3 (September 2005): 376–80. http://dx.doi.org/10.1177/154193120504900334.

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This paper tries to clarify contributing factors to achieving driver's mode awareness while he or she is driving a vehicle with a low-speed range and a high-speed range Adaptive Cruise Control (ACC) systems. We investigate the following three probable factors: (1) control logic for a low-speed range ACC when it loses sight of the target vehicle to follow, (2) previous acquaintance with the high-speed range ACC, and (3) control mode utilized more frequently. An experiment has been conducted with a fixed-based driving simulator to investigate effects of these factors on the driver's mode awareness.

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Lee, John D. "Driving Safety." Reviews of Human Factors and Ergonomics 1, no. 1 (June 2005): 172–218. http://dx.doi.org/10.1518/155723405783703037.

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Driving is a common and hazardous activity that is a prominent cause of death worldwide. Driver behavior represents a predominant cause, contributing to over 90% of crashes. In this review, I will focus on how driver behavior influences driving safety by describing the types of crashes and their general causes, the driving process, the perceptual and cognitive characteristics of drivers, and driver types and impairments. Evidence from each of these perspectives suggests that breakdowns of a multilevel control process are the fundamental factors that undermine driving safety. Drivers adapt and drive safely in a broad range of situations but fail when expectations are violated or when feedback is inadequate. The review concludes by considering driving safety from a societal risk management perspective.

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Kim, Kwanghyun, Seunghwan Moon, Jinhwan Kim, Yangkyu Park, and Jong-Hyun Lee. "Input Shaping Based on an Experimental Transfer Function for an Electrostatic Microscanner in a Quasistatic Mode." Micromachines 10, no. 4 (March 27, 2019): 217. http://dx.doi.org/10.3390/mi10040217.

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This paper describes an input shaping method based on an experimental transfer function to effectively obtain a desired scan output for an electrostatic microscanner driven in a quasistatic mode. This method features possible driving extended to a higher frequency, whereas the conventional control needs dynamic modeling and is still ineffective in mitigating harmonics, sub-resonances, and/or higher modes. The performance of the input shaping was experimentally evaluated in terms of the usable scan range (USR), and its application limits were examined with respect to the optical scan angle and frequency. The experimental results showed that the usable scan range is as wide as 96% for a total optical scan angle (total OSA) of up to 9° when the criterion for scan line error is 1.5%. The usable scan ranges were degraded for larger total optical scan angles because of the nonlinear electrostatic torque with respect to the driving voltage. The usable scan range was 90% or higher for most frequencies up to 160 Hz and was drastically decreased for the higher driving frequency because fewer harmonics are included in the input shaping process. Conclusively, the proposed method was experimentally confirmed to show good performance in view of its simplicity and its operable range, quantitatively compared with that of the conventional control.

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Shi, Qin, Bingjiao Liu, Qingsheng Guan, Lin He, and Duoyang Qiu. "A genetic ant colony algorithm-based driving cycle generation approach for testing driving range of battery electric vehicle." Advances in Mechanical Engineering 12, no. 1 (January 2020): 168781401990105. http://dx.doi.org/10.1177/1687814019901054.

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In this article, an approach of driving cycle generation for battery electric vehicle is proposed based on genetic ant colony algorithm. The real-world traffic information is utilized to build up a local driving cycle database, in which definitions of the short trip and kinematic characteristic parameters are discussed to describe the driving cycle. A method of principal component analysis is taken as a preprocessor for reducing the dimension of driving cycle data. And then, genetic ant colony algorithm is used to classify the type of short trips and generate the driving cycle. The experimental results on board indicate that, compared with the Economic Commission for Europe driving cycle, the error of driving range and characteristic parameters tested by genetic ant colony driving cycle are reduced by 18.1% and 18.3%, respectively. Therefore, genetic ant colony driving cycle is a good candidate to test driving range of battery electric vehicle.

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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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Wang, Shuhang, Jianfeng Li, Pengshuai Yang, Tianxiao Gao, Alex R. Bowers, and Gang Luo. "Towards Wide Range Tracking of Head Scanning Movement in Driving." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 13 (April 20, 2020): 2050033. http://dx.doi.org/10.1142/s0218001420500330.

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Gaining environmental awareness through lateral head scanning (yaw rotations) is important for driving safety, especially when approaching intersections. Therefore, head scanning movements could be an important behavioral metric for driving safety research and driving risk mitigation systems. Tracking head scanning movements with a single in-car camera is preferred hardware-wise, but it is very challenging to track the head over almost a [Formula: see text] range. In this paper, we investigate two state-of-the-art methods, a multi-loss deep residual learning method with 50 layers (multi-loss ResNet-50) and an ORB feature-based simultaneous localization and mapping method (ORB-SLAM). While deep learning methods have been extensively studied for head pose detection, this is the first study in which SLAM has been employed to innovatively track head scanning over a very wide range. Our laboratory experimental results showed that ORB-SLAM was more accurate than multi-loss ResNet-50, which often failed when many facial features were not in the view. On the contrary, ORB-SLAM was able to continue tracking as it does not rely on particular facial features. Testing with real driving videos demonstrated the feasibility of using ORB-SLAM for tracking large lateral head scans in naturalistic video data.

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Kim, Seiho, Jaesik Lee, and Chulung Lee. "Does Driving Range of Electric Vehicles Influence Electric Vehicle Adoption?" Sustainability 9, no. 10 (October 1, 2017): 1783. http://dx.doi.org/10.3390/su9101783.

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Dimitropoulos, Alexandros, Piet Rietveld, and Jos N. van Ommeren. "Consumer valuation of changes in driving range: A meta-analysis." Transportation Research Part A: Policy and Practice 55 (September 2013): 27–45. http://dx.doi.org/10.1016/j.tra.2013.08.001.

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Chen, Xiaoxi, Guangyong Li, Pengwei Li, and Mao Ye. "Driving method for liquid crystal lens to increase focus range." Electronics Letters 55, no. 6 (March 2019): 336–37. http://dx.doi.org/10.1049/el.2018.7146.

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Machura, Philip, Valerio De Santis, and Quan Li. "Driving Range of Electric Vehicles Charged by Wireless Power Transfer." IEEE Transactions on Vehicular Technology 69, no. 6 (June 2020): 5968–82. http://dx.doi.org/10.1109/tvt.2020.2984386.

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de Vries, Harwin, and Evelot Duijzer. "Incorporating driving range variability in network design for refueling facilities." Omega 69 (June 2017): 102–14. http://dx.doi.org/10.1016/j.omega.2016.08.005.

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Lin, Zhenhong. "Optimizing and Diversifying Electric Vehicle Driving Range for U.S. Drivers." Transportation Science 48, no. 4 (November 2014): 635–50. http://dx.doi.org/10.1287/trsc.2013.0516.

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Neaimeh, Myriam, Graeme A. Hill, Phil T. Blythe, and Yvonne Hübner. "Routing systems to extend the driving range of electric vehicles." IET Intelligent Transport Systems 7, no. 3 (September 1, 2013): 327–36. http://dx.doi.org/10.1049/iet-its.2013.0122.

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Tsai, Chun-Wei, Kai-hsin Chen, Ching-Kai Shen, and Jui-che Tsai. "A MEMS Doubly Decoupled Gyroscope With Wide Driving Frequency Range." IEEE Transactions on Industrial Electronics 59, no. 12 (December 2012): 4921–29. http://dx.doi.org/10.1109/tie.2011.2177612.

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Choi, Kyung-Min, and A.-Lam Jung. "Analysis of Priority of Reutilization Factor of Outdoor Driving Range." Journal of Golf Studies 14, no. 1 (March 31, 2020): 199–211. http://dx.doi.org/10.34283/ksgs.2020.14.1.17.

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Szumska, Emilia M., and Rafał S. Jurecki. "Parameters Influencing on Electric Vehicle Range." Energies 14, no. 16 (August 7, 2021): 4821. http://dx.doi.org/10.3390/en14164821.

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There is a range of anxiety-related phenomena among users and potential buyers of electric vehicles. Chief among them is the fear of the vehicle stopping and its users getting “stuck” before reaching their designated destination. The limited range of an electric vehicle makes EV users worry that the battery will drain while driving and the vehicle will stall on the road. It is therefore important to know the factors that could further reduce the range during daily vehicle operation. The purpose of this study was to determine the effect of selected parameters on a battery’s depth of discharge (DOD). In a simulation study of an electric vehicle, the effects of the driving cycle, ambient temperature, load, and initial state of charge of the accumulator on the energy consumption pattern and a battery’s depth of discharge (DOD) were analyzed. The simulation results confirmed that the route taken has the highest impact on energy consumption. The presented results show how significantly the operating conditions of an electric vehicle affect the energy life. This translates into an electric vehicle’s range.

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Iora, Paolo, and Laura Tribioli. "Effect of Ambient Temperature on Electric Vehicles’ Energy Consumption and Range: Model Definition and Sensitivity Analysis Based on Nissan Leaf Data." World Electric Vehicle Journal 10, no. 1 (January 7, 2019): 2. http://dx.doi.org/10.3390/wevj10010002.

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In this paper, a general quasi-steady backward-looking model for energy consumption estimation of electric vehicles is presented. The model is based on a literature review of existing approaches and was set up using publicly available data for Nissan Leaf. The model has been used to assess the effect of ambient temperature on energy consumption and range, considering various reference driving cycles. The results are supported and validated using data available from an experimental campaign where the Nissan Leaf was driven to depletion across a broad range of winter ambient temperatures. The effect of ambient temperature and the consequent accessories consumption due to cabin heating are shown to be remarkable. For instance, in case of Federal Urban Driving Schedule (FUDS), simplified FUDS (SFUDS), and New European Driving Cycle (NEDC) driving cycles, the range exceeds 150 km at 20 °C, while it reduces to about 85 km and 60 km at 0 °C and −15 °C, respectively. Finally, a sensitivity analysis is reported to assess the impact of the hypotheses in the battery model and of making different assumptions on the regenerative braking efficiency.

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Mao, Jia-Yu, Dong-Kai Li, Xin Ding, Hong-Min Zhang, Yun Long, Xiao-Ting Wang, and Da-Wei Liu. "Fluctuations of driving pressure during mechanical ventilation indicates elevated central venous pressure and poor outcomes." Pulmonary Circulation 10, no. 4 (October 2020): 204589402097036. http://dx.doi.org/10.1177/2045894020970363.

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Inappropriate mechanical ventilation may induce hemodynamic alterations through cardiopulmonary interactions. The aim of this study was to explore the relationship between airway pressure and central venous pressure during the first 72 h of mechanical ventilation and its relevance to patient outcomes. We conducted a retrospective study of the Department of Critical Care Medicine of Peking Union Medical College Hospital and a secondary analysis of the MIMIC-III clinical database. The relationship between the ranges of driving pressure and central venous pressure during the first 72 h and their associations with prognosis were investigated. Data from 2790 patients were analyzed. Wide range of driving airway pressure (odds ratio, 1.0681; 95% CI, 1.0415–1.0953; p < 0.0001) were independently associated with mortality, ventilator-free time, intensive care unit and hospital length of stay. Furthermore, wide range of driving pressure and elevated central venous pressure exhibited a close correlation. The area under receiver operating characteristic demonstrated that range of driving pressure and central venous pressure were measured at 0.689 (95% CI, 0.670–0.707) and 0.681 (95% CI, 0.662–0.699), respectively. Patients with high ranges of driving pressure and elevated central venous pressure had worse outcomes. Post hoc tests showed significant differences in 28-day survival rates (log-rank (Mantel–Cox), 184.7; p < 0.001). In conclusion, during the first 72 h of mechanical ventilation, patients with hypoxia with fluctuating driving airway pressure have elevated central venous pressure and worse outcomes.

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Wager, Guido, Jonathan Whale, and Thomas Braunl. "Driving electric vehicles at highway speeds: The effect of higher driving speeds on energy consumption and driving range for electric vehicles in Australia." Renewable and Sustainable Energy Reviews 63 (September 2016): 158–65. http://dx.doi.org/10.1016/j.rser.2016.05.060.

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Tamura, Ayataro, Takayuki Ishibashi, and Atsuo Kawamura. "EV Range Extender in a Two-Battery HEECS Chopper-Based Powertrain." World Electric Vehicle Journal 10, no. 2 (April 19, 2019): 19. http://dx.doi.org/10.3390/wevj10020019.

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This paper first presents a new powertrain based on a two-battery High-Efficiency Energy Conversion System (HEECS) chopper that is suitable for electric vehicles (EVs). The HEECS chopper is based on the principle of a partial power conversion circuit, and the overall efficiency is over 99% in a wide load range. The efficiency of this powertrain was measured in the steady state by two types of powertrains, a non-chopper powertrain and an HEECS chopper-based powertrain, using a motor test bench. On the basis of these data, several driving tests, such as the Worldwide-harmonized Light vehicles Test Cycle (WLTC), were simulated, and four driving cycle patterns were included. A 6.4% reduction in energy consumption was observed in WLTC low mode compared with the energy consumed by the non-chopper powertrain in the experiments. Thus, the HEECS chopper-based powertrain is more suitable for low-speed driving ranges than high-speed ranges.

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Wu, De Jun, Ting Yong Lu, Li Jun Zhang, and Xian Wu Gong. "Parameter Matching and Simulation Study of Powertrain for Extended-Range Electric Vehicle." Advanced Materials Research 926-930 (May 2014): 1387–91. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.1387.

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A method of parameter matching for extended-range electric vehicle (E-REV) was discussed to meet the requirements given, then using a model and genetic algorithm to optimize the transmission ratio of E-REV. The parameters of the battery and range extender (RE) are designed by driving range and power requirement. The simulation results shows that the parameter matching is reasonable, and the power performance and driving range could meet the design requirements.

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Li, Xuefei, Rajesh Balagam, Ting-Fang He, Peter P. Lee, Oleg A. Igoshin, and Herbert Levine. "On the mechanism of long-range orientational order of fibroblasts." Proceedings of the National Academy of Sciences 114, no. 34 (August 7, 2017): 8974–79. http://dx.doi.org/10.1073/pnas.1707210114.

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Long-range alignment ordering of fibroblasts have been observed in the vicinity of cancerous tumors and can be recapitulated with in vitro experiments. However, the mechanisms driving their ordering are not understood. Here, we show that local collision-driven nematic alignment interactions among fibroblasts are insufficient to explain observed long-range alignment. One possibility is that there exists another orientation field coevolving with the cells and reinforcing their alignment. We propose that this field reflects the mechanical cross-talk between the fibroblasts and the underlying fibrous material on which they move. We show that this long-range interaction can give rise to high nematic order and to the observed patterning of the cancer microenvironment.

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Lahlou, Anas, Florence Ossart, Emmanuel Boudard, Francis Roy, and Mohamed Bakhouya. "Optimal Management of Thermal Comfort and Driving Range in Electric Vehicles." Energies 13, no. 17 (August 31, 2020): 4471. http://dx.doi.org/10.3390/en13174471.

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The HVAC system represents the main auxiliary load in battery-powered electric vehicles (BEVs) and requires efficient control approaches that balance energy saving and thermal comfort. On the one hand, passengers always demand more comfort, but on the other hand the HVAC system consumption strongly impacts the vehicle’s driving range, which constitutes a major concern in BEVs. In this paper, a thermal comfort management approach that optimizes the thermal comfort while preserving the driving range during a trip is proposed. The electric vehicle is first modeled together with the HVAC and the passengers’ thermo-physiological behavior. Then, the thermal comfort management issue is formulated as an optimization problem solved by dynamic programing. Two representative test-cases of hot climates and traffic situations are simulated. In the first one, the energetic cost and ratio of improved comfort is quantified for different meteorological and traffic conditions. The second one highlights the traffic situation in which a trade-off between the driving speed and thermal comfort is important. A large number of weather and traffic situations are simulated and results show the efficiency of the proposed approach in minimizing energy consumption while maintaining a good comfort.

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Kontou, Eleftheria, Yafeng Yin, and Zhenhong Lin. "Socially optimal electric driving range of plug-in hybrid electric vehicles." Transportation Research Part D: Transport and Environment 39 (August 2015): 114–25. http://dx.doi.org/10.1016/j.trd.2015.07.002.

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Journal articles on the topic 'Driving range'

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