The Experts below are selected from a list of 40239 Experts worldwide ranked by ideXlab platform
Malcolm Mcculloch - One of the best experts on this subject based on the ideXlab platform.
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a robust data driven methodology for real world Driving Cycle development
Transportation Research Part D-transport and Environment, 2012Co-Authors: Justin D K Bishop, Colin J Axon, Malcolm MccullochAbstract:Abstract This paper develops a robust, data-driven Markov Chain method to capture real-world behaviour in a Driving Cycle without deconstructing the raw velocity–time sequence. The accuracy of the Driving Cycles developed using this method was assessed on nine metrics as a function of the number of velocity states, Driving Cycle length and number of Markov repetitions. The road grade was introduced using vehicle specific power and a velocity penalty. The method was demonstrated on a corpus of 1180 km from a trial of electric scooters. The accuracies of the candidate Driving Cycles depended most strongly on the number of Markov repetitions. The best Driving Cycle used 135 velocity modes, was 500 s and captured the corpus behaviour to within 5% after 1,000,000 Markov repetitions. In general, the best Driving Cycle reproduced the corpus behaviour better when road grade was included.
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A robust, data-driven methodology for real-world Driving Cycle development
Transportation Research Part D: Transport and Environment, 2012Co-Authors: Justin D K Bishop, Colin J Axon, Malcolm MccullochAbstract:This paper develops a robust, data-driven Markov Chain method to capture real-world behaviour in a Driving Cycle without deconstructing the raw velocity-time sequence. The accuracy of the Driving Cycles developed using this method was assessed on nine metrics as a function of the number of velocity states, Driving Cycle length and number of Markov repetitions. The road grade was introduced using vehicle specific power and a velocity penalty. The method was demonstrated on a corpus of 1180. km from a trial of electric scooters. The accuracies of the candidate Driving Cycles depended most strongly on the number of Markov repetitions. The best Driving Cycle used 135 velocity modes, was 500. s and captured the corpus behaviour to within 5% after 1,000,000 Markov repetitions. In general, the best Driving Cycle reproduced the corpus behaviour better when road grade was included. © 2012 Elsevier Ltd
Gaurav Sharma - One of the best experts on this subject based on the ideXlab platform.
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development of real world Driving Cycle case study of pune india
Transportation Research Part D-transport and Environment, 2009Co-Authors: Sanghpriya H. Kamble, Tom V. Mathew, Gaurav SharmaAbstract:The critical component of all emission models is a Driving Cycle representing the traffic behaviour. Although Indian Driving Cycles were developed to test the compliance of Indian vehicles to the relevant emission standards, they neglects higher speed and acceleration and assume all vehicle activities to be similar irrespective of heterogeneity in the traffic mix. Therefore, this study is an attempt to develop an urban Driving Cycle for estimating vehicular emissions and fuel consumption. The proposed methodology develops the Driving Cycle using micro-trips extracted from real-world data. The uniqueness of this methodology is that the Driving Cycle is constructed considering five important parameters of the time-space profile namely, the percentage acceleration, deceleration, idle, cruise, and the average speed. Therefore, this approach is expected to be a better representation of heterogeneous traffic behaviour. The Driving Cycle for the city of Pune in India is constructed using the proposed methodology and is compared with existing Driving Cycles.
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Development of real-world Driving Cycle: Case study of Pune, India
Transportation Research Part D: Transport and Environment, 2009Co-Authors: Sanghpriya H. Kamble, Tom V. Mathew, Gaurav SharmaAbstract:The critical component of all emission models is a Driving Cycle representing the traffic behaviour. Although Indian Driving Cycles were developed to test the compliance of Indian vehicles to the relevant emission standards, they neglects higher speed and acceleration and assume all vehicle activities to be similar irrespective of heterogeneity in the traffic mix. Therefore, this study is an attempt to develop an urban Driving Cycle for estimating vehicular emissions and fuel consumption. The proposed methodology develops the Driving Cycle using micro-trips extracted from real-world data. The uniqueness of this methodology is that the Driving Cycle is constructed considering five important parameters of the time–space profile namely, the percentage acceleration, deceleration, idle, cruise, and the average speed. Therefore, this approach is expected to be a better representation of heterogeneous traffic behaviour. The Driving Cycle for the city of Pune in India is constructed using the proposed methodology and is compared with existing Driving Cycles.© Elsevie
Justin D K Bishop - One of the best experts on this subject based on the ideXlab platform.
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a robust data driven methodology for real world Driving Cycle development
Transportation Research Part D-transport and Environment, 2012Co-Authors: Justin D K Bishop, Colin J Axon, Malcolm MccullochAbstract:Abstract This paper develops a robust, data-driven Markov Chain method to capture real-world behaviour in a Driving Cycle without deconstructing the raw velocity–time sequence. The accuracy of the Driving Cycles developed using this method was assessed on nine metrics as a function of the number of velocity states, Driving Cycle length and number of Markov repetitions. The road grade was introduced using vehicle specific power and a velocity penalty. The method was demonstrated on a corpus of 1180 km from a trial of electric scooters. The accuracies of the candidate Driving Cycles depended most strongly on the number of Markov repetitions. The best Driving Cycle used 135 velocity modes, was 500 s and captured the corpus behaviour to within 5% after 1,000,000 Markov repetitions. In general, the best Driving Cycle reproduced the corpus behaviour better when road grade was included.
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A robust, data-driven methodology for real-world Driving Cycle development
Transportation Research Part D: Transport and Environment, 2012Co-Authors: Justin D K Bishop, Colin J Axon, Malcolm MccullochAbstract:This paper develops a robust, data-driven Markov Chain method to capture real-world behaviour in a Driving Cycle without deconstructing the raw velocity-time sequence. The accuracy of the Driving Cycles developed using this method was assessed on nine metrics as a function of the number of velocity states, Driving Cycle length and number of Markov repetitions. The road grade was introduced using vehicle specific power and a velocity penalty. The method was demonstrated on a corpus of 1180. km from a trial of electric scooters. The accuracies of the candidate Driving Cycles depended most strongly on the number of Markov repetitions. The best Driving Cycle used 135 velocity modes, was 500. s and captured the corpus behaviour to within 5% after 1,000,000 Markov repetitions. In general, the best Driving Cycle reproduced the corpus behaviour better when road grade was included. © 2012 Elsevier Ltd
Sanghpriya H. Kamble - One of the best experts on this subject based on the ideXlab platform.
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development of real world Driving Cycle case study of pune india
Transportation Research Part D-transport and Environment, 2009Co-Authors: Sanghpriya H. Kamble, Tom V. Mathew, Gaurav SharmaAbstract:The critical component of all emission models is a Driving Cycle representing the traffic behaviour. Although Indian Driving Cycles were developed to test the compliance of Indian vehicles to the relevant emission standards, they neglects higher speed and acceleration and assume all vehicle activities to be similar irrespective of heterogeneity in the traffic mix. Therefore, this study is an attempt to develop an urban Driving Cycle for estimating vehicular emissions and fuel consumption. The proposed methodology develops the Driving Cycle using micro-trips extracted from real-world data. The uniqueness of this methodology is that the Driving Cycle is constructed considering five important parameters of the time-space profile namely, the percentage acceleration, deceleration, idle, cruise, and the average speed. Therefore, this approach is expected to be a better representation of heterogeneous traffic behaviour. The Driving Cycle for the city of Pune in India is constructed using the proposed methodology and is compared with existing Driving Cycles.
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Development of real-world Driving Cycle: Case study of Pune, India
Transportation Research Part D: Transport and Environment, 2009Co-Authors: Sanghpriya H. Kamble, Tom V. Mathew, Gaurav SharmaAbstract:The critical component of all emission models is a Driving Cycle representing the traffic behaviour. Although Indian Driving Cycles were developed to test the compliance of Indian vehicles to the relevant emission standards, they neglects higher speed and acceleration and assume all vehicle activities to be similar irrespective of heterogeneity in the traffic mix. Therefore, this study is an attempt to develop an urban Driving Cycle for estimating vehicular emissions and fuel consumption. The proposed methodology develops the Driving Cycle using micro-trips extracted from real-world data. The uniqueness of this methodology is that the Driving Cycle is constructed considering five important parameters of the time–space profile namely, the percentage acceleration, deceleration, idle, cruise, and the average speed. Therefore, this approach is expected to be a better representation of heterogeneous traffic behaviour. The Driving Cycle for the city of Pune in India is constructed using the proposed methodology and is compared with existing Driving Cycles.© Elsevie
Xuan Zhao - One of the best experts on this subject based on the ideXlab platform.
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Development of a representative urban Driving Cycle construction methodology for electric vehicles: A case study in Xi’an
Transportation Research Part D-transport and Environment, 2020Co-Authors: Xuan Zhao, Xiangmo ZhaoAbstract:Abstract This paper develops a systematic and practical construction methodology of a representative urban Driving Cycle for electric vehicles, taking Xi’an as a case study. The methodology tackles four major tasks: test route selection, vehicle operation data collection, data processing, and Driving Cycle construction. A qualitative and quantitative comprehensive analysis method is proposed based on a sampling survey and an analytic hierarchy process to design test routes. A hybrid method using a chase car and on-board measurement techniques is employed to collect data. For data processing, the principal component analysis algorithm is used to reduce the dimensions of motion characteristic parameters, and the K-means and support vector machine hybrid algorithm is used to classify the Driving segments. The proposed Driving Cycle construction method is based on the Markov and Monte Carlo simulation method. In this study, relative error, performance value, and speed-acceleration probability distribution are used as decision criteria for selecting the most representative Driving Cycle. Finally, characteristic parameters, Driving range, and energy consumption are compared under different Driving Cycles.
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development of a representative urban Driving Cycle construction methodology for electric vehicles a case study in xi an
Transportation Research Part D-transport and Environment, 2020Co-Authors: Xuan Zhao, Xiangmo ZhaoAbstract:Abstract This paper develops a systematic and practical construction methodology of a representative urban Driving Cycle for electric vehicles, taking Xi’an as a case study. The methodology tackles four major tasks: test route selection, vehicle operation data collection, data processing, and Driving Cycle construction. A qualitative and quantitative comprehensive analysis method is proposed based on a sampling survey and an analytic hierarchy process to design test routes. A hybrid method using a chase car and on-board measurement techniques is employed to collect data. For data processing, the principal component analysis algorithm is used to reduce the dimensions of motion characteristic parameters, and the K-means and support vector machine hybrid algorithm is used to classify the Driving segments. The proposed Driving Cycle construction method is based on the Markov and Monte Carlo simulation method. In this study, relative error, performance value, and speed-acceleration probability distribution are used as decision criteria for selecting the most representative Driving Cycle. Finally, characteristic parameters, Driving range, and energy consumption are compared under different Driving Cycles.
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Developing an electric vehicle urban Driving Cycle to study differences in energy consumption.
Environmental science and pollution research international, 2018Co-Authors: Xuan Zhao, Shu WangAbstract:This paper develops a methodology for constructing a representative electric vehicle (EV) urban Driving Cycle as a basis for studying the differences in estimated energy consumption, taking Xi'an as an example. The test route is designed in accordance with the overall topological structure of the urban roads in the study region and the results of a traffic flow survey. Wavelet decomposition and reconstruction are utilized to preprocess the original data. Principal component analysis (PCA) is used to reduce the number of the kinetic parameters. The fuzzy C-means (FCM) clustering algorithm is used to cluster the Driving segments. A representative EV urban Driving Cycle is constructed in accordance with the time proportions of three classes of Driving segments and the correlation coefficients of the characteristic parameters. Finally, the differences in energy consumption estimates obtained using the constructed Xi'an EV urban Driving Cycle (XA-EV-UDC) and the international Driving Cycles are studied. The comparison shows that when international Driving Cycles are used to estimate the energy consumption and Driving range of EVs, large relative errors will result, with energy consumption errors of 9.65 to 21.17% and Driving range errors of 20.10 to 38.14%. Therefore, to accurately estimate energy consumption and Driving range of EVs under real-world Driving conditions, representative EV Driving Cycles for each typical city and region should be constructed.
