A novel optimization-based method to develop representative driving cycle in various driving conditions

Cui, Yuepeng; Zou, Fang; Xu, Hao; Chen, Zhihui; Gong, Kuangmin

doi:10.1016/j.energy.2022.123455

Cited by 17 publications

(6 citation statements)

References 35 publications

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“…Another research that used the same data collection approach is as presented by Yuhui et al, [40]. In this research, in urban area of Fuzhou city, 211 buses' routes were selected.…”

Section: On-board Measurement Methodsmentioning

confidence: 99%

“…All the data are then retrieved manually from the device, then used MATLAB software to process the data. [40] Meanwhile, in Sangareddy district India, the on-board measurement device (video V-box HD 2) as shown in Figure 3 was used for collecting vehicle trajectory data and speed related parameters (Chandrashekar et al,) [41]. As explained in the research, all the data collected are then retrieved manually form the device.…”

Section: On-board Measurement Methodsmentioning

confidence: 99%

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Data Collection and Data Management Approach for Developing a Driving Cycle: A Review

Nurru Anida Ibrahim,

Arunkumar Subramaniam,

Siti Norbakyah Jabar

et al. 2024

ARASET

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Driving cycles were first proposed and developed for standard emission measurements used by legislative bodies to approve newly developed vehicles. There are many driving cycles that have been developed over the past few years. Most of the research on driving cycles is based on the condition of the vehicle in a specific location, such as the United States FTP75, European ECE 15, Japan 10115, and others. A driving cycle is a representative speed-time profile that reflects the typical driving pattern of a given city or region. A driving cycle consists of a sequence of vehicle operating conditions (idle, acceleration, cruise, and deceleration). The basic methodologies to construct a driving cycle are route selection, data collection, and the development of the driving cycle. However, as far as we know, detailed reviews and in-depth discussions of data collection instruments and data management are rarely present. As a result, this paper reviews and discusses the recent data collection approach, as well as providing a thorough analysis of the mechanisms that they employ. Furthermore, the disadvantages and advantages of the reviewed mechanisms have also been discussed.

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“…Another research that used the same data collection approach is as presented by Yuhui et al, [40]. In this research, in urban area of Fuzhou city, 211 buses' routes were selected.…”

Section: On-board Measurement Methodsmentioning

confidence: 99%

Section: On-board Measurement Methodsmentioning

confidence: 99%

Data Collection and Data Management Approach for Developing a Driving Cycle: A Review

Nurru Anida Ibrahim,

Arunkumar Subramaniam,

Siti Norbakyah Jabar

et al. 2024

ARASET

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“…In recent years, the main purpose of qualitative–quantitative measurements in research on exhaust gas emissions conducted around the world is the testing of driving cycles under real traffic conditions. , In addition, the studies are focused on testing new fuel mixtures , and systems of exhaust gas treatment. , Many publications also describe tests aiming to confirm compliance with emission standards . The researchers less frequently focus on secondary emissions of toxic compounds .…”

Section: Exhaust Emission Test Methodsmentioning

confidence: 99%

The Actual Toxicity of Engine Exhaust Gases Emitted from Vehicles: The Development and Perspectives of Biological and Chemical Measurement Methods

Kęska

2023

ACS Omega

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Most of the current studies on vehicle engine exhaust emissions are focused on qualitative and quantitative measurements. Approval tests for admitting vehicles to traffic and tests performed at vehicle inspection stations are limited to measuring the concentrations of individual compounds or selected groups of compounds. For vehicles with compression-ignition engines, the annual emission control comprises only an exhaust gas opacity test, performed with an opacimeter. This approach does not consider very harmful groups of compounds that determine the toxicity of exhaust gases but are not directly covered by the emission standards, such as polycyclic aromatic hydrocarbons and volatile organic compounds. Also, it does not provide a clear answer to the question of the actual toxicity of exhaust gases, understood as the harmful effect that a given substance causes on living organisms or biological processes. Studies on the actual toxicity of engine exhaust gases present a new area of interest, increasingly more discussed but still not approached in a comprehensive way. The studies include experiments using in vitro biological methods and chemical analyses of gas mixtures. In this Review, I present an overview of current research and a critical comparison of commonly used methods of testing engine exhaust emissions and methods that might supplement them in a significant manner. The development of in vitro biological methods, including methods of microscopic analysis of cells in the assessment of exhaust gas toxicity, provides an innovative approach to the problem of air pollution. This type of research presents the opportunity to indisputably answer the question of the actual toxicity of a given gas mixture and to make a new contribution to science in the field of molecular biology. Current data show that the survival of cells exposed to engine exhaust emissions from older generation vehicles is higher compared to that of newer generation vehicles.

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“…An innovative data-driven driving cycle development method based on minimum maximum ant colony optimization (MMACO) and the MC method was introduced to improve the representativeness of driving cycles in Ref. 20 , potentially serving as a benchmark for establishing fuel consumption standards. Gong et al 21 collected high-frequency operational data from battery electric vehicles (BEVs) and established the Beijing driving cycle through statistical and MC methods, laying a robust foundation for the precise evaluation of BEV performance in Beijing.…”

Section: Introductionmentioning

confidence: 99%

Construction and optimization of representative actual driving cycles based on the improved autoencoder

Zhao,

Sun,

Wang

et al. 2024

Sci Rep

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In this study, much work has been performed to accurately and efficiently develop representative actual driving cycles. Electric vehicle road tests were conducted and the associated data were gathered based on the manual driving method, and the Changsha Driving Cycle Construction (CS-DCC) method was proposed to achieve systematical construction of a representative driving cycle from the original data. The results show that the refined data exhibit greater stability and a smoother pattern in contrast to the original data after noise reduction by five-scale wavelet analysis. The Gaussian Kernel Principal Component Analysis (KPCA) algorithm is chosen to reduce the dimensionality of the characteristic matrix, and the number of principal components is selected as 5 with a cumulative contribution rate of 85.99%. The average error of the characteristic parameters between the optimized drive cycle and the total data is further reduced from 13.6 to 6.1%, with a reduction ratio of 55.1%. Meanwhile, the constructed driving cycle has prominent local characteristics compared with four standard driving cycles, demonstrating the necessity of constructing an actual driving cycle that reflects localized driving patterns. The findings present a powerful application of artificial intelligence in advancing engineering technologies.

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A novel optimization-based method to develop representative driving cycle in various driving conditions

Cited by 17 publications

References 35 publications

Data Collection and Data Management Approach for Developing a Driving Cycle: A Review

Data Collection and Data Management Approach for Developing a Driving Cycle: A Review

The Actual Toxicity of Engine Exhaust Gases Emitted from Vehicles: The Development and Perspectives of Biological and Chemical Measurement Methods

Construction and optimization of representative actual driving cycles based on the improved autoencoder

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