Development of Driving Cycle Construction Methodology in Malaysia's Urban Road System

Mahayadin, A. R.; Ibrahim, I.; Zunaidi, I.; Shahriman, A. B.; Faizi, M K; Sahari, Muhammad Amin; Hashim, M S M; Saad, Mohd Sazli; Sarip, M. S.; Razlan, Z. M.; Rani, Mukta; Isa, Zaidi; Kamarrudin, N S; Harun, A.; Nagaya, Yasunobu

doi:10.1109/icassda.2018.8477619

Cited by 14 publications

(7 citation statements)

References 8 publications

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“…The proportion of each cluster or traffic condition in the final DC is proportional to the duration of that condition in the collected data. Based on the WLTC and the present DC durations, we decided to use a DC duration between 1500 s [28] and 1800 s [4].…”

Section: Micro Trip Methods Of DC Constructionmentioning

confidence: 99%

A Neural Network and Principal Component Analysis Approach to Develop a Real-Time Driving Cycle in an Urban Environment: The Case of Addis Ababa, Ethiopia

et al. 2022

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This study aimed to develop the Addis Ababa Driving Cycle (DC) using real-time data from passenger vehicles in Addis Ababa based on a neural network (NN) and principal component analysis (PCA) approach. Addis Ababa has no local DC for automobile emissions tests and standard DCs do not reflect the current scenario. During the DC’s development, the researchers determined the DC duration based on their experience and the literature. A k-means clustering method was also applied to cluster the dimensionally reduced data without identifying the best clustering method. First, a shape-preserving cubic interpolation technique was applied to remove outliers, followed by the Bayes wavelet signal denoising technique to smooth the data. Rules were then set for the extraction of trips and trip indicators before PCA was applied, and the machine learning classification was applied to identify the best clustering method. Finally, after training the NN using Bayesian regularization with a back propagation, the velocity for each route section was predicted and its performance had an overall R-value of 0.99. Compared with target data, the DCs developed by the NN and micro trip methods have a relative difference of 0.056 and 0.111, respectively, and resolve the issue of the DC duration decision in the micro trip method.

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Section: Micro Trip Methods Of DC Constructionmentioning

confidence: 99%

A Neural Network and Principal Component Analysis Approach to Develop a Real-Time Driving Cycle in an Urban Environment: The Case of Addis Ababa, Ethiopia

et al. 2022

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“…It is evident that micro-trip methods can well represent the actual driving conditions. 43 In Markov Chain model work on transition probability matrix, the cycle construction depends only on the current driving modes selected and assessment criteria, it can end with non-realistic if the driving mode selection goes wrong. The adaption of micro simulation approach can reasonably produce synthesized driving cycle but it is data intensive.…”

Section: Methods Of Construction Of Driving Cyclementioning

confidence: 99%

A framework for development of real-world motorcycle driving cycle in India

Sithananthan

Kumar

2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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This paper proposed a framework for development of real-world driving cycle in India after a thorough review and comparison of motorcycle driving cycles used in different countries. A limited state-of-the art work for the development of driving cycles for motorcycles is available. The motorcycle driving cycles developed by different countries differ from each other in terms of their driving cycle characteristics, emission factors, and fuel economy. This paper reviewed the parameters of real-world driving cycles of motorcycles and compares the same with legislative cycles concerning their characteristics and emissions. The parameters of real-world driving cycles and Indian legislative cycle (IDC) deviate significantly from other legislative cycles in the range of −97% to +1172% and −74% to 284% respectively. The emission factors of the legislative cycle do not match with the realistic emissions measured by real-world driving cycles. This is due to the reason that the legislative cycles do not represent the current traffic scenario and hence need to be revised. A framework is proposed to develop a real-world driving cycle in India.

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“…Details can be found in the NF ISO 16769-2 norm and are summarized as follows (Figure 1a contrary, transient driving constitutes speed variation typical of real driving conditions and driver behavior. Thus, driving cycles are built according to vehicle, environment and road conditions in order to assess the performance of an internal combustion engine (ICE) vehicle or to define the range of an electric vehicle [40]. Common driving cycles for electric vehicles from the United States (FTP-75) and Europe (ARTEMIS, NEDC) can be found in [37], as well as the Worldwide Harmonized Light Vehicles Test Procedure (WLTP.…”

Section: Driving Cyclesmentioning

confidence: 99%

Sizing of Lithium-Ion Battery/Supercapacitor Hybrid Energy Storage System for Forklift Vehicle

et al. 2020

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Nowadays, electric vehicles are one of the main topics in the new industrial revolution, called Industry 4.0. The transport and logistic solutions based on E-mobility, such as handling machines, are increasing in factories. Thus, electric forklifts are mostly used because no greenhouse gas is emitted when operating. However, they are usually equipped with lead-acid batteries which present bad performances and long charging time. Therefore, combining high-energy density lithium-ion batteries and high-power density supercapacitors as a hybrid energy storage system results in almost optimal performances and improves battery lifespan. The suggested solution is well suited for forklifts which continuously start, stop, lift up and lower down heavy loads. This paper presents the sizing of a lithium-ion battery/supercapacitor hybrid energy storage system for a forklift vehicle, using the normalized Verein Deutscher Ingenieure (VDI) drive cycle. To evaluate the performance of the lithium-ion battery/supercapacitor hybrid energy storage system, different sizing simulations are carried out. The suggested solution allows us to successfully optimize the system in terms of efficiency, volume and mass, in regard to the battery, supercapacitors technology and the energy management strategy chosen.

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Development of Driving Cycle Construction Methodology in Malaysia's Urban Road System

Cited by 14 publications

References 8 publications

A Neural Network and Principal Component Analysis Approach to Develop a Real-Time Driving Cycle in an Urban Environment: The Case of Addis Ababa, Ethiopia

A Neural Network and Principal Component Analysis Approach to Develop a Real-Time Driving Cycle in an Urban Environment: The Case of Addis Ababa, Ethiopia

A framework for development of real-world motorcycle driving cycle in India

Sizing of Lithium-Ion Battery/Supercapacitor Hybrid Energy Storage System for Forklift Vehicle

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