Comparison of Real-World Urban Driving Route PEMS Fuel Economy with Chassis Dynamometer CVS Results

2019 ◽  
Author(s):  
Michael Akard ◽  
Nathan Gramlich ◽  
Tim Nevius ◽  
Scott Porter
Keyword(s):  
Fuel Economy ◽  
Real World ◽  
Chassis Dynamometer

An Investigation of the Fuel Economy of a Drive Cycle Developed Using the Road Load Energy Criterion

2021 ◽  
Author(s):  
Peter Vasquez ◽  
Edwin Quiros ◽  
Gerald Jo Denoga ◽  
Robert Michael Corpus ◽  
Robert James Lomotan
Keyword(s):  
Fuel Consumption ◽  
Fuel Economy ◽  
Energy Criterion ◽  
Chassis Dynamometer ◽  
Drive Cycle ◽  
The Road ◽  
Energy Error ◽  
Road Load ◽  
Metro Manila ◽  
Road Data

Abstract Efforts to mitigate climate change include lowering of greenhouse gas emissions by reducing fuel consumption in the transport sector. Various vehicle technologies and interventions for better fuel economy eventually require chassis dynamometer testing using drive cycles for validation. As such, the methodology to generate these drive cycles from on-road data should produce drive cycles that closely represent actual on-road driving from the fuel economy standpoint. This study presents a comparison of the fuel economy measured from a drive cycle developed using road load energy as a major assessment criterion and the actual on-road fuel economy of a 2013 Isuzu Crosswind utility vehicle used in the UV Express transport fleet in Metro Manila, Philippines. In this approach to drive cycle construction from on-road data, the ratio of the total road load energy of the generated drive cycle to that of the on-road trip is made the same ratio as their respective durations. On-road velocity and fuel consumption were recorded as the test vehicle traversed the 42.5 km. Sucat to Lawton route and vice versa in Metro Manila. Gathered data were processed to generate drive cycles using the modified Markov Chain approach. Three drive cycles of decreasing duration, based on the practicality of testing on a chassis dynamometer, were generated using three arbitrary data compression ratios. These drive cycles were tested using the same vehicle on the chassis dynamometer and compared with the on-road data using road load energy, fuel economy, average speed, and maximum acceleration. For the 893-seconds drive cycle generated, the road load energy error was 3.93% and fuel economy difference of 1.14%. For the 774-seconds cycle generated, the road load energy error was 4.34% and fuel economy difference was 0.91%. For the 664-seconds drive cycle, the road load energy error was 3.68% and fuel economy difference was 0.91%. On-road fuel economy for the 42.5-km. route averaged over nine round trips was 8.785 km/L. Based on the results, the road load energy criterion approach of drive cycle construction methodology can generate drive cycles which can very closely estimate on-road fuel economy.

Benchmarking Fuel Economy of Connected and Automated Vehicles in Real World Driving Conditions via Monte Carlo Simulation

2020 ◽  
Author(s):  
Shreshta Rajakumar Deshpande ◽  
Shobhit Gupta ◽  
Dennis Kibalama ◽  
Nicola Pivaro ◽  
Marcello Canova
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Fuel Economy ◽  
Real World ◽  
Human Subjects ◽  
Driver Model ◽  
Traffic Information ◽  
Vehicle Speed ◽  
Instrumented Vehicle ◽  
The Impact

Abstract Connectivity and automation have accelerated the development of algorithms that use route and real-time traffic information for improving energy efficiency. The evaluation of such benefits, however, requires establishing a reliable baseline that is representative of a real-world driving environment. In this context, virtual driver models are generally adopted to predict the vehicle speed based on route data and presence of lead vehicles, in a way that mimics the response of human drivers. This paper proposes an Enhanced Driver Model (EDM) that forecasts the human response when driving in urban conditions, considering the effects of Signal Phasing and Timing (SPaT) by introducing the concept of Line-of-Sight (LoS). The model was validated against data collected on an instrumented vehicle driven on public roads by different human subjects. Using this model, a Monte Carlo simulation is conducted to determine the statistical distribution of fuel consumption and travel time on a given route, varying driver behavior (aggressiveness), traffic conditions and SPaT. This allows one to quantify the impact of uncertainties associated to real-world driving in fuel economy estimates.

Fuel Economy Performance of the Highly Efficient Fuel Economy Oils Using Chassis Dynamometer Test

1993 ◽  
Author(s):  
Kenyu Akiyama ◽  
Fumio Ueda ◽  
Johji Miyake ◽  
Kazuyoshi Tasaka ◽  
Shinichi Sugiyama
Keyword(s):  
Fuel Economy ◽  
Chassis Dynamometer ◽  
Highly Efficient ◽  
Dynamometer Test

Evaluation of Fuel Economy and Emissions Reduction for a Motorcycle With Automatic Idling-Stop Device

2014 ◽  
Vol 136 (2) ◽  
Author(s):  
Chih-Hsien Yu ◽  
Chyuan-Yow Tseng ◽  
Shiunn-Cheng Chuang
Keyword(s):  
Fuel Economy ◽  
Fuel Injection ◽  
Control Unit ◽  
Engine Control Unit ◽  
Emissions Reduction ◽  
Fuel Saving ◽  
Chassis Dynamometer ◽  
Stop And Go ◽  
And Control ◽  
Co Emission

In an attempt to improve the fuel economy and reduce the exhaust emissions of motorcycles, some manufactures have developed commercialized motorcycles equipped with automatic idling-stop and go (AISG) functionality. Even though research efforts devoted to the idling-stop strategy have demonstrated its effectiveness, motorcycles equipped with the AISG device are not popular because the general public still has some concerns about them. This paper aims to evaluate the benefits and feasibility of a commercialized motorcycle with AISG functionality with regard to the public's concerns about fuel economy and emission problems during engine restart transients. In order to verify the accuracy of the analytical results and control for variable driver characteristics, a motorcycle chassis dynamometer was used to recreate the urban driving pattern. Furthermore, the feasibility of fuel-saving and emissions improvement by adjusting fuel-injection signal of the engine control unit (ECU) during engine restart operation was also evaluated. The experimental results showed that the addition of the fuel-injection modulation plus idling-stop strategy can improve the fuel economy rate by up to 12.2% and reduce carbon monoxide (CO) emission by up to 36.95% in comparison with the non-idling stop case.

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

2020 ◽  
pp. 095440702097753
Author(s):  
Masilamani Sithananthan ◽  
Ravindra Kumar
Keyword(s):  
Fuel Economy ◽  
Real World ◽  
State Of The Art ◽  
Emission Factors ◽  
Driving Cycle ◽  
Driving Cycles ◽  
Traffic Scenario

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.

scholarly journals An Enhanced Driver Model for Evaluating Fuel Economy on Real-World Routes

2019 ◽  
Vol 52 (5) ◽  
pp. 574-579 ◽  
Author(s):  
Shobhit Gupta ◽  
Shreshta R. Deshpande ◽  
Punit Tulpule ◽  
Marcello Canova ◽  
Giorgio Rizzoni
Keyword(s):  
Fuel Economy ◽  
Real World ◽  
Driver Model
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