Evaluation of Auxiliary Device Efficiency in Heavy Vehicle Application

2013 ◽  
Author(s):  
Carl-Johan Sjostedt ◽  
Jan Wikander ◽  
Henrik Flemmer
Keyword(s):  
Fuel Consumption ◽  
Large Population ◽  
Ambient Conditions ◽  
Power Steering ◽  
Simulink Model ◽  
Cooling Fan ◽  
Driving Cycles ◽  
Consumption Reduction ◽  
Road Data ◽  
Auxiliary Device

To investigate the energy consumption related to auxiliary devices, as a basis for optimizing the auxiliary systems’ layout and identifying potential for fuel consumption reduction, a study has been carried out at Scania CV AB. The following auxiliary devices have been investigated: Alternator, water pump, cooling fan, air compressor, air conditioning compressor, oil pump and power steering pump. Those auxiliary devices represent 5–7% of the total fuel consumption in long haulage truck applications, while for a city bus the figure could be as high as 50%. One of the challenges of evaluating the fuel consumption of auxiliary devices is the variance: it depends on many factors including road data, driver profile, ambient conditions (temperature, pressure, precipitation) and more. To investigate this variance, test data collected from the Scania test fleet has been analyzed. The data are used as input to a MATLAB/Simulink model of the auxiliary systems. This way, a large population of trucks has been investigated, for many driving cycles under realistic conditions. A general model for reducing fuel consumption for auxiliary systems has been developed.

Simple Methodology for the Development and Analysis of Local Driving Cycles Applied in the Study of Cars and Motorcycles in Recife, Brazil

2021 ◽  
pp. 036119812199185
Author(s):  
Guilherme Medeiros Soares de Andrade ◽  
Fernando Wesley Cavalcanti de Araújo ◽  
Maurício Pereira Magalhães de Novaes Santos ◽  
Silvio Jacks dos Anjos Garnés ◽  
Fábio Santana Magnani
Keyword(s):  
Fuel Consumption ◽  
Global Positioning System ◽  
Energy Analysis ◽  
Aerodynamic Drag ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Driving Cycles ◽  
Vehicle Category ◽  
Global Positioning ◽  
Utility Vehicle

Standard driving cycles are usually used to compare vehicles from distinct regions, and local driving cycles reproduce more realistic conditions in specific regions. In this article, we employed a simple methodology for developing local driving cycles and subsequently performed a kinematic and energy analysis. As an application, we employed the methodology for cars and motorcycles in Recife, Brazil. The speed profile was collected using a smartphone (1 Hz) validated against a high precision global positioning system (10 Hz), presenting a mean absolute error of 3 km/h. The driving cycles were thus developed using the micro-trip method. The kinematic analysis indicated that motorcycles had a higher average speed and acceleration (32.5 km/h, 0.84 m/s2) than cars (22.6 km/h, 0.55 m/s2). As a result of the energy analysis, it was found that inertia is responsible for most of the fuel consumption for both cars (59%) and motorcycles (41%), but for motorcycles the aerodynamic drag is also relevant (36%). With regards to fuel consumption, it was found that the standard driving cycle used in Brazil (FTP-75; 2.47 MJ/km for cars and 0.84 MJ/km for motorcycles) adequately represents the driving profile for cars (2.46 MJ/km), and to a lesser extent motorcycles (0.91 MJ/km) in off-peak conditions. Finally, we evaluated the influence of the vehicle category on energy consumption, obtaining a maximum difference of 38% between a 2.0 L sports utility vehicle and a 1.0 L hatchback.

scholarly journals A Thermodynamic Transient Model for Performance Analysis of a Twin Shaft Industrial Gas Turbine

2017 ◽  
Author(s):  
Samuel Cruz-Manzo ◽  
Vili Panov ◽  
Yu Zhang ◽  
Anthony Latimer ◽  
Festus Agbonzikilo
Keyword(s):  
Gas Turbine ◽  
System Performance ◽  
Computational Tool ◽  
Ambient Conditions ◽  
Transient Model ◽  
Simulink Model ◽  
Artificial Neural Network Ann ◽  
Industrial Gas Turbine ◽  
Scientific Engineering ◽  
Steady State Performance

In this study, a Simulink model based on fundamental thermodynamic principles to predict the dynamic and steady state performance in a twin shaft Industrial Gas Turbine (IGT) has been developed. The components comprising the IGT have been implemented in the modelling architecture using a thermodynamic commercial toolbox (Thermolib, EUtech Scientific Engineering GmbH) and Simulink environment. Measured air pressure and air temperature discharged by compressor allowed the validation of the modelling architecture. The model assisted the development of a computational tool based on Artificial Neural Network (ANN) for compressor fault diagnostics in IGTs. It has been demonstrated that modelling plays an important role to predict and monitor gas turbine system performance at different operating and ambient conditions.

Different Configurations of Exhaust Gas Heat Recovery in Internal Combustion Engine: Evaluation on Different Driving Cycles Using Numerical Simulations

2018 ◽  
Vol 10 (4) ◽  
Author(s):  
Hanna Sara ◽  
David Chalet ◽  
Mickaël Cormerais
Keyword(s):  
Numerical Simulations ◽  
Fuel Consumption ◽  
Heat Recovery ◽  
Combustion Engine ◽  
Management Strategies ◽  
Driving Cycle ◽  
Maximum Temperature ◽  
Exhaust Gas ◽  
Direct Heating ◽  
Driving Cycles

Exhaust gas heat recovery is one of the interesting thermal management strategies that aim to improve the cold start of the engine and thus reduce its fuel consumption. In this work, an overview of the heat exchanger used as well as the experimental setup and the different tests will be presented first. Then numerical simulations were run to assess and valorize the exhaust gas heat recovery strategy. The application was divided into three parts: an indirect heating of the oil with the coolant as a medium fluid, a direct heating of the oil, and direct heating of the oil and the coolant. Different ideas were tested over five different driving cycles: New European driving cycle (NEDC), worldwide harmonized light duty driving test cycle (WLTC), common Artemis driving cycle (CADC) (urban and highway), and one in-house developed cycle. The simulations were performed over two ambient temperatures. Different configurations were proposed to control the engine's lubricant maximum temperature. Results concerning the temperature profiles as well as the assessment of fuel consumption were stated for each case.

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.

scholarly journals Comparative Study on Fuel Consumption and Different Driving Cycles for a Passenger Car in Malaysia via 1-D Simulation

2018 ◽  
Vol 429 ◽  
pp. 012057
Author(s):  
M S Mustaqim ◽  
M S M Hashim ◽  
A B Shahriman ◽  
Z M Razlan ◽  
I Zunaidi ◽  
...  
Keyword(s):  
Comparative Study ◽  
Fuel Consumption ◽  
Passenger Car ◽  
Driving Cycles

scholarly journals Experimental Analysis of Calculation of Fuel Consumption Rate by On-Road Mileage in a 2.0 L Gasoline-Fueled Passenger Vehicle

2018 ◽  
Vol 8 (12) ◽  
pp. 2390 ◽  
Author(s):  
Jaehyuk Lim ◽  
Yumin Lee ◽  
Kiho Kim ◽  
Jinwook Lee
Keyword(s):  
Fuel Consumption ◽  
Fuel Economy ◽  
Fuel Efficiency ◽  
Co2 Concentration ◽  
The United States ◽  
Traffic Conditions ◽  
Fuel Consumption Rate ◽  
Driving Cycles ◽  
The Difference ◽  
Consumption Value

The five-driving test mode is vehicle driving cycles made by the Environment Protection Association (EPA) in the United States of America (U.S.A.) to fully reflect actual driving environments. Recently, fuel consumption value calculated from the adjusted fuel consumption formula has been more effective in reducing the difference from that experienced in real-world driving conditions, than the official fuel efficiency equation used in the past that only considered the driving environment included in FTP and HWFET cycles. There are many factors that bring about divergence between official fuel consumption and that experienced by drivers, such as driving pattern behavior, accumulated mileage, driving environment, and traffic conditions. In this study, we focused on the factor of causing change of fuel efficiency value, calculated according to how many environmental conditions that appear on the real-road are considered, in producing the fuel consumption formula, and that of the vehicle’s accumulated mileage in a 2.0 L gasoline-fueled vehicle. So, the goals of this research are divided into four major areas to investigate divergence in fuel efficiency obtained from different equations, and what factors and how much CO2 and CO emissions that are closely correlated to fuel efficiency change, depending on the cumulative mileage of the vehicle. First, the fuel consumption value calculated from the non-adjusted formula, was compared with that calculated from the corrected fuel consumption formula. Also, how much CO2 concentration levels change as measured during each of the three driving cycles was analyzed as the vehicle ages. In addition, since the US06 driving cycle is divided into city mode and highway mode, how much CO2 and CO production levels change as the engine ages during acceleration periods in each mode was investigated. Finally, the empirical formula was constructed using fuel economy values obtained when the test vehicle reached 6500 km, 15,000 km, and 30,000 km cumulative mileage, to predict how much fuel consumption of city and highway would worsen, when mileage of the vehicle is increased further. When cumulative mileage values set in this study were reached, experiments were performed by placing the vehicle on a chassis dynamometer, in compliance with the carbon balance method. A key result of this study is that fuel economy is affected by various fuel consumption formula, as well as by aging of the engine. In particular, with aging aspects, the effect of an aging engine on fuel efficiency is insignificant, depending on the load and driving situation.

scholarly journals Research on energy management strategy considering battery life for plug-in hybrid electric vehicle

2018 ◽  
Vol 10 (9) ◽  
pp. 168781401879776 ◽  
Author(s):  
Jianjun Hu ◽  
Zhihua Hu ◽  
Xiyuan Niu ◽  
Qin Bai
Keyword(s):  
Fuel Consumption ◽  
Electric Vehicle ◽  
Energy Management ◽  
Management Strategy ◽  
Hybrid Electric Vehicle ◽  
Battery Life ◽  
Energy Management Strategy ◽  
Driving Cycles ◽  
Comprehensive Test ◽  
Hybrid Electric

To improve the fuel efficiency and battery life-span of plug-in hybrid electric vehicle, the energy management strategy considering battery life decay is proposed. This strategy is optimized by genetic algorithm, aiming to reduce the fuel consumption and battery life decay of plug-in hybrid electric vehicle. Besides, to acquire better drive-cycle adaptability, driving patterns are recognized with probabilistic neural network. The standard driving cycles are divided into urban congestion cycle, highway cycle, and urban suburban cycle; the optimized energy management strategies in three representative driving cycles are established; meanwhile, a comprehensive test driving cycle is constructed to verify the proposed strategies. The results show that adopting the optimized control strategies, fuel consumption, and battery’s life decay drop by 1.9% and 3.2%, respectively. While using the drive-cycle recognition, the features of different driving cycles can be identified, and based on it, the vehicle can choose appropriate control strategy in different driving conditions. In the comprehensive test driving cycle, after recognizing driving cycles, fuel consumption and battery’s life decay drop by 8.6% and 0.3%, respectively.

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