A MODELING STUDY ON FUEL CONSUMPTION IMPROVEMENT OF A LIGHT-DUTY CNG TRUCK EQUIPPED WITH A HYBRID POWERTRAIN

2021 ◽  
Author(s):  
Ratnak Sok ◽  
Jin Kusaka ◽  
Hisaharu Nakashima ◽  
Makoto Akaike
Keyword(s):  
Fuel Consumption ◽  
Hybrid Powertrain ◽  
Light Duty ◽  
Fuel Consumption Improvement ◽  
Modeling Study

Powertrain Waste Heat Recovery: A Systems Approach to Maximize Drivetrain Efficiency

2012 ◽  
Author(s):  
Kevin Laboe ◽  
Marcello Canova
Keyword(s):  
Fuel Consumption ◽  
Waste Heat Recovery ◽  
Systems Approach ◽  
Waste Heat ◽  
Combustion Engine ◽  
Heat Energy ◽  
Engine Oils ◽  
Engine Cooling ◽  
Light Duty ◽  
Light Duty Vehicles

Up to 65% of the energy produced in an internal combustion engine is dissipated to the engine cooling circuit and exhaust gases [1]. Therefore, recovering a portion of this heat energy is a highly effective solution to improve engine and drivetrain efficiency and to reduce CO2 emissions, with existing vehicle and powertrain technologies [2,3]. This paper details a practical approach to the utilization of powertrain waste heat for light vehicle engines to reduce fuel consumption. The “Systems Approach” as described in this paper recovers useful energy from what would otherwise be heat energy wasted into the environment, and effectively distributes this energy to the transmission and engine oils thus reducing the oil viscosities. The focus is on how to effectively distribute the available powertrain heat energy to optimize drivetrain efficiency for light duty vehicles, minimizing fuel consumption during various drive cycles. To accomplish this, it is necessary to identify the available powertrain heat energy during any drive cycle and cold start conditions, and to distribute this energy in such a way to maximize the overall efficiency of the drivetrain.

The WLTC vs NEDC: A Case Study on the Impacts of Driving Cycle on Engine Performance and Fuel Consumption

2021 ◽  
Vol 18 (3) ◽  
pp. 9071-9081
Author(s):  
Jakub Lasocki
Keyword(s):  
Fuel Consumption ◽  
Combustion Engine ◽  
Engine Performance ◽  
Driving Cycle ◽  
Performance Characteristics ◽  
Pollutant Emission ◽  
Strong Impact ◽  
Light Duty ◽  
Light Duty Vehicles

The World-wide harmonised Light-duty Test Cycle (WLTC) was developed internationally for the determination of pollutant emission and fuel consumption from combustion engines of light-duty vehicles. It replaced the New European Driving Cycle (NEDC) used in the European Union (EU) for type-approval testing purposes. This paper presents an extensive comparison of the WLTC and NEDC. The main specifications of both driving cycles are provided, and their advantages and limitations are analysed. The WLTC, compared to the NEDC, is more dynamic, covers a broader spectrum of engine working states and is more realistic in simulating typical real-world driving conditions. The expected impact of the WLTC on vehicle engine performance characteristics is discussed. It is further illustrated by a case study on two light-duty vehicles tested in the WLTC and NEDC. Findings from the investigation demonstrated that the driving cycle has a strong impact on the performance characteristics of the vehicle combustion engine. For the vehicles tested, the average engine speed, engine torque and fuel flow rate measured over the WLTC are higher than those measured over the NEDC. The opposite trend is observed in terms of fuel economy (expressed in l/100 km); the first vehicle achieved a 9% reduction, while the second – a 3% increase when switching from NEDC to WLTC. Several factors potentially contributing to this discrepancy have been pointed out. The implementation of the WLTC in the EU will force vehicle manufacturers to optimise engine control strategy according to the operating range of the new driving cycle.

Low-Level Automated Light-Duty Vehicle Technologies Provide Opportunities to Reduce Fuel Consumption

2018 ◽  
Vol 2672 (24) ◽  
pp. 60-74 ◽  
Author(s):  
Saeed Vasebi ◽  
Yeganeh M. Hayeri ◽  
Constantine Samaras ◽  
Chris Hendrickson
Keyword(s):  
Fuel Consumption ◽  
Aerodynamic Force ◽  
The United States ◽  
Route Guidance ◽  
Conventional Vehicle ◽  
Automated Vehicle ◽  
Fuel Savings ◽  
Light Duty ◽  
Vehicle Systems ◽  
Light Duty Vehicle

Gasoline is the main source of energy used for surface transportation in the United States. Reducing fuel consumption in light-duty vehicles can significantly reduce the transportation sector’s impact on the environment. Implementation of emerging automated technologies in vehicles could result in fuel savings. This study examines the effect of automated vehicle systems on fuel consumption using stochastic modeling. Automated vehicle systems examined in this study include warning systems such as blind spot warning, control systems such as lane keeping assistance, and information systems such as dynamic route guidance. We have estimated fuel savings associated with reduction of accident and non-accident-related congestion, aerodynamic force reduction, operation load, and traffic rebound. Results of this study show that automated technologies could reduce light-duty vehicle fuel consumption in the U.S. by 6% to 23%. This reduction could save $60 to $266 annually for the owners of vehicles equipped with automated technologies. Also, adoption of automated vehicles could benefit all road users (i.e., conventional vehicle drivers) up to $35 per vehicle annually (up to $6.2 billion per year).

Smart rule-based diesel engine control strategies by means of predictive driving information

2019 ◽  
Vol 20 (10) ◽  
pp. 1047-1058 ◽  
Author(s):  
Giovanni Vagnoni ◽  
Markus Eisenbarth ◽  
Jakob Andert ◽  
Giuseppe Sammito ◽  
Joschka Schaub ◽  
...  
Keyword(s):  
Fuel Consumption ◽  
Control Strategies ◽  
Control Unit ◽  
Future Research ◽  
Engine Control ◽  
Test Cycle ◽  
Rule Based ◽  
Prediction Horizon ◽  
Light Duty ◽  
Path Control

The increasing connectivity of future vehicles allows the prediction of the powertrain operational profiles. This technology will improve the transient control of the engine and its exhaust gas aftertreatment systems. This article describes the development of a rule-based algorithm for the air path control, which uses the knowledge of upcoming driving events to reduce especially [Formula: see text] and particulate (soot) emissions. In the first section of this article, the boosting and the lean [Formula: see text] trap systems of a diesel powertrain are investigated as relevant sub-systems for shorter prediction horizons, suitable for Car-to-X communication range. Reference control strategies, based on state-of-the-art engine control unit algorithms and suitable predictive control logics, are compared for the two sub-systems in a model in the loop simulation environment. The simulation driving cycles are based on Worldwide harmonized Light-duty Test Cycle and Real Driving Emissions regulations. Due to the shorter, and consequently more probable, prediction horizon and the demonstrated emission improvements, a dedicated rule-based algorithm for the air path control is developed and benchmarked in the Worldwide harmonized Light-duty Test Cycle as described in the second part of this article. Worldwide harmonized Light-duty Test Cycle test results show an improvement potential for engine-out soot and [Formula: see text] emissions of up to 5.2% and 1.2%, respectively, for the air path case and a reduction of the average fuel consumption in Real Driving Emissions of up to 1% for the lean NOx trap case. In addition, the developed rule-based algorithm allows the adjustment of the desired NOx–soot trade-off, while keeping the fuel consumption constant. The study concludes with brief recommendations for future research directions, as for example, the introduction of a prediction module for the estimation of the vehicle operational profile in the prediction horizon.

scholarly journals Development of Hybrid Vehicle Energy Consumption Model for Transportation Applications—Part II: Traction Force-Speed Based Energy Consumption Modeling

2019 ◽  
Vol 10 (2) ◽  
pp. 22 ◽  
Author(s):  
Siriorn Pitanuwat ◽  
Hirofumi Aoki ◽  
Satoru IIzuka ◽  
Takayuki Morikawa
Keyword(s):  
Energy Consumption ◽  
Fuel Consumption ◽  
Traction Force ◽  
Hybrid Vehicles ◽  
Specific Power ◽  
Mode Transition ◽  
Hybrid Powertrain ◽  
Modeling Methods ◽  
Consumption Model ◽  
Energy Consumption Modeling

In the transportation sector, the fuel consumption model is a fundamental tool for vehicles’ energy consumption and emission analysis. Over the past decades, vehicle-specific power (VSP) has been enormously adopted in a number of studies to estimate vehicles’ instantaneous driving power. Then, the relationship between the driving power and fuel consumption is established as a fuel consumption model based on statistical approaches. This study proposes a new methodology to improve the conventional energy consumption modeling methods for hybrid vehicles. The content is organized into a two-paper series. Part I captures the driving power equation development and the coefficient calibration for a specific vehicle model or fleet. Part II focuses on hybrid vehicles’ energy consumption modeling, and utilizes the equation obtained in Part I to estimate the driving power. Also, this paper has discovered that driving power is not the only primary factor that influences hybrid vehicles’ energy consumption. This study introduces a new approach by applying the fundamental of hybrid powertrain operation to reduce the errors and drawbacks of the conventional modeling methods. This study employs a new driving power estimation equation calibrated for the third generation Toyota Prius from Part I. Then, the Traction Force-Speed Based Fuel Consumption Model (TFS model) is proposed. The combination of these two processes provides a significant improvement in fuel consumption prediction error compared to the conventional VSP prediction method. The absolute maximum error was reduced from 57% to 23%, and more than 90% of the predictions fell inside the 95% confidential interval. These validation results were conducted based on real-world driving data. Furthermore, the results show that the proposed model captures the efficiency variation of the hybrid powertrain well due to the multi-operation mode transition throughout the variation of the driving conditions. This study also provides a supporting analysis indicating that the driving mode transition in hybrid vehicles significantly affects the energy consumption. Thus, it is necessary to consider these unique characteristics to the modeling process.

scholarly journals Optimal and prototype dimensioning of 48V P0+P4 hybrid drivetrains

2020 ◽  
Vol 5 (3-4) ◽  
pp. 173-186
Author(s):  
Matthias Werra ◽  
Axel Sturm ◽  
Ferit Küçükay
Keyword(s):  
Fuel Consumption ◽  
Urban Areas ◽  
Combustion Engine ◽  
Electric Machine ◽  
Prototype Model ◽  
Vehicle Speed ◽  
Conventional Vehicle ◽  
Light Duty ◽  
Consumption Reduction ◽  
And Performance

Abstract This paper presents a virtual toolchain for the optimal concept and prototype dimensioning of 48 V hybrid drivetrains. First, this toolchain is used to dimension the drivetrain components for a 48 V P0+P4 hybrid which combines an electric machine in the belt drive of the internal combustion engine and a second electric machine at the rear axle. On an optimal concept level, the power and gear ratios of the electric components in the 48 V system are defined for the best fuel consumption and performance. In the second step, the optimal P0+P4 drivetrain is simulated with a prototype model using a realistic rule-based operating strategy to determine realistic behavior in legal cycles and customer operation. The optimal variant shows a fuel consumption reduction in the Worldwide harmonized Light Duty Test Cycle of 13.6 % compared to a conventional vehicle whereas the prototype simulation shows a relatively higher savings potential of 14.8 %. In the prototype simulation for customer operation, the 48 V hybrid drivetrain reduces the fuel consumption by up to 24.6 % in urban areas due to a high amount of launching and braking events. Extra-urban and highway areas show fuel reductions up to 11.6 % and 4.2 %, respectively due to higher vehicle speed and power requirements. The presented virtual toolchain can be used to combine optimal concept dimensioning with close to reality behaviour simulations to maximise realistic statements and minimize time effort.

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