A Simple Hybrid Electric Vehicle Fuel Consumption Model for Transportation Applications

Ahn, Kyoungho; Rakha, Hesham

doi:10.5772/intechopen.89055

Cited by 16 publications

(12 citation statements)

References 16 publications

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“…Analyzed is the fuel need for mileage in thousands of kilometers over a period of several years. That is in opposite to short-term testssuch as WLTP and RDE, various specialist tests [14] or simulations [7].…”

Section: Introductionmentioning

confidence: 95%

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Operational energy footprint of vehicle

Sitnik¹

2021

Combustion Engines

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One of the many important challenges of the modern world is the greenhouse effect of CO2. It is necessary to implement solutions that will eliminate it. Since the easiest way to eliminate the greenhouse effect is to reduce hydrocarbons fuel consumption, it is important to evaluate the fuel (energy) consumption in long term operation. The article presents a proposal for the use of the so-called vehicle energy footprint. The method of creating such a trace is presented and illustrated with specific results of data analyzes. The data is publicly available, which means that the presented method can be independently verified. It has also been shown that the use of the method not only organizes the analyzes, but also leads to information that cannot be obtained with other methods. It therefore seems that an attractive tool has been developed for future applications.

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Section: Introductionmentioning

confidence: 95%

“…However, it is still unsatisfactory. Hence the increasing use of the RDE test [4][5][6] or simulation [7]. This is indeed an emissions test [8,9], but can also be used to evaluate fuel consumption.…”

Section: Introductionmentioning

confidence: 99%

Operational energy footprint of vehicle

Sitnik¹

2021

Combustion Engines

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“…A previous study demonstrated that a fuel/energy model was developed with a special interest in measuring fuel/energy consumption for transportation planning, transportation impact assessments, vehicle technology evaluations, traffic simulation models, and specific control conditions [14]. In order to be utilized for real-time CAV transportation applications, there are several requirements for an ideal fuel/energy consumption model.…”

Section: Literature Reviewmentioning

confidence: 99%

Developing a Hydrogen Fuel Cell Vehicle (HFCV) Energy Consumption Model for Transportation Applications

Ahn

Rakha

2022

Energies

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This paper presents a simple hydrogen fuel cell vehicle (HFCV) energy consumption model. Simple fuel/energy consumption models have been developed and employed to estimate the energy and environmental impacts of various transportation projects for internal combustion engine vehicles (ICEVs), battery electric vehicles (BEVs), and hybrid electric vehicles (HEVs). However, there are few published results on HFCV energy models that can be simply implemented in transportation applications. The proposed HFCV energy model computes instantaneous energy consumption utilizing instantaneous vehicle speed, acceleration, and roadway grade as input variables. The mode accurately estimates energy consumption, generating errors of 0.86% and 2.17% relative to laboratory data for the fuel cell estimation and the total energy estimation, respectively. Furthermore, this work validated the proposed model against independent data and found that the new model accurately estimated the energy consumption, producing an error of 1.9% and 1.0% relative to empirical data for the fuel cell and the total energy estimation, respectively. The results demonstrate that transportation engineers, policy makers, automakers, and environmental engineers can use the proposed model to evaluate the energy consumption effects of transportation projects and connected and automated vehicle (CAV) transportation applications within microscopic traffic simulation models.

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“…Where ECbus(t) denotes the instantaneous energy consumption rate for a diesel bus; α 0 , α 1 and α 2 are vehicle-specific model coefficients that should be calibrated for each vehicle; is the mass factor accounting for rotational masses, where a value of 0.1 is used for HDVs [56]; is the term related to gear ratio, which is assumed to be zero due to the lack of gear data; a(t) is the instantaneous acceleration level; R(t) is the resistance force on the vehicle as given by Equation (4). An energy consumption model for a HEV was developed in [57]. This model estimates the instantaneous energy consumption rates of an HEV using instantaneous vehicle operational input variables, including instantaneous vehicle speed, acceleration and roadway grade, which can be easily obtained from GPS devices or smartphones.…”

Section: Energy Consumption Model For Busesmentioning

confidence: 99%

“…However, the study in [57] only investigated the energy data from LDVs. There is no evidence proving that this model also works for HDVs such as hybrid buses.…”

Section: Energy Consumption Model For Busesmentioning

confidence: 99%

Developing and Testing an Eco-Cooperative Adaptive Cruise Control System for Buses

Samira

Mansoureh

2020

International Conference on Transportation and Development 2020

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Studies over the past decade have shown that eco-driving systems which provide speed advisories to drivers/vehicles using data received via vehicle-to-infrastructure and vehicle-to-vehicle communications can help improve traffic mobility and reduce vehicle energy and emission levels. This study extends the Eco-Cooperative Adaptive Cruise Control (Eco-CACC) system previously developed for light duty vehicles to heavy duty vehicles (diesel and hybrid electric buses). First, the energy consumption models for diesel and hybrid buses are discussed and the field data collected by Blacksburg Transit are used to calibrate bus models. Thereafter, the bus Eco-CACC system is developed by incorporating the vehicle dynamic model and energy consumption model for buses. The developed Eco-CACC system has manual and automated modes to control buses. The manual Eco-CACC mode was tested by participants using driving simulators at Morgan State University under various scenarios that included different types of information. In addition, the automated bus Eco-CACC system was tested using the INTEGRATION microscopic simulation software to quantify the system-wide impacts of the proposed system under various traffic demand and vehicle types. The test results demonstrated that the proposed system could improve transit operations by reducing delay and helping transit agencies save on energy costs, resulting in an improved transit level of service, increased ridership, and improved traffic mobility.

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A Simple Hybrid Electric Vehicle Fuel Consumption Model for Transportation Applications

Cited by 16 publications

References 16 publications

Operational energy footprint of vehicle

Operational energy footprint of vehicle

Developing a Hydrogen Fuel Cell Vehicle (HFCV) Energy Consumption Model for Transportation Applications

Developing and Testing an Eco-Cooperative Adaptive Cruise Control System for Buses

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