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 Refined Node Energy Consumption Modeling in a LoRaWAN Network

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6398
Author(s):  
Sébastien Maudet ◽  
Guillaume Andrieux ◽  
Romain Chevillon ◽  
Jean-François Diouris
Keyword(s):  
Energy Consumption ◽  
Collision Probability ◽  
Maintenance Cost ◽  
Important Research Topic ◽  
Energy Consumption Model ◽  
Probability Of Collision ◽  
Consumption Model ◽  
Improved Model ◽  
Energy Consumption Modeling

LPWAN technologies such as LoRa are widely used for the deployment of IoT applications, in particular for use cases requiring wide coverage and low energy consumption. To minimize the maintenance cost, which can become significant when the number of sensors deployed is large, it is essential to optimize the lifetime of nodes, which remains an important research topic. For this reason, it is necessary that it is based on a fine energy consumption model. Unfortunately, many existing consumption models do not take into account the specifications of the LoRaWAN protocol. In this paper, a refined energy consumption model based on in-situ measurements is provided for a LoRaWAN node. This improved model takes into account the number of nodes in the network, the collision probability that depends on the density of sensors, and the number of retransmissions. Results show the influence of the number of nodes in a LoRaWAN network on the energy consumption of a node and demonstrate that the number of sensors that can be integrated into a LoRaWAN network is limited due to the probability of collision.

Modeling Urban Bus Fuel Consumption in Shanghai, China, Based on Localized MOVES

2018 ◽  
Vol 2672 (25) ◽  
pp. 150-162 ◽  
Author(s):  
Wenjian Jia ◽  
Xiaohong Chen ◽  
Xiaonian Shan
Keyword(s):  
Energy Consumption ◽  
Fuel Consumption ◽  
Motor Vehicle ◽  
Operating Mode ◽  
Environment Protection ◽  
Extended Application ◽  
Energy Consumption Model ◽  
Urban Bus ◽  
Consumption Model ◽  
Type Classification

In China, urban bus energy consumption is an increasing concern due to system expansion and poor energy efficiency due to frequent stopping and starting by buses. This study develops a mesoscopic bus energy consumption model based on the U.S. Environment Protection Agency’s Motor Vehicle Emission Simulator (MOVES). To localize MOVES, link operating mode distribution is calculated by bus GPS data collected from nine routes in Shanghai, China. A comparison of bus fuel economy between the U.S.A. and China is conducted to determine the model years in U.S.A. and China which have similar fuel consumption performance for buses with a certain weight. After MOVES localization, link energy consumption factors are estimated, and then the impacts of average speed, vehicle stops, acceleration, and road facility on link energy consumption factors are explored. Based on this exploration of influential variables, this study develops link-level bus energy consumption factor look-up tables for a variety of bus types. Model validation indicates that using link-level indicators to estimate bus energy consumption can achieve acceptable accuracy, and that the link type classification method can influence the accuracy of the mesoscopic bus energy consumption model. This study is useful to estimate bus energy consumption when instantaneous speed data is unavailable. This study also explores the extended application of MOVES by offering a procedure for applying MOVES to develop a bus energy consumption model in regions beyond the U.S.A.

scholarly journals Buildings energy consumption modeling methods

2020 ◽  
Vol 890 ◽  
pp. 012144
Author(s):  
Zaure Aimagambetova ◽  
Gulnara Bektemyssova ◽  
Zhanar Ibraeva
Keyword(s):  
Energy Consumption ◽  
Modeling Methods ◽  
Energy Consumption Modeling

scholarly journals Comparing Fuel Consumption and Emission Levels of Hybrid Powertrain Configurations and a Conventional Powertrain in Varied Drive Cycles and Degree of Hybridization

2020 ◽  
Vol 19 (1) ◽  
pp. 20-33
Author(s):  
W. U. Maddumage ◽  
K. Y. Abeyasighe ◽  
M. S. M. Perera ◽  
R. A. Attalage ◽  
P. Kelly
Keyword(s):  
Internal Combustion Engine ◽  
Fuel Consumption ◽  
Combustion Engine ◽  
Internal Combustion ◽  
Hybrid Vehicles ◽  
Exhaust Gas ◽  
Drive Cycle ◽  
Hybrid Powertrain ◽  
Powertrain Configuration ◽  
Exhaust Gas Emissions

Hybrid electric powertrains in automotive applications aim to improve emissions and fuel economy with respect to conventional internal combustion engine vehicles. Variety of design scenarios need to be addressed in designing a hybrid electric vehicle to achieve desired design objectives such as fuel consumption and exhaust gas emissions. The work in this paper presents an analysis of the design objectives for an automobile powertrain with respect to different design scenarios, i. e. target drive cycle and degree of hybridization. Toward these ends, four powertrain configuration models (i. e. internal combustion engine, series, parallel and complex hybrid powertrain configurations) of a small vehicle (motorized three wheeler) are developed using Model Advisor software and simulated with varied drive cycles and degrees of hybridization. Firstly, the impact of vehicle power control strategy and operational characteristics of the different powertrain configurations are investigated with respect to exhaust gas emissions and fuel consumption. Secondly, the drive cycles are scaled according to kinetic intensity and the relationship between fuel consumption and drive cycles is assessed. Thirdly, three fuel consumption models are developed so that fuel consumption values for a real-world drive cycle may be predicted in regard to each powertrain configuration. The results show that when compared with a conventional powertrain fuel consumption is lower in hybrid vehicles. This work led to the surprisingly result showing higher CO emission levels with hybrid vehicles. Furthermore, fuel consumption of all four powertrains showed a strong correlation with kinetic intensity values of selected drive cycles. It was found that with varied drive cycles the average fuel advantage for each was: series 23 %, parallel 21 %, and complex hybrids 33 %, compared to an IC engine powertrain. The study reveals that performance of hybrid configurations vary significantly with drive cycle and degree of hybridization. The paper also suggests future areas of study.

scholarly journals Research on innovative plug-in hybrid electric vehicle comprehensive energy consumption evaluation method based on statistic energy consumption

2021 ◽  
Vol 104 (4) ◽  
pp. 003685042110502
Author(s):  
Xinbo Chen ◽  
Jian Zhong ◽  
Feng Sha ◽  
Zaimin Zhong
Keyword(s):  
Energy Consumption ◽  
Fuel Consumption ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicle ◽  
Evaluation Method ◽  
The United States ◽  
Specific Power ◽  
The European Union ◽  
Hybrid Electric

The plug-in hybrid electric vehicle not only has the advantages of low emissions from electric vehicles, but also takes advantage of the high specific energy and high specific power of petroleum fuels, which can significantly improve the emissions and fuel economy of traditional vehicles. Studying its comprehensive energy consumption evaluation method is an important part of analyzing the economics of plug-in hybrid electric vehicles. This paper first puts forward the concept of statistical energy consumption and then proposes an innovative calculation method of plug-in hybrid electric vehicle energy consumption based on statistical energy consumption by referring to and analyzing the energy consumption test regulations of the United States, the European Union, and China. Given the two use case assumptions of charge depleting mode priority and charge sustaining mode only, considering the fuel consumption and the energy consumption that converts electrical energy consumption to fuel consumption, the probability density function of travel mileage distribution and energy consumption is derived. Finally, the interpretation and analysis of statistical energy consumption evaluation results are carried out.

Develop of a fuel consumption model for hybrid vehicles

2020 ◽  
Vol 207 ◽  
pp. 112546 ◽  
Author(s):  
Yan-Tao Zhang ◽  
Christian G. Claudel ◽  
Mao-Bin Hu ◽  
Yu-Hang Yu ◽  
Cong-Ling Shi
Keyword(s):  
Fuel Consumption ◽  
Hybrid Vehicles ◽  
Consumption Model

scholarly journals Real-Time Prediction of Fuel Consumption Based on Digital Map API

2019 ◽  
Vol 9 (7) ◽  
pp. 1369 ◽  
Author(s):  
Qi Zhao ◽  
Qi Chen ◽  
Li Wang
Keyword(s):  
Fuel Consumption ◽  
Application Programming Interface ◽  
Specific Power ◽  
Traffic Condition ◽  
Digital Map ◽  
Digital Maps ◽  
Forecasting Error ◽  
Trip Time ◽  
Application Programming ◽  
Consumption Model

At present, digital maps can estimate the travel time of each trip’s route but cannot offer a fuel consumption estimation at the same time. In this paper, we develop a fuel consumption model based on the Vehicle Specific Power (VSP) distribution, which can connect the traffic condition prediction with the fuel consumption model to predict fuel consumption. First, the traffic condition forecasting and the trip time of each route can be obtained through the digital map Application Programming Interface (API). Secondly, the users need to provide the engine displacement of their vehicles to match the fuel consumption model. Then, the fuel consumption prediction application based on Android is developed to forecast the fuel consumption by using traffic prediction data. Finally, the fuel consumption provided by the On-Board Diagnostic (OBD) data is used to verify the proposed application, and the forecasting error is less than 20%.

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