Modeling and Simulation of an Electric Vehicle with Independent Rear Motors to Estimate the Fuel Economy during EPA Drive Cycles

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
W. Browna ◽  
Y. Liu
Keyword(s):  
Modeling And Simulation ◽  
Electric Vehicle ◽  
Fuel Economy ◽  
Environmental Protection Agency ◽  
Hybrid Electric Vehicle ◽  
Combustion Engine ◽  
Rear Wheel ◽  
Wheel Drive ◽  
Technical Specifications ◽  
Critical Components

The “Car of the Future” project converted a 2015 rear-wheel drive (RWD) Subaru BRZ into a hybrid electric vehicle (HEV) with an intermediate milestone of a battery electric vehicle (BEV). BEV architecture required removal of the conventional powertrain components, such as internal combustion engine, transmission and differential, introduced an electric axle and battery. This intermediate BEV step provided a point at which the vehicle could be evaluated in its all electric operation with the absence of what was once critical components including its original powertrain and powertrain electronics. This step also ensures the electric components are working properly before more complexity is added to the system in building HEV. In our previous work, BEV Vehicle Technical Specifications (VTS) or requirements were developed and an electric axle was appropriately sized and selected to meet these requirements. After selecting the electrical axle with independent rear motors that will meet BEV performance requirements, Environmental Protection Agency (EPA) fuel economy rating of the BEV should be assessed. This paper presents a drive cycle analysis of the BEV vehicle using the EPA Urban Dynamometer Driving Schedule (UDDS) and Highway Fuel Economy Test (HWFET) drive cycles by means of dynamic modeling and simulation. In this study, the power required at the wheels, the efficiency of each motor and the energy required at the selected electrical axle were determined. In addition, the city, highway and combined miles per gallon equivalent (MPGe) fuel economy were determined.

scholarly journals Real-Time Co-optimization of Vehicle Route and Speed Using Generic Algorithm for Improved Fuel Economy

2019 ◽  
Vol 141 (03) ◽  
pp. S08-S15
Author(s):  
Guoming G. Zhu ◽  
Chengsheng Miao
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Fuel Economy ◽  
Autonomous Vehicles ◽  
Hybrid Electric Vehicle ◽  
High Efficiency ◽  
Combustion Engine ◽  
Cruise Control ◽  
Vehicle Performance ◽  
Hybrid Electric

Making future vehicles intelligent with improved fuel economy and satisfactory emissions are the main drivers for current vehicle research and development. The connected and autonomous vehicles still need years or decades to be widely used in practice. However, some advanced technologies have been developed and deployed for the conventional vehicles to improve the vehicle performance and safety, such as adaptive cruise control (ACC), automatic parking, automatic lane keeping, active safety, super cruise, and so on. On the other hand, the vehicle propulsion system technologies, such as clean and high efficiency combustion, hybrid electric vehicle (HEV), and electric vehicle, are continuously advancing to improve fuel economy with satisfactory emissions for traditional internal combustion engine powered and hybrid electric vehicles or to increase cruise range for electric vehicles.

Progression Over the Years: The EcoEagles Student-Designed Biodiesel Plug-In Hybrid Electric Vehicle

2011 ◽  
Author(s):  
Sean Carter ◽  
Jenna Beckwith ◽  
Marc Compere ◽  
Darris White ◽  
Brandon Smith ◽  
...  
Keyword(s):  
Electric Vehicle ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
Lithium Ion ◽  
Department Of Energy ◽  
Background Information ◽  
Biodiesel Fuel ◽  
Vehicle Development ◽  
Technical Specifications ◽  
Hybrid Electric

The Embry-Riddle Aeronautical University (ERAU) EcoEagles are participating in the EcoCar: The NeXt Challenge competition. The competition is a three-year collegiate event where 16 teams from North America compete to build a more efficient and better performing GM production vehicle. The three year collegiate competition is sponsored by the Department of Energy (DOE), General Motors (GM), and Argonne National Labs (ANL). The advanced vehicle technology competition has a history, and has been organized and ran for the past 20 years. The competition challenges collegiate minds to reduce the environmental impact of a Chevrolet EcoCAR by minimizing fuel consumption and reducing emissions while retaining the vehicle’s performance, safety, and consumer appeal. The main focus of the competition is to use real world vehicle development strategies and processes that would meet GM’s standard practices and safety protocols. All of the sponsors of the competition provide teams with engineering tools, equipment needed to create a realistic vehicle, and project design support to the teams throughout the competition. The ERAU team, the EcoEagles, has successfully devised a Plug-In Hybrid Electric Vehicle (PHEV) propulsion system that meets those requirements. The electrification of the powertrain and the use of biodiesel fuel are central themes in the EcoEagles’ strategy for improving fuel economy and tailpipe emissions. The team selected an electric range of approximately 25 miles based on the average commuter driving less than 33 miles per day [1]; meaning that most of the vehicle operation will be conducted using either fully electric or electric-assisted propulsion. The vehicle design consideration was accomplished by implementing a 1.3L GM Turbo Diesel coupled with a 2-Mode electrically variable transmission (EVT) and an A123 Lithium-Ion Iron-Phosphate 330V 12.8kWhr battery pack. The EcoEagles design will reduce petroleum energy consumption by 78%, improve fuel economy by 66%, and reduce well-to-wheel greenhouse gas (WTWGHG) emissions by 30%. The paper will focus on the 99% production readiness. The paper will also discuss and include vehicle test data supporting the energy efficiency, emissions, and performance / utility capabilities of the vehicle as determined by the first two years of vehicle development. The vehicle architecture and background information will also be presented to help the reader understand why the given architecture was chosen and how it might compare to the Chevrolet EcoCAR. Performance predictions made from simulations will be contrasted against those from the Hardware-in-the-Loop (HIL) development. Finally, on-road testing will also be compared with the same predictions with the goal of showing why the model-based, HIL enhanced, and vehicle technical specifications (VTS) did or did not agree.

Architecture Selection for Hybrid Electric Mobility As a Service Vehicle

2020 ◽  
Author(s):  
Noah Schaich ◽  
Christian Free ◽  
Nishan Nekoo ◽  
Michael J. Leamy
Keyword(s):  
Fuel Economy ◽  
Environmental Protection Agency ◽  
Hybrid Electric Vehicle ◽  
Combustion Engine ◽  
Hybrid Vehicle ◽  
Electric Machines ◽  
Georgia Tech ◽  
The Road ◽  
Simulink Model ◽  
Hybrid Electric

Abstract This paper documents the vehicle modeling and fuel economy simulation efforts for the Georgia Tech EcoCAR Mobility Challenge team during Year 1 of the current Advanced Vehicle Technology Competition (AVTC). The goal of the first year of the competition was to propose two possible hybrid electric vehicle (HEV) architectures based on a 2019 Chevrolet Blazer platform that could be built and refined throughout Years 2–4 of the competition. A Simulink vehicle model was used to compare a variety of HEV architectures with several different combinations of engines, batteries, and electric machines. An adaptive Equivalent Consumption Minimization Strategy was used to split torque between the internal combustion engine and two electric machines so that architectures with two electric machines could be modeled. The model was validating by comparing results from simulating a conventional Chevrolet Blazer with the 2.5L naturally aspirated engine to fuel economy results obtained by the Environmental Protection Agency (EPA). In addition to developing a hybrid vehicle, the competition is focused on exploring Mobility as a Service (MaaS), which introduces some special considerations when choosing a hybrid vehicle architecture. The results of the Simulink model as well as requirements set by the MaaS market led the Georgia Tech EcoCAR team to pursue a dual electric machine P0P4 parallel through the road hybrid vehicle.

Design and optimization of a modified series hybrid electric vehicle powertrain

2018 ◽  
Vol 233 (6) ◽  
pp. 1419-1435 ◽  
Author(s):  
Swagata Borthakur ◽  
Shankar C Subramanian
Keyword(s):  
Electric Vehicle ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
Combustion Engine ◽  
Operating Efficiency ◽  
Traction Motor ◽  
Vehicle Performance ◽  
Series Hybrid Electric Vehicle ◽  
Hybrid Electric ◽  
Vehicle Powertrain

Hybrid electric vehicles are emerging technologies that are considered as eco-friendly alternative solutions to internal combustion engine–driven vehicles. This paper proposes a modified hybrid electric vehicle powertrain system that addresses the shortcomings of a series hybrid electric vehicle powertrain. The proposed configuration replaces the conventional generator of a series hybrid electric vehicle with an integrated starter generator that supports the traction motor of the vehicle during acceleration and peak torque requirements and maintains the state of charge of the batteries to provide an extended electric range of the vehicle. The work done in this paper can be categorized into two stages. The first stage is the methodical development of the powertrain in terms of initial parameter matching and sizing of the vehicle components by considering the fundamentals of longitudinal vehicle dynamics. The second stage describes the optimization of the proposed configuration to meet the design objective of maximizing fuel economy subjected to a set of vehicle performance constraints. The performance of the proposed powertrain was evaluated and compared with a series hybrid electric vehicle powertrain for an on-road Indian driving cycle using AVL CRUISE, which is a commercially available software for the study and analysis of road vehicle powertrains. Result analysis during initial parameterization showed a reduction in gross vehicle weight of the proposed configuration by 244 kg (1.5%) and an improvement in the average operating efficiency of the traction motor by around 11%, when compared to a series hybrid electric vehicle. Furthermore, the optimization results for the proposed configuration established an improvement in the fuel economy by 21% while meeting vehicle performance requirements.

MPC-Based Energy Management of a Parallel Hybrid Electric Vehicle Using Terrain Information

2015 ◽  
Author(s):  
Mohamed Wahba ◽  
Sean Brennan
Keyword(s):  
Electric Vehicle ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
Combustion Engine ◽  
Simulation Software ◽  
Electrical Machine ◽  
Parallel Hybrid Electric Vehicle ◽  
Parallel Hybrid ◽  
Power Split ◽  
Hybrid Electric

A parallel hybrid electric vehicle (HEV) combines the power produced by electric machines and a combustion engine to enable improved fuel economy. Optimization of the power-split algorithm managing both torque sources can be readily achieved offline, but online implementation results often show great deviation from expected fuel economy due to traffic, hills, and similar effects that are not easily modeled. Of these external influences, the road grade for a travel route is potentially known a priori given a set destination choice from the driver. To examine whether grade information can improve the performance of a hybrid powertrain controller, we first formulate the vehicle model as a low-order dynamic model, recognizing that the primary dynamics of the energy system are slow. A model predictive control (MPC) strategy utilizing the terrain data is then developed to obtain a time-varying power split between the combustion engine and the electrical machine. Simulation results of the HEV model over multiple standard drive cycles, with different terrain profiles and different cost functions, are presented. Testing of the MPC performance compared to Argonne National Lab’s powertrain simulation software Autonomie shows that the MPC strategy utilizing terrain data gives an improvement of up to 2.2% in fuel economy with respect to the same controller without terrain information, on the same route.

Powertrain Matching Based on Driving Cycle for Fuel Cell Hybrid Electric Vehicle

2013 ◽  
Vol 288 ◽  
pp. 142-147 ◽  
Author(s):  
Shang An Gao ◽  
Xi Ming Wang ◽  
Hong Wen He ◽  
Hong Qiang Guo ◽  
Heng Lu Tang
Keyword(s):  
Fuel Cell ◽  
Electric Vehicle ◽  
Cell Hybrid ◽  
Hybrid Electric Vehicle ◽  
Combustion Engine ◽  
Driving Cycle ◽  
Power Performance ◽  
Matching Method ◽  
Hybrid Electric ◽  
Fuel Cell Hybrid

Fuel cell hybrid electric vehicle (FCHEV) is one of the most efficient technologies to solve the problems of the energy shortage and the air pollution caused by the internal-combustion engine vehicles, and its performance strongly depends on the powertrains’ matching and its energy control strategy. The theoretic matching method only based on the theoretical equation of kinetic equilibrium, which is a traditional method, could not take fully use of the advantages of FCHEV under a certain driving cycle because it doesn’t consider the target driving cycle. In order to match the powertrain that operates more efficiently under the target driving cycle, the matching method based on driving cycle is studied. The powertrain of a fuel cell hybrid electric bus (FCHEB) is matched, modeled and simulated on the AVL CRUISE. The simulation results show that the FCHEB has remarkable power performance and fuel economy.

Match and Optimization of Series Hybrid Bus Powertrain Parameters

2011 ◽  
Vol 121-126 ◽  
pp. 2710-2714
Author(s):  
Ling Cai ◽  
Xin Zhang
Keyword(s):  
Electric Vehicle ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
Simulation Platform ◽  
Optimal Method ◽  
Orthogonal Method ◽  
Series Hybrid Electric Vehicle ◽  
Hybrid Bus ◽  
Promising Solution ◽  
Hybrid Electric

With the requirements for reducing emissions and improving fuel economy, it has been recognized that the electric, hybrid electric powered drive train technologies are the most promising solution to the problem of land transportation in the future. In this paper, the parameters of series hybrid electric vehicle (SHEV), including engine-motor, battery and transmission, are calculated and matched. Advisor software is chosen as the simulation platform, and the major four parameters are optimized in orthogonal method. The results show that the optimal method and the parameters can improve the fuel economy greatly.

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