scholarly journals Hybrid Electric Vehicles: A Review of Existing Configurations and Thermodynamic Cycles

Thermo ◽  
2021 ◽  
Vol 1 (2) ◽  
pp. 134-150
Author(s):  
Rogelio León ◽  
Christian Montaleza ◽  
José Luis Maldonado ◽  
Marcos Tostado-Véliz ◽  
Francisco Jurado
Keyword(s):  
Literature Review ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric Vehicles ◽  
Combustion Engines ◽  
Thermodynamic Cycles ◽  
Conventional Combustion ◽  
Heat Engines ◽  
Hybrid Electric ◽  
Fast Evolution

The mobility industry has experienced a fast evolution towards electric-based transport in recent years. Recently, hybrid electric vehicles, which combine electric and conventional combustion systems, have become the most popular alternative by far. This is due to longer autonomy and more extended refueling networks in comparison with the recharging points system, which is still quite limited in some countries. This paper aims to conduct a literature review on thermodynamic models of heat engines used in hybrid electric vehicles and their respective configurations for series, parallel and mixed powertrain. It will discuss the most important models of thermal energy in combustion engines such as the Otto, Atkinson and Miller cycles which are widely used in commercial hybrid electric vehicle models. In short, this work aims at serving as an illustrative but descriptive document, which may be valuable for multiple research and academic purposes.

Analysis of Current Hybrid-Electric Automobile Drivetrains and Proposal of an Alternative Powertrain

2020 ◽  
Author(s):  
Andrew Ahn ◽  
Thomas S. Welles ◽  
Benjamin Akih-Kumgeh
Keyword(s):  
Electric Vehicles ◽  
Hybrid Electric Vehicle ◽  
Internal Combustion Engines ◽  
Hybrid Electric Vehicles ◽  
Global Climate ◽  
Low Cost ◽  
Fuel Efficiency ◽  
Combustion Engines ◽  
Hybrid Electric ◽  
Hybrid Configuration

Abstract Byproducts of fossil fuel combustion contribute to negative changes in the global climate. Specifically, emissions from automobiles are a major source of greenhouse gas pollution. Efforts to minimize these harmful emissions have led to the development and sustained improvement of hybrid drivetrains in automobiles. Despite many advancements, however, hybrid systems still face substantial challenges which bear on their practicality, performance, and competitive disadvantage in view of the low cost of today’s traditional internal combustion engines. These imperfections notwithstanding, hybrid electric vehicles have the potential to play significant roles in the future as cleaner transportation solutions. Actualization of this potential will depend on the ability of hybrid-electric vehicles to minimize their disadvantages while increasing their positive features relative to traditional combustion engines. This research investigates current hybrid electric architectures in automobiles with the aim of suggesting an alternative, more efficient hybrid configuration that utilizes current technology. This is completed by utilizing an iterative design process to compare how various components of existing hybrids can be combined and/or improved to develop a single, efficient and cohesive system that performs comparably to or surpasses existing ones in fuel efficiency and low emissions in all driving conditions. A critical and comparative analysis is provided based on current hybrid-electric vehicle architectures as well as a plausible alternative.

Multirate Integration in Hybrid Electric Vehicle Virtual Proving Grounds

1998 ◽  
Author(s):  
Dario Solis ◽  
Chris Schwarz
Keyword(s):  
Real Time ◽  
Electric Vehicle ◽  
Time Scales ◽  
Electric Vehicles ◽  
Time Scale ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric Vehicles ◽  
Differential Algebraic Equations ◽  
Vehicle Systems ◽  
Hybrid Electric

Abstract In recent years technology development for the design of electric and hybrid-electric vehicle systems has reached a peak, due to ever increasing restrictions on fuel economy and reduced vehicle emissions. An international race among car manufacturers to bring production hybrid-electric vehicles to market has generated a great deal of interest in the scientific community. The design of these systems requires development of new simulation and optimization tools. In this paper, a description of a real-time numerical environment for Virtual Proving Grounds studies for hybrid-electric vehicles is presented. Within this environment, vehicle models are developed using a recursive multibody dynamics formulation that results in a set of Differential-Algebraic Equations (DAE), and vehicle subsystem models are created using Ordinary Differential Equations (ODE). Based on engineering knowledge of vehicle systems, two time scales are identified. The first time scale, referred to as slow time scale, contains generalized coordinates describing the mechanical vehicle system that includs the chassis, steering rack, and suspension assemblies. The second time scale, referred to as fast time scale, contains the hybrid-electric powertrain components and vehicle tires. Multirate techniques to integrate the combined set of DAE and ODE in two time scales are used to obtain computational gains that will allow solution of the system’s governing equations for state derivatives, and efficient numerical integration in real time.

Minimizing Seat Track Vibration That is Caused by the Automatic Start/Stop of an Engine in a Power-Split Hybrid Electric Vehicle

2013 ◽  
Vol 135 (6) ◽  
Author(s):  
Hsiu-Ying Hwang
Keyword(s):  
Hybrid Electric Vehicle ◽  
Internal Combustion Engines ◽  
Hybrid Electric Vehicles ◽  
Control Strategies ◽  
Effective Means ◽  
Combustion Engines ◽  
Crank Angle ◽  
Power Split ◽  
Hybrid Electric ◽  
Reaction Torque

The use of hybrid electric vehicles is an effective means of reducing pollution and improving fuel economy. Certain vehicle control strategies commonly automatically shut down or restart the internal combustion engines of hybrid vehicles to improve their fuel consumption. Such an engine autostart/stop is not engaged or controlled by the driver. Drivers often do not expect or prepare for noticeable vibrations, noise, or an unsmooth transition when the engine is autostarted/stopped. Unsmooth engine autostart/stop transitions can cause driveline vibrations, making the ride uncomfortable and the customer dissatisfied with the vehicle. This research simulates the dynamic behaviors associated with the neutral starting and stopping of a power-split hybrid vehicle. The seat track vibration results of analysis and hardware tests of the baseline control strategy are correlated. Several antivibration control strategies are studied. The results reveal that pulse cancellation and the use of a damper bypass clutch can effectively reduce the fluctuation of the engine block reaction torque and the vibration of the seat track by more than 70% during the autostarting and stopping of the engine. The initial crank angle can have an effect on the seat track vibration as well.

Study and Investigation of Energy Management Techniques Used in Electric/Hybrid Electric Vehicles

2021 ◽  
Vol 54 (4) ◽  
pp. 599-606
Author(s):  
Punyavathi Ramineni ◽  
Alagappan Pandian
Keyword(s):  
Energy Management ◽  
Electric Vehicles ◽  
Internal Combustion Engines ◽  
Hybrid Electric Vehicles ◽  
Vital Role ◽  
Combustion Engines ◽  
Key Factor ◽  
Hybrid Electric ◽  
And Control ◽  
Intelligent Controllers

Many pollution-related issues are raising due to the usage of conventional internal combustion engines (ICEs) vehicles. Electric Vehicles/ Hybrid electric vehicles (EVs/HEVs) are the finest solutions to overcome those problems associated with ICE-based vehicles. The EVs are introduced with a signal energy source (SES), which is not a successful attempt, especially during transient vehicles, driving, etc. Multiple energy sources (MES) EVs are introduced to attain better performance than the SES vehicles, which is obtained by combining two sources like battery/fuel cells, ultracapacitor. In this contest, energy management (EMNG) plays a vital role in sharing the load to the sources as per the EVs requirement. In the case of MES-based EVs, the controller always plays a significant role in the related EMNG system because it is the key factor in improving vehicle efficiency. In this article, a study has mainly been done related to several conventional, intelligent controllers and control algorithms to do the proper EMNG between sources present in the EV.

Hybrid Particle Swarm and Gravitational Search Optimization Techniques for Charging Plug-In Hybrid Electric Vehicles

2016 ◽  
pp. 471-504 ◽  
Author(s):  
Imran Rahman ◽  
Pandian Vasant ◽  
Balbir Singh Mahinder Singh ◽  
M. Abdullah-Al-Wadud
Keyword(s):  
Electric Vehicles ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric Vehicles ◽  
Particle Swarm ◽  
Transportation Industry ◽  
Hybrid Particle ◽  
Search Optimization ◽  
Gravitational Search ◽  
Hybrid Electric ◽  
Computational Intelligence Methods

Electrification of Transportation has undergone major modifications since the last decade. Success of combining smart grid technology and renewable energy exclusively depends upon the large-scale participation of Plug-in Hybrid Electric Vehicles (PHEVs) towards reach the desired pollution-free transportation industry. One of the key Performance pointers of hybrid electric vehicle is the State-of-Charge (SoC) which needs to be enhanced for the advancement of charging station using computational intelligence methods. In this Chapter, authors applied Hybrid Particle swarm and gravitational search Optimization (PSOGSA) technique for intelligently allocating energy to the PHEVs considering constraints such as energy price, remaining battery capacity, and remaining charging time. Computational experiment results attained for maximizing the highly non-linear fitness function estimates the performance measure of both the techniques in terms of best fitness value and computation time.

Hybrid Particle Swarm and Gravitational Search Optimization Techniques for Charging Plug-In Hybrid Electric Vehicles

2020 ◽  
pp. 195-228
Author(s):  
Imran Rahman ◽  
Pandian Vasant ◽  
Balbir Singh Mahinder Singh ◽  
M. Abdullah-Al-Wadud
Keyword(s):  
Electric Vehicles ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric Vehicles ◽  
Particle Swarm ◽  
Transportation Industry ◽  
Hybrid Particle ◽  
Search Optimization ◽  
Gravitational Search ◽  
Hybrid Electric ◽  
Computational Intelligence Methods

Electrification of Transportation has undergone major modifications since the last decade. Success of combining smart grid technology and renewable energy exclusively depends upon the large-scale participation of Plug-in Hybrid Electric Vehicles (PHEVs) towards reach the desired pollution-free transportation industry. One of the key Performance pointers of hybrid electric vehicle is the State-of-Charge (SoC) which needs to be enhanced for the advancement of charging station using computational intelligence methods. In this Chapter, authors applied Hybrid Particle swarm and gravitational search Optimization (PSOGSA) technique for intelligently allocating energy to the PHEVs considering constraints such as energy price, remaining battery capacity, and remaining charging time. Computational experiment results attained for maximizing the highly non-linear fitness function estimates the performance measure of both the techniques in terms of best fitness value and computation time.

Control Strategy of Hybrid Electric Vehicles Based on Driving Style Identification

2014 ◽  
Vol 945-949 ◽  
pp. 1587-1596
Author(s):  
Xian Zhi Tang ◽  
Shu Jun Yang ◽  
Huai Cheng Xia
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric Vehicles ◽  
Entropy Theory ◽  
Driving Style ◽  
Identification Error ◽  
Identification Method ◽  
Hybrid Electric

The driving style comprehensive identification method based on the entropy theory is presented. The error and error proportion of each identification result are calculated. The entropy and the variation degree of the identification error of each identification method are calculated based on the definition of information entropy. According to the entropy and the variation degree of the identification error, the weight of each kind of identification method can be determined in the comprehensive identification method, and the driving style comprehensive identification algorithm is derived. The control strategy of hybrid electric vehicles based on the driving style identification is proposed. The economic control strategy and dynamic control strategy are established. Depending on the results of driving style identification, aiming at different kinds of drivers, the mode of control strategies can be adjusted, so the demands of different kinds of drivers can be satisfied. The hybrid electric vehicle simulation model and control strategy model are built, and the simulations have been done. Due to the simulation results, the drivers’ intention comprehensive identification method based on the entropy theory is proved to represent the driver’s driving style systematically and comprehensively, and the hybrid electric vehicle control strategy based on the driving style identification can make the vehicles satisfy different drivers’ demands.

scholarly journals Model Predictive Control for Connected Hybrid Electric Vehicles

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Kaijiang Yu ◽  
Xiaozhuo Xu ◽  
Qing Liang ◽  
Zhiguo Hu ◽  
Junqi Yang ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Electric Vehicles ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric Vehicles ◽  
Control Method ◽  
Fuel Efficiency ◽  
Computation Method ◽  
Hybrid Electric

This paper presents a new model predictive control system for connected hybrid electric vehicles to improve fuel economy. The new features of this study are as follows. First, the battery charge and discharge profile and the driving velocity profile are simultaneously optimized. One is energy management for HEV forPbatt; the other is for the energy consumption minimizing problem of acc control of two vehicles. Second, a system for connected hybrid electric vehicles has been developed considering varying drag coefficients and the road gradients. Third, the fuel model of a typical hybrid electric vehicle is developed using the maps of the engine efficiency characteristics. Fourth, simulations and analysis (under different parameters, i.e., road conditions, vehicle state of charge, etc.) are conducted to verify the effectiveness of the method to achieve higher fuel efficiency. The model predictive control problem is solved using numerical computation method: continuation and generalized minimum residual method. Computer simulation results reveal improvements in fuel economy using the proposed control method.

scholarly journals Comparisons of Real-World Vehicle Energy Efficiency with Dynamometer-Based Ratings and Simulation Models

2021 ◽  
Vol 12 (4) ◽  
pp. 161
Author(s):  
Karim Hamza ◽  
Kang-Ching Chu ◽  
Matthew Favetti ◽  
Peter Keene Benoliel ◽  
Vaishnavi Karanam ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Electric Vehicles ◽  
Fuel Economy ◽  
Real World ◽  
Hybrid Electric Vehicle ◽  
Energy Intensity ◽  
Hybrid Electric Vehicles ◽  
Combustion Engine ◽  
Simulation Models ◽  
Hybrid Electric

Software tools for fuel economy simulations play an important role during design stages of advanced powertrains. However, calibration of vehicle models versus real-world driving data faces challenges owing to inherent variations in vehicle energy efficiency across different driving conditions and different vehicle owners. This work utilizes datasets of vehicles equipped with OBD/GPS loggers to validate and calibrate FASTSim (software originally developed by NREL) vehicle models. The results show that window-sticker ratings (derived from dynamometer tests) can be reasonably accurate when averaged across many trips by different vehicle owners, but successfully calibrated FASTSim models can have better fidelity. The results in this paper are shown for nine vehicle models, including the following: three battery-electric vehicles (BEVs), four plug-in hybrid electric vehicles (PHEVs), one hybrid electric vehicle (HEV), and one conventional internal combustion engine (CICE) vehicle. The calibrated vehicle models are able to successfully predict the average trip energy intensity within ±3% for an aggregate of trips across multiple vehicle owners, as opposed to within ±10% via window-sticker ratings or baseline FASTSim.

scholarly journals Using Hybrid MCDM Methods to Assess Fuel Cell Technology for the Next Generation of Hybrid Power Automobiles

2011 ◽  
Vol 15 (4) ◽  
pp. 406-417 ◽  
Author(s):  
Chi-Yo Huang ◽  
◽  
Yi-Hsuan Hung ◽  
Gwo-Hshiung Tzeng ◽  
◽  
...  
Keyword(s):  
Fuel Cells ◽  
Fuel Cell ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric Vehicles ◽  
Green Energy ◽  
Next Generation ◽  
Cell Technology ◽  
Fuel Cell Technology ◽  
Hybrid Electric

With their huge consumption of petroleum and creation of a large number of pollutants, traditional vehicles have become one of the major creators of pollution in the world. To save energy and reduce carbon dioxide emissions, in recent years national governments have aggressively planned and promoted energy-saving vehicles that use green energy. Thus, hybrid electric vehicles have already become the frontrunners for future vehicles while fuel cells are considered the most suitable energy storage devices for future hybrid electric vehicles. However, various competing fuel cell technologies do exist. Furthermore, very few scholars have tried to investigate how the development of future fuel cells for hybrid electric vehicles can be assessed so that the results can serve as a foundation for the next generation of hybrid electric vehicle developments. Thus, how to assess various fuel cells is one the most critical issues in the designing of hybrid electric vehicles. This research intends to adopt a framework based on Hybrid Multiple-Criteria Decision Making (MCDM) for the assessment of the development in fuel cells for future hybrid electric vehicles. The analytic framework can be used for selecting the most suitable fuel cell technology for future hybrid electric vehicles. The results of the analysis can also be used for designing the next generation of hybrid electric vehicles.

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