scholarly journals A Method and System for Combining the Advantages of Gasoline Compression Ignition (GCI) Engine Technologies into Hybrid Electric Vehicles (HEVs)

2021 ◽  
Vol 11 (21) ◽  
pp. 9934
Author(s):  
Hyun Woo WON
Keyword(s):  
Electric Vehicles ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric Vehicles ◽  
High Efficiency ◽  
Cetane Number ◽  
Co2 Reduction ◽  
Thermodynamic Cycle ◽  
Low Temperature Combustion ◽  
Compression Ignition ◽  
Hybrid Electric

By combining a clean fuel such as gasoline with a high efficiency thermodynamic cycle (compression ignition), it is possible to demonstrate a powertrain that is clean and efficient, thus breaking the historical trade-off between decreasing CO2 and reducing criteria pollutants. The gasoline compression ignition (GCI) engine is a promising technology that can be used to improve thermal efficiency while reducing emissions. Its low temperature combustion does however lead to several problems that need to be overcome. The present study relates to a method and system for combining the advantages of GCI engine technology into a hybrid electric vehicle (HEV) to maximize the benefits. A plausible path is to operate the GCI engine at conditions where the benefits of a GCI engine could be maximized and where an electric motor can supplement the conditions where the GCI is less beneficial. In this study, GCI engines with different cetane number (CN) fuels were selected, and a hybrid simulation tool was used to address the potential of the GCI engines into hybrid electric vehicles. Co-developments can demonstrate efficiency and emission solutions through the achievements of the study, which will address examples of the competitive powertrain and will introduce more than 30% of CO2 reduction vehicle by 2030.

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.

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.

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.

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.

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.

Market penetration speed and effects on CO2 reduction of electric vehicles and plug-in hybrid electric vehicles in Japan

Energy Policy ◽  
2012 ◽  
Vol 45 ◽  
pp. 529-540 ◽  
Author(s):  
Kuniaki Yabe ◽  
Yukio Shinoda ◽  
Tomomichi Seki ◽  
Hideo Tanaka ◽  
Atsushi Akisawa
Keyword(s):  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Co2 Reduction ◽  
Market Penetration ◽  
Hybrid Electric

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.

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