High-Performance Heat Sink for Hybrid Electric Vehicle Inverters

2010 ◽  
Author(s):  
John Vetrovec
Keyword(s):  
Heat Sink ◽  
Heat Load ◽  
Hybrid Electric Vehicle ◽  
High Performance ◽  
Hybrid Electric Vehicles ◽  
Coolant Fluid ◽  
Ambient Conditions ◽  
Power Inverters ◽  
Hybrid Electric ◽  
Engine Coolant

We report a novel active heat sink (AHS) that allows high-density electronic components to operate at a stable temperature over a broad range of ambient conditions. AHS receives heat at high flux and transfers it at reduced flux to environment, coolant fluid (e.g., air or engine coolant), heat pipe, or structures. Temperature of the heat load can be controlled electronically. Target applications for AHS include thermal management of high-power inverters for hybrid electric vehicles. Depending on the configuration, AHS can handle a heat load of several hundred watts at a heat flux over 1,000 W/cm2 with a thermal resistance as low as 0.1 °C/W. AHS physics, engineering design for inverter applications, performance simulations, and initial test data are presented.

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.

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.

Modeling of Thermal Dynamics of a Connected Hybrid Electric Vehicle for Integrated HVAC and Powertrain Optimal Operation

2019 ◽  
Author(s):  
Nehal Doshi ◽  
Drew Hanover ◽  
Sadra Hemmati ◽  
Christopher Morgan ◽  
Mahdi Shahbakhti
Keyword(s):  
Electric Vehicle ◽  
Hybrid Electric Vehicle ◽  
Optimal Operation ◽  
Operating Conditions ◽  
System Level ◽  
Electric Heater ◽  
Ambient Conditions ◽  
Ic Engine ◽  
Thermal Dynamics ◽  
Hybrid Electric

Abstract Integrated energy management across system level components in electric vehicles (EVs) is currently an under-explored space. Opportunity exists to mitigate energy consumption and extend usable range of EVs through optimal control strategies which exploit system dynamics via controls integration of vehicle subsystems. Additionally, information available in connected vehicles like driver schedules, trip duration and ambient conditions can be leveraged to predict the operating conditions for a vehicle when a validated model of the vehicle is known. In this study, data-driven and physics-based models for heating, ventilation and air-conditioning (HVAC) are developed and utilized along with the vehicle dynamics and powertrain (VD&PT) models for a hybrid electric vehicle (HEV). The integrated HVAC and VD&PT models are then validated against real world data. Next, an integrated relationship between the internal combustion (IC) engine coolant and the cabin electric heater is established and used to promote potential energy savings in cabin heating when the operating schedule is known. Finally, an optimization study is conducted to establish a control strategy which maximizes the HVAC energy efficiency whilst maintaining occupant comfort levels according to ASHRAE standards and improving usable range of the vehicle relative to its baseline calibration.

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.

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