Multi-Lookup Table Based Regenerative Braking Strategy of Plug-in Hybrid Electric Vehicle

2010 ◽  
Vol 44-47 ◽  
pp. 1509-1513 ◽  
Author(s):  
Qing Sheng Shi ◽  
Xiao Ping Zhang ◽  
Fuan Chen
Keyword(s):  
Electric Vehicles ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric Vehicles ◽  
Control Strategies ◽  
Distribution Coefficients ◽  
Lookup Table ◽  
Regenerative Braking ◽  
Hybrid Electric ◽  
Braking Control ◽  
Novel Strategy

. In order to improve the energy efficiency of plug-in hybrid electric vehicles, it is important to design a suitable regenerative braking strategy. There are many control strategies that have been developed and presented for plug-in hybrid electric vehicles. Most of them are aimed to energy flow management, and seldom involves regenerative braking control. In this paper, a regenerative braking strategy based on multi-lookup table method is proposed for plug-in hybrid electric vehicles. Decelerations are introduced as the index of Table Selector, so braking force distribution coefficients can be flexibly adjusted using the proposed strategy. Finally, the simulation results show the validity of the novel strategy.

Analyses of the Relation Between Degree of Mixing and Regenerative Braking in Hybrid Electric Vehicles

2014 ◽  
Vol 926-930 ◽  
pp. 743-746 ◽  
Author(s):  
Jing Ming Zhang ◽  
Jin Long Liu ◽  
Ming Zhi Xue
Keyword(s):  
Electric Vehicles ◽  
Control Strategy ◽  
Recovery Rate ◽  
Hybrid Electric Vehicles ◽  
Front Wheel ◽  
Regenerative Braking ◽  
Theoretical Derivation ◽  
Matlab Simulink ◽  
Hybrid Electric ◽  
Braking Control

The introduction of driving motors brings in the function of regenerative braking for Hybrid Electric Vehicles (HEV). In order to study the further information of regenerative braking, the relation between the degree of mixing in HEV and the recovery rate of regenerative braking was analyzed. The study object was the front-wheel driving HEV with the wire-control composite regenerative braking control strategy. Conclusions were deduced through the theoretical derivation. The braking model was established on the platform in MATLAB/SIMULINK and it was simulated within a HEV. The results indicate that the recovery rate would increase if the degree of mixing rises.

Research on regenerative braking control strategy of plug-in hybrid electric vehicle considering CVT ratio rate of change

2017 ◽  
Vol 232 (14) ◽  
pp. 1931-1943 ◽  
Author(s):  
Jianjun Hu ◽  
Zihan Guo ◽  
Hang Peng ◽  
Dawei Zheng
Keyword(s):  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Control Strategies ◽  
Dynamic Change ◽  
Optimization Method ◽  
Rate Of Change ◽  
Regenerative Braking ◽  
Hybrid Electric ◽  
Braking Control ◽  
Braking Process

At present, the regenerative braking control strategies for hybrid electric vehicles equipped with continuously variable transmission (CVT) mainly focus on improving the regenerative braking efficiency. But the influence of dynamic change of the CVT ratio is not considered with regard to the intended braking effect. For a CVT ratio control strategy based on steady-state optimal efficiency, the performance of motor-only braking and engine/motor combined braking modes are analyzed. The analysis of these modes shows that actual braking strength deviates from that required during the dynamic braking process. After analyzing the dynamic characteristics of a transmission system, a CVT ratio control strategy based on the limitations of the ratio rate of change is proposed, with the use of a discrete exhaustive optimization method. The simulation results show that, under a variety of braking conditions, the proposed regenerative braking control strategy can make the actual braking strength follow the requirements and recover more braking energy.

Regenerative Braking Control Development for P2 Parallel Hybrid Electric Vehicles

2017 ◽  
Author(s):  
Yanan Zhao ◽  
Ming Kuang ◽  
Bernard Nefcy ◽  
Dan Colvin ◽  
Stuart Ford ◽  
...  
Keyword(s):  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Regenerative Braking ◽  
Parallel Hybrid ◽  
Hybrid Electric ◽  
Braking Control

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.

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