Research on parameter matching of electric vehicle power system based on genetic algorithm

2020 ◽  
Author(s):  
Hongtao Wang ◽  
Honglian Zhou ◽  
Qian Cao ◽  
Mengke Liao ◽  
Bin Wang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Power System ◽  
Electric Vehicle ◽  
Parameter Matching

scholarly journals Parameter matching and simulation analysis of power system of pure electric vehicle

2021 ◽  
Vol 2076 (1) ◽  
pp. 012091
Author(s):  
Jiwei Geng ◽  
Qun Chi
Keyword(s):  
Power System ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Simulation Analysis ◽  
Vehicle Speed ◽  
Critical Component ◽  
Power Performance ◽  
Environmental Destruction ◽  
Parameter Matching

Abstract The consumption of gasoline and diesel for cars is beyond imagination. Along with these problems comes environmental destruction. In order to achieve the two major indicators of power performance and economy, the most critical component of electric vehicles --- the power system is optimized and improved, and simulation is used to verify it on this basis‥ It can be concluded that the maximum vehicle speed is greater than 120km/h, the maximum gradeability exceeds 30%, the cruising range reaches 178km.

Parameter Matching and Simulation Study of Powertrain for Extended-Range Electric Vehicle

2014 ◽  
Vol 926-930 ◽  
pp. 1387-1391 ◽  
Author(s):  
De Jun Wu ◽  
Ting Yong Lu ◽  
Li Jun Zhang ◽  
Xian Wu Gong
Keyword(s):  
Genetic Algorithm ◽  
Electric Vehicle ◽  
Power Performance ◽  
Power Requirement ◽  
Range Extender ◽  
Extended Range ◽  
Parameter Matching ◽  
Driving Range ◽  
Design Requirements ◽  
Simulation Results

A method of parameter matching for extended-range electric vehicle (E-REV) was discussed to meet the requirements given, then using a model and genetic algorithm to optimize the transmission ratio of E-REV. The parameters of the battery and range extender (RE) are designed by driving range and power requirement. The simulation results shows that the parameter matching is reasonable, and the power performance and driving range could meet the design requirements.

scholarly journals Multi-Objective Optimization and Matching of Power Source for PHEV Based on Genetic Algorithm

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1127 ◽  
Author(s):  
Pengxiang Song ◽  
Yulong Lei ◽  
Yao Fu
Keyword(s):  
Genetic Algorithm ◽  
Power System ◽  
Fuel Economy ◽  
Power Source ◽  
Multi Objective Optimization ◽  
Matching Method ◽  
Optimal Matching ◽  
Multi Objective ◽  
Parameter Matching ◽  
Hybrid Electric

Power system parameter matching is one of the key technologies in the development of hybrid electric vehicles. The power source is the key component of the power system which composed of engine, motor, and battery. Reasonable power source parameters are conducive to improve the power, fuel economy, and emission performance of vehicles. In this paper, regarding the problem that the plug-in hybrid electric vehicle (PHEV) parameter matching needs to weigh different design objectives, a multi-objective optimization and matching method based on a genetic algorithm is proposed. The vehicle dynamic model is established based on MATLAB/Simulink (Mathworks in Natick, Massachusetts, USA), and the feasibility of the model is verified by simulation. The main performance parameters of the power source are matched by theoretical analysis, and the PHEV integrated optimization simulation platform is established based on Isight(Dassault Systemes in Paris, France) and MALTAB/Simulink. Power source components are optimized considering fuel economy and lightweight objectives under the performance constraints. Firstly, the optimal matching results under different weights are obtained by transforming different objectives into single objective, and the multi-island genetic algorithm is used to obtain the optimal matching results in which the equivalent fuel consumption of 100km is reduced by 1%. Then the Pareto solution is obtained using the NSGA-II algorithm. The optimal matching results can be found after determining the weights of different design objectives, which proves the effectiveness and superiority of the multi-objective optimization matching method. The optimization results show that compared with the original vehicle, the fuel economy effect is increased by 2.26% and the lightweight effect is increased by 8.26%.

scholarly journals Power system parameters matching of in-wheel motor drive electric vehicle

2021 ◽  
Vol 257 ◽  
pp. 01044
Author(s):  
Wei Chen Ren ◽  
Yi Fang ◽  
Xiao Peng Li ◽  
Zheng Xu ◽  
Xu Long Wang
Keyword(s):  
Power System ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Motor Drive ◽  
System Parameters ◽  
Type Selection ◽  
Power Battery ◽  
Outer Rotor ◽  
Parameter Matching ◽  
Avl Cruise

Based on the line-controlled chassis platform, the low-speed outer rotor motor is used to drive the wheel directly in the designed in-wheel motor line-controlled electric vehicle. The power system parameters of the designed four-wheel independent drive electric vehicle are matched and simulated by offline simulation. Firstly, based on the theoretical basis of the research on the parameter matching of the power system of pure electric vehicles, the type selection and parameter matching of the hub motor and power battery are carried out according to the established dynamic and economic indicators. Then, the designed component parameters are repeatedly corrected until the design goal is achieved; Finally, the vehicle model is built in AVL cruise, and the design results are simulated by software.

scholarly journals Optimized Strategic Planning of Future Norwegian Low-Voltage Networks with a Genetic Algorithm Applying Empirical Electric Vehicle Charging Data

Electricity ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 91-109
Author(s):  
Julian Wruk ◽  
Kevin Cibis ◽  
Matthias Resch ◽  
Hanne Sæle ◽  
Markus Zdrallek
Keyword(s):  
Genetic Algorithm ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Low Voltage ◽  
Network Planning ◽  
Voltage Regulation ◽  
Electric Vehicle Charging ◽  
Vehicle Charging ◽  
The Impact

This article outlines methods to facilitate the assessment of the impact of electric vehicle charging on distribution networks at planning stage and applies them to a case study. As network planning is becoming a more complex task, an approach to automated network planning that yields the optimal reinforcement strategy is outlined. Different reinforcement measures are weighted against each other in terms of technical feasibility and costs by applying a genetic algorithm. Traditional reinforcements as well as novel solutions including voltage regulation are considered. To account for electric vehicle charging, a method to determine the uptake in equivalent load is presented. For this, measured data of households and statistical data of electric vehicles are combined in a stochastic analysis to determine the simultaneity factors of household load including electric vehicle charging. The developed methods are applied to an exemplary case study with Norwegian low-voltage networks. Different penetration rates of electric vehicles on a development path until 2040 are considered.

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