Abstract
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%.
Highlights
Nowadays, the number and annual production of internal combustion engine vehicles are increasing rapidly, resulting in energy crisis, urban air pollution, and other issues that are becoming increasingly serious [1]
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%
In this paper, regarding the problem that Plug-in hybrid electric vehicle (PHEV) parameter matching needs to weigh different design objectives, a multi-objective optimization matching method based on genetic algorithm is proposed
Summary
The number and annual production of internal combustion engine vehicles are increasing rapidly, resulting in energy crisis, urban air pollution, and other issues that are becoming increasingly serious [1]. Zhao et al [12] uses SQP to match the power system parameters of four-wheel drive fuel cell hybrid electric vehicle, but the objective function is the size of power transmission system components. Compared withefficiency a conventional genetic algorithm,the comprehensive performance single-objective optimization with optimum fuel economy or minimum cost, and few studies focus of fuel consumption and emission can be improved by 4%. A hybrid electric bus with this configuration can achieve variety ofbus, working modes (as shown in parameters and performance indexes of the vehicle, which is the design goal of matching and Figure 2), including pure electric mode, engine driving mode, hybrid driving mode, charging mode optimization in this recovery paper.
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