Parameter optimization design method of parallel hybrid electric vehicle based on genetic algorithm

A technology of hybrid electric vehicles and genetic algorithm, which is applied in the field of parameter optimization design of parallel hybrid electric vehicles, can solve problems that rarely consider the influence of transmission system resonance and noise, and achieve the advantages of easy solution of multi-objective optimization problems and easy performance improvement Effect

Active Publication Date: 2018-09-28
JILIN UNIV
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Problems solved by technology

However, in the current transmission system matching, the matching issues of vehicle power, economy, emission and drivability are mainly considered, and the impact of transmission system matching on vehicle body resonance and noise is rarely considered; in addi

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  • Parameter optimization design method of parallel hybrid electric vehicle based on genetic algorithm
  • Parameter optimization design method of parallel hybrid electric vehicle based on genetic algorithm
  • Parameter optimization design method of parallel hybrid electric vehicle based on genetic algorithm

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Embodiment Construction

[0047] The applicant gives an embodiment of the present invention according to a specific parallel city bus development process, in order to describe the technical solution of the present invention clearly and completely.

[0048] In this embodiment, the process flow of the parameter optimization design method for parallel hybrid electric vehicles based on genetic algorithm is as follows: figure 1 shown, including:

[0049] (1) Establishment of multi-objective optimization mathematical model: First, the main parameters are shown in Table 1, including vehicle parameters, engine parameters, and motor parameters;

[0050] Table 1 List of main parameters

[0051]

[0052]

[0053] On the basis of the original commonly used power index and economic index, the present invention adds consideration to the resonance of the vehicle body, in order to reduce the probability of vehicle body resonance within a certain speed range; considering the complexity and calculation time of th...

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Abstract

The invention provides a parameter optimization design method of a parallel hybrid electric vehicle based on genetic algorithm, aiming at avoiding the phenomenon that through the traditional matchingmethod, a vehicle body has obvious resonance and large noise in a certain vehicle speed range after the matching is completed, including the following steps: firstly, establishing a multi-objective optimization mathematical model to determine calculation formulas of 0-50 km/h acceleration time, fuel economy and vehicle body resonance probability; secondly, using a weight coefficient method to transform the multi-objective optimization problem into a single-objective problem, and applying the genetic algorithm to calculate a quasi-optimal solution of the optimization problem under the premise of meeting the constraint condition; and finally, comparing performances before and after optimization of transmission system parameters to analyze the improvement of the optimization effect.

Description

technical field [0001] The invention belongs to the field of hybrid electric vehicle optimization, and in particular relates to a parameter optimization design method of a parallel hybrid electric vehicle. Background technique [0002] Compared with traditional vehicles, parallel hybrid vehicles have more power system components and parameters, so the matching optimization problem is more complicated; the existing hybrid vehicle matching design methods are divided into two categories: constraint matching method and optimal matching method, Compared with the traditional constraint matching method, the optimal matching method can guide and improve the design of the transmission system through the optimization algorithm, so that it can ensure the optimal performance of multiple performance indicators while satisfying the constraint conditions. Among all optimization algorithms, genetic algorithm designs iterative formulas by simulating the characteristics of biological groups, ...

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Application Information

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IPC IPC(8): G06F17/50G06N3/12
CPCG06N3/126G06F30/15G06F30/20Y02T10/40Y02T90/00
Inventor 曾小华王振伟宋大凤李广含钱琦峰张轩铭崔皓勇董兵兵孙楚琪雷宗坤
Owner JILIN UNIV
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