Simulated annealing particle swarm algorithm based hybrid power automobile parameter optimization method

A technology of hybrid electric vehicles and particle swarm algorithm, applied in calculation, calculation model, biological model, etc., can solve the problem of not being able to quickly obtain the optimal solution, and achieve the effect of reducing emissions and reducing fuel consumption

Inactive Publication Date: 2015-03-11
JIANGSU UNIV
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Problems solved by technology

[0005] Aiming at problems such as the empiricization of hybrid vehicle control parameters during the setting process and the inability to quickly obtain the optimal solution, the present invention proposes a hybrid vehicle parameter opt

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  • Simulated annealing particle swarm algorithm based hybrid power automobile parameter optimization method
  • Simulated annealing particle swarm algorithm based hybrid power automobile parameter optimization method
  • Simulated annealing particle swarm algorithm based hybrid power automobile parameter optimization method

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Embodiment Construction

[0042] The specific implementation process of the invention patent will be further described below in conjunction with the accompanying drawings.

[0043] The present invention selects the parallel hybrid electric vehicle as a specific embodiment, and calls the built-in function in the analysis software ADVISOR of the hybrid electric vehicle in the form of code. The calling format is: [error_code, resp]=adv_no_gui(action, input);

[0044] There are many control strategies for parallel hybrid electric vehicles, and the most commonly used one is the logic threshold control strategy. The logic threshold control strategy controls the working mode of the engine and the motor by setting a series of logic thresholds, so that the engine and the motor work in a high-efficiency area. There are 9 control parameters in the logic threshold control strategy, as shown in the following table:

[0045] parameters

definition

cs_hi_soc

battery limit

cs_lo_soc

...

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Abstract

The invention discloses a simulated annealing particle swarm algorithm based hybrid power automobile parameter optimization method. A threshold value in a hybrid power automobile control strategy is converted into a group of particles to be optimized, the automobile fuel consumption rate and emissions are utilized as an optimization objective function, the simulated annealing process is performed on the particles in a parallel mode, the new state of every particle is selectively accepted according to the Metropolis criterion in the annealing process, the local optimum is jumped out through the jumping characteristic of a simulated annealing particle swarm algorithm, and the global optimal solution is achieved through convergence finally. According to the simulated annealing particle swarm algorithm based hybrid power automobile parameter optimization method, the problems that the setting process of hybrid power automobile control parameters is based on the experience and an optimal threshold value cannot be obtained are solved, the optimal threshold value can be obtained rapidly, and the vital significance is brought to the automobile energy conservation and emissions reduction and the theory research of hybrid power automobiles.

Description

technical field [0001] The invention belongs to the field of automobile parameter optimization, in particular to a hybrid electric automobile parameter optimization method based on simulated annealing particle swarm algorithm. Background technique [0002] Hybrid electric vehicle has the advantages of low emission, less pollution and good fuel economy, and is an important direction of future automobile development. However, the operation mode of hybrid electric vehicle is complicated, and its control strategy is not very mature. At present, only the logic threshold control strategy designed based on engineering experience is widely used in commercialized hybrid electric vehicles. In engineering practice, the logic threshold setting is mainly based on engineering experience and intuitive judgment, and then through a large number of experimental comparisons and verifications to find the best value, which often takes a long time. [0003] Particle swarm optimization algorithm ...

Claims

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Application Information

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IPC IPC(8): G06F17/50G06N3/00
Inventor 陈龙姚勇袁朝春杨军任皓肖飞高泽宇
Owner JIANGSU UNIV
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