Neural network-based automobile power control method considering dynamic response capability

Abstract

The invention relates to a neural network-based automobile power control method considering dynamic response capability. The method comprises the following steps of S1, acquiring the whole automobile working condition characteristic speed of an automobile, the required power of a power system, the power of an energy source and the SOC of a storage battery in real time, S2, judging whether the fuel cell is started or not according to the SOC of the storage battery, and if the fuel cell is started, executing the step S3, S3, loading the whole automobile working condition characteristic speed, the required power of the power system and the power of the energy source into a neural network, and obtaining the current optimal power distribution parameter of the energy source, S4, correcting the current optimal power distribution parameter of the energy source according to the dynamic response capability curve of the fuel cell system, and S5, performing distribution control on the output power of the energy source according to the corrected current optimal power distribution parameter of the energy source. Compared with the prior art, the dynamic response capability of the fuel cell is considered, and the system has the advantages of being good in fuel economy, simple in structure, easy to implement in a real automobile and the like.

Description

technical field [0001] The invention relates to the field of fuel cell vehicle power control, in particular to a neural network-based vehicle power control method considering dynamic response capability. Background technique [0002] Pure fuel cell vehicles have the following disadvantages: long start-up time, poor cold start performance; slow dynamic response of the system; low efficiency of fuel cells when the output power is low and high; energy cannot be recovered through regenerative braking. To overcome these disadvantages, fuel cells are generally used in combination with other energy storage devices such as batteries and supercapacitors. Therefore, the energy distribution among multiple energy sources, that is, the energy management strategy, is one of the key research issues in the design of fuel cell vehicles. The performance of fuel cell vehicles is closely related to its energy management strategy. The optimal energy management strategy can not only improve the ...

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Problems solved by technology

[0002]Pure fuel cell vehicles have the following disadvantages: long start-up time, poor cold start performance; slow dynamic response of the system; when the output power is low and high, Fuel cells are inefficient; cannot recover energy through regenerative braking

[0006] During the training process of the neural network, the solution obtains the power allocation results through the minimum power loss algorithm, and does not fully consider the working performance of the fuel cell and storage battery. It is concluded that when this scheme is adopted, the life of the fuel cell is reduced

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