Hybrid electric vehicle hydrogen consumption and load change energy management method

Abstract

The invention discloses a hybrid electric vehicle hydrogen consumption and load change energy management method. Through measures of model establishment, fuzzy rule introduction, energy management controller design and optimization and the like, a fuzzy energy management control method optimized through a genetic algorithm is established, and the operation efficiency of a fuel cell vehicle is improved. A subordinating degree function and a fuzzy rule base of the subordinating degree function are defined, and expert knowledge can be used for grasping a main output rule. The core of the method is optimization of parameters of the subordinating degree function, and inputs, outputs and fuzzy division of the fuzzy rule base of the inputs and the outputs are optimized. Due to the fact that the structure of a fuzzy rule cannot be directly expressed mathematically, an optimization problem is solved through the genetic algorithm.

Description

technical field [0001] The invention belongs to the technical field of automation and relates to an energy management method for hydrogen consumption and load changes of a hybrid electric vehicle. Background technique [0002] With the aggravation of energy crisis and environmental pollution, fuel cell powered vehicles have received more and more attention. However, due to slow dynamic response, limited load following capability or insufficient hydrogen under transient and fluctuating power demands, fuel cells are usually mixed with one or two batteries or supercapacitors. Hybridization of fuel cells with supercapacitors can meet large instantaneous power demands, absorb feedback energy and miniaturize fuel cells. The mixed use of fuel cells and storage batteries or supercapacitors will inevitably have the ratio of the two power sources and the management and control of energy flow, which requires an energy management solution. [0003] Aiming at the energy management in h...

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Problems solved by technology

[0003] Aiming at the energy management in hybrid electric vehicles, many control schemes have been proposed, such as fuzzy logic controller, fuzzy logic controller plus Haar wavelet energy management system, heuristic controller, distributed power management controller, etc., these methods are relatively stable and can be used in real time, however, the design process of these control strategies is relatively complicated and not suitable for optimization

Dynamic programming, neural network, genetic algorithm, and particle swarm optimization have all been used to optimize energy management systems, however, these methods only consider reducing fuel or energy consumption, without considering factors such as drive capacity, emissions, etc., and most optimization Algorithms are too complex to operate in real time

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