An energy management method for hybrid electric vehicles based on adaptive fuzzy control

Abstract

The invention relates to an energy management method for hybrid electric vehicles based on adaptive fuzzy control, comprising the following steps: initializing fuzzy rules and membership functions based on prior knowledge and vehicle system structure and expressing them with chromosomes; Next, the fuzzy rules and membership function are optimized by genetic algorithm respectively, and the optimal fuzzy control parameters with small fuel consumption and small SOC change value are found; the hybrid electric vehicle energy management is carried out according to the optimal fuzzy control parameters of each working condition. Compared with the prior art, the present invention iteratively optimizes the fuzzy rules and membership functions under different working conditions through the genetic algorithm, and then uses the optimized fuzzy rules and membership functions for energy management, which reduces the energy management strategy of fuzzy control. The dependence on expert knowledge and experience, compared with the method of artificially determining fuzzy control parameters, can quickly find the optimal fuzzy control parameters under different working conditions.

Description

technical field [0001] The invention relates to the field of hybrid power energy management, in particular to an adaptive fuzzy control-based hybrid power vehicle energy management method. Background technique [0002] The current situation of global environmental pollution and the depletion of proven oil resources is an urgent issue that the auto industry must face. The increasing number of cars in the world has further exacerbated the sharp contradiction between energy consumption and the increasingly limited supply of oil resources. , the development of new energy vehicles has become one of the effective ways to alleviate the energy and environmental crisis. In this context, hybrid electric vehicles, which combine the advantages of traditional internal combustion engine vehicles and electric vehicles, have become low-emission and low-energy vehicles with good application prospects. [0003] As the core part of hybrid electric vehicles, the energy management strategy of h...

AI Technical Summary

Problems solved by technology

Although the fuzzy control strategy can ensure good fuel economy and emission performance of hybrid electric vehicles, the fuzzy rules and membership function jointly determine the control performance of the fuzzy controller for energy management, and it is often difficult to determine the fuzzy controller's performance. Values ​​of Fuzzy Rules and Membership Functions

In the prior art, there is no theoretical guidance method for the formulation of fuzzy rules and the selection of membership functions. Generally, fuzzy rules and membership functions are set through expert experience, which is subject to great subjectivity and arbitrariness, and it is difficult to determine the fuzzy control parameters. The value can only be adjusted continuously by trial and error, which takes a long time. When the hybrid electric vehicle is in use, it will drive under different working conditions, and the fuzzy control parameters under various working conditions need to be determined separately, and the time required is longer. long

In addition, the fuzzy control parameters are generally directly written into the controller, once the system of the hybrid electric vehicle changes, the original fuzzy control parameters may lead to a decrease in control performance

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