Design method and system of data-driven predictive controller for hev mode switching

Abstract

The invention discloses an HEV mode switching data driving prediction controller design method. The HEV mode switching data driving prediction controller design method comprises steps that a whole hybrid electric vehicle simulation model is established in vehicle simulation software; motor torque, engine torque, and clutch torque are designed as excitation data, and the designed excitation data is used for the established hybrid electric vehicle simulation model; the input and output data block Hankel matrix of the hybrid electric vehicle is established based on the acquired open loop data, and then a prediction output equation of a hybrid electric vehicle mode switching system is established based on an identification model; an increment of an input sequence, an output sequence, and a future input sequence of a data driving prediction controller are defined, and the prediction output equation is expressed in an increment way; the data driving prediction controller is solved in an on-line manner according to a corresponding input constraint and a corresponding output constraint. Calculation amount is reduced, and a modeling error problem caused by a switching process from the data identification model to a state space model is prevented.

Description

technical field [0001] The invention belongs to the field of hybrid electric vehicles, and in particular relates to a data-driven predictive controller design method and system for HEV mode switching. Background technique [0002] The English full name of HEV is Hybrid Electric Vehicle, and the Chinese name is: Hybrid Electric Vehicle. [0003] With the aggravation of energy shortage and environmental pollution, hybrid electric vehicle (Hybrid Electric Vehicle, HEV) can not only greatly improve the fuel economy and emission performance of the vehicle, but also ensure sufficient mileage. Feasible solutions to fossil fuel consumption and carbon emissions have become a research hotspot and mainstream direction today. [0004] At present, some advanced theories, such as: fuzzy adaptive sliding mode method, model matching control, model reference control, switching control, model predictive control and other methods, have been applied to this field and achieved many research res...

AI Technical Summary

Problems solved by technology

Higher-order nonlinear models are difficult to handle with mechanism-based control methods

Even if it can be processed, it will inevitably lead to a high-level and complex controller with a huge amount of calculation, which cannot meet the fast and real-time requirements of HEV.

In view of this, the existing studies have done some simplification in modeling, but there are bound to be many unmodeled dynamics, resulting in poor control effects

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