Hybrid electric vehicle control parameter calibrating method oriented to working conditions

Abstract

The invention discloses a hybrid electric vehicle control parameter calibrating method oriented to working conditions, and relates to the technical field of hybrid electric vehicles. The hybrid electric vehicle control parameter calibrating method mainly comprises the steps of establishment of working condition samples, optimization of the control parameters under different independent working conditions based on a particle swarm optimization, selection of working condition characteristic indexes based on correlation, multiple linear regression analysis and calibration of optimal control parameters for new working conditions. A multiple linear regression model between the optimal control parameters and the working condition characteristic indexes is established by fully considering the relationship between the working condition characteristics and the optimal control parameters, the control parameters for different working conditions can be quickly calibrated, and on the one hand, theinfluence of the working conditions on the optimal control parameters is better understood; and on the other hand, calibrators can quickly determine the optimal control parameters conveniently, and the calibration period is shortened.

Description

technical field [0001] The invention belongs to the technical field of hybrid electric vehicles, in particular to a method for calibrating control parameters of hybrid electric vehicles. Background technique [0002] Energy saving is one of the main goals of vehicle hybridization. Since hybrid vehicles involve two or more power sources, the coupling relationship is complex, and the energy management control strategy (hereinafter referred to as the control strategy) and its key control parameters have an important impact on fuel consumption. Therefore, a series of control parameter optimization methods with minimum fuel consumption as the goal are derived, such as dynamic programming algorithm, minimum equivalent fuel consumption method, genetic algorithm, particle swarm optimization and so on. Among them, the dynamic programming algorithm can achieve the optimal economy under a certain working condition, but it needs to extract complex control rules before it can be applied ...

AI Technical Summary

Problems solved by technology

[0004] The Chinese patent publication number is CN104071161A, and the publication date is 2014-10-01, which discloses a method for identification and energy management of plug-in hybrid electric vehicles. First, the support vector machine is used to identify the operating conditions, and the operating conditions are divided into Specific types, and then use different fuzzy methods to control the engine torque under different types of working conditions, so as to optimize fuel economy. This method only divides the working conditions into a limited number of categories, and cannot reflect the characteristics of the working conditions in detail. The fuzzy method simulates human behavior. Judgment is equivalent to formulating the relationship between working condition characteristics and control parameters based on experience, and the optimization effect is limited; the Chinese patent publication number is CN102717797A, and the publication date is 2012-10-10, which discloses a hybrid vehicle energy management method And energy management system, this method takes fuel consumption, engine emission, battery SOC as the cost function, uses the motor output torque as the calibration value, uses the stochastic dynamic programming method to solve the energy management problem, is different from the optimization method of this patent, and does not explore Relationship between Optimal Control Parameters and Working Condition Characteristics

Images