A working condition-oriented method for calibration of hybrid electric vehicle control parameters

Abstract

The invention discloses a working condition-oriented method for calibrating control parameters of a hybrid electric vehicle and relates to the technical field of hybrid electric vehicles. The method mainly includes the establishment of working condition samples, the optimization of control parameters under each independent working condition based on particle swarm optimization, the screening of working condition characteristic indicators based on correlation, multiple linear regression analysis, and the calibration of optimal control parameters for new working conditions. step. Fully consider the relationship between the working condition characteristics and the optimal control parameters, establish a multiple linear regression model between the optimal control parameters and the working condition characteristic indicators, and quickly calibrate the control parameters for different working conditions. On the one hand, it is helpful to understand The influence of working conditions on the optimal control parameters, on the one hand, facilitates the calibration personnel to quickly determine the optimal control parameters and shorten the calibration cycle.

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. , so a series of control parameter optimization methods aiming at minimum fuel consumption are derived, such as dynamic programming algorithm, minimum equivalent fuel consumption method, genetic algorithm, particle swarm algorithm, etc. Among them, the dynamic programming algorithm can achieve the optimal economy under a specific working condition, but it needs complex control rule extraction before it can be applied to actual control, an...

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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

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