Prediction model establishing method for driving cycle of plug-in hybrid electric vehicle and vehicle energy management method

Abstract

The invention discloses a predication model establishing method for a driving cycle of a plug-in hybrid electric vehicle and a plug-in hybrid electric vehicle energy management method based on the prediction model establishing method, wherein the methods realize online application based on a model prediction control energy management strategy. Based on a basic principle of model prediction, through changing the time scale of the future driving condition of the predicated vehicle, controlling for the future vehicle speed predication precision is realized. Furthermore a predicated time domain transforming principle and a dynamic planning algorithm are introduced into a model predicating control frame, thereby forming a variable-time-domain model prediction energy management method for aimingat the plug-in hybrid electric vehicle. Particularly the prediction model establishing method comprises the steps of predicating a settling solution of condition loss problems which may occur in theactual driving process of the vehicle, predicating a process precision defining formula in real time, and forming a prediction energy management method based on a variable time domain model through apredicated time domain transforming principle which is composed of main component analysis, clustering analysis, relativity analysis and the like and introducing the principle and a dynamic planning algorithm into the model prediction control frame.

Description

technical field [0001] The present invention relates to an energy management method of a plug-in hybrid electric vehicle, in particular to the construction of a predictive model with variable predictive time domain. Background technique [0002] The model predictive control method has been gradually applied to the design of hybrid vehicle energy management strategy due to its good online application potential. This method realizes the rolling optimization of the future power demand of the vehicle in the forecast time domain based on the future driving conditions. The real-time optimal control decision is used to approach the global optimal control effect. A typical model predictive control process includes four parts: predictive model, rolling optimization, extraction of SOC reference trajectory, and online correction. Among them, the predictive model is the core part of the above process and even the entire energy management strategy. It mainly affects the future driving of...

AI Technical Summary

Problems solved by technology

[0003] The Markov chain-based method is a commonly used predictive model construction method. Due to the comprehensive consideration of the predictive accuracy of specific cycle conditions and the time cost of predictive optimization, the traditional Markov chain-based model predictive control methods are mostly Use a fixed forecast duration

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