Vehicle operating condition multi-scale predicting method based on Markov chain

Abstract

The invention discloses a vehicle operating condition multi-scale predicting method based on the Markov chain. The method establishes a Markov chain prediction model for the vehicle operating condition. The method comprises the steps of computing a state transferring matrix by maximum likelihood estimation according to the history information of vehicle operating condition; performing the vehicle operating condition predicting of different time scales according to the obtained state transferring matrix by utilizing the Markov chain and Monte Carlo analogy method; restoring the predicted outcomes of different time scales into data under a history operating condition sampling frequency through linear interpolation; dividing the predicted outcomes of different time scales into different confidence grades according to simulated sample quantity, and computing the linear weight coefficient under different confidence grades of the predicted outcome every time by adopting a linear weighting method; and merging all the predicted values of each scale of predicted outcome every time according to the weight coefficients and merging the different scales of predicted outcomes under the original data frequency to obtain the vehicle operating condition multi-scale predicting outcome. The vehicle operating condition multi-scale predicting method based on the Markov chain can meet the predicting precision requirements of the vehicle operating condition and the requirements of vehicle real-time control.

Description

technical field [0001] The invention relates to a method for predicting vehicle operating conditions, in particular to a Markov chain-based multi-scale forecasting method for vehicle operating conditions. Background technique [0002] The energy management strategy of hybrid electric vehicle is to optimize the fuel consumption and emission, take the operating conditions of the vehicle as the design basis, and take the dynamic performance as the constraint condition to calculate the energy distribution of the two power sources. However, the current energy management strategy development method based on the so-called standard vehicle operating conditions emphasizes "universal representation", but does not express the characteristics of differences and diversity of vehicle operating conditions over time in different cities, at different times and in different locations . The effective energy management strategies designed according to specific representative working conditions...

AI Technical Summary

Problems solved by technology

[0010] The above has introduced the current situation of the prediction method of automobile operating conditions based on historical information, and there are mainly the following problems: (1) Foreign scholars have begun to try to use the automobile operating conditions as a Markov chain to carry out automobile operating conditions. (2) The prediction method based on fuzzy logic and historical information has rough classification and complex calculation, and it is difficult to guarantee the accuracy and real-time requirements of prediction

Therefore, there is no method for predicting vehicle operating conditions at present that can clearly explain the principle of the vehicle operating condition prediction method, and can ensure high prediction accuracy and good real-time performance

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