A Method for Predicting the Future Speed ​​Trajectory of a Hybrid Electric Bus

A technology of hybrid power and trajectory prediction, applied in neural learning methods, traffic flow detection, traffic control systems of road vehicles, etc., can solve problems such as insufficient prediction accuracy of future driving conditions

CN103914985BInactive Publication Date: 2015-10-28DALIAN UNIV OF TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Publication Date
2015-10-28
Estimated Expiration
Not applicable · inactive patent

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Abstract

The invention discloses a method for predicting the future speed trajectory of a hybrid power bus. The method includes the following steps that parameters are obtained and normalized; an input parameter vector and an output parameter vector of an RBF neural network are determined; the RBF neural network is offline trained; the future speed trajectory is online predicted. Based on online learning of the RBF neural network, prediction of the speed trajectory is studied under the precondition that global optimization of the control strategy of the hybrid power bus is achieved from the aspect of a people-bus-environment system, the method for predicting the bus future driving trajectory is provided and fully takes bus state parameters, driver driving styles and front road environment and traffic state parameters into consideration, and accuracy of prediction of the bus speed is improved. The influences of the people-bus-environment system on the future working condition is fully taken into consideration, accuracy of prediction of the working condition is improved while the bus has the precognition capacity, and a good foundation is laid for global optimization of the control strategy.
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Description

technical field

[0001] The invention relates to a method for predicting the future speed trajectory of a hybrid electric bus, in particular to a method for predicting the future speed trajectory of a hybrid electric bus based on online learning of a Radial Basis Function (RBF) neural network. Background technique

[0002] Due to its good fuel economy and low emissions, hybrid electric vehicles have become one of the most practical ways to solve energy and emission problems. The fuel economy and emissions of hybrid electric vehicles are mainly determined by the energy management strategy of the multi-energy power system. From the perspective of control effect, the global optimization strategy can be regarded as the most ideal control method with the most fuel-saving potential for the hybrid system, and the prediction of future driving conditions is a prerequisite for the global optimization of the energy management strategy. The prediction of future driving conditions is to ...

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Embodiment Construction

[0039] The specific embodiments of the present invention will be described in detail below in conjunction with the technical solutions and accompanying drawings.

[0040] Taking the hybrid city bus actually running in a certain place as the research object, such as figure 1 Shown is the schematic diagram of the future vehicle speed trajectory prediction method based on RBF neural network online learning. The core is RBF neural network construction and offline training, and RBF neural network online prediction of future vehicle speed trajectory. It includes the following steps:

[0041] A. Acquisition and normalization of parameters

[0042] A1. Acquisition of parameters: Based on the on-board information acquisition system, the real-time operation data of each data point is collected by different drivers when driving on different road conditions, and stored in the road database. For example, randomly select 4 drivers and 5 driving routes of hybrid electric buses, record the ...