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

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.

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 ...

AI Technical Summary

Problems solved by technology

However, most of the existing methods for predicting future driving conditions at present collect vehicle driving parameters, combine GPS positioning information, and use methods based on probability statistics to predict future vehicle speed trajectories and provide them for optimal control strategies, ignoring the driving conditions of different drivers. style and many real-time changes in the road environment and traffic state parameters on future driving conditions, resulting in insufficient accuracy in predicting future driving conditions

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