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Online Optimal Energy Distribution of Composite Power Vehicles Based on BP Neural Network Velocity Prediction
Author(s) -
Qingjian Jiang,
Zhijun Fu,
Qiang Hu
Publication year - 2021
Publication title -
mathematical problems in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.262
H-Index - 62
eISSN - 1026-7077
pISSN - 1024-123X
DOI - 10.1155/2021/2519569
Subject(s) - artificial neural network , power (physics) , energy (signal processing) , driving cycle , automotive engineering , control theory (sociology) , composite number , engineering , computer science , simulation , control (management) , electric vehicle , algorithm , artificial intelligence , mathematics , statistics , physics , quantum mechanics
In this paper, an online optimal energy distribution method is proposed for composite power vehicles using BP neural network velocity prediction. Firstly, the predicted vehicle speed in the future period is obtained via the output of a BP neural network, where the current vehicle driving state and elapsed vehicle speed information is used as the input. Then, according to the predicted vehicle speed, an energy management method based on model predictive control is proposed, and online real-time power distribution is carried out through rolling optimization and feedback correction. Cosimulation results under urban drive cycle show that the proposed method can effectively improve the energy efficiency of composite power sources compared with the commonly used method with the assumption of prior known driving conditions.

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