Energy Management Strategy based on Hybrid-Systems Algorithm and Radial Basis Function Neural Network for HEV

Baoshuai Liu, Hui Liu, Lijin Han, Xiaolei Ren

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In the paper, an energy management strategy (EMS) based on the hybrid system predictive control algorithm and radial basis function neural network algorithm (RBF-NN) is constructed for hybrid electric vehicle(HEV). First, an velocity predictor is proposed based on the RBF-NN and the Chebyshev filter, which aim to improve the calculated accuracy. Based on the detailed analysis, the driveline system is simplified into an hybrid dynamic system and optimized by Mixed integer linear programming algorithm. Through the simulation, the effectiveness of the proposed method is validated in two driving cycles. Results show that the proposed approach improve the internal combustion engine (ICE) fuel economy.

Original languageEnglish
Title of host publicationProceeding - 2021 China Automation Congress, CAC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages571-576
Number of pages6
ISBN (Electronic)9781665426473
DOIs
Publication statusPublished - 2021
Event2021 China Automation Congress, CAC 2021 - Beijing, China
Duration: 22 Oct 202124 Oct 2021

Publication series

NameProceeding - 2021 China Automation Congress, CAC 2021

Conference

Conference2021 China Automation Congress, CAC 2021
Country/TerritoryChina
CityBeijing
Period22/10/2124/10/21

Keywords

  • HEV
  • RBF-NN
  • energy management
  • hybrid systems

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