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Journal of Control Science and Engineering
Volume 2019, Article ID 1496202, 14 pages
https://doi.org/10.1155/2019/1496202
Research Article

Control Strategy for PHEB Based on Actual Driving Cycle with Driving Style Characteristic

1Vehicle & Transportation Engineering Institute, Henan University of Science and Technology, Luoyang 471003, China
2Collaborative Innovation Center of Machinery Equipment Advanced Manufacturing of Henan, Luoyang 471003, China
3Suzhou Automotive Research Institute, Tsinghua University, Suzhou 215200, China

Correspondence should be addressed to Zhenhai Xu; nc.ude.tsuah.uts@241063303061

Received 7 October 2018; Revised 29 March 2019; Accepted 10 April 2019; Published 2 May 2019

Academic Editor: Carlos-Andrés García

Copyright © 2019 Jianping Gao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

To exert fully the energy economy performance of plug-in hybrid electric buses (PHEBs) and enhance the adaptability to different drivers and driving cycles, a control strategy for PHEB based on actual driving cycle with driving style characteristic is proposed in this paper. Through the actual city bus driving data, collected in real time, 6 actual driving cycles with driving style characteristic are fitted by using Principal Component Analysis (PCA) and Cluster Analysis (CA). Based on the 6 driving cycles, the key parameters of rule-based control strategy are optimized and established by a combinatorial optimization algorithm in Isight. Then, an identification model to recognize the current condition based on the Learning Vector Quantization (LVQ) neural network has been built and trained offline, which is integrated in the control strategy for PHEB to invoke the corresponding optimized key control parameters in real time. A hardware-in-the-loop (HIL) test is conducted, and the result shows that the proposed strategy could improve the energy consumption by 4.94%, compared with the original rule-based control strategy, and its validity and practicability are fully verified.