A Method for Identification of Driving Patterns in Hybrid Electric Vehicles Based on a LVQ Neural Network

Hongwen He, Chao Sun and Xiaowei Zhang
Additional contact information
Hongwen He: National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
Chao Sun: National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
Xiaowei Zhang: National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China

Energies, 2012, vol. 5, issue 9, 1-18

Abstract: Driving patterns exert an important influence on the fuel economy of vehicles, especially hybrid electric vehicles. This paper aims to build a method to identify driving patterns with enough accuracy and less sampling time compared than other driving pattern recognition algorithms. Firstly a driving pattern identifier based on a Learning Vector Quantization neural network is established to analyze six selected representative standard driving cycles. Micro-trip extraction and Principal Component Analysis methods are applied to ensure the magnitude and diversity of the training samples. Then via Matlab/Simulink, sample training simulation is conducted to determine the minimum neuron number of the Learning Vector Quantization neural network and, as a result, to help simplify the identifier model structure and reduce the data convergence time. Simulation results have proved the feasibility of this method, which decreases the sampling window length from about 250–300 s to 120 s with an acceptable accuracy. The driving pattern identifier is further used in an optimized co-simulation together with a parallel hybrid vehicle model and improves the fuel economy by about 8%.

Keywords: hybrid electric vehicles; LVQ; neural network; driving pattern recognition; simulation; fuel economy (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.mdpi.com/1996-1073/5/9/3363/pdf (application/pdf)
https://www.mdpi.com/1996-1073/5/9/3363/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:5:y:2012:i:9:p:3363-3380:d:19897

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().