A Method for Identification of Driving Patterns in Hybrid Electric Vehicles Based on a LVQ Neural Network
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...
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2012-09-01
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Online Access: | http://www.mdpi.com/1996-1073/5/9/3363 |
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doaj-b44f1bf7354e43419c0ec3dc7f214de22020-11-24T21:32:09ZengMDPI AGEnergies1996-10732012-09-01593363338010.3390/en5093363A Method for Identification of Driving Patterns in Hybrid Electric Vehicles Based on a LVQ Neural NetworkXiaowei ZhangChao SunHongwen HeDriving 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%.http://www.mdpi.com/1996-1073/5/9/3363hybrid electric vehiclesLVQneural networkdriving pattern recognitionsimulationfuel economy |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xiaowei Zhang Chao Sun Hongwen He |
spellingShingle |
Xiaowei Zhang Chao Sun Hongwen He A Method for Identification of Driving Patterns in Hybrid Electric Vehicles Based on a LVQ Neural Network Energies hybrid electric vehicles LVQ neural network driving pattern recognition simulation fuel economy |
author_facet |
Xiaowei Zhang Chao Sun Hongwen He |
author_sort |
Xiaowei Zhang |
title |
A Method for Identification of Driving Patterns in Hybrid Electric Vehicles Based on a LVQ Neural Network |
title_short |
A Method for Identification of Driving Patterns in Hybrid Electric Vehicles Based on a LVQ Neural Network |
title_full |
A Method for Identification of Driving Patterns in Hybrid Electric Vehicles Based on a LVQ Neural Network |
title_fullStr |
A Method for Identification of Driving Patterns in Hybrid Electric Vehicles Based on a LVQ Neural Network |
title_full_unstemmed |
A Method for Identification of Driving Patterns in Hybrid Electric Vehicles Based on a LVQ Neural Network |
title_sort |
method for identification of driving patterns in hybrid electric vehicles based on a lvq neural network |
publisher |
MDPI AG |
series |
Energies |
issn |
1996-1073 |
publishDate |
2012-09-01 |
description |
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%. |
topic |
hybrid electric vehicles LVQ neural network driving pattern recognition simulation fuel economy |
url |
http://www.mdpi.com/1996-1073/5/9/3363 |
work_keys_str_mv |
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1725958388780630016 |