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Energies 2012, 5(9), 3363-3380; doi:10.3390/en5093363
Article

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

* , *  and
National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
* Authors to whom correspondence should be addressed.
Received: 9 July 2012 / Revised: 17 August 2012 / Accepted: 24 August 2012 / Published: 5 September 2012
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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 hybrid electric vehicles; LVQ; neural network; driving pattern recognition; simulation; fuel economy
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).
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He, H.; Sun, C.; Zhang, X. A Method for Identification of Driving Patterns in Hybrid Electric Vehicles Based on a LVQ Neural Network. Energies 2012, 5, 3363-3380.

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