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Algorithms 2015, 8(3), 632-644; doi:10.3390/a8030632

Data Fusion Modeling for an RT3102 and Dewetron System Application in Hybrid Vehicle Stability Testing

1,2,†,* and 1,2,†
1
College of Power and Energy Engineering, Harbin Engineering University, Harbin 150001, China
2
Heilongjiang Institute of Technology, Harbin 150050, China
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Jun-Bao Li
Received: 12 June 2015 / Accepted: 7 August 2015 / Published: 12 August 2015
(This article belongs to the Special Issue Machine Learning Algorithms for Big Data)
View Full-Text   |   Download PDF [1643 KB, uploaded 14 August 2015]   |  

Abstract

More and more hybrid electric vehicles are driven since they offer such advantages as energy savings and better active safety performance. Hybrid vehicles have two or more power driving systems and frequently switch working condition, so controlling stability is very important. In this work, a two-stage Kalman algorithm method is used to fuse data in hybrid vehicle stability testing. First, the RT3102 navigation system and Dewetron system are introduced. Second, a modeling of data fusion is proposed based on the Kalman filter. Then, this modeling is simulated and tested on a sample vehicle, using Carsim and Simulink software to test the results. The results showed the merits of this modeling. View Full-Text
Keywords: data fusion; Kalman algorithm; vehicle stability; hybrid vehicle data fusion; Kalman algorithm; vehicle stability; hybrid vehicle
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. (CC BY 4.0).

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MDPI and ACS Style

Miao, Z.; Zhang, H. Data Fusion Modeling for an RT3102 and Dewetron System Application in Hybrid Vehicle Stability Testing. Algorithms 2015, 8, 632-644.

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