Using Statistically Based Modeling for Vehicle Dynamics

Alexa

Lespezeanu

et al. 2016

AMM

Modern data analysis techniques has been successfully applied in many technical disciplines to understand the complexity of the system. The growing volume of theoretical knowledge about systems dynamic's offered researchers the opportunity to look for non-linear dynamics in data whose evolution linear models are unable to explain in a satisfactory manner. One of the most recent approach in this respect is Recurrence Analysis - RA. It is a graphical method designed to locate hidden recurring patterns, nonstationarity and structural changes. RA approach have first been applied in natural sciences like physics and biology. Quickly, was adopted in economics and engineering. Meanwhile, the fast development of computer resources has provided powerful tools to perform this new and complex model. One free software which we used to perform the analysis is Visual Recurrence Analysis - VRA developed by Eugene Kononov. As is presented in this paper, the recurrence plot investigation for the analyzing of the gasoline engine shows some of the its capabilities in this domain. We chose two specific engine parameters measured in two different tests to perform the RPA. These parameters are ignition angle and engine angular speed. There were computed graphs for each of them. Graphs were analyzed and compared to obtain a conclusion. This work is an incipient research, being one of the first attempt of using recurrence plot for analyzing automotive dynamics. It opens a wide field of action for future research programs.

Section: Introductionmentioning

confidence: 99%

Recurrence Plot Analysis to Study Parameters of a Gasoline Engine

Alexa

Lespezeanu

et al. 2016

AMM

“…Nevertheless, they were computed using static characteristics of the engine that had been obtained on the test bench. Eventually, we selected 64 tests, every one and each of them consisting of 15 parameters and saved the data as a tensor [2]. The tensor was used to issue mathematical models that describe vehicle's dynamics.…”

Section: Introductionmentioning

confidence: 99%

Using Statistically Based Modeling for Vehicle Dynamics

Grosu

Mocian

et al. 2014

AMR

This paper is a result of a research focused on statistical vehicles dynamics. Its main purpose is to establish mathematical description of vehicle dynamics based on statistically sufficient experimental data and using statistical instruments. The results are analytical expressions and graphical representations that can be used in situations other than those the data were obtained. Experimental research program objective was to obtain a variety of data to define the dynamics of a vehicle. It involved a large number of tests, more than 100, on different runways, pavement, mosaic tiles or asphalt. They were performed in various weather conditions, sunny and warm weather or rain or sleet and snow. The driving style varied between normal and sport ones. The experimental data were used in obtaining mathematical models that define certain dependency between dynamic parameters. There were issued multiple linear regressions with one resulting parameter. If we analyzed the models we issued we notice that the more factorial parameters are involved, the higher the accuracy of the model we get.

“…The result of the experimental research program is a set of 64 th tests. Each test contains 16 th time-series of the measured parameters [1].…”

Section: Introductionmentioning

confidence: 99%

Using Neural Networks to Modeling Vehicle Dynamics

Alexa

Vilau

et al. 2014

AMM

This paper is the result of a research program which focused on the statistical dynamics of vehicles. Most of the inputs of man-machine-field system have a random variation, so a systemic and statistical analysis of vehicle dynamics is obvious. In our study, data were obtained by measuring the dynamic parameters of vehicles and engines. Testing program aimed to capture a large range of operating regimes. To analyze the data the authors have used neural networks. There was adopted a NNARX (Neural Network Auto-Regressive with eXogene inputs) model with 4 inputs, 5 hidden units and 1 output. It can be concluded that the development of mathematical modeling using non-linear neural network can ensure the desired accuracy, conveniently is obtained by increasing the number of neurons in the hidden laws.