Development of a Data-Driven Methodology for Rapid Identification of Key Performance Indicators in Motorcycle Racing

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F26%3A0200050" target="_blank" >RIV/00216305:26210/26:0200050 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.mdpi.com/2673-4591/113/1/12" target="_blank" >https://www.mdpi.com/2673-4591/113/1/12</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/engproc2025113012" target="_blank" >10.3390/engproc2025113012</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Development of a Data-Driven Methodology for Rapid Identification of Key Performance Indicators in Motorcycle Racing

Popis výsledku v původním jazyce

This study presents a novel method for the rapid identification of key performance indicators (KPIs) from measured riding data of a Ducati Panigale V2 motorcycle, aimed at enhancing racing performance through a deeper understanding of rider-vehicle interaction. The methodology involves the design and implementation of mathematical tools within the RaceStudio3 software to analyze data from the motorcycle’s sensor system. This approach facilitates the swift detection of critical events, including gearshift delays, improper throttle control, and suspension issues. The fusion of data from the motorcycle enables a comprehensive evaluation of the rider’s influence on performance. The results demonstrate the potential of the proposed method to provide valuable insights for optimizing motorcycle setup and rider technique.

Název v anglickém jazyce

Development of a Data-Driven Methodology for Rapid Identification of Key Performance Indicators in Motorcycle Racing

Popis výsledku anglicky

This study presents a novel method for the rapid identification of key performance indicators (KPIs) from measured riding data of a Ducati Panigale V2 motorcycle, aimed at enhancing racing performance through a deeper understanding of rider-vehicle interaction. The methodology involves the design and implementation of mathematical tools within the RaceStudio3 software to analyze data from the motorcycle’s sensor system. This approach facilitates the swift detection of critical events, including gearshift delays, improper throttle control, and suspension issues. The fusion of data from the motorcycle enables a comprehensive evaluation of the rider’s influence on performance. The results demonstrate the potential of the proposed method to provide valuable insights for optimizing motorcycle setup and rider technique.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

20301 - Mechanical engineering

Projekt

<a href="/cs/project/EH23_020%2F0008528" target="_blank" >EH23_020/0008528: Inovativní technologie pro chytrou nízkoemisní mobilitu</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Engineering Proceedings

ISSN

—

e-ISSN

2673-4591

Svazek periodika

113

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

10

Strana od-do

—

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-105025667062