Upravljačka kompenzacija zračnosti prigonskog sklopa električnog vozila s ciljem smanjenja vibracija i trzaja pogona

master's thesis

• Title (english): Control compensation of electric vehicle drivetrain backlash aimed at reduction of powertrain vibrations and jerk
• Author: Ivan Ruškan
• Mentor: Joško Deur (mentor)
• Granter: University of Zagreb; Faculty of Mechanical Engineering and Naval Architecture; Zagreb
• Defense date and country: 2024-03-28, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Mechanical Engineering; General Mechanical Engineering (Construction)

Abstract

Zadnjih godina električna vozila postaju sve zastupljenija. Karakterizira ih učinkovit i tih rad, smanjena potrošnja energije i nulte emisije stakleničkih plinova, kao i visoke vozne performanse koje donose i potrebu za razvojem novih tehnologija i rješenja. Jedna od takvih potreba je ona vezana uz kompenzaciju utjecaja zračnosti prigonskog sklopa. Zračnost se javlja unutar zupčanika te u sprezi s elastičnošću poluvratila izaziva jake udare te torzijske vibracije pogona pri promjeni smjera okretnog momenta, koji narušavaju udobnost vožnje. Jedan od načina rješavanja ovog problema je korištenje upravljačkih algoritama za kompenzaciju utjecaja zračnosti. U ovom radu dan je prvo pregled postojećih postupaka kompenzacije zračnosti. Zatim su postavljeni detaljni i upravljanju orijentirani modeli pogona električnog vozila. Na temelju pojednostavljenog modela pogona provedena je sinteza sustava za prigušenje torzijskih vibracija pogona. Kako bi se potrebna mjerenja ograničila na lokalno dostupnu i preciznu informaciju o brzini pogonskog motora, razvijen je estimator brzine kotača. Konačno, razvijen je sustav adaptacije prigušnog pojačanja vezano uz detekciju zone zračnosti. Za potrebe detekcije zone zračnosti razvijen je estimator momenta tereta motora koji je implementiran kao adaptivni Kalmanov filtar. Projektirani sustav kompenzacije zračnosti ispitan sistematskim simulacijskim testovima.

Abstract (english)

In recent years, electric vehicles have become increasingly prevalent. They are characterized by efficient and quiet operation, reduced energy consumption and zero greenhouse gas emissions, as well as high driving performance that brings about the need for the development of new technologies and solutions. One such need is related to compensating for drivetrain backlash effects. Backlash occurs within the gears, and when coupled with the half-shaft compliance, it causes strong impacts and torsional vibrations in the powertrain when there is a change in the direction of torque, thereby compromising driving comfort. One way to address this issue is by using control algorithms for backlash compensation. This thesis first provides an overview of existing methods for backlash compensation. Then, detailed and control oriented models of electric vehicle powertrains are established. Based on the simplified powertrain model, a powertrain torsional vibrations damping system is designed. To limit the required measurements to locally available and precise information about the driving motor speed, a wheel speed estimator is developed. Finally, a damping gain adaptive mechanism related to backlash detection is designed. For the purpose of backlash zone detection, a motor load torque estimator is developed and implemented as an adaptive Kalman filter. The designed backlash compensation system is systematically verified through simulation tests.

• Keywords: električno vozilo; kompenzacija zračnosti; prigušenje torzijskih vibracija; regulacija; estimacija.
• Keywords (english): electric vehicle; backlash compensation; drivetrain vibration damping; control; estimation.
• Language: croatian
• URN:NBN: urn:nbn:hr:235:605821
• Study programme: Title: Mechanical Engineering; Study programme type: university; Study level: graduate; Academic / professional title: magistar/magistra inženjer/inženjerka strojarstva (magistar/magistra inženjer/inženjerka strojarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Open access
• Created on: 2024-03-27 13:53:50