EconPapers    
Economics at your fingertips  
 

Real-Time Processor-in-Loop Investigation of a Modified Non-Linear State Observer Using Sliding Modes for Speed Sensorless Induction Motor Drive in Electric Vehicles

Mohan Krishna Srinivasan, Febin Daya John Lionel, Umashankar Subramaniam, Frede Blaabjerg, Rajvikram Madurai Elavarasan, G. M. Shafiullah, Irfan Khan and Sanjeevikumar Padmanaban
Additional contact information
Mohan Krishna Srinivasan: Department of Electrical and Electronics Engineering, Alliance College of Engineering and Design, Alliance University, Bangalore 562 106, India
Febin Daya John Lionel: SELECT, Vellore Institute of Technology, Chennai 600127, India
Umashankar Subramaniam: Renewable Energy Lab, College of Engineering, Prince Sultan University, Riyadh 11586, Saudi Arabia
Frede Blaabjerg: CORPE, Department of Energy Technology, Aalborg University, 9000 Aalborg, Denmark
Rajvikram Madurai Elavarasan: Electrical and Automotive parts Manufacturing unit, AA Industries, Chennai 600123, India
G. M. Shafiullah: Discipline of Engineering and Energy, Murdoch University, Murdoch 6150, Australia
Irfan Khan: Marine Engineering Technology Department in a joint appointment with Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA
Sanjeevikumar Padmanaban: Department of Energy Technology, Aalborg University, 6700 Esbjerg, Denmark

Energies, 2020, vol. 13, issue 16, 1-22

Abstract: Tracking performance and stability play a major role in observer design for speed estimation purpose in motor drives used in vehicles. It is all the more prevalent at lower speed ranges. There was a need to have a tradeoff between these parameters ensuring the speed bandwidth remains as wide as possible. This work demonstrates an improved static and dynamic performance of a sliding mode state observer used for speed sensorless 3 phase induction motor drive employed in electric vehicles (EVs). The estimated torque is treated as a model disturbance and integrated into the state observer while the error is constrained in the sliding hyperplane. Two state observers with different disturbance handling mechanisms have been designed. Depending on, how they reject disturbances, based on their structure, their performance is studied and analyzed with respect to speed bandwidth, tracking and disturbance handling capability. The proposed observer with superior disturbance handling capabilities is able to provide a wider speed range, which is a main issue in EV. Here, a new dimension of model based design strategy is employed namely the Processor-in-Loop. The concept is validated in a real-time model based design test bench powered by RT-lab. The plant and the controller are built in a Simulink environment and made compatible with real-time blocksets and the system is executed in real-time targets OP4500/OP5600 (Opal-RT). Additionally, the Processor-in-Loop hardware verification is performed by using two adapters, which are used to loop-back analog and digital input and outputs. It is done to include a real-world signal routing between the plant and the controller thereby, ensuring a real-time interaction between the plant and the controller. Results validated portray better disturbance handling, steady state and a dynamic tracking profile, higher speed bandwidth and lesser torque pulsations compared to the conventional observer.

Keywords: machine model; adaptive control; model reference; disturbance; stability; real-time; processor-in-loop (PIL); electric vehicles (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/13/16/4212/pdf (application/pdf)
https://www.mdpi.com/1996-1073/13/16/4212/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:13:y:2020:i:16:p:4212-:d:399090

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().

 
Page updated 2025-03-19
Handle: RePEc:gam:jeners:v:13:y:2020:i:16:p:4212-:d:399090