Virtual Sensor Using a Super Twisting Algorithm Based Uniform Robust Exact Differentiator for Electric Vehicles

Hassam Muazzam, Mohamad Khairi Ishak, Athar Hanif, Ali Arshad Uppal, Bhatti Ai and Nor Ashidi Mat Isa
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Hassam Muazzam: School of Electrical and Electronics Engineering, Universiti Sains Malaysia, Nibong Tebal, Seberang Perai 14300, Penang, Malaysia
Mohamad Khairi Ishak: School of Electrical and Electronics Engineering, Universiti Sains Malaysia, Nibong Tebal, Seberang Perai 14300, Penang, Malaysia
Athar Hanif: Center for Automotive Research (CAR), The Ohio State University, Columbus, OH 43212, USA
Ali Arshad Uppal: Department of Electrical and Computer Engineering, Comsats University Islamabad, Islamabad 44000, Pakistan
Bhatti Ai: Electrical Engineering Department, Capital University of Science and Technology, Islamabad 44000, Pakistan
Nor Ashidi Mat Isa: School of Electrical and Electronics Engineering, Universiti Sains Malaysia, Nibong Tebal, Seberang Perai 14300, Penang, Malaysia

Energies, 2022, vol. 15, issue 5, 1-18

Abstract: The highly efficient Interior Permanent Magnet Synchronous Motor (IPMSM) is ubiquitous choice in Electric Vehicles (EVs) for today’s automotive industry. IPMSM control requires accurate knowledge of an immeasurable critical Permanent Magnet (PM) flux linkage parameter. The PM flux linkage is highly influenced by operating temperature which results in torque derating and hence power loss, unable to meet road loads and reduced life span of electrified powertrain in EVs. In this paper, novel virtual sensing scheme for estimating PM flux linkage through measured stator currents is designed for an IPMSM centric electrified powertrain. The proposed design is based on a Uniform Robust Exact Differentiator (URED) centric Super Twisting Algorithm (STA), which ensures robustness and finite-time convergence of the time derivative of the quadrature axis stator current of IPMSM. Moreover, URED is able to eliminate chattering without sacrificing robustness and precision. The proposed design detects variation in PM flux linkage due to change in operating temperature and hence is also able to establish characteristics of fault detection. The effectiveness and accuracy in different operating environments of the proposed scheme for nonlinear mathematical IPMSM model with complex EV dynamics are verified thorough extensive simulation experiments using MATLAB/Simulink.

Keywords: electric vehicles (EVs); electrified powertrain; interior permanent magnet synchronous motor (IPMSM); permanent magnet (PM) flux linkage; super twisting algorithm (STA); uniform robust exact differentiator (URED) (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: 2022
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