Advanced motor control and motion control for electric vehicle (EV) applications
Bibliographic record
Abstract
The groundbreaking Vector Control methods, also known as Field Oriented Control (FOC), pioneered by Hasse and Blaschke more than 50 years ago, emerged as essential technologies that have facilitated the replacement of DC motors with AC motors in high-performance industrial applications. Presently, commercial drive systems have reached an exceptional level of quality in terms of dynamics and precision. Recent research in motor drives has been directed towards specific areas, such as improving performance and efficiency, as well as increasing reliability through design optimization and sophisticated control techniques. The shift towards electrified transportation over the past two decades has ushered in a new era for the automotive industry: the EV revolution. Several factors have contributed to this transition, such as government policies, energy storage systems, charging infrastructure, …, but the fundamental technologies reside in the motors that have replaced internal combustion engines (ICE) in traditional vehicles. Part 1: Advanced Motor Control In Ev Applications: The first part of this tutorial will commence by addressing the specific performance requirements for motors suitable for these emerging EV applications. We will provide the most important control techniques employed in the common types of motors used in EVs, which include permanent magnet synchronous motors (PMSM), induction motors (IM), and brushless DC (BLDC) motors. Part 2: Ev Motion Control: In the second part of the tutorial, the focus will be made on EV motion control, with an emphasis on motor drive. We will introduce the configuration and modeling of EVs, including motor(s), inverter(s), batteries, and mechanical parts. Subsequently, we will discuss the key aspects of EV control, namely the control of longitudinal and lateral movements. State-of-the-art techniques, including driving force distribution, disturbance observer-based road condition estimation, Fuzzy logic-based control, etc. will be covered in detail. Drawing on a literature review and from our recent research results at the CTI Lab for EVs, HUST, Vietnam, and the e-TESC Lab, University of Sherbrooke, Canada, this tutorial aims to offer participants valuable insights on the EV control, highlighting the remarkable advantage of electric motors over ICE due to their ability to deliver rapid and precise torque development. Several remarks on propulsion system perspectives will also be provided, which inspire research and innovation in this direction. This effort would contribute to the ambitious goal of achieving Net Zero Emissions by 2050, a commitment shared by many countries to address global climate challenges.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".