The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Design Optimization of a Surface-Mounted Permanent-Magnet Motor With Concentrated Windings for Electric Vehicle Applications
Abstract
This paper describes design techniques for electric vehicle (EV) traction machines to achieve high efficiency against a defined driving cycle such as the New European Drive Cycle (NEDC) while satisfying the required torque-speed operating range. A fractional-slot concentrated-winding (FSCW) surface-mounted permanent-magnet (SPM) machine has been identified as a suitable candidate for EV applications due to its high power/torque density, high efficiency, and good flux-weakening capability compared with other competing machine topologies. Based on the vehicle characteristics and the reference driving cycle, the motor specifications are established, and the design constraints for the SPM machine to satisfy the peak torque and flux-weakening capabilities are derived. Furthermore, the influence of the key parameters, such as slot-pole number combination, machine inductance, axial length, and number of turns, on the machine copper and iron losses over the NEDC is evaluated. Optimizations were carried for these parameters to minimize the total energy losses over the driving cycle. It has been shown that conventional design methodologies that aim to maximize efficiency in the region close to the rated operating condition may lead to less optimal designs and higher energy losses over the NEDC. A prototype motor for a front- and rear-wheel-driven EV has been designed, manufactured, and tested. The experimental results validate the proposed design methodology.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Electrical Motor Drivelines in Commercial All-Electric Vehicles: A Review
de Santiago, J., Bernhoff, H., Ekergård, B., Eriksson, S., Ferhatovic, S., Waters, R., & Leijon, M. (2012) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2011.2177873 · Cited by 669
Cited by
Yang, Z., Shang, F., Brown, I. P., & Krishnamurthy, M. (2015) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2015.2470092 · Cited by 718
Metwly, M. Y., Abdel-Majeed, M. S., Abdel-Khalik, A. S., Hamdy, R. A., Hamad, M. S., & Ahmed, S. (2020) · IEEE Access · doi:10.1109/access.2020.2992741 · Cited by 229
Metadata
- Authors
- Wang, J., Yuan, X., & Atallah, K.
- Venue
- IEEE Transactions on Vehicular Technology
- Published
- 2013-03
- Volume
- 62
- Issue
- 3
- Pages
- 1053-1064
- ISSN
- 0018-9545 (ISSN-L)
- 1939-9359
- OpenAlex
- W2012451811
- Cited by
- 184
- References
- 1
- FWCI
- 6.31
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Design Optimization of a Surface-Mounted Permanent-Magnet Motor With Concentrated Windings for Electric Vehicle Applications. https://app.cochat.ai/papers/10.1109/tvt.2012.2227867
- The paper
- Wang, J., Yuan, X., & Atallah, K. (2013). Design Optimization of a Surface-Mounted Permanent-Magnet Motor With Concentrated Windings for Electric Vehicle Applications. IEEE Transactions on Vehicular Technology. https://doi.org/10.1109/tvt.2012.2227867
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.