{
      "rev_number": 3,
      "ispublished": "pub",
      "publisher": "Institute of Electrical and Electronics Engineers Inc.",
      "datestamp": "2026-05-05 03:41:11",
      "publication": "IEEE Region 10 Annual International Conference, Proceedings/TENCON",
      "refereed": "TRUE",
      "pagerange": "86 \u2013 91",
      "date": 2025,
      "type": "conference_item",
      "status_changed": "2026-05-05 03:41:11",
      "lastmod": "2026-05-05 03:41:11",
      "creators": [
        {
          "name": {
            "lineage": null,
            "honourific": null,
            "family": "Sepeeh",
            "given": "Muhamad Syazmie"
          }
        },
        {
          "name": {
            "given": "Shamsul Aizam",
            "lineage": null,
            "honourific": null,
            "family": "Zulkifli"
          }
        },
        {
          "name": {
            "lineage": null,
            "honourific": null,
            "family": "Chiu",
            "given": "Huang-Jen"
          }
        },
        {
          "name": {
            "lineage": null,
            "honourific": null,
            "family": "Jamahori",
            "given": "Hanis Farhah"
          }
        }
      ],
      "title": "Recent Evolution of Intelligent Approach in Electric Motor Drive System for EV Applications: A Concise Review",
      "issn": 21593442,
      "id_number": "10.1109/TENCON66050.2025.11375007",
      "metadata_visibility": "show",
      "userid": 1,
      "keywords": "Adaptive control systems; Deep learning; Electric drives; Electric machine control; Fuzzy logic; Induction motors; Intelligent systems; Learning algorithms; Learning systems; Power electronics; Synchronous motors; Traction motors; Drive systems; Electric motor drives; Intelligent method; Management techniques; Motor control; Motor drive system; Motor management; Optimal performance; Performance efficiency; Vehicle applications; Electric vehicles",
      "official_url": "https://www.scopus.com/inward/record.uri?eid=2-s2.0-105034127358&doi=10.1109%2fTENCON66050.2025.11375007&partnerID=40&md5=8a6217e1d1f35a86ae06c509a3deeac5",
      "uri": "https://khub.utp.edu.my/scholars/id/eprint/20565",
      "isbn": "979-833153772-2",
      "eprint_status": "archive",
      "abstract": "The fast development of electric vehicles (EVs) has motivated major studies on smart management techniques for electric motors, which are essential for guaranteeing optimal performance, efficiency, and dependability. This brief paper offers a summary of recent advances in artificial intelligence (AI)-based techniques used in electric motor management inside EV applications. The conversation covers several artificial intelligence models, including fuzzy logic (FL) systems, evolutionary algorithms (EA), machine learning (ML), and deep learning (DL). Every approach is quickly examined in terms of its working principles, benefits, drawbacks, and appropriateness for particular motor control duties, including speed control, torque optimisation, defect identification, and adaptive control. Particular focus is on how AI methods combine with motor types often used in EVs, such as permanent magnet synchronous motor (PMSM) and induction motor (IM). The paper highlights current trends and research opportunities to provide researchers and practitioners a modest but instructive reference for enhancing intelligent motor control in modern EV systems. \u00a9 2025 IEEE.",
      "eprintid": 20565,
      "full_text_status": "none",
      "dir": "disk0/00/02/05/65"
    }