Introduction to Predictive Models for Motor Dielectric Aging

Jones, Gavin; Frost, Nancy; Mosier, Aaron P.

doi:10.1109/eic51169.2022.9833207

Cited by 1 publication

(1 citation statement)

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“…It is used to solve general problems related to the application of electric motors in the automotive industry, such as estimating the driving torque or the rotor temperature to improve cooling control. The authors in [70] introduce how motor dielectric aging can be prevented. In [71], the authors present a DT-based optimization for optimally adjusting parameters in ultraprecision motion systems.…”

Section: Performance Optimization and Virtual Testingmentioning

confidence: 99%

Review of Electric Vehicle Testing Procedures for Digital Twin Development: A Comprehensive Analysis

Rjabtšikov,

Rassõlkin,

Kudelina

et al. 2023

Energies

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This article explores the transformative potential of digital twin (DT) technology in the automotive sector, focusing on its applications in enhancing propulsion drive systems. DT technology, a virtual representation of physical objects, has gained momentum due to its real-time monitoring and analysis capabilities. Within the automotive industry, where propulsion systems dictate vehicle performance, DTs offer a game-changing approach. Propulsion drive systems encompass electric motors, transmissions, and related components, significantly impacting efficiency and power delivery. Traditional design and testing methods need help addressing these systems’ intricate interactions. This article aims to investigate how DTs can revolutionize propulsion systems. The study examines various applications of DTs, ranging from predictive maintenance to performance optimization and energy efficiency enhancement. The article underscores the technology’s potential by reviewing case studies and real-world implementations. It also outlines challenges tied to integration and validation. In unveiling the capabilities of DT technology for propulsion systems, this article contributes to a comprehensive understanding of its role in shaping a more data-driven and efficient automotive industry.

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Section: Performance Optimization and Virtual Testingmentioning

confidence: 99%