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February 2, 20260 citationsOpen Access

Design and Optimization of a Non-Contact Current Sensor for EVs Based on a Hybrid Semi-Circular Array of Hall-Effect and TMR Elements

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XYXiaopeng YuanHWHaoyu WangLZLei Zhang

Key Points

  • The aim is to design a non-contact current sensor that enhances measurement reliability in automotive wiring systems.
  • Develop a hybrid sensing array with Hall-effect and TMR elements.
  • Create an eccentricity error compensation algorithm to improve accuracy under misalignment.
  • Implement an equivalent modeling method to assess interference in complex wiring environments.
  • Achieved 97.07% accuracy in aligned misalignment conditions.
  • Maintained 94.31% accuracy despite external disturbances.
  • Reached 99.05% accuracy when the conductor is centered in the array.

Abstract

This paper presents a semi-circular, non-contact current sensor designed to simplify the layout of automotive wiring harnesses and enhance measurement convenience and reliability. The sensor integrates a hybrid sensing array consisting of Hall-effect and tunnel magnetoresistance (TMR) elements. To address common challenges in automotive power systems and vehicle wiring—such as conductor eccentricity and magnetic interference from adjacent cables—two key techniques are proposed. First, an eccentricity error compensation algorithm is developed, achieving a measurement accuracy of 97.07% under specific misalignment conditions. Second, an equivalent modeling method based on eccentricity principles is introduced to characterize interference fields in complex wiring environments, maintaining 94.31% accuracy in the presence of external disturbances. When the conductor is centered within the array, the average measurement accuracy reaches 99.05%. Experimental results demonstrate that the proposed sensor can reliably measure large currents from 0 to 210 A, making it highly suitable for applications in electric vehicles, high-voltage harness monitoring, power electronics, and intelligent transportation systems.

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Cite This Study

Yuan et al. (2026) studied this question.

synapsesocial.com/papers/6980ffb4c1c9540dea812635https://doi.org/10.3390/vehicles8020027
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