Uncertainty assessment of electromagnetic exposure safety for human body with intracranial artery stent around EV-WPT based on K-GRU surrogate model

As electric vehicles (EVs) continue to gain popularity and wireless power transfer (WPT) advances, protecting human health from electromagnetic exposure during EV-WPT operation has become a critical research priority. Given the rising number of patients with metallic medical devices implanted, this article presents a human model of an adult male with an intracranial arterial stent exposed to electromagnetic field leakage from WPT. Considering uncertainties in WPT manufacturing errors and human positioning relative to WPT, this article employs a modified Gate Recurrent Unit (GRU) architecture based on the Kolmogorov–Arnold Network (K-GRU) to quantify these uncertainties in electromagnetic safety assessment. Compared to the Monte Carlo (MC) method, the K-GRU proxy modeling approach reduces assessment time to just 5% of that required by MC. The findings indicate intracranial artery stent implantation significantly influences the distribution of the induced electric field within the human body. Specifically, there is a 96% probability that the induced electric field exceeds the limits set by the International Commission on Non-Ionizing Radiation Protection (ICNIRP) guidelines at the EV’s outer side, and a 71.5% probability at the rear. These findings suggest that for patients with intracranial arterial stents, maintaining appropriate safety distances and implementing power restrictions in EV-WPT systems may be necessary to ensure compliance with electromagnetic exposure limits.

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Publication Details

Journal
Alexandria Engineering Journal
Published
2025-04-23
DOI
https://doi.org/10.1016/j.aej.2025.03.112
Citations
1
Primary Topic
Noise Effects and Management
Type
article
Field-Weighted Citation Impact
3.12
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article

Uncertainty assessment of electromagnetic exposure safety for human body with intracranial artery stent around EV-WPT based on K-GRU surrogate model

Hong-Wei Zhou, Jiaxin Liu, Quanyi Yu, Tianhao Wang et al.
1 citations
Alexandria Engineering Journal
Noise Effects and Management
3.12
article

Uncertainty assessment of electromagnetic exposure safety for human body with intracranial artery stent around EV-WPT based on K-GRU surrogate model

Hong-Wei Zhou, Jiaxin Liu, Quanyi Yu, Tianhao Wang, Kaifeng Zhao, Yaodan Chi
article en
1 citations

Abstract

As electric vehicles (EVs) continue to gain popularity and wireless power transfer (WPT) advances, protecting human health from electromagnetic exposure during EV-WPT operation has become a critical research priority. Given the rising number of patients with metallic medical devices implanted, this article presents a human model of an adult male with an intracranial arterial stent exposed to electromagnetic field leakage from WPT. Considering uncertainties in WPT manufacturing errors and human positioning relative to WPT, this article employs a modified Gate Recurrent Unit (GRU) architecture based on the Kolmogorov–Arnold Network (K-GRU) to quantify these uncertainties in electromagnetic safety assessment. Compared to the Monte Carlo (MC) method, the K-GRU proxy modeling approach reduces assessment time to just 5% of that required by MC. The findings indicate intracranial artery stent implantation significantly influences the distribution of the induced electric field within the human body. Specifically, there is a 96% probability that the induced electric field exceeds the limits set by the International Commission on Non-Ionizing Radiation Protection (ICNIRP) guidelines at the EV’s outer side, and a 71.5% probability at the rear. These findings suggest that for patients with intracranial arterial stents, maintaining appropriate safety distances and implementing power restrictions in EV-WPT systems may be necessary to ensure compliance with electromagnetic exposure limits.

Alexandria Engineering JournalVol. 125
Jilin University (CN), Jilin Jianzhu University (CN), First Hospital of Jilin University (CN)
Openalex Percentile: Top 12%
Noise Effects and Management
3.12
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