Evidence map›Paper›PMID 42590338›Full record

ReviewMaterials (Basel, Switzerland)2026

Vibroacoustic Metamaterials for Low-Frequency Sound and Vibration Attenuation in Electric Vehicles: A Review.

Krisztian Horvath

Abstract readReview
In one paragraph

Review in Materials (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Krisztian HorvathDepartment of Vehicle Development, Audi Hungaria Faculty of Engineering, Széchenyi István University, Egyetem tér 1, 9026 Győr, Hungary.ORCID 0009-0007-1655-2255

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The transition from internal combustion engine vehicles to battery electric vehicles has changed the acoustic design problem in automotive engineering. The absence of combustion-related masking increases the perceptibility of tonal and narrowband sources, including gear whine, electric motor orders, inverter-related components, tire cavity resonances, auxiliary system noise, and lightweight-panel radiation. At the same time, mass-based acoustic treatments conflict with electric vehicle lightweighting, range, cost, and sustainability targets. Vibroacoustic metamaterials offer an alternative route by manipulating elastic and acoustic wave propagation through architected geometries, local resonances, periodicity, membranes, lattice architectures, and adaptive or topological wave-control mechanisms. This review examines vibroacoustic metamaterials for low-frequency electric vehicle noise, vibration, and harshness (EV NVH) from an engineering perspective. It covers mechanisms, EV-specific NVH problems, component applications, materials, manufacturing, modeling, validation, AI-assisted design, sustainability, and technology readiness. Particular emphasis is placed on order-targeted, path-oriented, manufacturable, and experimentally validated solutions for electric-drive (e-drive) housings, wheel arches, battery enclosures, body panels, covers, and auxiliary systems. The review concludes that vibroacoustic metamaterials are most promising when integrated into conventional NVH workflows through order analysis, transfer path ranking, robust resonator tuning, durability validation, and multi-objective design optimization.

Indexed as

electric vehicleslightweight structureslocally resonant metamaterialslow-frequency vibrationNVHvibroacoustic metamaterials

Identifiers

PMID42590338
PMCPMC13468135

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.