Sound Quality Optimization in Electric Vehicles

The transition from internal combustion engines to electric drivetrains has transformed automotive acoustics, presenting both opportunities and challenges for sound quality design. In contrast to conventional vehicles, electric vehicles (EVs) exhibit a markedly lower noise floor, enabling finer control over residual and artificial soundscapes. This shift demands a holistic approach that integrates vibration isolation, active sound generation and suppression, psychoacoustic evaluation and user-centred design. Engineers must balance pedestrian safety requirements—often met through externally mounted warning tones—with interior comfort and brand identity conveyed via bespoke sound signatures. Advances in sensor fusion, real-time processing and machine learning now allow adaptive acoustic feedback that responds to driving conditions and user preferences. At the same time, the reduced masking effect of mechanical noise obliges noise-vibration-harshness specialists to address tonal artefacts arising from inverter switching, motor harmonics and aerodynamic excitations. The overarching goal is to deliver an auditory environment that enhances perceived refinement, supports situational awareness and reinforces product appeal without compromising efficiency or regulatory compliance.

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Sound Quality Optimization in Electric Vehicles publication trend

The graph below shows the total number of articles in sound quality optimization in electric vehicles across all publications each year (not limited to Nature Index journals).

Technical terms

NVH: A multidisciplinary field addressing noise, vibration and harshness characteristics in vehicles, crucial for interior comfort and structural integrity.

Psychoacoustic parameter: A quantitative measure of sound attributes (such as loudness, roughness or sharpness) reflecting human auditory perception rather than physical amplitude alone.

Genetic algorithm–optimised back-propagation artificial neural network (GA-BP ANN): A hybrid machine learning model in which a genetic algorithm selects optimal network weights and structures for accurate prediction of subjective sound quality.

Sound pressure level (SPL): A logarithmic measure (in decibels) of the acoustic pressure deviation from atmospheric ambient pressure, commonly used to quantify noise exposure.

References

1. Effect of Driving Sound of Electric Vehicle on Product Attractiveness. Human-Centric Intelligent Systems (2023).
2. Sound Quality Estimation of Electric Vehicles Based on GA-BP Artificial Neural Networks. Applied Sciences (2020).
3. Artificial Engine Sound Synthesis Method for Modification of the Acoustic Characteristics of Electric Vehicles. Shock and Vibration (2018).
4. A literature review [2000–2022] on vehicle acoustics: Investigations on perceptual parameters of interior soundscapes in electrified vehicles. Frontiers in Mechanical Engineering (2022).