###### Abstract  
Tonal powertrain noise can have a strong negative impact on passengers' quality and comfort perception in the interior of electric vehicles. Therefore, in the vehicle development process, the assessment of the perceptibility of tonal powertrain noise is essential. As wind and tire noise can possibly mask tonal noises, engineers use modern masking models to determine the masking threshold of tonal powertrain noise from vehicle interior measurements. In the presently used method, the masking threshold is mostly generated with torque-free deceleration measurements. However, the influence of torque on masking tire noise must be considered. As this requires time-consuming and costly road measurements, an extension of the method is being developed, which will also enable the use of roller dynamometer measurements for the assessment. For the extension of the method, however, the influence of the torque must also be considered. This paper presents a novel calculation method that quantifies the influence of torque on the masking threshold and converts masking thresholds from an arbitrary torque level to another. By identifying the frequency and speed range that is mainly affected by the torque-dependent tire noise, a regression model with respect to the tractive force on the tires can be used to calculate a torque-dependent correction factor. The developed method can significantly improve the validity of masking thresholds and quantitatively, the method generalizes well across different vehicle segments. The error can be reduced to below 2 dB above 2000 rpm and to below 1 dB above 4000 rpm. By using this method, more valid target level settings for tonal powertrain noise can be derived. m 2024202  
production vehicles. These elevated noise levels result in increased masking thresholds, which can lead to the fact that tonal noise that is audible in the series production vehicle might not be heard in the prototype vehicle [4]. Masking models can be utilized to predict the masking threshold of the production vehicle in an early phase of the development process [5]. Many models exist that evaluate the tonality and perceptibility of tonal noise [3; 4; 6; 7; 8; 9; 10; 11]; however, when deriving target levels for tonal powertrain noise, none of these models take torque-dependent effects into account. In previous works, a masking model has been developed that determines the masking threshold for specific engine orders as a function of the rotational speed of the engine or the driving speed of the vehicle. Additionally, the masking threshold can be evaluated as a 2-dimensional function of the driving speed and the frequency of the tone, which can be represented as \"masking threshold maps\". In this way, the masking threshold can be represented as a vehicle-specific masking threshold. From the masking threshold map, arbitrary order-, frequency- or velocity-specific masking thresholds can be extracted. The audibility of tonal noise can then be assessed by comparing the sound pressure level of the order with the order-specific masking threshold [12]. For the assessment, the model evaluates the vehicle interior noise that is currently obtained from torque-less coast-downmeasurements [13]. While road measurements are time-consuming and can produce fluctuating results, roller dynamometer measurements are frequently preferred for their high repeatability [14]. However, the masking noise characteristics observed on a roller dynamometer deviate considerably from those under actual road driving conditions. Consequently, masking thresholds calculated from roller dynamometer measurements using conventional masking models need to be adjusted to reflect road driving conditions. To enable the usage of roller dynamometer data for the assessment of the perceptibility of tonal powertrain noise, an advanced model is under development. This model predicts road driving masking thresholds based on roller dynamometer data. Previous analysis has revealed that the newly developed method must account for the influence of torque on tire noise.