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The high-frequency whining noise produced by motors in modern electric vehicles causes a significant issue, leading to annoyance among passengers. This noise becomes even more noticeable due to the quiet nature of electric vehicles, which lack other noises to mask the high-frequency whining noise. To improve the noise caused by motors, it is essential to optimize various motor design parameters. However, this task requires expert knowledge and a considerable time investment. In this study, we explored the application of artificial intelligence to optimize the NVH performance of motors during the design phase. Firstly, we selected and modeled three benchmark motor types using Motor-CAD. Machine learning models were trained using Design of Experiment methods to simulate batch runs of Motor-CAD inputs and outputs.

Over the past decade, there have been many efforts to generate engine sound inside the cabin either in reducing way or in enhancing way. To reduce the engine noise, the passive way, such as sound absorption or sound insulation, was widely used but it has a limitation on its reduction performance. In recent days, with the development of signal processing technology, ANC (Active Noise Control) is been used to reduce the engine noise inside the cabin. On the other hand, technologies such as ASD (Active Sound Design) and ESG (Engine Sound Generator) have been used to generate the engine sound inside the vehicle. In the last ISNVH, Hyundai Motor Company newly introduced ESEV (Engine Sound by Engine Vibration) technology. This paper describes the ESEV Plus Minus that uses engine vibration to not only enhance the certain engine order components but reduce the other components at the same time. Consequently, this technology would produce a much more diverse engine sound.

In general, when a problem occurs in a component of powertrains, various phenomena appear, and abnormal noise is one of them. The service mechanics diagnose the noise through analysis by using their ears and equipment. However, depending on their experiences, analysis time and diagnostic accuracy vary greatly. To shorten the analysis time and improve the diagnostic accuracy, we have developed a technology to diagnose powertrain parts that cause abnormal noises. To create the best deep learning model for our diagnosis, we tried to collect many abnormal noises from various parts. The collected noise data was measured under idle and various operating conditions from our vehicles and test cells. This noise data is abnormal noises generated from engines, transmissions, drive system and PE (Power Electric) parts of eco-friendly vehicles. From the collected data, we distinguished good and bad data through detailed analysis in time and frequency domain.

Automotive companies are trying to enhance the customer’s impression by improving engine sound quality. The target of this sound quality is to create a brand sound that is preferred by their customers as well as quietness of interior noise. Over the past decade there have been many studies in the field of automotive sound quality. These have included the technologies such as tuning of intake orifice and exhaust orifice, tuning of structure-borne, intake feedback devices, active exhaust valves, ANC (Active Noise Cancellation) and ASD (Active Sound Design). The three elements of the sound that affect the feeling of the customer are known as engine order arrangement, frequency balance, and linearity. Here, the most important thing in sound quality development is the order arrangement.

In the automotive industry, various simulation-based analysis methods have been suggested and applied to reduce the time and cost required to develop the engine structure to improve the NVH performance of powertrain. This simulation is helpful to set the engine design concept in the initial phase of the powertrain development schedules. However, when using the conventional simulation method with a uniformed force, the simulation results sometimes show different results than the test results. Therefore, in this paper, we propose a method for predicting the radiated noise level of a diesel engine using actual combustion excitation force. Based on the analytical radiated noise development target, we identify the major components of the engine that are beyond this development target by in the frequency range. The components of the problem found in this way are reflected in the engine design of the early development stage to shorten the development time.

This paper presents two closed-loop control methods for monitoring and improving the combustion behavior and the combustion noise on two 4-cylinder diesel engines, in which an in-cylinder pressure and an accelerometer transducer are used to monitor and control them. Combustion processes are developed to satisfy the stricter and stricter regulations on emissions and fuel consumption. These combustion processes are influenced by the factors such as engine durability, driving conditions, environmental influences and fuel properties. Combustion noise could be increased by these factors and is detrimental to interior sound quality. Therefore, it is necessary to develop robust combustion behaviors and combustion noise. For this situation, we have developed two closed-loop control methods. Firstly, a method using in-cylinder pressure data was developed for monitoring and improving the combustion noise of a 1.7L engine. A new index using the values calculated from the data was proposed.