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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.

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.

In order to align with net-zero CO2 ambitions, automotive OEMs have been developing increasingly sophisticated strategies to minimise the impact that combustion engines have on the environment. Intelligent thermal management systems to actively control coolant flow around the engine have a positive impact on friction generated in the power cylinder by improving the warmup rate of cylinder liners and heads. This increase in temperature results in an improved frictional performance and cycle averaged fuel consumption, but also increases the piston to liner clearances due to rapid warm up of the upper part of the cylinder head. These increased clearances can introduce piston slap noise and substantially degrade the NVH quality to unacceptable levels, particularly during warmup after soak at low ambient temperatures. Using analytical techniques, it is possible to model the thermo-structural and NVH response of the power cylinder with different warm up strategies.

This paper presents NVH optimization procedure for the 2.5L I4 common rail diesel engine. Distinctive design feature is the low noise timing chain system which realized by chain case modification and meshing noise improvement. The former was effective for reduction of sound level and the later was for sound quality. Combustion excitation force, vibration transmission in engine structure were also improved and countermeasured to achieve powerful and comfortable Sports Utility Vehicle.

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.

The combustion noise of a diesel engine can be deteriorated by combustion characteristics such as the maximum rate of heat release and the start of combustion. These combustion characteristics in turn are influenced by the factors such as the engine NVH durability, driving conditions, environmental factors and fuel properties. Therefore, we need to develop the robust combustion noise that is insensitive to these factors. To achieve this aim, methods for predicting combustion characteristics has been developed by analyzing the vibration signal measured from the engine cylinder block. The closed-loop control of injection parameters through combustion characteristics prediction has been performed to produce the desired engine combustion performance. We constructed an ECU logic for the closed-loop control and verified the design in a diesel passenger car. We also evaluated the effect of combustion noise and fuel consumption by applying the closed-loop control.

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.