Increased customer expectations lead the automobile manufacturers to develop innovative solutions, such as mode selection functions that provide different performance and comfort settings for the drivers. Almost all brands have different types of driving modes installed on their vehicles, such as sport mode, economy mode, off-road mode, etc. In the current technology, the mode selection is manually done by the driver. Thus, no effort is taken to match the driver style with available driving modes. However, driving mode selection should be done through an intelligent system such as vehicle control unit, in order to optimize customer expectations related to vehicle performance, driving comfort, and fuel consumption. This can be achieved by the analysis of all drivability maneuvers during any driving cycle. Based on the results of these analyses, drivability calibration settings of the vehicle can be adjusted depending on driver behaviors. In addition, fuel consumption can be improved using suitable calibration for each driver type. In this study, an experimental investigation is carried out in which vehicle data is collected for eleven different drivers at three different drivability calibrations. Furthermore, fuzzy logic algorithms are utilized in order to distinguish the driver characteristics. First, data from nine drivers are used in order to train the fuzzy logic approach. Then, the trained fuzzy logic scheme is used to assess the characteristics of two other drivers, who were left out in the training data set. Hence, it is aimed to obtain an intelligent prediction procedure that can estimate the characteristics of a driver based on their driving styles.

Human perception in terms of vehicle comfort problems is a significant issue for automotive manufacturers and academic researchers as evident from scientific papers available in the literature. In this study, the maximum vehicle speed is predicted for comfortable driving of school bus drivers at certain working conditions. First, a full-vehicle school bus model, which consists of a seat, vehicle body, wheels and suspension systems, is developed to evaluate vehicle seat comfort in accordance with International Organization for Standardization (ISO) 2631-1 and Occupational Health and Safety (OHS) legislation. Second, collected experimentally power spectrum densities of road roughness are converted to amplitude form in order to be an input to the developed full-vehicle model. Third, the frequency weighting factor, which is determined by International Organization for Standardization 2631-1, is applied to the calculated RMS acceleration of the seat. Finally, frequency-weighted RMS accelerations of the seat for various conditions of road roughness and vehicle speeds are obtained, and it is used to evaluate the bus driver seat comfort in accordance with ISO 2631-1. In addition, RMS accelerations of the bus driver seat are used to evaluate vehicle seat comfort in accordance with OHS legislation. It is concluded that the effects of vehicle speed and road roughness on comfortable driving are observed and maximum vehicle speed for comfortable driving decreases as the power spectrum density of road roughness increases. According to the results, measures to be taken in accordance with the OHS legislation are suggested.

This study focuses on developing a piezoelectric-based passive damper to mitigate high- frequency brake noise. The research investigates different designs of piezoelectric transducer elements to convert mechanical energy into electrical energy using a mass sliding belt test system that simulates real disc brake noise mechanisms. Integration of these electrical elements into the existing mass-sliding belt system is investigated. Five distinct piezoelectric transducer designs (Design-1, Design-2, Design-3, Design-4 and Design-5), each with unique technical specifications, boundary conditions, and scaling, are developed to suppress high frequency noise from friction-induced vibrations. To facilitate effective selection, a scoring method is employed, identifying Design-5 with a cylindrical model as the most efficient transducer design. Evaluation criteria include energy generation, compressive strength, operating temperature, weight, ergonomic usability, cost, and safety. Analyses are performed to assess potential stress-induced damage to the piezoelectric material resulting from the selected design. This study aims to contribute a novel perspective to noise reduction techniques and successfully demonstrates the integration of a piezoelectric transducer and an RLC circuit into a functional system.

In the present study, a fast and accurate procedure for the inverse identification of material properties of a brake disc is proposed through the results of numerical and experimental modal analyses, where a particular attention is given to different objective functions and weighting. First, experimental and numerical modal analyses are conducted to obtain the natural frequencies and corresponding mode shapes. Second, design of experiment (DoE) methods are utilized to generate the meta-model of natural frequency results that are calculated numerically through finite element methods (FEMs). Several FEM simulations are run by altering critical material properties (density, Young’s modulus, and Poisson’s ratio) as independent variables according to the determined sample points by DoEs. Third, a second-degree response surface model is built by using linear regressions of simulation results. Predictive accuracies of two DoEs are investigated by the analysis of the residuals between FEM and meta-model results. Fourth, three different objective functions are adopted and used with 10 and 16 natural frequencies, with and without weighting, and compared with each other to investigate the effectiveness of functions and weighting for the particular problem. The sequential quadratic programming method is preferred for the minimization of the objective functions. As opposed to the related literature, weights of the objective functions are determined by variances of natural frequencies measured at different locations of the disc which are already required to obtain mode shapes. Therefore, a computationally and experimentally low-cost and fast identification process is attained. The results showed that the use of weights usually improves the accuracy of identification. Changing the number of included natural frequencies in the optimization process, where the third objective function is used, has a significant effect, especially on the identification of the Poisson’s ratio. Finally, a good correlation is achieved between experimental and computational frequencies updated by the optimum properties.