Due to the considerable increase in car parc and the remarkable improvement in vehicle properties and cost performance, higher requirements are being placed on driving comfort. With regard to the braking system, the enhancement of the brake pedal feel is one of the ways to improve the comfort, and affecting factors include spatial layout, dynamic characteristics, and the characteristics matching with the vehicle motion, so a comprehensive evaluation of a brake pedal is highly essential. This paper, by taking passenger cars in China as the research object, established a comprehensive evaluation system for pedals based on the improved analytic hierarchy process. The index weight in each index level was determined through the G1 method, and an experimental design was conducted on individual indices on the lowest level. The relationship parameters between the indices were measured through the test and comprehensive scores of each automobile pedal were obtained in combination with the evaluation method designed. Based on comprehensive scores, a typical analysis on 16 test vehicles was carried out, followed by a comparison of different index levels. Through result analysis, the direction for pedal improvement was proposed, and the feasibility of the method with the case is verified, which provides a scientific basis and a reference for vehicle pedal development, the calibration in position layout, and the pedal design.
In the process of automobile electronic accelerator pedal development, it is a critical and challenging issue to evaluate the rationality and comfort of the design of an automotive electronic accelerator pedal. Many factors influence the comfort of the accelerator pedal, such as the spatial layout, dynamic characteristics, and matching characteristics of the accelerator pedal and vehicle motion. Since comfort evaluation requires a lot of manpower and material resources, this paper proposes a prediction model based on support vector machine regression algorithm (SVR) for comprehensive evaluation of Chinese passenger car pedals. It uses the known evaluation results to predict the unknown evaluated accelerator pedal parameters to achieve a more efficient and accurate assessment of electronic accelerator pedal design. Firstly, the article performs pedal position scans, pedal static, and road tests to give criteria, limitations, and recommended design ranges for pedal operation. Then, the vehicle performance was predicted and evaluated using a support vector machine prediction model and back propagation (BP) neural network prediction model for comparison. The correlation coefficient for the prediction results of the SVR model was 0.9024 with a mean square error was 0.00195. The correlation coefficient for the BP neural network model prediction result was 0.8694 with a mean square error of 0.00582. Finally, the simulation results were analyzed, and the results showed that support vector regression outperformed the neural network in predicting the validity and reliability of pedal design and performance evaluation, and can facilitate automotive pedal design and development.
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