Application of fuzzy dynamic weights drivability evaluation model in tip-in condition

Abstract

Only the subjective or objective method is employed to calculate the index weight in the evaluation of drivability, which makes the evaluation result less accurate. In this paper, a drivability evaluation model was proposed on the basis of fuzzy dynamic weights, and the drivability was evaluated in tip-in conditions. The evaluation indexes affecting drivability in tip-in conditions were determined using multi-dimensional analysis. The dynamic weight was built using an improved analytic hierarchy process and improved entropy method in accordance with the principle of “function driving and difference driving.” The fuzzy matrix was built by drawing upon the improved membership function of the evaluation index. Finally, the drivability evaluation model of the tip-in condition was built with dynamic weight and fuzzy matrix. The evaluation model was employed to evaluate the drivability of the vehicle, which obtained the drivability score. As the experiment results suggest, the evaluation model is accurate and effective in comparison with the real score and the score obtained from the fuzzy analytic hierarchy process. The evaluation model is employed to formulate a control strategy for a vehicle that will allow better drivability. Besides, the method makes the performance development of the vehicle more knowledgeable and agile.

A novel objective evaluation method of drivability for passenger cars considering subjective and objective consistency

“…Tip-in condition is described as the process of rapid acceleration for the vehicle when the driver quickly triggers the accelerator pedal, and it is closely related to the transient characteristics of the vehicle. 25 When vehicle speed reaches the target speed (±5%), the accelerator pedal rapidly increases to the target opening within 0.2 s (greater than 30%) and it is maintained it for 3 s.…”

“…However, the information redundancy between objective indicators has not been considered. For the objective scoring models and methods, 24 fuzzy analytic hierarchy process (FAHP), 25 gray relational analysis (GRA), 26 and FCE 21 have good subjective and objective consistency for a single working condition, but their accuracy is limited and the different working condition types have a great influence on its evaluation accuracy. Based on learning methods, BP neural network 27 and optimal extreme learning machine (OELM) 21 can improve the consistency of subjective and objective to 95%.…”

Testing and analysis of automotive gearshift performance based on gear shift analysis

“…Especially in urban conditions, the driver is very sensitive to the comfort of gear selection performance because of the uninterrupted shifting action, which directly affects the driver's evaluation of a certain type of vehicle. [1][2][3] It can be said that as a user's perceptible item, the shifting performance of transmission is directly related to the degree of user complaints. Therefore, in order to meet the needs of consumers, automobile manufacturers need to improve the shifting quality and driving comfort as much as possible through continuous design optimization in the process of transmission development and matching.…”

“…The poor performance of manual transmission in automobile would reduce the driver's driving pleasure and make the driver feel tired. Especially in urban conditions, the driver is very sensitive to the comfort of gear selection performance because of the uninterrupted shifting action, which directly affects the driver's evaluation of a certain type of vehicle 1‐3 . It can be said that as a user's perceptible item, the shifting performance of transmission is directly related to the degree of user complaints.…”

Experimental Evaluation of an RWD Vehicle With Parameter Extraction for Analytical Modeling and Evaluation

“…The study concluded that vehicle body response was an indicator of comfort and did not find any actionable driveline dynamics that predicted subjective evaluation of the gearshift. Another study that uses their own metrics to evaluate the response to tip-in conditions was done in [79]. In this evaluation they took jury study results and compared the results with eight vehicle signals including; thumb rise rate, acceleration peak, response time, response delay, engine speed fluctuation, throttle change rate, kick, and jerk.…”