نوع مقاله : مقاله پژوهشی
نویسندگان
1 مهندسی مکانیک بیوسیستم، دانشگاه تربیت مدرس، تهران، ایران
2 گروه مهندسی خورو، دانشگاه علم و صنعت، تهران، ایران
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Driving cycle assessment is one of common methods to evaluate vehicle’s real-world condition also monitoring fuel consumption and emissions. Basic challenges in extraction of driving cycle are data analysis for develop and define suitable behavior of device. Clustering, classification and recognition of driving pattern are important steps on extraction of suitable driving cycle. Generally, the accuracy of modeling and recognition of AI-based methods is indicated more than 90% and other outputs are in compliance with big data. Thus, in this research we endeavored to evaluate the effect of using artificial intelligence on driving cycle of off-road vehicles. The major part of off-road vehicles are agricultural vehicles such as tractors which they are divided into three categories based on agriculture operations; light, heavy and extra heavy.in addition, the procedure of agricultural operation is effective on fuel consumption, loading and exhaust emissions. The results of this research showed that the use of conventional machine learning methods for clustering and classification can be used for any volume of features. However, with increase in features, complexity of region segmentation and the effect of farm management factors cause overtraining overtraining condition in learning algorithm and reduce the accuracy of extracted driving cycle and prediction of driving behavior. Therefore, it is necessary to use advanced algorithms with deep learning capabilities. Therefore, extracting the intelligent driving cycle for agricultural tractors based on the type of agricultural operation with the help of artificial intelligence methods can reduce fuel consumption, pollution and optimal farm management.
کلیدواژهها [English]
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