توسعه یک چرخه رانندگی براساس داده‌های ضبط‌شده از یک خودروی هیبرید بنزینی- الکتریکی در دو مسیر از شهر تهران با الگوریتم میانگین کی

نوع مقاله : مقاله پژوهشی (توسعه ای)

نویسندگان

1 دانشیار، دانشکده مهندسی مکانیک، دانشگاه سمنان، سمنان، ایران.

2 دانشجوی کارشناسی ارشد، دانشکده مهندسی مکانیک، دانشگاه سمنان، سمنان، ایران.

3 دانشجوی کارشناسی، دانشکده مهندسی برق، دانشگاه علم و صنعت ایران، تهران، ایران.

4 کارشناسی ارشد، دانشکده فنی مهندسی، دانشگاه آزاد اسلامی واحد تهران شمال، تهران، ایران

5 دانشجوی کارشناسی ارشد، دانشکده مهندسی مکانیک، دانشگاه بین‌المللی امام خمینی، قزوین، ایران.

6 دانشجوی کارشناسی، دانشکده مهندسی کامپیوتر، دانشگاه صنعتی امیرکبیر، تهران، ایران.

چکیده

در این مقاله، یک چرخه رانندگی براساس داده­های ضبط‌شده از یک خودروی هیبرید بنزینی الکتریکی در دو مسیر شهر تهران با الگوریتم میانگین کی (K-means) توسعه داده شده است. برای این منظور، در دو مسیر متفاوت در شهر تهران، داده­های سرعت و مکان جغرافیایی خودرو برحسب زمان با یک نرم­افزار تلفن همراه ضبط شد و نتایج آن با استفاده از داده­ها در نمایشگر خودرو، صحه­گذاری گردید. در ادامه، با استفاده از الگوریتم میانگین کی، دو چرخه رانندگی برای دو مسیر فوق استخراج شد و مشخصه­های آنها با مشخصه­های کل داده­ها مقایسه گردید. نتایج به‌دست‌آمده نشان داد که برای نرم‌افزار توسعه‌یافته، داده‌برداری از دقت و کیفیت مناسبی برخوردار است. چرخه­های رانندگی معرفی‌شده نهایی دارای خطاهای نسبی کمتر از 10 درصد هستند که نشان از انتخاب مناسب آنها در کلان داده ضبط‌شده می‌باشند. همچنین، در مقایسه با چرخه‌های تولیدشده با خودروهای بنزینی در شهر تهران می‌توان ادعا کرد که بیش از آن‌که نوع وسیله نقلیه بر وضعیت حرکت خودرو مؤثر باشد فرهنگ رانندگی و نیز ناهمواری‌های جاده در این شهر تأثیرگذار هستند؛ زیرا این چرخه‌ها همگی اعدادی بیش از 80 درصد و حدود 30 کیلومتر بر ساعت را به‌ترتیب برای درصد زمان رانندگی و میانگین سرعت سفر ثبت کرده‌اند. نکته جالب دیگر به‌دست‌آمده از تحلیل کلان داده ضبط‌شده، این است که با توجه به درصد زمان توقف چرخه‌ها که همگی کمتر 20 درصد هستند، رانندگان در تهران تمایل کمی به توقف دارند و لذا برای کنترل سرعت خودرو در ترافیک، نیازمند ایجاد تغییرات شدید در سرعت خودرو می‌شوند که همین اتفاق سبب ثبت انحراف حدود 40 واحد از میانگین سرعت می‌شود که در مقایسه با سایر چرخه‌ها اندکی بیشتر است. 

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Development of a Driving Cycle Based on Data Recorded from an Electric-Gasoline Hybrid Vehicle on Two Routes in Tehran City with K-means Algorithm

نویسندگان [English]

  • Mohammad Azadi 1
  • Shokouh Dezianian 2
  • Alireza Navi 3
  • Alireza Salmani 4
  • Tabanmehr Gharaati 5
  • Ali Faraji 6
1 Associate Professor, Faculty of Mechanical Engineering, Semnan University, Semnan, Iran.
2 MSc Student, Faculty of Mechanical Engineering, Semnan University, Semnan, Iran.
3 BSc Student, Faculty of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran.
4 MSc, Faculty of Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran.
5 MSc Student, Faculty of Mechanical Engineering, Imam Khomeini International University, Qazvin, Iran.
6 BSc Student, Faculty of Computer Engineering, Amirkabir University of Technology, Tehran, Iran.
چکیده [English]

In this article, a driving cycle was developed based on acquired data from a hybrid gasoline-electric vehicle on two routes in Tehran city, using the K-mean algorithm. For this objective, speed data and the geographical locations of the vehicle were acquired with a mobile application and results were validated by data in the vehicle monitor on two different routes in Tehran city. Then, using the K-mean algorithm, two driving cycles were extracted and their characteristics compared to those of big data. Obtained results demonstrated that for the developed software, data acquisition was accurate and of appropriate quality. Final introduced driving cycles had relative errors below 10%, which indicated their appropriate selection from acquired big data. Moreover, compared to extracted cycles by gasoline vehicles in Tehran city, it could be claimed that the vehicle type had impact on vehicle driving; however, this impact was more related to the driving culture and the roughness of the roads in this city. This is because these cycles all record figure of more than 80% and approximately 30 km / h for the percentage of driving time and average travel speed, respectively. Another interesting result obtained from the analysis of big data is that the stop time in cycles was less than 10% and therefore, drivers in Tehran had low tendency to stop. Thus, for controlling the vehicle speed under traffic conditions, high variations in speed was needed. This led to approximately 40% of the variations from the average speed compared to other cycles, which were a little higher.      

کلیدواژه‌ها [English]

  • Driving cycle Hybrid vehicle Tehran city K
  • means algorithm Global positioning system

مراجع

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فصلنامه علمی کارافن
دوره 19، شماره 1 - شماره پیاپی 57
فنی و مهندسی
خرداد 1401
صفحه 629-653

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  • تاریخ دریافت: 07 آذر 1400
  • تاریخ بازنگری: 28 دی 1400
  • تاریخ پذیرش: 14 اسفند 1400

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