Electric vehicle model parameter estimation with combined least squares and gradient descent method

Name: Electric vehicle model parameter estimation with combined least squares and gradient descent method
Author: Gözüküçük, Mehmet Ali, Uğurdağ, Hasan Fatih, Dedeköy, Mert, Çelik, Mert, Akdoğan, Taylan

İsim	Electric vehicle model parameter estimation with combined least squares and gradient descent method
Yazar	Gözüküçük, Mehmet Ali, Uğurdağ, Hasan Fatih, Dedeköy, Mert, Çelik, Mert, Akdoğan, Taylan
Basım Tarihi:	2019
Basım Yeri	- IEEE
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-605011275-7
Kayıt Numarası	8cd94af3-7b5c-49e7-9a34-7269f31c7dc2
Lokasyon	Natural and Mathematical Sciences, Electrical & Electronics Engineering
Tarih	2019
Notlar	TÜBİTAK
Örnek Metin	Energy management algorithms have a crucial role in electric vehicles due to their limited driving range. For an energy management algorithm to be effective, we should model the vehicle as accurately as possible. That is, not only the structure of the model should be accurate, but also the parameters of the model should be accurate. In this work, we take the model of an electric vehicle and tune three parameters in it based on trip data, namely, vehicle mass, air drag coefficient, and rolling resistance coefficient. We do this by using Least Squares method to set the initial guess and then by optimizing the parameters using Gradient Descent. To the best of our knowledge, this is the first work that simultaneously estimates these three parameters. Our work is also unique in the sense that it combines Least Squares and Gradient Descent.
DOI	10.23919/ELECO47770.2019.8990393

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Electric vehicle model parameter estimation with combined least squares and gradient descent method

Yazar Gözüküçük, Mehmet Ali, Uğurdağ, Hasan Fatih, Dedeköy, Mert, Çelik, Mert, Akdoğan, Taylan

Basım Tarihi 2019

Basım Yeri - IEEE

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-605011275-7

Kayıt Numarası 8cd94af3-7b5c-49e7-9a34-7269f31c7dc2

Lokasyon Natural and Mathematical Sciences, Electrical & Electronics Engineering

Tarih 2019

Notlar TÜBİTAK

Örnek Metin Energy management algorithms have a crucial role in electric vehicles due to their limited driving range. For an energy management algorithm to be effective, we should model the vehicle as accurately as possible. That is, not only the structure of the model should be accurate, but also the parameters of the model should be accurate. In this work, we take the model of an electric vehicle and tune three parameters in it based on trip data, namely, vehicle mass, air drag coefficient, and rolling resistance coefficient. We do this by using Least Squares method to set the initial guess and then by optimizing the parameters using Gradient Descent. To the best of our knowledge, this is the first work that simultaneously estimates these three parameters. Our work is also unique in the sense that it combines Least Squares and Gradient Descent.

DOI 10.23919/ELECO47770.2019.8990393