Position detection for electric vehicle DWCS using VI-SLAM method

The dynamic wireless charging system (DWCS) is developed to solve the problems of large battery volume and mileage anxiety of electric vehicles. However, the accurate position detection for electric vehicle DWCS is facing challenge. The traditional communication, detection and estimation methods are...

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Autores principales:	Jun Cheng, Liyan Zhang, Qihong Chen, Rong Long
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Electric vehicle DWCS VI-SLAM Graph optimization Position detection Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/e9eb0e26d36c4d5eb0c0e6a3db01413f
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spelling	oai:doaj.org-article:e9eb0e26d36c4d5eb0c0e6a3db01413f2021-11-26T04:34:26ZPosition detection for electric vehicle DWCS using VI-SLAM method2352-484710.1016/j.egyr.2021.09.086https://doaj.org/article/e9eb0e26d36c4d5eb0c0e6a3db01413f2021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352484721008921https://doaj.org/toc/2352-4847The dynamic wireless charging system (DWCS) is developed to solve the problems of large battery volume and mileage anxiety of electric vehicles. However, the accurate position detection for electric vehicle DWCS is facing challenge. The traditional communication, detection and estimation methods are difficult to accurately obtain the position. To tackle this problem, the visual inertial simultaneous localization and mapping (VI-SLAM) method is applied to the electric vehicles DWCS. Firstly, the graph optimization based tight coupling method is used to integrate the monocular visual and inertial measurement unit (IMU) measurements. Secondly, the NVIDIA TX2 and MYNT VI-sensor suite are assembled, which the MTi300-IMU is treated as the ground truth system. Finally, the mobile vehicle is controlled to race on the simulated DWCS pathway. The experimental result shows that the method achieves great performance with the accuracy of centimeter level. In particular, the root mean square error (RMSE) of pose (i.e. position and orientation) in X, Y, Z directions are 0.086 m, 0.092 m, 0.102 m and 2.423°, 1.682°, 2.501°, respectively. Compared with the smooth variable structure filter (SVSF) based SLAM method, 0.106 m, 0.098 m, 0.130 m and 3.069°, 3.261°, 2.961°, the accuracy of ours is increased by 18.87%, 6.12%, 21.54% and 21.05%, 45.42%, 15.54%, respectively.Jun ChengLiyan ZhangQihong ChenRong LongElsevierarticleElectric vehicleDWCSVI-SLAMGraph optimizationPosition detectionElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENEnergy Reports, Vol 7, Iss , Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Electric vehicle DWCS VI-SLAM Graph optimization Position detection Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Electric vehicle DWCS VI-SLAM Graph optimization Position detection Electrical engineering. Electronics. Nuclear engineering TK1-9971 Jun Cheng Liyan Zhang Qihong Chen Rong Long Position detection for electric vehicle DWCS using VI-SLAM method
description	The dynamic wireless charging system (DWCS) is developed to solve the problems of large battery volume and mileage anxiety of electric vehicles. However, the accurate position detection for electric vehicle DWCS is facing challenge. The traditional communication, detection and estimation methods are difficult to accurately obtain the position. To tackle this problem, the visual inertial simultaneous localization and mapping (VI-SLAM) method is applied to the electric vehicles DWCS. Firstly, the graph optimization based tight coupling method is used to integrate the monocular visual and inertial measurement unit (IMU) measurements. Secondly, the NVIDIA TX2 and MYNT VI-sensor suite are assembled, which the MTi300-IMU is treated as the ground truth system. Finally, the mobile vehicle is controlled to race on the simulated DWCS pathway. The experimental result shows that the method achieves great performance with the accuracy of centimeter level. In particular, the root mean square error (RMSE) of pose (i.e. position and orientation) in X, Y, Z directions are 0.086 m, 0.092 m, 0.102 m and 2.423°, 1.682°, 2.501°, respectively. Compared with the smooth variable structure filter (SVSF) based SLAM method, 0.106 m, 0.098 m, 0.130 m and 3.069°, 3.261°, 2.961°, the accuracy of ours is increased by 18.87%, 6.12%, 21.54% and 21.05%, 45.42%, 15.54%, respectively.
format	article
author	Jun Cheng Liyan Zhang Qihong Chen Rong Long
author_facet	Jun Cheng Liyan Zhang Qihong Chen Rong Long
author_sort	Jun Cheng
title	Position detection for electric vehicle DWCS using VI-SLAM method
title_short	Position detection for electric vehicle DWCS using VI-SLAM method
title_full	Position detection for electric vehicle DWCS using VI-SLAM method
title_fullStr	Position detection for electric vehicle DWCS using VI-SLAM method
title_full_unstemmed	Position detection for electric vehicle DWCS using VI-SLAM method
title_sort	position detection for electric vehicle dwcs using vi-slam method
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/e9eb0e26d36c4d5eb0c0e6a3db01413f
work_keys_str_mv	AT juncheng positiondetectionforelectricvehicledwcsusingvislammethod AT liyanzhang positiondetectionforelectricvehicledwcsusingvislammethod AT qihongchen positiondetectionforelectricvehicledwcsusingvislammethod AT ronglong positiondetectionforelectricvehicledwcsusingvislammethod
_version_	1718409874862243840

Position detection for electric vehicle DWCS using VI-SLAM method

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