Identification of Potential Electromagnetic Disturbances at 2172 MHz in Autonomous EV Routes

Trihatmo, Sardjono; Hendrantoro, Gamantyo; Septiawan, Reza Rendian; Setijadi, Eko; Rufiyanto, Arief; Sulistya, Budi

doi:10.1109/icramet56917.2022.9991219

Cited by 2 publications

(2 citation statements)

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“…The area is quite spacious and surrounded by small to medium buildings and high vegetation. The method of the measurement is described comprehensively in [15].…”

Section: A Electromagnetic Field Observationmentioning

confidence: 99%

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An Autoregressive Model of Electromagnetic Disturbances in An Autonomous Electric Vehicle’s Route

Trihatmo,

Hendrantoro,

Septiawan

et al. 2024

JAREE

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Electromagnetic Interference (EMI) can cause a malfunction of on-board electronic circuits in an autonomous electric vehicle and supporting electronic devices located in the environment of autonomous electric vehicles as well. In order to navigate an autonomous electric vehicle safely, it is important to have electromagnetic field characteristic in the environment. Since the information of electromagnetic field characteristic is hard to find, it needs to be modeled. This paper presents a model of electromagnetic field characteristic that is generated by using autoregression in order to estimate potential EMI. The EMI estimation is based on electromagnetic characteristic in an environment. Unlike other applications that use time history of data to build a model, we present a spatial electromagnetic field strength data in a previous route to estimate the future data in a new route. To obtain historical data for auto-regression process, we measured electric field strengths along a circular route in a campus near Jakarta. This surrounding environment represents a typical area of suburbs. The input variables for auto-regression process are the first 27 correlated data of 155 measured data. The result shows that the use of 13 predictor coefficient produces a variance of prediction error near to zero, with an improvement from maximum prediction error of 15.1257 to prediction error of 0.1862.

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“…The area is quite spacious and surrounded by small to medium buildings and high vegetation. The method of the measurement is described comprehensively in [15].…”

Section: A Electromagnetic Field Observationmentioning

confidence: 99%

“…Accordingly, we use spatial previous measured data in a circular route to estimate the future data for another route. The input data for the model are obtained by observing electromagnetic field in a typical area of suburbs as it is discussed in [15].…”

Section: Introductionmentioning

confidence: 99%