An Autoregressive Model of Electromagnetic Disturbances in An Autonomous Electric Vehicle’s Route - Trihatmo, Sardjono; Hendrantoro, Gamantyo; Septiawan, Reza; Setijadi, Eko; Rufiyanto, Arief (Department of Electrical Engineering ITS and FORTEI, 2024)
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.