Journal on Advanced Research in Electrical Engineering, Vol. 8, No. 1, Jan. 2024 34 © 2024 by the authors. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. How to cite: Trihatmo, S., Hendrantoro, G., Septiawan, Reza., Setijadi, Eko., Rufiyanto, A. (2024). An Autoregressive Model of Electromagnetic Disturbances in An Autonomous Electric Vehicle’s Route. JAREE (Journal on Advanced Research in Electrical Engineering), 8(1). An Autoregressive Model of Electromagnetic Disturbances in An Autonomous Electric Vehicle’s Route Sardjono Trihatmo Departement of Electrical Engineering Institut Teknologi Sepuluh Nopember Surabaya, Indonesia sard001@brin.go.id Eko Setijadi Departement of Electrical Engineering Institut Teknologi Sepuluh Nopember Surabaya, Indonesia ekoset@ee.its.ac.id Gamantyo Hendrantoro Departement of Electrical Engineering Institut Teknologi Sepuluh Nopember Surabaya, Indonesia gamantyo@ee.its.ac.id Arief Rufiyanto Departement of Electrical Engineering Institut Teknologi Sepuluh Nopember Surabaya, Indonesia arie014@brin.go.id Reza Septiawan Research Center for Electronics National Agency for Research and Innovation (BRIN) Jakarta, Indonesia reza001@brin.go.id Abstract—Electromagnetic Interference (EMI) can cause a malfunction of on-board electronic circuits in an autonomous electric vehicle (AEV) and supporting electronic devices located in the environment of autonomous electric vehicleAEVs as well. In order to navigate an AEV safely, it is important to have electromagnetic field characteristic in the environment. As the details about the electromagnetic field's characteristics are elusive, there's a requirement to create a model for it. 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 presents X-Y plane of electromagnetic field strength (E-field) real data in a previous route to estimate the future data in a new route. To obtain historical data for auto-regression process, E-Field measurement was conducted 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 by using of 13 predictor coefficient produces a variance of prediction error with an improvement from maximum prediction error of 15.1257 to prediction error of 0.1862. Keywords—autoregressive model, electromagnetic field, electromagnetic interference, electric vehicles, random process. I. INTRODUCTION Electromagnetic field can interfere electronic circuits. It cause a malfunction of electronic devices in the automotive research area [1]. In commercial automotive industries, it is reported in [2] that there is strong indication of malfunctions of electronic devices caused by EMI. Since AEVs are predicted as the future transportation [3], it is necessary to consider the effect of EMI to electronic control and navigation devices in the vehicles. A common and powerful tool for navigation of vehicles is a GPS receiver. A study of the performance of GPS receivers has been made comprehensively in [4]. However, since a GPS receiver can be affected by EMI [5], an AEV may take a wrong direction if the supporting GPS fails to provide correct data. The sources of electromagnetic field are not only on-board electrical or electronic devices but also transmitter stations in the environment as it is indicated in [6]. Accordingly it is essential to have a characteristic of electromagnetic field in the environment of AEVs. Based on the existing map of electromagnetic field characteristic, one can determine which routes are dangerous for AEVs. This kind of map can be used to try out path-planning algorithms of an electric vehicle before it starts travelling. Unfortunately, there is scarce information available in the map depicting electromagnetic field characteristics. Even Although some experiments have been conducted to investigate the characteristic of electromagnetic field, however the area of measurement is limited. In the previous research [7] and [8], the measurement was conducted only in a campus area. Another measurement of the experiment in [9] was conducted also in a relative particular area. In addition, the characteristic in this area can change with the time. In order to provide a map of electromagnetic characteristic independently from the real environment, we needs to simulate and create a model of a electromagnetic field characteristic. A powerful tool for modeling is autoregression. Autoregression estimates the current values from the existing previous values of electromagnetic field strength in dBm . In the research area of telecommunication, this method is used for estimating the rain attenuation on multiple short radio links [10]. In biomedical applications, autoregressive modeling is used to analyses cardiovascular response signals [11]. It is reported in [12] that by using autoregressive modelling, the power spectrum in the spectral analysis of heart rate variability can be more easily interpreted than using the discrete Fourier transform. Such an autoregressive model of electromagnetic field is useful in evaluating the effectiveness of path-planning method of an EV, by which excessive electromagnetic field must be avoided. The limits and frequencies of electromagnetic field that are harmful for on-board electronic devices are determined in [13]. This paper presents an autoregressive model of an electromagnetic field characteristic. Commonly, autoregressive modeling is used for analyzing electric or electromagnetic responses use time history of previous data to predict future data. In our work, it is a huge time consuming to observe and to collect data in a relative wide area. There is an alternative way to model a random process