Electric Vehicles: A Data Science Perspective Review

Abstract

Current trends are showing that the popularity of electric vehicles (EVs) has significantly increased over the last few years, causing changes not only in the transportation industry but generally in business and society. This paper covers one possible angle to the (r)evolution instigated by EVs, i.e., it provides the data science perspective review of the interdisciplinary area at the intersection of green transportation, energy informatics, and economics. Namely, the review summarizes data-driven research in EVs by identifying two main research streams: (i) socio–economic, and (ii) socio–technical. The socio–economic stream includes research in: (i) acceptance of green transportation in countries and among different populations, (ii) current trends in the EV market, and (iii) forecasting future sales for the green transportation. The socio–technical stream includes research in: (i) electric vehicle battery price and capacity and (ii) charging station management. This kind of study is especially important now when the question is no longer whether the transition from internal-combustion engine vehicles to clean-fuel vehicles is going to happen but how fast it will happen and what are going to be implications for society, governmental policies, and industry. Based on the presented literature review, the paper also outlines the most significant open questions and challenges that are yet to be solved: (i) scarcity of trustworthy (open) data, and (ii) designing a generalized methodology for charging station deployment.

Synthetic Data Generator for Electric Vehicle Charging Sessions: Modeling and Evaluation Using Real-World Data

“…Instead of generating the next arrival of EV, here we focus on generating the number of arrivals in a given t s (timeslot, e.g., slots of 60 min). The number of arrivals N in t s can be generated as a random sample from a discrete probability distribution Equation (7). This distribution can be characterized using parameters P, and Equation (6) can be modified to Equation (8), wherein we model these parameters.…”

“…However, studies require reliable session data for understanding behaviors and exploring flexibility. The scarcity of reliable data has been discussed previously [7], and its necessity has been pointed out for further research purposes. Where data are available, they may still be protected under confidentiality by private data collectors, and not freely available for academic or public use.…”

An Adaptive Prediction Model for the Remaining Life of an Li-Ion Battery Based on the Fusion of the Two-Phase Wiener Process and an Extreme Learning Machine

“…Due to their ability to decrease environmental pollution, electric vehicles (EVs) have become a research hotspot [1]. An EV system consists of a differential, a mechanical transmission system, an electric motor, a power converter, a battery management system (BMS), and a battery pack [2].…”

A Road Condition Classification Algorithm for a Tire Acceleration Sensor using an Artificial Neural Network

“…The automotive industry is experiencing a period of innovation, represented by the term CASE (connected, autonomous, shared, and electric). This term symbolizes the application of technologies from various fields to automobiles [1][2][3]. Considerable effort has also been directed toward improving driving stability and convenience by utilizing various vehicle technologies, such as sensors and wired/wireless communications [4][5][6].…”