System for Ranking and Matching Electric Vehicles: History
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Electric vehicles (EVs) have become popular in the last decade because of their advantages compared to conventional vehicles. The market offers dozens of EV models in a large range of prices, performances, and specifications. It is very important to have a decision support system that will help buyers and sellers navigate the mission to match the customer’s requirements and the preferred car.

  • energy
  • electric vehicle (EV)
  • expert system

1. Introduction

Electric vehicles (EVs) have become increasingly common over the past decade because of their advantages compared to conventional vehicles [1]. Electric vehicles have made a significant breakthrough in the automotive industry, giving consumers a way to travel on the roads more confidently and providing people with opportunities to switch to more environmentally friendly ways of driving. Today’s EVs are gradually taking over more roads and replacing polluting conventional vehicles. They appear to promote the ability to store energy in clean and smart grids to reduce greenhouse gas emissions and eliminate harmful traffic loads [2]. The market offers dozens of models of electric cars with a large range of prices, performance, and other specifications. The growth rate of introducing EVs into use increased in the entire EU. In 2019, the growth rate was 48%, while in 2020, it was 86% [3]. The development of EVs is highly favored because activities related to the development of the electro-mobility sector match the need to reduce environmental pollution. Producing fully electric-powered vehicles is now a reality for major passenger vehicle manufacturers. There is a dynamic change in the percentage of production volumes relating to vehicles powered by petroleum, electricity, or hybrid combinations of the two. In 2018, the estimated number of electric cars was around 750 thousand, and the projection for 2020 was around 10 million [4]. The number of electric car models in 2023 has already exceeded 300 [5].
The variety of EVs is a challenge to anyone who wants to buy an EV that properly matches specific requirements and needs. Finding the best match is also challenging for sellers and retailers who try to help their customers purchase an EV from a given set of cars they sell. Purchasing an EV is a multicriteria decision analysis problem with many criteria, including price, energy consumption, technical specifications, and ergonomic specifications. Therefore, there is a need for a decision support system that will help buyers and sellers navigate the mission to match the customer’s requirements and the preferred car. In [6], the authors developed a forecasting model that used machine-learning methods to identify factors for predicting consumer behavior regarding willingness to purchase an EV. They found that the factors stimulating the decision to purchase an EV to the greatest extent are price, design, car class, equipment, and EV driving advantages.

2. System for Ranking and Matching Electric Vehicles to Customer Specifications and Requirements 

The interest in EVs in the scientific literature has increased with the popularity of EVs and the understanding that EVs are probably the next generation of vehicles. In recent years (2018–2022), the number of published papers on the “electric vehicles” topic (as counted in Google Scholar), is high, with about 40,000–60,000 publications each year. Here is an example of several papers.
In [11], the authors point out four factors for the expanded use of EVs: government policy, economic advantages, environmental considerations, and technological development. They concluded that governments should invest in developing EVs and battery technologies, provide subsidies, and develop charging infrastructure. The use cost of EVs is influenced by grid electricity price, carbon tax, and other factors. In [12], the authors presented an expert system for the decision support of an EV driver in minimizing energy consumption. Their proposed system was based on the use of multi-valued logic trees, which enabled minimizing objective functions. One of those functions was aimed at minimizing EV energy consumption at different ambient temperatures. In [13], the authors explained that the major criteria for developing electric car chassis are stiffness and strength enhancement, subject to mass reduction, cost, and time. Toward this direction, they proposed an integrated methodology for developing an electric car chassis considering the modeling and simulation concurrently.
EVs have been the subject of much research over the last few years. This selection of papers aims to highlight some of the most significant findings. In [14], the authors explored consumer intentions to adopt, buy, and use EVs by analyzing 211 peer-reviewed research articles published between 2009 and 2019. They identified four main types of influential factors: demographic, situational, contextual, and psychological. In [15], the authors focused on EV operations management based on mathematical modeling for EV charging infrastructure planning, EV charging operations, public policy, and business models. In [16], the authors evaluated the progress in EVs regarding battery technology trends and charging methods, considering various battery technologies, standards available for EV charging, power control, and battery energy management proposals. In [17], the authors analyzed the policies, strategies, and technical requirements for EV development in India and globally. The authors focused on the situation in India, highlighting the current deployment of EVs and the existing challenges and opportunities. In [18], the authors examined the developments in EV policy in China, assessing national-level policy measures and financial incentives to develop the sustainable EV industry over the past decade. They also developed a mathematical model to quantify the credit policy regime’s impact, finding a significant gap between recent EV sales and projected EV production requirements. In [19], the authors covered the development of EVs and government policies in the UK, reviewing charging equipment protocols and standards, existing EV charging facilities, and charging infrastructure circuit topologies. The authors also discussed site factors, the operation and management of charging infrastructure, and various business models. Finally, in [20], the authors proposed a multi-criteria decision-making approach to identify and classify the factors of successful EV adoption in emerging economies. They concluded that EV performance and reliability, power and charging infrastructure, and government policies were the most influential factors for EV adoption.
In [21], the authors described the characteristics and typical models of energy sources of EVs, the existing EV types, and their environmental impacts. They also investigated energy management strategies for EVs, the charging technologies, the challenges faced by EVs, and the corresponding solutions. In [1], the authors investigated the effects of an uncontrolled charging process in a low-voltage distribution grid case study and proposed a charging coordination management strategy. Their simulation results indicate that the voltages in the system’s buses and the lines’ thermal limits are major limiting factors for the integration of EVs in energy distribution networks. In [2], the authors proposed a stochastic procedure for modeling and analyzing a fleet of EVs to generate accurate charging and discharging profiles. They used a genetic algorithm to determine the optimal charging/discharging schedule for each EV in the fleet. In [22], the authors proposed a concept of multi-objective techno-economic-environmental optimization for scheduling electric vehicle charging/discharging. They optimized end-user energy cost, battery degradation, grid interaction, and CO2 emissions in the home micro-grid context. Results from their case studies show reductions in energy cost, battery degradation, CO2 emissions, and grid utilization. In [23], the authors stated that regular gasoline vehicles have high emission levels that contribute significantly to climate change and pollution-related health problems. EVs are a promising alternative to gasoline vehicles because they do not directly release emissions or pollutants. The authors note that the sales of EVs are affected due to a variety of limitations, such as the lack of available charging infrastructure, the long charging time, the high cost of long-range EVs, and a limited supply of affordable EVs.
Several papers dealt with EVs’ ranking and selection. For example, the authors in [24] proposed a model for selecting and ranking a group of EVs using the multi-attributive border approximation area comparison (MABAC) method. They employed the MABAC method to evaluate various technical and operational attributes, such as fuel economy, base model pricing, quick accelerating time, battery range, and top speed. In [25], the authors presented an integrated approach of AHP and MABAC methods for selecting and ranking the best EV. The AHP method is used to obtain weight coefficients of criteria, and the selection of EVs alternatives is evaluated using the MABAC method (AHP-MABAC). In [26], the authors proposed a comprehensive tool for robustness analysis based on TOPSIS, which they demonstrated on EVs. Their method releases the decision-maker from setting the criteria weighting. Their framework was then used for sensitivity analysis based on interval arithmetic and the MCDA approach. While the primary assessment gives initial recommendations, only after thorough analysis can the ranking of alternatives be reliably treated. In [27], the author developed a multi-criteria stochastic selection of EVs for sustainable development in Poland. 
Ranking and selecting an EV is a multi-criteria decision problem. Ranking according to several criteria (outputs and inputs) usually results in a multi-criteria decision analysis method (MCDA). The use of MCDA focuses on designing mathematical and computational tools to support the subjective evaluation of a finite number of alternatives under a finite number of performance criteria. In an MCDM problem, a variety of alternatives is evaluated according to several criteria that characterize these alternatives; the goal is to choose the best alternative [28,29]. The use of MDCA methods is a popular tool in complex issues. These methods are applied to problems considering selecting the most satisfying choices or evaluating the quality of solutions [30]. Methods incorporating MCDA have received much attention from researchers and practitioners in evaluating, assessing, and ranking alternatives across diverse industries [31]. Numerous methods have been proposed for ranking alternatives according to multiple criteria. For example, [32,33] surveyed several MCDM and ranking methods. One of the most popular methods of MCDA is TOPSIS [8]. Over the years, it has gained many followers due to its simplicity, efficiency, and high correlation of results with other well-known multi-criteria methods. Numerous models and decision support systems have been developed based on the TOPSIS method of performance [34].
The TOPSIS method and its applications are widely used in the literature. In [35], the authors claimed that TOPSIS [8] is one of the most well-known classical MCDM approaches. The essential goal of the TOPSIS approach is that the most preferred alternative is not only the shortest distance from the positive ideal solution but also the farthest distance from the negative ideal solution [36]. For example, in [37], the authors used linguistic variables to show how energy policy goals toward sustainable development and options for renewable energy sources are linked and evaluated. In [38], the authors developed a TOPSIS-based MCDM approach for whole-building energy comparison, using a specific objective weighting procedure for cost-accuracy modification. In [39], the authors presented a concept of sustainable development of EVs in Tehran. They expended the conventional definition of sustainable development based on a philosophy of key aspects of human, nature, and systems performances. In their proposed method, the coefficient of each policy scenario was calculated utilizing fuzzy TOPSIS, and the various policies affecting the development of EVs in Tehran were ranked.

This entry is adapted from the peer-reviewed paper 10.3390/en16114283

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