An Integrated AHP-MABAC Approach for Electric Vehicle Selection
Introduction
Due to the rising global population and a rapid diminution of fossil fuels, many of the countries are responsible for producing greenhouse gas by carbon dioxide (CO2) emissions causing global warming all over the globe. The maximum percentage of greenhouse gas emissions has been caused by the transport sector contributing to climate change (Vassileva & Campillo, 2016). Introduction of alternative fuels, which includes natural gas, ethanol, methanol, propane, electricity, biodiesel, and hydrogen, are considered as substitutes for gasoline or fossil fuels. Therefore, global warming can be eliminated or mitigated by promoting the adoption of electric vehicles and can contribute to sustainable development (Deuten, Gómez Vilchez, & Thiel, 2020; Egnér & Trosvik, 2018). In the context of decarbonization, electric vehicles (EVs) play a vital role and recognized as the most feasible technology to mitigate CO2 emission by deploying alternative energy sources (Feng & Magee, 2020; Loganathan, Mishra, Tan, Kongsvik, & Rai, 2020; Muneer et al., 2015). As compared to conventional vehicles like internal combustion (IC) engines, electric vehicles are encouraged to reduce greenhouse gas emissions by minimizing fossil fuel dependency (Egbue & Long, 2012; Liu et al., 2020).
Nowadays, electric vehicles are gaining popularity due to several advantages, which include- efficient battery capacity, reduced hazardous gas emission, government subsidies and other incentives on the purchase, improved performance of vehicles, and other environmental benefits (Biswas & Das, 2019; Langbroek, Franklin, & Susilo, 2016). It is projected that the total number of electric vehicles around the globe will reach 35 million by 2022 (Heredia, Chaudhari, Meintz, Jun, & Pless, 2020). Besides, due to technological advancements and the increased price of fossil fuels, it is forecasted that total sales of electric vehicles could reach up to 75% growth by 2050. Nowadays, many government organizations are promoting the adoption of electric vehicles by introducing various policies, incentives, and subsidies for reducing CO2 emission (Brady & O'Mahony, 2011). Therefore, the emission of gases like carbon dioxide and carbon monoxide into the environment may decrease, resulting in reducing the problem of global warming. Most of the researchers conducted experimental analyses and concluded the contribution of electric vehicles for reducing greenhouse gas emissions in many regions (Yagcitekin, Uzunoglu, Karakas, & Erdinc, 2015). Mitigation of greenhouse gases not only reduces the climate change scenario but also reduces air pollution, which benefits the natural ecosystems by minimizing the consumption of fossil fuels (Javid & Nejat, 2017; Loganathan, Ming Tan, Mishra, Msagati, & Snyman, 2019). Most of the customers are not willing to purchase electric vehicles due to the high cost compared to the IC engine (conventional) vehicles (Sierzchula, Bakker, Maat, & Van Wee, 2014).
Considering the rapid expansion of the electric vehicles market worldwide, various EV models with a variety of salient features have come up to satisfy the dynamic demand of customers (Yagcitekin et al., 2015; Zarazua de Rubens, Noel, Kester, & Sovacool, 2020). Given this, various multinational automobile companies, including Ford, Hyundai, Fiat, General Motors, BMW, Tata Motors, Mitsubishi, etc., have already started commercial production of electric vehicles with distinct salient features. Therefore, the customer must choose the best alternative among various models of EVs with these distinct features. Das, Pandey, Mahato, and Singh (2019) was measured customer preferences for the selection of electric vehicles, including battery capacity, charging time, driving range, acceleration, etc. Similarly, various performance measure criteria were available for numerous electric vehicle models. So, it is difficult for the customers to choose appropriate criteria and the best alternative.
Therefore, to fill this research gap, it is necessary to introduce a decision-making tool to select performance measures, which may be conflicting in nature and the best alternatives among available variants of electric vehicles. Thus, we have used an integrated approach of the AHP-MABAC method as a multi-criteria decision-making tool to select and rank the best alternative of the electric vehicle. Most of the researcher used the MABAC method for a variety of applications, including a selection of loading and unloading resources (Pamučar & Ćirović, 2015), hotel selection on a tourism website (Yu, Wang, & Wang, 2017), selection of defensive operation of the guided anti-tank missile battery (Bojanic, Kovač, Bojanic, & Ristic, 2018), q-rung orthopair fuzzy set (Wang, Wei, Wei, & Wei, 2020), health tourism strategy selection (Büyüközkan, Mukul, & Kongar, 2020), evaluation of web-pages (Pamučar, Stević, & Zavadskas, 2018) and selection of commercially available scooters (Biswas & Saha, 2019).
The remainder of this paper is organized as follows: the next section continues with a motivation for this study. Section 3 introduces the identification of selection criteria. Section 4 reports the integrated method of AHP-MABAC. Data collection has been discussed in Section 5. Section 6 reports a detailed analysis of the AHP-MABAC method followed by a discussion in Section 7. Finally, Section 8 summarises the main conclusion and potential research directions.
Section snippets
Motivation
Although extensive literature available on electric vehicle adoption, however major studies (Brady & O'Mahony, 2011; Langbroek et al., 2016; Sierzchula et al., 2014; Vidhi & Shrivastava, 2018) only focused on government subsidy, incentive scheme, improved infrastructure, and climate change. Despite the rapid expansion of electric vehicles worldwide, selecting the best alternative among available variants of EVs becomes difficult. The literature lacks a comprehensive study for selecting the best
Identification of selection criteria
Nowadays, the automobile market offers a wide variety of variants, including various performance measures. These variants include style, color, quality, sizes, and performances. Similarly, there are various criteria that purchasers considered while selecting suitable vehicle, which includes- battery capacity, seating capacity, driving range, price, torque, acceleration, charging time, charging infrastructure, etc. Therefore, it is difficult for the purchaser to measure the performance of
Overview of integrated AHP-MABAC method
In this study, for doing the performance evaluation and ranking of best alternatives of electric vehicles, an integrated AHP-MABAC method has been proposed. The schematic diagram of the steps involved in the proposed method is presented in Fig. 1. The method deals with the selection of criteria using AHP and finding out the best alternatives using the MABAC method.
Data collection
The objective of the study is to prioritize and ranking of various criteria and alternatives for electric vehicles. Initially, from the extent literature review, six criteria have been identified based on purchasers' preferences while selecting an electric vehicle. These criteria were prioritized using the AHP method, as discussed earlier. Thereafter, multiple electric vehicle alternatives are available in the market. Each alternative has been ranked using the MABAC method. All criteria were
Prioritizing criteria using AHP
Initially, a pair-wise comparison matrix was formed based on the relative importance given by experts (Table 5, Table 6). The consensus method has been used for compiling the responses. The pair-wise comparison matrix, normalization matrix, and weighted sum value for evaluating selected criteria are summarized next.
Discussion
An integrated AHP-MABAC method has been established for effective decision-making for the selection of electric vehicles. The results show that the highest priority of various criteria and best alternatives among available electric vehicles. Fig. 3 deals with the prioritization of each criterion based on expert preferences. It shows that purchasers gave more weightage to the driving range (39%) followed by price (23%) of an electric vehicle. Nowadays, customers are more concerned about the
Conclusion
Most of the customers are still struggling to select the best alternative of available electric vehicles based on the collective performance measurements like driving range, battery capacity, etc. This work aims to prioritize various performance measurements using AHP and the ranking of various alternatives for electric vehicles available in the market. The AHP method allows researchers to capture both tangible and intangible criteria of complex real-life problems. A total of six criteria were
Funding
Not Applicable
Conflicts of interest
No conflict of interest
Availability of data and material
Not Applicable
Acknowledgement
The infrastructural support provided by FORE School of Management, New Delhi is greatly acknowledged. Authors would also like to thanks Mr. Faisal Shaikh for data collection support.
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