A Study on Selection Strategies for Battery Electric Vehicles Based on Sentiments, Analysis, and the MCDM Model

Abstract

Under the goal of carbon peak and carbon neutrality, developing battery electric vehicles (BEVs) is an important way to reduce carbon emissions in the transportation sector. To popularize BEVs as soon as possible, it is necessary to study selection strategies for BEVs from the perspective of consumers. Therefore, the Latent Dirichlet Allocation (LDA) model based on fine-grained sentiment analysis is combined with the multi-criteria decision-making (MCDM) model to assess ten types of BEV alternatives. Fine-grained sentiment analysis is applied to find the vehicle attributes that consumers care about the most based on the word-of-mouth data. The LDA model is suggested to divide topics and construct the indicator system. The MCDM model is used to rank vehicles and put forward the corresponding optimization path to increase consumer purchases of BEVs in China. The results show that (a) via the LDA model based on fine-grained sentiment analysis, attributes that consumers care most about are divided into five topics: dynamics, technology, safety, comfort, and cost; (b) based on the DEMATEL technique, the dimensions in the order of importance are as follows: safety, technology, dynamics, comfort, and cost; (c) the price is the most important criteria that affect customers’ satisfaction by the DANP model; and (d) based on the VIKOR model, the selection strategies present that Aion S is highlighted as the best choice, and the optimization path is discussed to promote the performance of BEVs to increase customers’ satisfaction. The findings can provide a reference for improving the sustainable development of the automobile industry in China. The proposed framework serves as the basis for further discussion of BEVs.

Selection of EVs as Tourist and Logistic Means of Transportation in Bosnia and Herzegovina’s Nature Protected Areas Using Z-Number and Rough Set Modeling

“…In Bosnia and Herzegovina, two areas have been designated as strict nature reserves (SNRs): SNR Prašuma Janj and SNR Prašuma Lom. Furthermore, the country is home to a total of four national parks, including National Park (NP) Kozara, NP Sutjeska, NP Una, and the most recently established NP [20] Advanced battery technologies selection MCDM-WPM Babar et al [21] Viability of EV in the current market Fuzzy SWOT, fuzzy LP Büyüközkan and Uztürk [22] Sustainable urban logistics SAW, VIKOR Hamurcu and Eren [23] Electric bus selection AHP, TOPSIS Khan et al [24] HEV selection TOPSIS Ziemba [25] EV selection for government use PROSA-C, Monte Carlo Ecer [26] Comprehensively assessing BEV alternatives SECA, MARCOS, MAIRCA, COCOSO, ARAS, COPRAS Khan and Ali [27] Smart waste management adoption framework Fuzzy SWARA, fuzzy VIKTOR Ren et al [28] Selection strategies for BEVs LDA, DEMATEL, DANP, VIKTOR Ziemba [29] Selection of city and compact EVs NEAT F-PROMETHEE Ziemba [30] Analysis and recommendation of the EV Monte Carlo, fuzzy TOPSIS, fuzzy SAW, NEAT F-PROMETHEE II Hamurcu and Eren [31] EV selection for transportation in inner city AHP, GP, TOPSIS He [32] Selection of battery electric bus EWM Oztaysi et al [33] EV selection problem F-SMART Ozdagoglu et al [34] Bus selection for intercity transportation PIPRECIA, COPRAS-G Stopka et al [35] Evaluation of selected passenger EVs AHP Štilić et al [36] Taxi service EV selection SWARA, MSDM, MABAC Wei and Zhou [37] EV supplier selection BWM, fuzzy VIKOR Ziemba and Gago [38] Selection and analysis of e-scooters PROMETHEE GDSS, GAIA Puška et al [39] EV selection DNMEREC, DNCRADIS Ba ˛czkiewicz and Wa ˛tróbski [40] EV selection Entropy, standard deviation (SD), CRITIC, Gini coefcient-based, MEREC, statistical variance, CILOS, IDOCRIW, VIKOR Dwivedi and Sharma [41] EV selection Entropy, TOPSIS Drina. In this research, the focus will be on creating a sustainable transportation system based on the selection of EVs for the needs of the NP Kozara.…”

Data-Driven Decision Aids for Purchasing Battery Electric Vehicles Based on PROMETHEE-II Methodology

“…Ecer [13] proposed a BEVs' evaluation tool by integrating six MCDM technologies based on price, acceleration, battery, driving range and other indicators. Subsequently, Ren et al [14] divided topics by LDA and established an evaluation standard system. VIKOR method was adopted to rank BEVs based on six criteria.…”

Integrated Machine Learning Algorithms and MCDM Techniques in Optimal Ranking of Battery Electric Vehicles

“…Loganathan et al, [17] used MCDM method to make selection of Li-ion batteries of electric vehicles, R. Wang et al, [18] used Triangular fuzzy entropy method to find the criterion weights and MULTIMOORA method to rank the suppliers of batteries. Aboushaqrah et al, [19] made use of the combination of Life cycle assessment and neutrosophic MCDM to select alternate fuel taxis, Ren et al, [20] used sentimental analysis and MCDM methods to select strategies for battery selection, Tian et al, [21] applied hierarchical MCDM and decision tools based on data driven for choosing battery electric vehicles, Patil & Mujumdar [22,23], presented the key factors persuading electric vehicles. Nayana [24], used genetic algorithms together with MCDM to make optimal scheduling of electric vehicles, A. Ghosh et al, [25] applied the MCDM methods of AHP and TOPSIS to select the optimum electric rickshaws, Yang et al, [26] presented hesitant fuzzy MULTIMOORA method in supplier selection of batteries.…”