*4.3. Statistical Results*

This integrated fuzzy AHP-TOPSIS methodology was used to evaluate the performance of different EVs. To acquire accurate information and insights, the investigators used comparative opinions of 75 automobile specialists from varied organization and scholarly backgrounds. It has been previously discussed that analyzing the performance of various EVs is extremely difficult in terms of competitive efficacy. The EVs were selected by using predetermined qualitative and quantitative assessment criteria during the process of different EVs' evaluation; however, the criteria demonstrated the requirement of the judgment, so herein unpredictability and fuzziness were included in the statistical and observational data evaluated by the decision makers with specific intelligence. A total of 75 automobile specialists, DM (k = 1, 2, 3,..., 75), were involved in analyzing the optimum available decision in linguistic variables. These 75 decision makers comprised 20 academics with 15 years of expertise, 20 researchers with 7 years of vehicle research experience, and 35 professionals from various automobile firms with 15 years of experience. The weights of the local criterion and subcriteria were derived using pairwise comparative matrices.

The aggregated fuzzify pairwise comparison matrix at Level 1 was formed, and can be seen in Table 1. The fuzzy-aggregated pairwise comparison matrix at Level 2 for regulatory, technical, business, and design is presented in Tables 2–5. For each second-layer aspect, the global weights were deliberate. These are tabulated in Tables 6–10. Further, Table 11 shows the overall weights and rankings of the methods. Table 12 presents the subjective cognition results for evaluators in linguistic terms. Table 13 shows the normalized fuzzy-decision matrix. Table 14 shows the weighted normalized fuzzy-decision matrix. In addition, with the support of the hierarchical structure, Table 15 and Figure 10 show the complete and final relative closeness of the alternatives.

**Table 1.** The aggregated fuzzify pairwise comparison matrix at Level 1.


**Table 2.** The fuzzy-aggregated pairwise comparison matrix at Level 2 for regulatory.


**Table 3.** The fuzzy-aggregated pairwise comparison matrix at Level 2 for technical.



**Table 4.** The fuzzy-aggregated pairwise comparison matrix at Level 2 for business.

**Table 5.** The fuzzy-aggregated pairwise comparison matrix at Level 2 for design.


#### **Table 6.** The defuzzified pairwise comparison matrix.


#### CR = 0.000602.

**Table 7.** The aggregated pairwise comparison matrix at Level 2 for regulatory.


#### CR = 0.0488003.

**Table 8.** The aggregated pairwise comparison matrix at Level 2 for technical.


CR = 0.034904.

#### **Table 9.** The aggregated pairwise comparison matrix at Level 2 for business.


CR = 0.002506.


**Table 10.** The aggregated pairwise comparison matrix at Level 2 for design.


**Table 11.** The overall weights and rankings of methods.

**Table 12.** The subjective cognition results for evaluators in linguistic terms.



**Table 13.** The normalized fuzzy-decision matrix.

**Table 14.** The weighted normalized fuzzy-decision matrix.


**Table 15.** The closeness coefficients for the aspired level among the different alternatives.


In this context, an evaluation of different electric vehicle alternatives was conducted for the inclusion of fuzzy AHP in the fuzzy TOPSIS; i.e., two major MCDM techniques. Although the proportional relevance of each aspect to the other can be expressed, the intricacies of subjective judgments in the description of the challenge were taken into consideration by fuzzy numbers. Ultimately, the suggested model was tested using a statistical method showing how the highest efficient electric vehicle type was chosen. The satisfaction degree (CC−i) of different alternatives was estimated as 0.38712741, 0.64714356, 0.44421424, 0.43471445, and 0.45485126 for T1, T2, T3, T4, and T5, respectively. As per the findings shown in Figure 10, the second alternative (T2) was highly effective and proficient among several other EV alternatives.

### *4.4. Comparison with the Classical AHP-TOPSIS Method*

Whenever similar statistics are handled with different approaches, it produces contradictory interpretations [29]. Researchers have employed one or more techniques to check the correctness of anticipated methodology findings [30]. Therefore, in this study, we employed the classical AHP-TOPSIS approach [31] to estimate the findings using another approach and to check the effectiveness of consequences using fuzzy AHP-TOPSIS. The procedure of accumulating and projecting relevant information in classical AHP-TOPSIS is analogous to analyzing fuzzy AHP-TOPSIS without fuzzification. As a result, data were used in their actual numerical format to evaluate the different EV performances using traditional AHP-TOPSIS. Table 16 and Figure 11 show the differences in outcomes between fuzzy and classical AHP-TOPSIS. The findings produced using the traditional approach had a strong association with the ones produced using the fuzzy methodology. The outcomes of the comparative analysis were not as varied and distinct from one another; however, the precision of the findings varied. The correctness of the fuzzy-based methodology was higher and more accurate than that of the conventional methodology, as it was a more powerful approach over the classical AHP-TOPSIS. The fuzzy-based AHP-TOPSIS offered the capability of providing fuzzy set numbers for different parameters during the evaluation process.


**Table 16.** Comparison of the AHP-TOPSIS techniques.

**Figure 11.** A graphical representation of the comparison of the AHP-TOPSIS techniques.

### *4.5. Sensitivity Analysis*

The sensitivity evaluation was accomplished by altering the variables that influenced the report's correctness. During this statistical study, the sensitivity of the generated weights (variables) was assessed [32]. Following this, 13 variables were used throughout the analysis to evaluate sensitivity with the use of 13 experiments. The rate of satisfaction (CC−i) was calculated for each trial by taking into account weight alterations within each variable, whereas the weights of some other full variables were maintained constant by integrating the fuzzy AHP-TOPSIS methodology. The actual weights obtained in this research work are shown in the first row of Table 17. Alternative-2 (T2) had a significant satisfaction degree (CCi), as per the research findings. Thirteen experiments were performed, ranging from Experiment 1 to Experiment 13. In these 13 experiments, the obtained conclusions revealed that alternative-2 (T2) still had a higher satisfaction degree (CCi). Alternative-1 (T1) also was the lowest-weighted alternative in every trial. The variability in outcome suggested that alternative rankings were sensitive to weights. Table 17 and Figure 12 show the projected consequences.


**Table 17.** The sensitivity analysis.

**Figure 12.** A graphical representation of the sensitivity analysis.

#### **5. Conclusions**

Although technological increases in worldwide transportation and society have enhanced life on this planet, they also have resulted in massive environmental devastation. As a result, people are paying close attention to the environment and its long-term sustainability. Renewable-power vehicles are one contributor to global challenges. BEVs have a reasonable consumption and good power generation, as long as the overall weight is not excessive. The vehicle weight depends on the number and capacity of the batteries installed. As a result, light BEVs that travel short distances have the highest efficiencies.

FCEV automobiles can store a greater amount of energy in comparison to their vehicle weight, and fuel-cell recharging can be done more speedily. FCEVs are thus ideal for longdistance travel and resources to create little interruption. The prospect of transportation will play a significant role in energy in systems centered on the interchange of modes of transportation, a future in which battery electric vehicles, as well as fuel-cell electric vehicles, will be supportive instead of combative.

**Author Contributions:** All authors contributed equally to the manuscript. All authors have read and agreed to the published version of the manuscript.

**Funding:** The project has been funded by Taif University, Kingdom of Saudi Arabia, under grant no: TURSP-2020/306.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Acknowledgments:** This research was supported by Taif University Researchers Supporting Project Number TURSP-2020/306, Taif University, Taif, Saudi Arabia.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

