Optimizing Electric Mobility: A Multi-Criteria Decision-Making Approach for Sustainable Future of Electric Vehicles Through Smart Motor Choices

Over the past few decades, there has been a heightened scientific consciousness regarding the critical importance of developing sustainable, environmentally friendly alternatives to conventional solutions. This was prompted by issues such as air pollution, air resource depletion, and the search for a pollution-free, healthy environment. Human health in developing countries, particularly in major cities, can be adversely affected by several significant contributors to the increase in harmful exhaust emissions into the atmosphere, such as sulfur dioxide (CO), nitrogen oxides with particulate matter (NO_X), and sulfur dioxide (PM). There is a prevalent belief among many individuals that powertrains represent a viable approach to optimize unleaded emissions and enhance fuel economy within the automotive industry [1]. EV technology is classified as operating on the principle of electric force. It is then transferred to the vehicle's axles via the most efficient gearbox configuration possible after being converted to mechanical energy by the motor. Reversible and highly efficient, this mechanism must provide the wheels with the necessary torque and speed. A hybrid storage system is typically utilized to store the energy. Single-speed gear ratios are the prevailing transmission mechanism chosen for electric powertrains. The electric motor must provide the requisite energy for the EV to advance. Selecting the proper electric motor for a chassis could be difficult [2]. Currently, a wide variety of EVs are available for purchase. Numerous engines in these automobiles perform a variety of functions. Depending on their configuration or intended function, EVs may employ direct (DC) or alternating (AC) motors. Considerable research has been devoted to electric motors, resulting in the creation of numerous varieties. EV manufacturers can select from various electric motors following their unique requirements. The motor's characteristics impact the vehicle's overall performance, so care should be taken when choosing a particular EV motor type [3]. Several considerations must be considered, including management, efficacy, cost-effectiveness, and reliability. In addition to industrial applications and EV usage, additional factors must be considered [4]. The most widely recognized EV motors include induction motors (IM), permanent magnet synchronous motors (PMSM), and brushless DC motors. The traction motor candidates for the electric traction system should supply an economical solution for high-performance, speed sensor-free control and be dependable and effective. They should also possess an extended point of stability at varying velocities [5].

In addition to analyzing electric motors and their interactions, this article provides a synopsis of current market trends in the design of electric motors. In addition, a multi-criteria evaluation of the use of electric motors in EV applications is provided. Figure 1 illustrates many types of electric motors. EVs require appropriate electric machinery to power their propulsion systems efficiently. According to literature analysis, academics are increasingly interested in applying the MCDM theory to selecting electric motors for EVs. This approach facilitates a methodical and unbiased evaluation of diverse motor solutions by considering a range of criteria, such as energy efficiency, cost, performance, and dependability. An assortment of electric devices is utilized to power EVs. A frequently employed variety is the induction motor, renowned for its exceptional reliability, minimal upkeep requirements, and economical nature [6]. Another type is the PMSM, which offers a high power density and efficiency. The SRM is also gaining popularity due to its straightforward design, robustness, and cost-effectiveness. Each form of electric machine has both advantages and downsides. Induction motors, for example, may be less efficient than PMSM or SRM, despite their dependability and inexpensive cost. PMSM provides excellent power density and efficiency but may be more expensive [7].

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Figure 1. Types of electric motors/machines for EVs

In contrast, SRM has a basic structure and is less expensive but may cause more noise and vibration. The right electric machine is selected based on the EV application's requirements and limits. Factors such as driving range, vehicle weight, power requirements, and pricing must be considered during decision-making. MCDM theory promotes the development of efficient and sustainable EVs by identifying the most appropriate electric motor for specific EV applications, considering both qualitative and quantitative parameters [8].

Selecting proper electric motors for EVs is critical to advancing efficient and sustainable transportation. Recent years have witnessed a surge in interest regarding the application of MCDM theory to the selection procedure [9]. Researchers have acknowledged the importance of considering numerous factors when selecting electric motors for EVs, including performance, affordability, dependability, and energy efficiency [10]. The MCDM theory allows for a systematic and objective evaluation of various electric motor solutions, considering qualitative and quantitative parameters. The MCDM theory provides a framework for decision-making procedures with numerous criteria and competing goals. When applied to electric motor selection for EVs, this theory allows for the simultaneous examination of several parameters, allowing for a thorough study of alternative motor possibilities. This comprehensive approach helps determine the most suitable electric motor for individual EV applications, considering the vehicle's unique requirements and limits [11]. One of the primary advantages of selecting electric motors using MCDM theory is the capacity to consider qualitative and quantitative factors. Subjective qualitative factors, including customer satisfaction and dependability, can be challenging to integrate into conventional decision-making processes [12]. However, MCDM theory provides a structured approach to quantifying and comparing these qualitative factors, enabling a more comprehensive evaluation.

On the other hand, quantitative factors, such as performance metrics (e.g., torque, power, and efficiency) and cost considerations, can be easily measured and compared. MCDM theory allows for integrating these quantitative factors into the decision-making process, providing a robust basis for evaluating different electric motor options [13]. Choosing the most suitable electric motor for an EV is contingent upon many application-specific factors. The elements above encompass traveling range, vehicle mass, power demands, and financial implications [14]. An assortment of electric machine varieties, including induction motors, PMSM, and SRM, present a range of benefits and drawbacks. Due to their straightforwardness, durability, and economical nature, induction motors are extensively employed in EVs. However, their efficiency may be inferior to other motor varieties [15].

In contrast, PMSMs provide superior power density and efficiency but are more complicated and costly. Although SRMs offer benefits like increased torque density and defect tolerance, they may produce more acoustic noise and vibration. Future research and development will enhance the dependability, performance, and efficiency of electric apparatus for EVs [16]. This includes advancements in motor design, materials, control strategies, and power electronics. Additionally, efforts will be made to develop innovative motor technologies that address the specific requirements of EV applications, such as high power-to-weight ratio, compact size, and improved thermal management [17].

In the fast-changing environment of EVs, the search for sustainable energy solutions in electric motors has become increasingly important. MCDM approaches are a powerful tool in the decision sciences and are critical in navigating the complicated trade-offs connected with electric motors for EVs. With numerous elements to consider, ranging from energy efficiency and environmental impact to cost-effectiveness and technological feasibility, MCDM offers a structured strategy for evaluating and prioritizing these criteria. In the context of electric cars, where the transition to sustainability is a major priority, MCDM guides stakeholders in selecting electric motors that meet the broad goals of eco-friendliness and efficiency. As the automotive industry accelerates its transition to electrification, MCDM emerges as a key driver in steering decision-makers towards electric motor options that meet performance requirements and contribute significantly to the larger agenda of creating a greener and more sustainable future for EVs. In this delicate dance of criteria and considerations, MCDM stands as a beacon, guiding the automobile industry into choices that harmonize technological progress with environmental responsibility to create a cleaner and more energy-efficient transportation ecosystem.

In response to the critical conditions of global climate change and the exhaustion of fossil fuel reserves, there has been a surge in both investment and demand for EVs. It is crucial to shift towards transportation alternatives that are more environmentally sustainable, as conventional internal combustion engine vehicles significantly contribute to air pollution and greenhouse gas emissions. Powered by batteries built into the vehicle, EVs present an enticing remedy to these ecological issues. Despite the undeniable environmental advantages of EVs, their extensive adoption is contingent upon resolving several technological challenges, with energy storage systems and propulsion mechanism optimization being the most critical. Electric vehicles rely on propulsion batteries to supply the necessary energy to operate the electric motor. The electric engine propels the automobile concurrently, transforming electrical energy into mechanical energy. Overall efficacy and success of EVs depend heavily on the interplay between these two components.

This study aims to investigate and implement MCDM techniques to optimize the selection of batteries and actuators for EVs. Key Objectives of this study are:

• The study emphasizes the importance of MCDM in guiding electric motor selection, recognizing the complexity and interdependence of parameters such as torque, power, efficiency, weight, cost, and environmental impact.

• Validating the MCDM-based method using real-world electric motor data for EVs adds a practical layer. This case study provides real-world evidence to back up the suggested method.

• The research enhances scholarly comprehension of the automotive sector and actively promotes the widespread implementation of intelligent and eco-friendly EVs. These vehicles are marketed as potential resolutions to urgent environmental issues.

The findings of this research article are presented clearly and coherently by division into sections. After this introductory segment, the second section investigates into significant scholarly works on the present condition of EVs, advancements in battery and motor technologies, and established methodologies for their selection. The third section details the research methodology, including the process of identifying criteria, developing the decision framework, and applying MCDM methods. The analysis results, presented in the fourth component, offer valuable insights regarding the prioritization of criteria and the assessment of different combinations of batteries and motors. In the fifth segment, recommendations for stakeholders in the EV industry are provided in light of the findings. In summary, the conclusion elucidates the significant contributions of the research, underscores its constraints, and proposes avenues for future investigation in the perpetually evolving and dynamic domain of EV technology. The primary objective of this study is to enhance comprehension of the decision-making process involved in EV design. Additionally, it offers a pragmatic and executable framework for stakeholders interested in optimizing the choice of batteries and motors to promote efficient and sustainable electric mobility.

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Figure 2. Flowchart of methodology

On the other hand, ELECTRE prioritizes the outranking of alternatives. In contrast, TOPSIS evaluates alternatives according to their proximity to an ideal solution, as opposed to AHP's hierarchical approach to problem organization. Fuzzy logic is employed to reduce uncertainties, whereas Data Envelopment Analysis (DEA) is utilized to assess the performance of decision-making appliances. Contemporary progressions in computational techniques and Artificial Intelligence (AI) have significantly broadened the functionalities of MCDM, furnishing decision-makers with sophisticated instruments to navigate intricate, multifaceted dilemmas in various domains, including business, engineering, and environmental management. Maintaining its status as a dynamic and developing field, MCDM is essential for traversing the complexities of decision landscapes in an ever more complex world [62]. Figure 2 shows the methodology flow chart of different MCDM techniques implemented in this article and other important parameters. The significance of MCDM resides in its capacity to methodically tackle decision-making scenarios that involve numerous, frequently contradictory objectives. By employing decision matrices and methodologies such as AHP, TOPSIS, and ELECTRE, decision-makers can navigate the intricacies of assessing alternatives based on various criteria using a structured approach. Fuzzy logic is a method that can be applied to real-world decision problems to account for uncertainties, and recognizing the intrinsic ambiguity of preferences. Furthermore, the integration of DEA enables the evaluation of efficacy in the management of numerous decision-making entities [63]. Incorporating sophisticated AI and advanced computational methods signifies the ongoing development of MCDM, which facilitates the implementation of more sophisticated analyses and adaptive decision support systems. This interdisciplinary domain has become essential in contemporary decision-making procedures, as it offers a structured approach to tackle the intricate difficulties presented by conflicting goals and unpredictable contexts. In an increasingly linked and complex global world, MCDM is a leading framework that enables industry-spanning decision-makers to create well-informed and strategic judgements that harmonise with various, frequently competing objectives [64].

There are a few crucial phases or steps in the MCDM process for choosing appropriate electric motors for application in EVs with certain parameters that are included in this study, such as efficiency, power density, reliability, controllability, cost, and technical maturity. The AHP method has different steps for getting the results for selecting appropriate motors. In contrast, the SAW and TOPSIS are the MCDM techniques that are implemented for EV motors using the below steps [65]:

Identification of criteria: The initial phase is establishing the essential criteria for selecting a suitable electric car motor. The criteria above may comprise motor horsepower, efficiency, cost, and mass.

Assign weights to the criteria: Once the criteria have been defined, each criterion is weighted according to its relative value. Weights can be allotted in various ways, including pairwise comparison or expert judgment.

Normalize the data: Normalization should be performed on the data for each criterion to ensure that they all fall on the same scale. Several approaches to accomplish this include min-max normalization and z-score normalization.

Calculate the weighted score: To compute the weighted score for each motor, multiply the normalized value for each criterion by its associated weight and then sum the results.

Rank the motors: Finally, the motors can be evaluated based on their weighted scores, and the motor with the greatest score is the most appropriate choice [66].

3.1 SAW method

The most prevalent and uncomplicated MCDM method is weighted sum. Considering this attribute, the overall significance of the system must be equal to one. Every potential option is considered for a given attribute. The subsequent measurements employ this methodology:

• Decide on the objective and have an understanding of the relevant evaluation criteria for this objective.

• A choice matrix can be obtained from Eq. (1). Every row in this matrix represents a different option or alternative, which in this case is motors, and every column represents a different characteristic, which includes power density, efficiency, controllability, dependability, technological maturity, and cost [32]. As an outcome, an element E_ij is utilized as an input from the decision matrix 'DM' [E_ij; i = 1, 2,..., the number of alternatives (n); j = 1,2,..., number of attributes (m)] [67].

$\text{DM=}\left[ \begin{matrix}   \begin{matrix}   {{\text{E}}_{\text{11}}} & {{\text{E}}_{\text{12}}} & --  \\   {{\text{E}}_{\text{21}}} & {{\text{E}}_{\text{22}}} &--  \\  -- &--&-- \\ \end{matrix}  \\   \begin{matrix}   {{E}_{\text{i1}}} & {{\text{E}}_{\text{i2}}} & --\\  --&-- & --\\   {{\text{E}}_{\text{n1}}} & {{\text{E}}_{\text{n2}}} &-- \\ \end{matrix}  \\ \end{matrix}\begin{matrix}   \begin{matrix}   {{\text{E}}_{\text{1j}}} & -- & {{\text{E}}_{\text{1m}}}  \\   {{\text{E}}_{\text{2j}}} &-- & {{\text{E}}_{\text{2m}}}  \\  --&--& --\\ \end{matrix}  \\   \begin{matrix}   {{\text{E}}_{\text{ij}}} & --& {{\text{E}}_{\text{im}}}  \\  --&--& -- \\   {{\text{E}}_{\text{nj}}} & -- & {{\text{E}}_{\text{nm}}}  \\\end{matrix}  \\ \end{matrix} \right]$                 (7)

• In order to construct the normalized decision matrix, the linear normalization approach is utilized, NDM_ij, for favourable conditions (profit) and non-en.

$\text{ND}{{\text{M}}_{\text{ij}}}=\frac{{{E}_{ij}}}{Max~{{E}_{ij}}}$ For advantageous criteria, j = 1, 2, …, m                 (8)

$\text{ND}{{\text{M}}_{\text{ij}}}=\frac{Min~{{E}_{ij}}}{{{E}_{ij}}}$ For non-advantageous criteria, j = 1, 2, …, m         (9)

• Make your selection based on the relative importance of a multitude of objective criteria. Assign importance weights (w_j) to attributes so that ∑w_j = 1. The present investigation employs the Equal weights method to assign weights to attributes [68].

3.1.1 Equal weights method

Eq. (10) calculates the weight of each attribute (m) by dividing 1 by the total amount of attributes (using the equal weight technique).

w_j = 1/m for j =1, 2,…, m              (10)

The result of multiplying each column aspect of NDM_ij by w_j is the weighted and normalized matrix WZ_ij. Eq. (11) displays the components of the weighted, normalized matrix WZ_ij [69].

$\text{W}{{\text{Z}}_{\text{ij}}}\text{=}\left[ {{\text{w}}_{\text{j}}}ND{{\text{M}}_{\text{ij}}} \right]$                (11)

Eq. (12) gives an alternative composite performance score (CPS).

$CP{{S}_{SAW}}=\underset{j=1}{\overset{m}{\mathop \sum }}\,W{{Z}_{ij}}$               (12)

The technique generates alternatives based on CPS value, identifying the most and least superior solutions [70].

3.2 TOPSIS method

Throughout the 1980s, TOPSIS emerged as an MCDM methodology. When determining the optimal or negative ideal solution, TOPSIS selects the option that is the greatest Euclidean distance from either. A methodology for selecting the most effective motors for EVs is utilized in this investigation. The factors above comprise technological maturity, power density, efficiency, dependability, controllability, and cost [68].

Step 1: Calculate the normalised matrix and weighted normalized matrix

Weighted Normalised Matrix and Normalised Matrix Calculations. Each value is normalized as follows: where n is the number of columns and m is the number of rows in the dataset. While j varies along the columns, I fluctuate along the rows.

${{\bar{X}}_{ij}}=\frac{{{X}_{ij}}}{\sqrt{\mathop{\sum }_{i=1}^{n}X_{ij}^{2}}}$                (13)

  ${{V}_{ij}}={{\bar{X}}_{ij}}\times {{W}_{j}}$                       (14)

​Step 2: Calculate the ideal best and ideal worst value

The calculations to determine the ideal best and ideal worst values for our scenario are currently underway. The minimum allowable value and maximum acceptable value of the cost factor also referred to as criterion C1, are in opposition to being beneficial. Similar to how the minimum value represents the optimal worst-case scenario, the maximum value corresponds to the optimal best-case scenario concerning each Beneficial Criteria (C2, C3, C4, and C5). The ideal best and worst values must be determined to initiate the process. Here, it is necessary to ascertain the direction of the influence ('+' or '-'). The "+" impact denotes the condition that the minimum and maximum values of the column are ideal worst and ideal best, respectively. The "-" impact signifies the contrary [69].

Step 3: Calculate the Euclidean distance from the ideal best and ideal worst

At this point, calculate the Euclidean distance between each row element and the ideal worst. The ideal best Euclidean distance for the ith row is S_i⁺, where V_i,j is the element value, and V_j⁺ is the ideal worst for that column. Similarly, the worst Euclidian distance on the ith row is S_i^-. Eq. (15) calculates the ideal best-case Euclidean distance, whereas Eq. (16) computes the ideal worst-case [70].

$S_{i}^{+}={{\left[ \mathop{\sum }_{j=1}^{M}{{\left( {{V}_{IJ}}-V_{J}^{+} \right)}^{2}} \right]}^{0.5}}$             (15)

$S_{i}^{-}={{\left[ \mathop{\sum }_{j=1}^{M}{{\left( {{V}_{IJ}}-V_{J}^{-} \right)}^{2}} \right]}^{0.5}}$                 (16)

​Step 4: Calculation of performance score and distribution of ranks

The TOPSIS Score is computed. Let us compute the TOPSIS score for each row using the Euclidean distances for the Ideal Best and Ideal Worst cases, which are now in our possession.

${{P}_{i}}=\frac{S_{i}^{-}}{S_{i}^{+}+S_{i}^{-}}$                      (17)

The alternatives are subsequently ranked following their relative proximity; the option receiving the highest recommendation is the one farthest from the anti-ideal solution and closest to the ideal solution [71].

3.3 Analytical hierarchy process (AHP)

In 1980, Saaty introduced a practicable and resilient instrument designed to supervise quantitative and qualitative multi-criteria factors influencing the decision-making process. The AHP paradigm is constructed hierarchically. AHP is a highly inclusive system for evaluating judgments by considering multiple factors. Its distinctive feature is its belief in combining qualitative and quantitative parameters and its ability to facilitate hierarchical problem descriptions. To commence, the problem must be arranged hierarchically. Assigning a nominal value to each level of the hierarchy and constructing a matrix of pairwise comparison judgments comprise the second stage [72]. Figure 3 shows the hierarchal structure of AHP for motor selection.

Utilizing a step-by-step process, the relative weights of criteria were determined. Those above are the stages comprised of AHP.

Step 1: Determination of the issue about decision-making.

Step 2: Establishment of the bilateral comparison matrix.

Step 3: Construct the normalized matrix through calculation.

Step 4: Establish the coefficient of weight for each criterion.

Step 5: Determining the ratio of consistency.

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As shown in Table 1, each of these evaluations is designated a numerical value ranging from one to nine. The consistency of decisions executed in AHP is denoted by the consistency ratio (CR) [73]. The CR is calculated using the following formula:

$CR=\frac{Consistency~Index~\left( CI \right)}{Random~Index~\left( RI \right)}$                    (18)

The N-order matrix is computed through the implementation of the given formula.

$CI=\frac{{{\lambda }_{Max-N}}}{N-1}$                     (19)

In this context, λ_Max represents the highest eigenvalue, while N signifies the Random Index (RI) or criteria (CN) for the matrix order specified in Figure 4. When dealing with matrices of greater scale, a CR below 0.1 is considered acceptable. An evaluation is considered suitable and maintains an adequate level of consistency when the observed value is equal to or lower than the predetermined value [74].

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Figure 4. RI values for matrix order preferences

SAW, TOPSIS, and AHP are effective MCDM strategies for supporting complicated scenario decision-making. However, like any other approach, they have a set of limits. One key disadvantage of these systems is that they are prone to the initial subjective judgments provided by the individuals making the decisions [82]. Pairwise comparisons in AHP, for example, necessitate those participants designate numerical values to the relative importance of alternatives and criteria, which can be difficult due to ignorance or personal biases. The precision of the ultimate determination is substantially contingent upon the caliber and uniformity of these assessments. Likewise, within SAW and TOPSIS, the determination of weights and the normalization process entail subjective judgments, and even minor discrepancies in these parameters can result in notably distinct conclusions [83]. An additional significant limitation pertains to the presumption of autonomous criteria, which might not consistently hold when confronted with true-life dilemmas. Criteria can occasionally be interconnected, so alterations to one criterion may influence others. Frequently, MCDM approaches operate under the assumption that the criteria are autonomous from each other. This supposition can lead to the abandonment of intricate interrelationships that possess the capacity to impact the decision-making process [84]. Such oversimplification may result in less-than-ideal consequences, particularly when applied to dynamic and complex environments.

Furthermore, the computing complexity of these methods may limit their effectiveness. The computation of eigenvectors and eigenvalues in AHP is a computationally costly procedure, especially when dealing with large decision matrices. Furthermore, SAW and TOPSIS necessitate many mathematical computations, which become increasingly difficult as the number of choices and criteria analyzed grows. This may provide a pragmatic problem when presented with enormous datasets or the necessity for quick decision-making [85, 86].

Additionally, these methods are constrained by the assumption of linearity. The assumptions made by SAW, TOPSIS, and AHP generally posit linear associations between criteria and alternatives. Although this simplification contributes to improved computational efficiency, it might not faithfully depict the intricacies of real-life decision scenarios, which frequently involve non-linear relationships. Inaccurate assessments may result from the inaccurate representation of decisions impacted by non-linear factors [87]. Moreover, MCDM techniques may encounter difficulties when encountering ambiguous or imprecise data. Numerous decision problems are inherently uncertain, and methods that rely on precise numerical inputs may fail to sufficiently account for the intrinsic vagueness or ambiguity in decision data. This constraint assumes particular significance in decision-making situations where data is limited, insufficient, or susceptible to substantial fluctuations. An additional obstacle emerges due to the possible lack of consistency in decision-maker preferences [88]. As an illustration, the CR is employed by AHP to evaluate the consistency of pairwise comparisons. However, achieving absolute consistency in practice proves challenging; decision-makers may inadvertently introduce inconsistencies into their assessments. The existence of these inconsistencies possesses the capacity to compromise the reliability of the decision model and erode the caliber of the results [89].

Moreover, it is common for these MCDM techniques to operate under the assumption that preferences remain constant over time, thereby disregarding the possibility of shifts in decision-makers preferences or the ever-changing characteristics of the decision environment [90]. Preferences are susceptible to change due to many factors, including external circumstances, newly acquired information, and organizational priorities. Neglecting to consider these ever-changing elements may lead to decisions that are superseded or less under the present objectives of the organization. Ethical and social considerations are not explicitly integrated into the development of these methodologies. MCDM techniques emphasize quantitative factors and might not sufficiently consider ethical or qualitative considerations when making decisions [91]. For example, inadequate consideration of social and environmental consequences, cultural subtleties, and ethical ramifications of choices could result in omitting vital ethical aspects.

In summary, although MCDM techniques such as SAW, TOPSIS, and AHP offer valuable decision support frameworks, their implementation is not devoid of constraints [92]. Critical drawbacks include sensitivity to subjective judgments, assumptions of independence and linearity, computational complexity, difficulties in managing uncertainty, and disregard for dynamic preferences. Professionals must acknowledge these constraints to employ these approaches prudently. They should augment them with qualitative observations and consistently improve and validate models to fortify their resilience across various decision-making scenarios [93, 94].

5.1 Future recommendations

MCDM is an exceptional approach for addressing complex decision-making problems involving numerous diametrically opposed criteria. The traditional MCDM approach referred to as the SAW method, rates alternatives according to the performance of criteria that are assigned weights. As society progresses, several suggestions can be put forth to enhance the effectiveness and practicality of the SAW methodology across diverse decision-making procedures [95]. In the first place, technological progress has enabled the integration of SAW with other nascent technologies, including AI and ML. This results in a multitude of benefits. SAW could become more dynamic and adaptable by incorporating predictive analytics into its design. This will allow decision-makers to react in real-time to changing environmental conditions. Combining regular MCDM procedures with cutting-edge technologies can improve decision-making accuracy and efficiency. This is especially useful in firms requiring immediate flexibility to change market trends.

Furthermore, resolving uncertainties and including risk management strategies, which include the SAW approach, are critical to the future of MCDM [96]. Ambiguity & unpredictability are usually identified as defining aspects of the decision-making environments. Developing robust models that effectively manage uncertainty, unpredictability, and risk should be the primary objective of forthcoming SAW research and applications. To offer decision-makers a comprehensive comprehension of potential outcomes and their associated risks, this involves incorporating probabilistic methodologies, scenario analysis, and sensitivity analysis into the SAW framework. The SAW approach can be modified to incorporate ecological, social, and economic factors when environmental and sustainable decision-making is considered. Decision-makers require tools capable of conducting comprehensive analyses of the environmental, social, and economic repercussions of different alternatives, given the current global predicament, which includes the acceleration of climate change and the depletion of natural resources. Subsequent investigations should explore alternative methodologies for incorporating sustainability considerations into the SAW process. This facilitates decision-makers ability to make selections that align with enduring environmental and social goals [97].

Collaborative decision-making is an additional domain in which the SAW technique may encounter future expansion. Problems involving decision-making are frequently complex and involve many parties with varying interests. To optimize the advantages offered by this methodology, subsequent SAW implementations ought to explore alternative methods of engaging a diverse array of stakeholders in the deliberative procedure. This improves the judgments' validity and ensures that a broader range of perspectives and values are considered during the assessment process. Integrating SAW with other MCDM approaches is a potentially fruitful field for further study [98]. Hybrid models, which combine the benefits of many methodologies, can provide more robust and nuanced decision support. Researchers should look into how SAW can be efficiently combined with other methods like AHP, TOPSIS, and ELECTRE to create hybrid models that benefit from the advantages of each methodology. Creating user-friendly software and decision support tools is one of the future recommendations for utilizing the SAW technology. This refers to the method's practical implementation. The ease of access to MCDM tools across various enterprises and decision settings is critical for their widespread adoption. User interfaces should be built to make the input of criteria, weights, and performance data as simple as possible while delivering clear and intelligible results [99].

5.2 Future scope of electric motors for sustainable transportation

A wealth of opportunities for innovation and optimization exists in EVs and sustainable transportation, as evidenced by the extensive research conducted on electric motor optimization for environmentally friendly EVs. Subsequent research endeavors might explore enhancing motor performance by integrating cutting-edge technologies such as intelligent control systems, enhanced materials, and artificial intelligence [100]. Electric motor efficiency and power-to-weight ratios could be greatly improved using novel materials, such as advanced alloys or composites, and exploring 3D-printed components. Further optimization of electric motor functions, guaranteeing dynamic responsiveness to driving conditions, and maximizing energy economy may be possible by integrating AI algorithms for real-time performance monitoring and adaptive control systems [101]. In the future, EV sustainability may surpass basic motor economy concerns. The comprehensive life cycle of an EV is examined to mitigate its environmental footprint, encompassing activities such as responsible waste disposal or recycling and raw material extraction. Coordination between material scientists, environmental engineers, and recycling specialists would be required to implement a closed-loop system following circular economy principles [102].

Electric motor optimization, intelligent transportation systems, and vehicle-to-everything (V2X) communication are all areas that could be investigated in the context of connected and autonomous EVs, which could be the focus of the study. Better traffic management, less congestion, and more efficient use of energy could result from taking this strategy. Exploring the convergence of EVs with smart grids is also crucial, as is investigating how improved electric motors might contribute to grid stability, demand response, and energy storage options, thereby encouraging a more sustainable energy ecosystem [103]. The advent of Industry 4.0 technology presents doors for new manufacturing procedures, and the study could look into adopting smart manufacturing processes for electric motor production. The creation and customization of electric motors could benefit from investigating techniques like additive manufacturing and digital twins, which can streamline the manufacturing process, decrease waste, and speed up the prototype process [104]. Potential future extensions of the study may encompass socio-economic assessments, legislative recommendations, and technological advancements. A comprehensive shift towards intelligent, eco-friendly EVs necessitates knowledge of the policy frameworks necessary to incentivize sustainable practices in the automotive sector and an evaluation of the economic feasibility of adopting optimized electric motors on a large scale. The development of strategies that support global sustainability goals could be enhanced through cooperation with industry participants, policymakers, and economists. Ultimately, research into the optimization of electric motors for intelligent, eco-friendly EVs has a vast and multifaceted future [105]. The research possesses the capacity to make a substantial contribution to the continuous development of sustainable transportation through the integration of emerging technologies, examination of the complete life cycle of EV components, exploration of the intersections between connected and autonomous systems, adoption of advanced manufacturing techniques, and incorporation of socio-economic factors. This all-encompassing strategy keeps the study cutting edge, directing the development of EV technology toward a more sustainable and performance-driven future [106].

5.3 Implications of the study for providing sustainable energy

Exploring the field of MCDM procedures in sustainable energy is analogous to beginning a voyage through a terrain filled with various possibilities and challenges. These methods, which have become influential tools in decision science, are essential for dealing with the complex network of factors related to sustainable energy solutions. The essence of MCDM resides in the capacity to concurrently evaluate and harmonize several factors, thereby converting decision-making processes into a refined art that smoothly corresponds to the intricacies of the sustainable energy domain [107].

The consequences of employing MCDM techniques are especially significant when striving to achieve sustainable energy objectives, as the risks involved are substantial, and the decisions made have enduring effects on our environment and future welfare. Imagine a scenario where the world is actively working towards shifting away from its reliance on fossil fuels and instead embracing a balanced combination of renewable energy sources [108]. MCDM techniques serve as a dependable instrument that aids decision-makers in navigating the intricate array of alternatives. These methodologies empower decision-makers to contemplate the environmental and social ramifications of their choices in addition to the economic ones. One significant benefit of MCDM is its ability to provide a systematic structure for evaluating numerous possibilities. Envision oneself at the juncture of an extensive array of renewable energy systems, including but not limited to solar, wind, and hydro. MCDM enables a comprehensive analysis by considering societal acceptance, carbon footprint, and energy efficacy [109]. It is akin to possessing a multidimensional lens that enables decision-makers to comprehend the comprehensive consequences of their decisions. Being new to this sector, I am amazed by how MCDM converts potentially daunting decisions into an organized, well-informed procedure [110]. Let's examine the concept of Pareto efficiency in MCDM, which I find fascinating due to its elegant and practical nature. Pareto efficiency is achieved when an option is considered ideal and cannot be improved without hurting another alternative. Depicting solutions that provide comprehensive benefits to the environment, society, and economy is a critical undertaking in the pursuit of sustainable energy; thus, this notion is extremely significant. After initially confronting its foundational principles, progressively comprehending the profound ramifications of Pareto efficiency has proven to be an intellectually stimulating and enlightening voyage [111].

5.4 Sustainable development goals (SDGs) for EV

EVs are critical in facilitating sustainable development goals (SDGs), including features connected to the environment, economy, and civilization. By increasing the use of renewable energy sources and decreasing reliance on fossil fuels, EVs contribute to generating affordable, environmentally friendly energy. This satisfies the criteria outlined in SDG 7. SDG 9, which encompasses industry, infrastructure, and innovation, is enhanced by advocating for innovation in the automotive industry. EVs are of utmost importance in urban settings for achieving Sustainable Development Goal 11, as they reduce air pollution and greenhouse gas emissions, thereby fostering the development of sustainable communities and cities [112]. SDG 13 objectives for climate action are furthered by the extensive adoption of EVs, which significantly mitigate the adverse effects of climate change. Following SDG 12, the production and operation of EVs adhere to responsible consumption and production patterns. Employment opportunities are also created as a result of the expansion of the EV industry, which contributes to the attainment of SDG 8 (decent work and economic growth). Enhanced EV utilization positively impacts public health and contributes to SDG 3 [113]. Providing populations that are disproportionately affected by air pollution with access to cleaner alternatives through the use of EVs is one way in which accessible and sustainable transportation options contribute to the reduction of inequalities SDG 10. Although only indirectly, the transition to environmentally responsible modes of transportation, such as EVs, correlates with SDG 16 by supporting environmental sustainability and ethical business practices. Overall, the incorporation of EVs into transportation systems plays a varied role in the advancement of interconnected SDGS, contributing to a future that is more sustainable and inclusive [114]. The United Nations has identified a set of SDGs, and EVs play a significant part in helping to contribute to those goals. Several important SDGs can be addressed more effectively by promoting the widespread usage and acceptance of EVs). An examination of the EV's role in advancing sustainable development is presented here. The SDGs that are pertinent to the development of EVs are depicted in Figure 5. These SDGs will play an essential part in the process.