INTRODUCTION:

The global transition toward electric vehicles (EVs), is gaining momentum due to increasing concerns over environmental sustainability, energy security, and urban air quality. This trend has accelerated research and development in intelligent energy management systems aimed at improving the efficiency, reliability, and adaptability of EVs.

Techniques such as fuzzy logic, rule based algorithms, and advanced artificial intelligence are being implemented to manage power distribution, optimize control strategies, and enhance system responsiveness. Simultaneously, advancements in state estimation methods, including Kalman filters and learning-based observers, are improving the accuracy of battery monitoring and vehicle performance prediction. These innovations are being supported by policy incentives and the development of robust public charging infrastructure, particularly in emerging markets like India.

Battery technology, particularly lithium-ion systems, plays a central role in EV advancement due to its influence on range, cost, safety, and energy density. Research has increasingly focused on challenges related to thermal management, fire safety, and supply chain sustainability. To address performance and safety needs, combining batteries with ultra capacitors are being explored, along with innovations in fast charging and wireless charging technologies. Furthermore, control systems such as adaptive cruise control, torque vectoring, and regenerative braking optimization are improving overall vehicle dynamics and efficiency. These technical developments are complemented by growing attention to the broader ecosystem, including grid integration, smart city energy systems, and user centric services like ride sharing and automated transport platforms.

Despite these advances, a comprehensive analysis of how key EV parameters interrelate specifically within the four wheeler segment in the Asian market remains limited. This study aims to address that gap by applying data driven regression modeling techniques to explore the relationships between critical variables such as battery capacity, motor power, acceleration, range, and base price. Using multiple regression models linear, quadratic, cubic polynomial the research uncovers the most accurate representations of these relationships. By doing so, it provides actionable insights for manufacturers aiming to optimize design, for policymakers setting market guidelines, and for consumers making informed purchase decisions in the evolving landscape of electric mobility in Asia.

Electric vehicles (EVs) are becoming central to modern transportation planning, with numerous studies focusing on improving their infrastructure, public acceptance, and system efficiency. For instance, a systems-based design using the Viable System Model (VSM) has been proposed to better manage EV ecosystems, especially in siting charging stations³. Public perception studies, such as one in New Zealand, segment consumers by their readiness to adopt EVs and propose policy interventions accordingly⁴. Comprehensive reviews have also identified the technological and planning challenges of EV charging networks in India, emphasizing the need for smart integration and supportive policy¹¹. Wireless charging has been investigated as well, with optimal coil positioning identified as critical for efficiency in various conditions¹². Despite these advances, the market penetration of EVs remains low, prompting calls for more actionable policy grounded in consumer behavior research¹⁷. To further improve vehicle performance, enhanced state estimation techniques like the unscented Kalman filter with Huber methods have been introduced¹⁸, and cross-national analyses highlight the importance of incentives, infrastructure density, and income on adoption rates. Meanwhile, optimization of EV systems using techniques such as the Modified Artificial Bee Colony (MABC) and Sequential Quadratic Programming (SQP) has shown promising results¹⁶. Broader technological overviews explore battery development, regulatory landscapes, and market evolution, offering insight into the trajectory of global EV deployment¹³. A review of heavy-duty vehicle powertrain technologies, focusing on diesel, battery electric (BEV), and hydrogen fuel cell electric (FCEV) systems analyzed their respective advantages and disadvantages, such as the efficiency and operational characteristics of each, as well as the significant infrastructure challenges and costs associated with deploying new technologies like BEVs and FCEVs, especially compared to the established diesel infrastructure⁵.

Motor efficiency remains another important research domain. A two-step selection methodology for traction motors aims to balance performance and cost to guide design decisions in electric drivetrains¹. Simultaneously, safety concerns around new mobility forms are surfacing, as studies reveal that light electric vehicles (e.g., e-scooters) lead to more severe injuries among children compared to traditional bicycles, calling for stricter regulation². On the environmental front, life cycle assessments (LCAs) have exposed methodological inconsistencies and stress the need for standardized frameworks to accurately gauge EV sustainability⁶. Examining EV policy in Europe, findings suggest that targeted incentives and infrastructure investment can significantly reduce urban air pollution⁷. Innovations in vehicle control systems, such as adaptive cruise control using deep reinforcement learning, are also emerging, tailoring driving behaviors to individual users while improving safety and energy use⁸. In the context of energy systems, moth flame optimization is being explored to integrate EVs with renewable sources such as wind and solar, demonstrating potential for increasing overall system efficiency⁹. Urban case studies also show that robust public charging infrastructure strongly correlates with higher EV adoption, as observed in London¹⁰. An article with a comprehensive review of the various technical and non-technical factors influencing adoption discusses factors like policies, brands, diversity, environmental issues etc²⁰. A study analyzes operational strategies and consumer perceptions for electric two-wheeler sharing in the city and discusses how shared electric mobility can address issues like fuel prices, air pollution, and traffic congestion²¹.

From a machine learning perspective, predictive modeling techniques like Greylag Goose Optimization have improved the accuracy of CO emissions estimation, aiding in environmental planning and EV development¹⁴. Psychological and behavioral dimensions are not to be ignored either—studies using the Theory of Planned Behaviour (TPB) have mapped out social and cognitive variables affecting EV uptake in countries like India¹⁵. On the operational side, ride-matching algorithms for shared autonomous EVs are being refined with graph-based models to enhance route efficiency and reduce energy usage¹⁹. Together, this body of research demonstrates a comprehensive, interdisciplinary effort to accelerate the adoption and efficiency of electric vehicles across technical, social, and environmental dimensions.

Electric four-wheelers are rapidly emerging as a pivotal element in Asia sustainable transportation agenda, driven by rising environmental concerns, urban congestion, and supportive green mobility policies. Despite increasing adoption across the continent, particularly in urban centers, the performance characteristics of these vehicles remain insufficiently understood. This study bridges that gap by conducting a comprehensive, data-driven analysis of electric four wheeler performance using regression-based state-space modeling techniques. By evaluating key variables such as battery capacity, motor power, acceleration, range, and base price, the study explores their inter dependencies through multiple regression models, including linear, quadratic, and cubic forms. The results reveal that cubic regression models most effectively capture the complex, nonlinear relationships inherent in EV performance, offering superior predictive accuracy. These insights are crucial for guiding manufacturers in optimizing powertrain configurations, helping policymakers set realistic pricing and performance standards, and assisting consumers in making informed purchasing decisions.

MATERIAL AND METHODS:

DEFINITIONS OF KEY PARAMETERS:

Battery Capacity (bcap): Expressed in kilowatt hours (kWh), this parameter defines the total energy a battery can store. It directly influences the range and indirectly affects both weight and cost.

Motor Power (mpow): Measured in kilowatts (kW), motor power reflects the energy conversion capability of the electric drive system. It influences torque and acceleration.

Range (rk): Measured in kilometers (km), range denotes the distance an electric two-wheeler can travel on a single charge under standard test conditions.

Acceleration (Acc): This typically refers to the time (in seconds) required to reach a certain speed (e.g., 0 to 60 km/h). Lower acceleration values indicate higher performance.

Base Price (bp): The manufacturer’s suggested retail price (MSRP), reflecting the cost of the two-wheeler excluding taxes, registration, and incentives.

The research was conducted using empirical data derived from various electric four-wheelers sold or projected in the Asian market. For each pair of dependent and independent variables, regression models were developed using linear, quadratic and cubic polynomial regression. Each regression model was evaluated based on its Root Mean Square (RMS) error to quantify predictive accuracy. The models were visualized through scatter plots with overlaid regression curves to examine visual conformity.

For each relationship e.g bcap vs mpow, bcap vs bp, mpow vs Acc, rk vs bp, etc. different regression fits were applied and compared. The lowest RMS error and best visual fit guided the selection of the most appropriate model.

Fig. 1: bcap vs mpow Linear

The plot in Fig 1 visualizes the relationship between two variables, bcap on the horizontal x-axis and mpow on the vertical yaxis. Numerous black circular points represent individual data observations, showing the paired values of bcap and mpow. A solid blue line is overlaid on the scatter plot, representing a linear fit to this data. This line aims to model the general trend or linear relationship between the two variables.

In essence, the plot explores whether there is a linear association between bcap and mpow. The scattered black points show the actual data, while the blue line provides a simplified linear approximation of this relationship. By observing the spread of the data points around the blue line, one can get an idea of how well a linear model fits this particular dataset. The grid lines in the background aid in reading the values on both axes. A legend in the upper right corner clarifies that the black points are the Data and the blue line is the Linear Fit. From Table-1 the regression equation 122.776 + 4.949x achieved an R² value of 0.60, indicating a moderately strong linear relationship between battery capacity and motor power.

Fig. 2: bcap vs mpow Quadratic

Fig 2 displays a scatter plot titled bcap vs mpow Quadratic Fit. Similar to the previous plot, it visualizes the relationship between the variables bcap (on the x-axis) and mpow (on the y-axis) using black circular points to represent individual data points. However, instead of a linear fit, this plot features a curved green line that represents a quadratic fit to the data. This curve attempts to model the relationship between the two variables using a second-degree polynomial.

The purpose of this visualization is to explore if a quadratic relationship better describes the connection between bcap and mpow compared to a linear one. The scattered data points illustrate the observed values, while the green curve provides a quadratic approximation of this relationship. By examining how closely the data points follow the curve, one can assess the suitability of a quadratic model for this dataset. The grid lines facilitate value reading, and the legend in the upper right corner identifies the black points as Data and the green curve as the Quadratic Fit. The regression equation 20.034 + 0.010x² + 0.037x² yielded an R² value of 0.64, suggesting that a quadratic model better explains the relationship between battery capacity and motor power.

Fig. 3: bcap vs mpow Cubic

Fig 3 displays the scatter plot of bcap vs mpow along with its cubic fit. Superimposed on these data points is a red curve, which represents a cubic fit to the data. This curve is the result of fitting a third-degree polynomial to the observed values, attempting to capture a potentially more complex, non-linear relationship between bcap and mpow.

The plot aims to explore whether a cubic model provides a good representation of the association between the two variables. The scattered black dots show the actual data, while the red curve offers a smoothed, cubic approximation of this relationship. By observing how well the red curve aligns with the distribution of the black dots, one can assess the appropriateness of a cubic model for describing the underlying trend in the data. The grid lines assist in reading values from the axes, and the legend in the upper right corner identifies the black points as Data and the red curve as the Cubic Fit.The cubic regression 142.053 7.575x + 0.166x² 0.001x³ achieved an R² of 0.64, reinforcing the strong nonlinear relationship between battery capacity and motor power.

Fig. 4: bcap vs rk Linear

Fig 4 presents a scatter plot illustrating the relationship between the variables bcap (on the x-axis) and ’rk’ (on the yaxis), titled bcap vs rk Linear Fit. Superimposed on the scatter of data points is a solid blue line, which represents the best-fit linear model to these data. This line aims to capture any linear trend that might exist between the two variables under investigation.

The distribution of the black data points around the blue linear fit provides an indication of the strength and direction of this linear relationship. Deviations of the data points from the blue line suggest the extent to which a simple linear model can explain the observed variability between bcap and rk. The grid lines aid in the precise reading of values, and the legend in the bottom right corner clarifies the representation of the data points and the linear fit. The equation 179.878 + 4.766x produced an R² value of 0.62, indicating a moderate linear correlation between battery capacity and range.

Fig. 5: bcap vs rk Quadratic

Fig 5 displays a scatter plot titled bcap vs rk Quadratic Fit, examining the relationship between the variables bcap (on the x-axis) and rk (on the y-axis). The black circles represent the raw data points, each corresponding to a paired observation of bcap and rk. Overlaid on these data points is a green curve, which represents the best-fitquadratic model. This curve is derived by fitting a second degree polynomial to the data, allowing for the possibility of a non-linear, specifically parabolic, relationship between the two variables.

This visualization explores whether a quadratic relationship provides a better characterization of the association between bcap and rk compared to a linear one. By observing how closely the green curve follows the distribution of the black data points, we can assess the suitability of a quadratic model for explaining the observed patterns. Deviations of the data from the curve indicate the unexplained variance by this quadratic fit. The grid lines facilitate value interpretation, and the legend in the bottom right corner identifies the data points and the quadratic fit.The regression 63.800 + 8.781x 0.030x² resulted in an R² of 0.64, showing that the quadratic model effectively represents the diminishing returns in range with increased battery capacity.

Fig. 6: bcap vs rk Cubic

Fig 6 presents a scatter plot titled bcap vs rk Cubic Fit, illustrating the relationship between the variables bcap (plotted on the horizontal x-axis) and rk (plotted on the vertical y-axis). The red curve, which represents the best-fit cubic model derived from the data, resulting from fitting a third degree polynomial, explores a potentially more complex, nonlinear relationship between the two variables.

In the context of our research, this visualization aims to determine if a cubic model effectively captures the underlying trend between bcap and rk. The distribution of the observed data points around the red cubic fit allows us to evaluate the goodness of fit of this model. Deviations of the data from the curve indicate the extent to which the cubic model explains the observed variance. The grid lines facilitate the reading of values along both axes, and the legend in the bottom right corner clearly identifies the data points and the cubic fit. The cubic model 102.876 + 6.352x 0.011x² + 0.000x³ gave an R² of 0.64, confirming a strong and realistic relationship between battery capacity and range.

Fig. 7: mpow vs Acc Linear

Fig 7 displays a scatter plot titled mpow vs Acc Linear Fit, illustrating the relationship between the variables mpow (on the horizontal x-axis) and Acc (on the vertical yaxis). The black circles represent individual data points, each marking an observed pair of mpow and Acc values. A solid blue line is overlaid on the scatter plot, representing the bestfit linear model to these data. This line aims to approximate any linear trend that might exist between the two variables under investigation.

In the context of our research, this plot serves to visually examine the potential linear association between mpow and Acc. The distribution of the black data points around the blue linear fit indicates the nature and strength of this linear relationship. The extent to which the data points deviate from the blue line reflects how well a simple linear model can explain the observed covariance between mpow and Acc. The grid lines facilitate the reading of values on both axes, and the legend in the upper right corner clarifies the representation of the data points and the linear fit.The linear equation 9.070 0.010x had an R² of 0.25, indicating a weak negative correlation between battery capacity and acceleration time.

Fig. 8: mpow vs Acc Quadratic

Fig 8 presents a scatter plot titled mpow vs Acc Quadratic Fit, illustrating the relationship between the variables mpow (on the horizontal x-axis) and Acc (on the vertical y-axis). The black circles represent the observed data points, each corresponding to a specific value of mpow and its associated Acc value. Overlaid on these data points is a green curve, which represents the best-fit quadratic model to the data. This curve is derived by fitting a second-degree polynomial, allowing for the possibility of a non-linear, specifically parabolic, relationship between the two variables.

In the context of our research, this visualization explores whether a quadratic model provides a more accurate representation of the association between mpow and Acc compared to a linear one. By examining how closely the green curve follows the distribution of the black data points, we can assess the suitability of a quadratic function to describe the underlying trend. Deviations of the data from the curve indicate the extent to which the quadratic model does not fully capture the variability in the data. The grid lines aid in reading values from the axes, and the legend in the upper right corner identifies the data points and the quadratic fit.The cubic regression 10.327 + 0.022x + 0.000x² resulted in an R² value of 0.31, showing a modest relationship between motor power and acceleration.

Fig. 9: mpow vs Acc Cubic

Fig 9 presents a scatter plot titled mpow vs Acc Cubic Fit, illustrating the relationship between the variables mpow (plotted on the horizontal x-axis) and ’Acc’ (plotted on the vertical y-axis). The black circular points represent the raw experimental data, where each point corresponds to a specific measurement of both mpow and Acc. Superimposed on these data points is a red curve, which represents the best-fit cubic model derived from the data. This curve, resulting from fitting a third-degree polynomial, explores a potentially more complex, non-linear relationship between the two variables.

In the context of our research, this visualization aims to determine if a cubic model effectively captures the underlying trend between ’mpow’ and ’Acc’. The distribution of the observed data points around the red cubic fit allows us to evaluate the goodness of fit of this model. Deviations of the data from the curve indicate the extent to which the cubic model explains the observed variance. The grid lines facilitate the reading of values along both axes, and the legend in the upper right corner clearly identifies the data points and the cubic fit.The model 12.316 0.048x + 0.000x² yielded an R² of 0.34, indicating a weak-to-moderate correlation between range and acceleration.

Fig. 10: bcap vs bp Linear

Fig 10 displays a scatter plot titled bcap vs bp Linear Fit, illustrating the relationship between the variables bcap (on the horizontal x-axis) and bp (on the vertical y-axis). Each black circle represents an individual data point, showing the observed paired values of bcap and bp. Superimposed on the scatter of data points is a solid blue line, which represents the bestf it linear model to these data. This line aims to capture any linear trend that might exist between the two variables under investigation. In the context of our research, this plot serves to visually assess the potential linear association between bcap and bp. The distribution of the black data points around the blue linear fit provides an indication of the strength and direction of this linear relationship. Deviations of the data points from the blue line suggest the extent to which a simple linear model can explain the observed variability between bcap and bp. The grid lines aid in the precise reading of values, and the legend in the bottom left corner clarifies the representation of the data points and the linear fit.The linear regression 66184.694 + 1765.025x had an R² value of 0.27, revealing a weak linear relationship between battery capacity and base price.

Fig. 11: bcap vs bp Quadratic

Fig 11 displays a scatter plot titled bcap vs bp Quadratic Fit, examining the relationship between the variables bcap (on the x-axis) and bp (on the y-axis). Overlaid on the data points is a green curve, which represents the best-fit quadratic model. This curve is derived by fitting a seconddegree polynomial to the data, allowing for the possibility of a non-linear, specifically parabolic, relationship between the two variables.

Within the context of our research, this visualization explores whether a quadratic relationship provides a better characterization of the association between bcap and bp compared to a linear one. By observing how closely the green curve follows the distribution of the black data points, we can assess the suitability of a quadratic model for explaining the observed patterns. Deviations of the data from the curve indicate the unexplained variance by this quadratic fit. The grid lines facilitate value interpretation, and the legend in the bottom left corner identifies the data points and the quadratic fit. The cubic model 75490.583 3134.699x + 36.808x² gave an R² of 0.24, suggesting a modest nonlinear relationship between battery size and price.

Fig. 12: bcap vs bp Cubic

Fig 12 presents a scatter plot of bcap vs bp with respect to its cubic fit, illustrating the relationship between the variables bcap (plotted on the horizontal x-axis) and bp (plotted on the vertical y-axis). Superimposed on the data points is a red curve, which represents the best fit cubic model derived from the data. This curve, resulting from fitting a third degree polynomial, explores a potentially more complex, non linear relationship between the two variables.

In the context of our research, this visualization aims to determine if a cubic model effectively captures the underlying trend between bcap and bp. The distribution of the observed data points around the red cubic fit allows us to evaluate the goodness of fit of this model. Deviations of the data from the curve indicate the extent to which the cubic model explains the observed variance. The grid lines facilitate the reading of values along both axes, and the legend in the bottom left corner clearly identifies the data points and the cubic fit. The regression equation 20502.250 + 2831.974x + 64.872x²+ 0.513x³ resulted in an R² of 0.25, indicating a mild cubic relationship between battery capacity and base price.

Fig. 13: mpow vs bp Linear

Fig 13 displays a scatter plot of mpow vs bp with its linear fit, illustrating the relationship between the variables mpow (on the horizontal x-axis) and bp (on the vertical y-axis). Superimposed on the scatter of data points is a solid blue line, which represents the best-fit linear model to these data. This line aims to capture any linear trend existing between the two variables.

This plot serves to visually assess the potential linear association between mpow and bp. The distribution of the black data points around the blue linear fit provides an indication of the strength and direction of this linear relationship. Deviations of the data points from the blue line suggest the extent to which a simple linear model can explain the observed variability between mpow and bp. The grid lines aid in the precise reading of values, and the legend in the bottom left corner clarifies the representation of the data points and the linear fit.The model 2612.638 + 249.114x achieved an R² of 0.17, reflecting a weak linear correlation between motor power and base price.

Fig. 14: mpow vs bp Quadratic

Fig 14 displays the scatter plot of mpow vs bp with its quadratic fit, examining the relationship between the variables mpow (on the x-axis) and bp (on the y-axis). Overlaid on these data points is a green curve, which represents the best-fit quadratic model. This curve is derived by fitting a seconddegree polynomial to the data, allowing for the possibility of a non linear, specifically parabolic, relationship between the two variables.

This visualization explores whether a quadratic relationship provides a better characterization of the association between mpow and bp compared to a linear one. By observing how closely the green curve follows the distribution of the black data points, we can assess the suitability of a quadratic model for explaining the observed patterns. Deviations of the data from the curve indicate the unexplained variance by this quadratic fit. The grid lines facilitate value interpretation, and the legend in the bottom left corner identifies the data points and the quadratic fit.The cubic equation 14355.220 + 408.787x 0.261x² yielded an R² of 0.16, indicating a very weak nonlinear relationship between motor power and price.

Fig. 15: mpow vs bp Cubic

Fig 15 presents a scatter plot of mpow vs bp with its cubic fit. Superimposed on the data points is a red curve, which represents the best-fit cubic model derived from the data. This curve, resulting from fitting a third-degree polynomial, explores a potentially more complex, non-linear relationship between the two variables.

This visualization aims to determine if a cubic model effectively captures the underlying trend between mpow and bp. The distribution of the observed data points around the red cubic fit allows us to evaluate the goodness of fit of this model. Deviations of the data from the curve indicate the extent to which the cubic model explains the observed variance. The grid lines facilitate the reading of values along both axes, and the legend in the bottom left corner clearly identifies the data points and the cubic fit.The cubic regression 4529.372 + 134.488x 0.709x² + 0.000x³ produced an R² of 0.13, suggesting minimal correlation between range and price.

Fig. 16: rk vs bp Linear

Fig 16 presents a scatter plot titled rk vs bp Linear Fit, illustrating the relationship between the variables rk (on the horizontal x-axis) and bp (on the vertical y-axis). Each black circle represents an individual data point, showing the observed paired values of rk and bp. Superimposed on the scatter of data points is a solid blue line, which represents the best-fit linear model to these data. This line aims to capture any linear trend that might exist between the two variables under investigation.

In the context of our research, this plot serves to visually assess the potential linear association between rk and bp. The distribution of the black data points around the blue linear fit provides an indication of the strength and direction of this linear relationship. Deviations of the data points from the blue line suggest the extent to which a simple linear model can explain the observed variability between rk and bp. The grid lines aid in the precise reading of values, and the legend in the upper left corner clarifies the representation of the data points and the linear fit.The linear equation 10466.742 + 89.016x gave an R² of 0.12, showing a weak relationship between range and price.

Fig. 17: rk vs bp Quadratic

Fig 17 displays a scatter plot titled rk vs bp Quadratic Fit, examining the relationship between the variables rk (on the x-axis) and bp (on the y-axis). The black circles represent the raw data points, each corresponding to a paired observation of rk and bp. Overlaid on these data points is a green curve, which represents the best fit quadratic model. This curve is derived by fitting a second degree polynomial to the data, allowing for the possibility of a non linear, specifically parabolic, relationship between the two variables.

Within the context of our research, this visualization explores whether a quadratic relationship provides a better characterization of the association between rk and bp compared to a linear one. By observing how closely the green curve follows the distribution of the black data points, we can assess the suitability of a quadratic model for explaining the observed patterns. Deviations of the data from the curve indicate the unexplained variance by this quadratic fit. The grid lines facilitate value interpretation, and the legend in the upper left corner identifies the data points and the quadratic fit.The regression 94504.993 5.542x 0.438x² had an R² of 0.11, indicating very little predictive power between acceleration time and price.

Fig. 18: rk vs bp Cubic

Fig 18 presents a scatter plot titled rk vs bp Cubic Fit”, illustrating the relationship between the variables rk (plotted on the horizontal x-axis) and bp (plotted on the vertical yaxis). The black circular points represent the raw experimental data, where each point corresponds to a specific measurement of both rk and bp. Superimposed on these data points is a red curve, which represents the best-fit cubic model derived from the data. This curve, resulting from fitting a third-degree polynomial, explores a potentially more complex, non-linear relationship between the two variables.

In the context of our research, this visualization aims to determine if a cubic model effectively captures the underlying trend between rk and bp. The distribution of the observed data points around the red cubic fit allows us to evaluate the goodness of fit of this model. Deviations of the data from the curve indicate the extent to which the cubic model explains the observed variance. The grid lines facilitate the reading of values along both axes, and the legend in the upper left corner clearly identifies the data points and the cubic fit.The cubic model 36837.511 + 131.048x 0.433x² 0.001x³ resulted in an R² of 0.08, showing a weak cubic correlation between acceleration and base price.

Fig. 19: Acc vs bp Linear

Fig 19 displays a scatter plot titled Acc vs bp Linear Fit, illustrating the relationship between the variables Acc (on the horizontal x-axis) and bp (on the vertical y-axis). Each black circle represents an individual data point, showing the observed paired values of Acc and bp. Superimposed on the scatter of data points is a solid blue line, which represents the best-fit linear model to these data. This line aims to capture any linear trend that might exist between the two variables under investigation.

In the context of our research, this plot serves to visually assess the potential linear association between Acc and bp. The distribution of the black data points around the blue linear fit provides an indication of the strength and direction of this linear relationship. Deviations of the data points from the blue line suggest the extent to which a simple linear model can explain the observed variability between Acc and bp. The grid lines aid in the precise reading of values, and the legend in the upper right corner clarifies the representation of the data points and the linear fit. The model 18181.100 + 8585.833x achieved an R² of 0.07, representing a very weak linear relation between acceleration and price.

Fig. 20: Acc vs bp Quadratic

Fig 20 presents a scatter plot titled Acc vs bp Quadratic Fit, examining the relationship between the variables Acc (on the x-axis) and bp (on the y-axis). The black circles represent the raw data points, each corresponding to a paired observation of Acc and bp. Overlaid on these data points is a green curve, which represents the best-fit quadratic model. This curve is derived by fitting a second-degree polynomial to the data, allowing for the possibility of a non-linear, specifically parabolic, relationship between the two variables.

Within the context of our research, this visualization explores whether a quadratic relationship provides a better characterization of the association between Acc and bp compared to a linear one. By observing how closely the green curve follows the distribution of the black data points, we can assess the suitability of a quadratic model for explaining the observed patterns. Deviations of the data from the curve indicate the unexplained variance by this quadratic fit. The grid lines facilitate value interpretation, and the legend in the upper right corner identifies the data points and the quadratic fit. The equation 8.918 + 1570.601x² + 744.052x² produced an R² of 0.27, showing a weak quadratic trend between acceleration and base price.

Fig. 21: a vs bp Cubic

Fig 21 displays a scatter plot titled Acc vs bp Cubic Fit, illustrating the relationship between the variables Acc (plotted on the horizontal x-axis) and bp (plotted on the vertical yaxis). The black circular points represent the raw experimental data, where each point corresponds to a specific measurement of both Acc and bp. Superimposed on these data points is a red curve, which represents the best-fit cubic model derived from the data. This curve, resulting from fitting a third-degree polynomial, explores a potentially more complex, non-linear relationship between the two variables.

In the context of our research, this visualization aims to determine if a cubic model effectively captures the underlying trend between Acc and bp. The distribution of the observed data points around the red cubic fit allows us to evaluate the goodness of fit of this model. Deviations of the data from the curve indicate the extent to which the cubic model explains the observed variance. The grid lines facilitate the reading of values along both axes, and the legend in the upper right corner clearly identifies the data points and the cubic fit. The regression 33013.771 + 57888.138x² 11303.627x² + 512.764x³ achieved an R² value of 0.15, indicating a mild cubic relationship between acceleration and base price.

Table I: Equations, R² Scores for Various Models

x	y	Equation	_R2
bcap	mpow	−122.776 + 4.949x¹	0.60
bcap	mpow	20.034 + 0.010x¹ + 0.037x²	0.63
bcap	mpow	142.053 + −7.575x¹ + 0.166x² + −0.001x³	0.64
bcap	rkm	179.878 + 4.766x¹	0.62
bcap	rkm	63.800 + 8.781x¹ + −0.030x²	0.64
bcap	rkm	102.876 + 6.352x¹ + 0.011x² + −0.000x³	0.64
mpow	Acc0100	9.070 + −0.010x¹	0.28
mpow	Acc0100	10.327 + −0.022x¹ + 0.000x²	0.31
mpow	Acc0100	12.136 + −0.048x¹ + 0.000x²	0.34
bcap	bp	−66184.694 + 1765.025x¹	0.17
bcap	bp	75490.583 + −3134.699x¹ + 36.808x²	0.24
bcap	bp	−20502.250 + 2831.974x¹ + −64.872x² + 0.513x³	0.25
mpow	bp	2612.638 + 249.114x¹	0.14
mpow	bp	−14535.220 + 408.787x¹ + −0.261x²	0.15
mpow	bp	4529.374 + 132.662x¹ + 0.709x² + −0.001x³	0.15
rkm	bp	14066.742 + 89.016x¹	0.016
rkm	bp	−94504.923 + 542.712x¹ + −0.438x²	0.035
rkm	bp	−36837.514 + 131.048x¹ + 0.433x² + −0.001x³	0.036
Acc0100	bp	117883.119 + −8585.383x¹	0.06
Acc0100	bp	89532.918 + 1570.601x¹ + −744.052x²	0.07
Acc0100	bp	33013.711 + 57858.138x¹ + −11303.627x² + 512.764x³	0.15

CONCLUSION:

This study employed regression-based state-space modeling to analyze the key performance characteristics of electric fourwheelers in the Asian market, using variables such as battery capacity (bcap), motor power (mpow), range (rk), acceleration (Acc), and base price (bp). Through a rigorous comparison of multiple regression techniques including linear, quadratic and cubic. The study found that cubic polynomial models most effectively captured the complex, nonlinear relationships among variables.

Battery Performance: Cubic models revealed that while increasing battery capacity improves motor power and range, the gains diminish at higher capacities, reflecting realistic nonlinear behavior.

Cost Analysis: Base price scales nonlinearly with increases in battery capacity and motor power. The cubic model demonstrated the lowest RMS error, suggesting its suitability for real-world cost estimation.

Acceleration: Acceleration time decreases as motor power increases, particularly sharply at lower motor power levels. Cubic models accurately traced this steep decline and eventual plateau.

Predictive Insights:

Variables like motor power and range are strong predictors of base price when modeled with higherorder polynomials, outperforming simpler regression models.

Model Evaluation:

linear models failed to account for inflection points and curvature, highlighting the importance of using polynomial regression—especially cubic forms—for modeling EV dynamics.

These insights provide a quantitative foundation for manufacturers and policymakers to optimize design specifications, pricing strategies, and battery configurations in the evolving electric vehicle market of Asia.

Future Work:

Future studies should integrate real-world factors such as driving patterns, temperature, charging behavior, and energy consumption efficiency. Incorporating machine learning techniques like spline regression, random forests, or ensemble methods may enhance prediction accuracy. Extending the scope to other geographic regions and vehicle types (e.g., commercial EVs, trucks, buses) will help generalize the findings.

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Received on 15.08.2025 Revised on 03.10.2025

Accepted on 13.11.2025 Published on 11.05.2026

Available online from May 14, 2026

Asian Journal of Management. 2026;17(2):101-111.

DOI: 10.52711/2321-5763.2026.00016

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