Enhancing Electric Vehicle Performance: A Co-Relation Study of Key Performance Parameters

The urgent issues of greenhouse gases and global warming have drawn more attention globally. The adoption of Electric Vehicle (EV) technology has altered the transportation industry and progressively decreased the market share of fossil fuel-powered automobiles as a crucial step to solve these pressing concerns. EVs provide emissions-free mobility since they run on chemical energy stored in rechargeable battery packs. These Battery Electric Vehicles (BEVs) can recover kinetic energy lost while braking by utilizing regenerative braking technology, further boosting their environmental friendliness. Performance has significantly improved as a result of manufacturer’s ongoing advances to EV technology. The effectiveness of regenerative braking and battery capacity are two critical performance factors for EVs that have a substantial impact on their total performance. A thorough examination of key performance metrics and their connection becomes essential for achieving additional improvement. Industries use simulation tools for evaluation, but they frequently ignore the intricate relationship between many performance-influencing factors. In order to address this, we provide a Co-Relation research study on a number of commercially available electric vehicles, concentrating on the battery capacity, power, driving range, and pick-up as the key performance aspect. We determine the Co-Relation between these factors using Karl Pearson Co-Relation coefficient, which offers important insights regarding EV performance. The findings of this research will be a useful tool for EV makers, promoting a better knowledge of parametric connection and assisting them in optimizing the performance of their EV model. Additionally, the finding of post-sale analysis of performance indicators may provide crucial confirmation for the results of simulation. This study helps direct to the automobile sector toward a future that is more efficient and sustainable.


INTRODUCTION
In electric automobiles, an internal combustion engine which generates power by burning fossil fuels is swapped out for one or more electric motor.The need to address important challenges including the depletion of fossil fuels, global warming, and greenhouse gas emission are what has sparked this movement.As a result, electric vehicles have become a competitive substitute for the current generation of EVs.The idea of electric automobiles was first created in the middle of the 20th century, but due to considerable technological breakthrough and a growing interest in alternative energy sources, they have seen a comeback in the 21st century.The most recent model, referred to as BEV, include electric motors Fig. 1 Schematic Diagram of EV [1] For a Hybrid Electric Tracked Vehicle (HETV), J. Wu et al. [5] proposed a hierarchical energy management strategy to minimize energy usage while retaining the EV's path tracking accuracy.In comparison to the rule-based method, the results demonstrated a much higher fuel economy.The interconnection of the performance parameters was not made evident in the research mentioned above.In order to solve the problems of mass movement and charge transfer in the traction battery, Y. Liu et al. [6] conducted a bibliographic study on the most current developments in battery materials including electrolyte and electrodes.The authors also spoke about several characterization methods for material study.The primary method to improve electrolyte conductivity and electrode rate capacity in EV batteries were provided by G. L. Zhu et al. in their paper published in Science [7].Even though the research examined several characteristics of battery materials, they omitted parametric EV analyses.F. Un-Noor et al. [8] reviewed and analysed the data that was available on EV designs including the battery, traction motor, charging procedures, performance optimization and provided potential future development possibilities.M. El.Baghdadi et al. [9] tested the EVs of two distinct model and made.They used a chassis dynamometer to assess EV performance under various torque-speed operating situation, and they confirmed their finding using data collected from actual road condition.A simulation approach was used by C. M. Chang et al. [10] to compare the performance of an EV with a 5-speed gearbox versus a single-ratio gearbox.According to their research, An EV with a multi-speed transmission performs better than one with a single-ratio gearbox in terms of on-road load-bearing.Although independence among parameters that impact EV performance is important throughout the life cycle of the vehicle, it was not given much consideration in the research mentioned above, which assessed performance.Pearson Co-Relation is a statistical measure that quantifies the strength and direction of a linear relationship between two variables, typically represented as a value between -1 and +1.In the context of electric vehicles (EVs), Pearson Co-Relation can be a useful tool for analysing and understanding various parameters and factors that affect EV performance, efficiency, and overall functionality.From the above, it is clear that EV analysis in terms of correlated elements is crucial in light of potential performance improvements when the EVs are put on the road.In this paper, a straightforward analysis utilizing Pearson Co-Relation is carried out to show how these elements interact and how performance might be improved.A variable that alters the desired behaviour, or performance of the EV, is referred to as a factor or parameter of the EV.The next section discusses some EV parameters.

PERFORMANCE PARAMETERS OF ELECTRIC VEHICLE
To evaluate the various commercially available EV's parameters including EV range, battery capacity, power, and pick-up are discovered.These are the variables that affect and reflect the performance of the EV, and knowing how these variables interact will enable you to gauge the EV's performance at any point in its lifespan.This will also serve as guidance for the designers as they work to improve EV performance.By using Pearson Co-Relation which will be covered in the section below, the relationship between these elements will be examined.The factors that were chosen for the Co-Relation analysis are provided in this section.

Driving Range
Operating range on a single charge is one of the most crucial factors when it comes to electric automobiles.Range per hour is a measure of an EV effectiveness.EVs with a longer range are more productive.The average range of electric vehicles is only around half that of conventional vehicles, and petrol stations are far more common than EV charging stations, making it crucial to increase the range of these vehicles.Because high and low temperatures have an adverse effect on battery performance and range, several EVs are initially being launched in regions that are neither extremely hot nor extremely cold [11].

Battery Capacity
The battery, an electrical storage device that transforms chemical energy into electrical energy and serves as a source of electrical power, is a crucial part of an EV.The most common unit of measurement for battery capacity is kilowatt-hours (KWh), which is equal to the number of gallons of gasoline consumed by fossil fuel-powered vehicles.A fully charged battery, however, cannot compete with a gasoline tank that is fully charged.Generally, the battery's rated capacity can never be reached [12].

Power
The traction motor of an electric vehicle generates power that propel the road wheels, much like an internal combustion engine (ICE).The EVs supply electricity in a manner that is distinctly different from ICE. Kilowatt or horsepower are frequently used to measure the power output of electrical motor.

Acceleration Time (0-60 second)
In USA and United Kingdom, 0 to 97 km/h pick-up or acceleration time is a common performance indicator.When an EV accelerates from 0 to 100 km/h (0 to 62.1 mph), it is recorded elsewhere.The finest attribute of an EV is its instantaneous, quick, smooth and noiseless acceleration.Most of the version that are now on the market can accelerate from 0 to 60 mph (or 96.5 km/h) in eight seconds [13][14].

METHODOLOGY
In this section, performance variables of EVs are evaluate, and Pearson's Co-Relation is used to determine the Co-Relation between the performance factors.The coefficient (r) stands for Pearson's Co-Relation and measures the strength of a linear link between two variables.It is not only aids in indicating the presence of a connection between two parameters, but also establishes the proper degree of this association.Additionally, the Pearson Co-Relation analysis makes the desired and statistically significant assumption that the associated components have a normal distribution.The Pearson Co-Relation coefficient is used to calculate the strength of the Co-Relation between the data for the chosen factors.The formula returns a value between '-1' and '1', where '1' denotes a strong positive relationship while '-1' denotes a strong negative relationship and '0' denotes that there is no relationship.It is preferable to examine the trend of all chosen performance criteria before conducting a Co-Relation Analysis Fig. 2 illustrates the trend that shows how various battery-operated electric vehicles' performance characteristics have changed over time.It is clear from Fig. 2 that there may be a link between the chosen performance criteria.Co-Relation analysis may use to determine this.The information used in this investigation was taken from a number of commercially available EV database websites [12].The data of different variables are shown in the following table 1.
Where 'r' is the Pearson Co-Relation coefficient, p and q are the values for the first and second parameters respectively and n is the total number of value (Vehicles).The following table 2 lists the Co-Relation coefficients that were discovered by equation (1)'s Co-Relation analysis of four separate performance parameters.The Pearson Co-Relation coefficient (r) between the variables "Battery Capacity" and "range" is 0.88 in the table above, indicating a fairly significant positive link between these two variables.The linear equation for the relationship between battery capacity and driving range is as follows: Where, accordingly, p represents the battery capacity and q the vehicles range.Additionally, it has been found that various EV-related characteristics are negatively correlated.'Battery capacity' and 'pick-up' have a negative link [ Fig 6], as indicated by the Co-Relation coefficient, r = -0.90.By utilizing the following equation, the relationship between battery capacity and pick-up may be established: Where 'q' is the pick-up time and 'p' is the battery capacity.The relationships between the other components are examined along similar lines.According to Table 2, there is a strong positive Co-Relation between battery capacity and range (0.89), battery capacity and power (0.95), range and power (0.85) while there is a strong negative Co-Relation between battery capacity and pick-up (0.90), range and pick-up (0.90), power and pick-up (0.91).Scatter plots, which are produced using the data taken from Table 1 and shown in figure 3, 4, 5, 6, 7, and 8. Dotted points in graph are different vehicles that is given in Table 1.
Fig. 3 Battery Capacity V/S Range R 2 is often used to assess how well the equation's line fits the data points.Specifically, for a linear regression model, R 2 measures how well the linear equation explains the variation in the dependent variable.It tells you how much of the variation in the dependent variable can be attributed to the linear relationship with the independent variable(s).R2 values range from 0 to 1. R 2 =0 implies that the independent variable(s) do not explain any of the variability in the dependent variable.In other words, the model does not fit the data well while R 2 =1 implies that the independent variable(s) perfectly explain all of the variability in the dependent variable.The model fits the data perfectly.R 2 is often interpreted as the percentage of variation in the dependent variable that is explained by the independent variable(s).
For example, from figure 3 R 2 =0.7887, it means that 78.87% of the variation in the battery capacity is explained by driving range.It should be noted that battery capacity and pick-up time have a very significant negative Co-Relation (see Fig. 6).It implies that the pick-up or acceleration rate of the vehicle would linearly decrease as Battery capacity increases.It is clear from Fig. 3 and 4 that the parameters power and range (also known as dependent factors) change linearly with battery capacity (also known as an independent factor).This implies that increasing the battery capacity can increase an EV's power and driving range.The derived linear equation may be used to anticipate the battery capacity needed to achieve the desired driving range and power and vice versa.Range and power are coupled linearly to one another as seen in Fig. 5 because range and battery capacity have a linear connection, as does power and battery capacity.

RESULTS AND DISCUSSIONS
It can be seen from the above that a greater capacity battery will result in a slower rate of acceleration.This indicates that an increase in battery capacity results in an increase in vehicle weight.An EV travel range is extended by adding additional batteries of the same technology, but doing so increases the vehicle's overall weight, degrades its dynamic and braking performance and reduce the amount of cargo and people it can carry.The vehicle's acceleration characteristics (i.e., pick-up) will be greatly impacted Power V/S Pick-up by the weight increase, demonstrating a negative link between battery capacity and pick-up.Further observation reveals a substantial negative association between the factors range and power and the factor pick-up.Because they are directly connected to battery capacity and battery weight, an increase in either of these elements will result in a linear reduction in the vehicle pickup.

CONCLUSION
Co-Relation coefficients are the crucial tool in analysing and understanding various aspects of the electric vehicle industry, including factors affecting adoption, performance, and environmental impact.
Researchers, Policymakers and Businesses use these tools to make informed decisions and develop strategies for the EV industry.The current research compared the performance of several batteryoperated electric automobile industries.The analysis was conducted by using the Pearson's Co-Relation-based feature selection approach, as stated in earlier parts, and the study entailed analysing the performance requirements of these electric automobiles.The results showed a clear Co-Relation between the EV performance metrics.These Co-Relations, which were seen to be either positive or negative, showed how various vehicle performance characteristics interacted with one another.The investigation also raises the possibility of expanding future research to examine connections between additional EV performance metrics.In particular, characteristics like energy consumption, efficiency, speed and other pertinent variables weren't examined in this research but may be in subsequent ones for a more thorough analysis.As a result, it is determined that for the best acceleration rate, battery capacity should be limited to a minimum.Furthermore, by carefully selecting performance variables, EV performance may be improved.

Fig. 2
Fig.2 Performance of Various Vehicles

Fig. 4
Fig.4 Battery Capacity V/S Power

Table 2
Co-Relation coefficient (r) of some parameter