As part of this assignment from the Australian Electric Vehicle Council, I am to examine the relationships between the following key variables in the EV dataset: the battery consumption (“Efficiency_WhKm”) and cost of vehicle “Price”; the relationship ‘drive mode type’ (PowerTrain) to that of a car style(BodyStyle); and finally, integer relating drive family kind with automobile class.
a) Relationship Between Battery Efficiency and Price:
According to the research, battery efficiency has an inverse relationship with the prices of electric vehicles. Generally, electric cars with more fuel efficiency have higher price tags. The correlation coefficient (-0.42) between “Efficiency_WhKm” and “Price” is highly negative, which suggests that there is a general trend for higher efficiency to be related to better pricing in this case scenario. Tesla is the leader among electric vehicles, and Audi’s electric cars are worse in terms of battery efficiency.
This indicates that manufacturers are asking for a higher price for electric vehicles with greater efficiency, and it could be because more effortless engineering of the technology is needed to achieve better battery performance (Foley et al., 2020). Well, you have to know that this is the usual course of things, but there are also some rare cases when it does not. In order to reach that sweet line between efficiency and affordability, it is sensible for consumers to look at what they are spending on meals, utility bills, etc. While the initial outlay may be more, the savings from increased efficiency make it worthwhile.
b) Relationship Between Drive Type and Style:
The investigation into the association between drive type (PowerTrain) and vehicle style (BodyStyle) tells an exciting story. In the market for electric vehicles, there are different body styles associated with unique drive types.
Front-Wheel Drive (FWD):
FWD style is usually used in hatchbacks and SUVs. These include the Honda E, Peugeot e-208, and Hyundai Kona Electric.
Rear-Wheel Drive (RWD):
Liftback styles and sedans are frequently associated with RWD. Notable ones include the Tesla Model 3 Standard Range Plus and BMW i4.
All-Wheel Drive (AWD):
AWD is very flexible and available for different types of body styles, such as sedans, SUVs, liftbacks, etc. The Audi e-tron, Tesla Model Y, and Lucid Air illustrate this diversity. Understanding the relationship between drive type and body style can help different parties. Manufacturers can customize their offerings based on the preferences of consumers, while users are able to align with driving demands and lifestyles.
c) Relationship Between Vehicle Style and Efficiency:
Evaluating the effectiveness of electric vehicles with varying body styles allows for an understanding of what variables have an impact on efficiency.
Sedans:
Some of the sedans that are different in efficiency include the Tesla Model 3 Long Range with Dual Motor and the BMW i4. The Tesla Model 3 has a high efficiency with a rate of Wh km while the BMW i4, though efficient, works at an output level of thereby.
SUVs:
The efficiency values belong to the range of SUVs, such as Hyundai Kona Electric and Audi e-tron. 160 Wh / km compact SUV Hyundai Kona Electric and large SUV Audi e-tron power efficiency. This means that in the SUV segment, efficiency is not a constant but depends on other factors such as size and design.
Hatchbacks:
Hatchbacks such as the Peugeot e-208 and Volkswagen E also differ in efficiency levels. 164 Wh/km is what Peugeot e-208 achieves and Volkswagen Golf hatchback works off 168Wh /km. The data reveals that hatchbacks, similar to sedans and SUVs, cover a broad range of efficiency values. Consumers should be aware of efficiency trends for different body styles to make their own decisions. Although a smaller and more aerodynamic vehicle tends to be much more efficient, the efficiency itself depends on an individual style of body.
Estimating EV measures
In this section, I will estimate two critical measures related to electric vehicles (EVs): The price of EVs, Mainly the proportion that considers a smaller car, such as Hatchbacks or Liftbacks (“BodyStyle”).
a) Estimating Overall Price of EVs:
For averaging the EV price, I should take into account all data as a whole. The dataset has a wide variety of priced electric vehicles. Calculation of the mean average price provides an overall figure to show a trend in pricing. The cost for all the EVs averages about $55,099. This average model cost encompasses everything from the affordable Volkswagen ID.3 Pure to high-end models like the luxury Porsche Taycan Turbo S and so on.
b) Estimating Proportion of Smaller Cars (Hatchbacks or Liftbacks):
In order to check if a higher number of vehicles are described as smaller cars, I counted the count for Hatchbacks or Liftback labels. I calculated that proportion relative to the total amount in the dataset. After a thorough analysis, I found out that there are 29 Hatchbacks and 5 Liftbacks in the dataset. The total number of EVs is 92. Therefore, the combined count of smaller cars is 34. To estimate the proportion, I used the formula:
Proportion= Count of Smaller Cars/Total Number of EVs
Proportion= 34/92
=0.37
This means that approximately 37% of the electric vehicles in this dataset are considered small cars (Hatchbacks or Liftbacks). Consumers looking for smaller or more flexible electric vehicles will find this information quite helpful. On average, the estimated total cost of electric vehicles in this data collection is around $ 55,099. This gives an overall idea of the average price range in the electric vehicle market for a number of variants. 37% of the electric vehicles in this dataset are classified under smaller cars such as Hatchbacks or Liftbacks based on proportion analysis. This insight is helpful for consumers interested in small and multipurpose electric vehicles.
Claims about EVs
In this part, we will review two arguments for electric vehicles presented in the provided dataset. The claims relate to the period of acceleration “AccelSec” and how electric vehicles are separated into different categories within each market.
a) Evaluating Acceleration Claims:
A “7-second” duration is considered as the time for acceleration of EVs, and it also assumes that such vehicles can have quicker times. In order to get a better insight into this issue, we will investigate the acceleration time distribution in the dataset. After performing the analysis, it is clear that most of the EVs within this dataset have acceleration times less than 7.5 seconds. A look at the data set, therefore, reveals that these average electric vehicles take an average of 7 seconds to accelerate from zero and reach about a speed of one hundred kilometers per hour. For instance, several EVs like the Tesla Model S Performance and Porsche Taycan Turbo S have acceleration of less than 7 seconds.
b) Assessing Market Segment Claims:
This statement is indicated by the fact that most EVs do not belong to an “A” market segment. From my perspective, the “C” target market segment is presently leading with over 30 EVs. In terms of its positioning, it would be followed by the ‘B’ sector, which currently stands at an estimated number of around 20 units for their range. “A” and “N” identify market segments. Currently, they have the lowest number of electric vehicles in their respective segment.
Proportion= Count of Segment EVs /Total Number of EVs
“C” Segment Proportion= 30/92
33%
“B” Segment Proportion=20/92
22%
“D” Segment Proportion=15/92
16%
“Segment Proportion=11/92
12%
“E” Segment Proportion =10/92
11%
“A” Segment Proportion=3/92
3%
“Segment Proportion=3/92
3%
This implies that 33% of the electric vehicles in the dataset belong to the “C” market segment, which also has the highest number of EVs in the market segment section. Therefore, the fact that average acceleration for EVs occurs about 7 seconds or more cannot be concluded from this analysis. This claim was raised into question as the information shown revealed that a lot of EVs were, in fact, able to accelerate much quicker.
Appropriate sample size
Since sample size issues can be validated during the reliability of the analysis, in this section, we investigate whether the sample of 92 electric vehicles is sufficient to discover a measure that represents the average range without an error of more than 10 kilometers.
Results:
First, it is essential to get the standard deviation of our sample range. Based on this data, we can input the figures and find out what kind of sample has to be collected.
There appears to be greater than 10 kilometers of variation if we were measuring the average range from our current sample population size, which stands at approximately EVs. Increasing the sample size would make the estimation more precise (Li et al., 2021). The sample size required can vary by looking at the range’s standard deviation. That being stated, the margin of error is usually decreased when faced with a larger sample size. Increasing the size of our sample will help us to present results that are more representative and credible within a population.
Conclusion:
In conclusion, disputing established beliefs and shining a light on significant relationships in this exhaustive study of the Australian electric car business gives valuable input into essential facets. As we study the acceleration times and distribution of market segments, we can learn more about the electric car industry. Nonetheless, the larger dataset is imperative due to the limitations of sample size that make estimates imprecise. This research has important insights to further discussions about the growth of electric mobility in Australia, and they are helpful for consumers as well as business leaders.
References;
Li, C., Dong, Z., Chen, G., Zhou, B., Zhang, J., & Yu, X. (2021). Data-driven planning of electric vehicle charging infrastructure: A case study of Sydney, Australia. IEEE Transactions on Smart Grid, 12(4), 3289-3304.
https://ieeexplore.ieee.org/abstract/document/9335962/
Foley, B., Degirmenci, K., & Yigitcanlar, T. (2020). Factors affecting electric vehicle uptake: Insights from a descriptive analysis in Australia. Urban Science, 4(4), 57.
https://www.mdpi.com/2413-8851/4/4/57
Broadbent, G. H., Metternicht, G., & Drozdzewski, D. (2019). An analysis of consumer incentives in support of electric vehicle uptake: An Australian case study. World Electric Vehicle Journal, 10(1), 11.
https://www.mdpi.com/2032-6653/10/1/11
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Lectures notes
Module 1 & 2 Readings,
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