Statistical Analysis of the Interdependence between the Technical and Functional Parameters of Electric Vehicles in the European Market
Abstract
:1. Introduction
2. Materials and Methods
2.1. Primary Statistical Analysis
2.2. Multivariate Statistical Correlation Analysis
- A first statistical correlation analysis, in which we wanted to highlight the causal interdependence (causal link) between a series of performance parameters (autonomy, maximum speed, acceleration) and a series of technical parameters (battery capacity, energy efficiency, fast charging speed, weight, electric motor power) of the electric vehicle. The statistical correlation method based on the Pearson correlation coefficient is a technique used to measure the degree of the linear relationship between two continuous variables. The Pearson correlation coefficient is based on the covariance and standard deviation of the two variables and ranges from −1 to +1. The formula used for calculating the Pearson correlation coefficient is:
- rxy is the Pearson correlation coefficient;
- n is the number of observation pairs (sample size);
- xi are the individual values of the x variable;
- yi are the individual values of the y variable;
- is the arithmetic mean of all x values;
- is the arithmetic mean of all y values.
- If rxy = 1, there is a perfect positive relationship between the two variables.
- If rxy = −1, there is a perfect negative relationship between the two variables.
- If rxy is close to 0, there is no linear relationship between the two variables.
- If rxy is positive, then the two variables are positively associated, meaning that if one increases, the other also increases.
- If rxy is negative, then the two variables are negatively associated, meaning that if one increases, the other decreases.
- (a)
- Accuracy: statistical correlation analysis measures the degree of the linear relationship between two variables, which means that it can be used to precisely evaluate the interdependence between the commercial performance of the electric vehicle and its technical parameters. Thus, the prediction model can be highly accurate in estimating the performance of the electric vehicle.
- (b)
- Ease of use: statistical correlation analysis is a metric and simple method that is easy to understand, which makes the prediction model accessible and easy to understand and interpret for specialists in the automotive industry.
- (c)
- Scalability: the prediction model is scalable and can be applied to a wide range of electric vehicles, regardless of their size or performance.
- (d)
- Identification of significant relationships: the model can identify significant relationships between commercial performance and technical parameters, so electric vehicle manufacturers can adjust and make improvements to their vehicle design and specifications to increase their performance and efficiency.
- 2.
- Construction of a causal model and continuous interdependence of the technical and performance parameters of electric vehicles (based on the links/interdependence between them), and model moderated/influenced by customer needs and requests (fast charging and constructive type).
2.2.1. Research Hypotheses
2.2.2. Data Analysis and Interpretation
- 1.
- Hypothesis H1.
- 2.
- Hypothesis H2.
- 3.
- Hypothesis H3.
3. Conclusions
- (1)
- A first level aiming at an interdependence due to market demands (requests) of customers and the performance and technical parameters of electric vehicles. The positive interdependence shows an increase over time of both the performance parameters of electric vehicles (especially in terms of autonomy and acceleration) and the technical parameters (especially related to the fast charging speed).
- (2)
- A second level of interdependence between performance parameters and technical parameters of electric vehicles (positive interdependence). In this case, the greatest interdependence refers to the connection between the autonomy of electric vehicles, vehicle’s curb weight, and energetic capacity of the battery (+0.355 and +0.687, respectively), and between the dynamics of an electric vehicle (acceleration and maximum speed) and the power of the electric motor (+0.633 and +0.661, respectively). The only negative interdependence in the model is between vehicle’s curb weight and energy efficiency (−0.181) (see Figure 20).
- (3)
- A third level of interdependence, with the negative link between the weight and the energy efficiency of electric vehicles, which highlights the need to continue research and the development of new solutions by the automotive industry to improve this problem.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Direction of Study | Topics | Ref. |
---|---|---|
Consumer attitudes |
| [19] |
| [20] | |
| [22] | |
| [23] | |
| [24] | |
Barriers to integration |
| [21] |
| [25] | |
| [26] | |
| [27] | |
Energy efficiency of battery |
| [28] |
| [29] | |
| [30] | |
| [31] | |
| [32] | |
EV components |
| [33] |
| [34] | |
| [35] | |
| [36] | |
| [37] | |
Integration in power grids |
| [38] |
| [39] | |
| [40] | |
Use of sustainable energy |
| [41] |
| [42] | |
| [43] |
Type/Class | Frequency | Percentage (%) | Cumulative Percentage (%) |
---|---|---|---|
Hatchback | 29 | 14.3 | 14.3 |
Coupe | 4 | 2.0 | 16.3 |
Sedan | 11 | 5.4 | 21.7 |
Cabriolet | 1 | 0.5 | 22.2 |
Sport Utility Vehicle | 77 | 37.9 | 60.1 |
Crossover | 10 | 4.9 | 65.0 |
Van | 51 | 25.1 | 90.1 |
Station | 20 | 9.9 | 100.0 |
Total | 203 | 100.0 | - |
Parameter | Class 1 | Class 2 | Class 3 | Class 4 |
---|---|---|---|---|
Battery energy capacity (kWh) | <27 | 28–54 | 55–80 | >80 |
Fast charging speed (km/1 h) | 100–300 | 301–600 | 601–900 | >900 |
Electric engine power (kW) | <150 | 151–250 | 251–350 | >350 |
Energy efficiency (kWh/km) | >27 | 22–27 | 16–21 | 10–15 |
Car weight (kg) | <1500 | 1501–2000 | 2001–2500 | >2500 |
Maximum speed (km/h) | <130 | 131–180 | 181–230 | >230 |
Acceleration (m/s2) | >12.0 | 8.1–12.0 | 4.1–8.0 | <4.0 |
Autonomy (km) | <150 | 151–300 | 301–450 | >451 |
Parameter | Autonomy (km) | Maximum Speed (km/h) | Acceleration (m/s2) | |
---|---|---|---|---|
Battery energy capacity (kWh) | Pearson Correlation | 0.606 ** | 0.434 ** | 0.462 ** |
Sig. (2-tailed) | 0.000 | 0.000 | 0.000 | |
N | 203 | 203 | 203 | |
Energetic efficiency (Wh/km) | Pearson Correlation | 0.425 ** | 0.359 ** | 0.455 ** |
Sig. (2-tailed) | 0.000 | 0.000 | 0.000 | |
N | 203 | 203 | 203 | |
Electric engine power (kW) | Pearson Correlation | 0.489 ** | 0.661 ** | 0.633 ** |
Sig. (2-tailed) | 0.000 | 0.000 | 0.000 | |
N | 203 | 203 | 203 | |
Fast charging speed (km/1 h) | Pearson Correlation | 0.401 ** | 0.409 ** | 0.425 ** |
Sig. (2-tailed) | 0.000 | 0.000 | 0.000 | |
N | 203 | 203 | 203 | |
Car weight (kg) | Pearson Correlation | 0.518 ** | 0.309 ** | 0.332 ** |
Sig. (2-tailed) | 0.000 | 0.000 | 0.000 | |
N | 203 | 203 | 203 |
Parameter | Battery Energy Capacity (kWh) | Energy Efficiency (kWh/km) | Electric Engine Power (kW) | Car Weight (kg) | Fast Charging Speed (km/1 h) | |
---|---|---|---|---|---|---|
Battery energy capacity (kWh) | Pearson Corr. | 1 | 0.037 | 0.454 ** | 0.687 ** | 0.564 ** |
Sig. (2-tailed) | 0.597 | 0.000 | 0.000 | 0.000 | ||
N | 203 | 203 | 203 | 203 | 203 | |
Energy efficiency (kWh/km) | Pearson Corr. | 0.037 | 1 | 0.299 ** | −0.181 ** | 0.391 ** |
Sig. (2-tailed) | 0.597 | 0.000 | 0.010 | 0.000 | ||
N | 203 | 203 | 203 | 203 | 203 | |
Electric engine power (kW) | Pearson Corr. | 0.454 ** | 0.299 ** | 1 | 0.355 ** | 0.488 ** |
Sig. (2-tailed) | 0.000 | 0.000 | 0.000 | 0.000 | ||
N | 203 | 203 | 203 | 203 | 203 | |
Car weight (kg) | Pearson Corr. | 0.687 ** | −0.181 ** | 0.355 ** | 1 | 0.385 ** |
Sig. (2-tailed) | 0.000 | 0.010 | 0.000 | 0.000 | ||
N | 203 | 203 | 203 | 203 | 203 | |
Fast charging speed (km/1 h) | Pearson Corr. | 0.564 ** | 0.391 ** | 0.488 ** | 0.385 ** | 1 |
Sig. (2-tailed) | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | |
N | 203 | 203 | 203 | 203 | 203 |
Years (2019–2022) | ||
---|---|---|
Years (2019–2022) | Pearson Correlation | 1 |
Sig. (2-tailed) | ||
N | 203 | |
Autonomy (km) | Pearson Correlation | 0.397 ** |
Sig. (2-tailed) | 0.000 | |
N | 203 | |
Maximum speed (km/h) | Pearson Correlation | 0.297 ** |
Sig. (2-tailed) | 0.000 | |
N | 203 | |
Acceleration (m/s2) | Pearson Correlation | 0.413 ** |
Sig. (2-tailed) | 0.000 | |
N | 203 |
Years (2019–2022) | ||
---|---|---|
Battery energy capacity (kWh) | Pearson Correlation | 0.244 ** |
Sig. (2-tailed) | 0.000 | |
N | 203 | |
Energy efficiency (kWh/km) | Pearson Correlation | 0.325 ** |
Sig. (2-tailed) | 0.000 | |
N | 203 | |
Electric engine power (kW) | Pearson Correlation | 0.259 ** |
Sig. (2-tailed) | 0.000 | |
N | 203 | |
Fast charging speed (km/h) | Pearson Correlation | 0.374 ** |
Sig. (2-tailed) | 0.000 | |
N | 203 | |
Car weight (kg) | Pearson Correlation | 0.194 ** |
Sig. (2-tailed) | 0.000 | |
N | 203 |
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Mariasiu, F.; Chereches, I.A.; Raboca, H. Statistical Analysis of the Interdependence between the Technical and Functional Parameters of Electric Vehicles in the European Market. Energies 2023, 16, 2974. https://doi.org/10.3390/en16072974
Mariasiu F, Chereches IA, Raboca H. Statistical Analysis of the Interdependence between the Technical and Functional Parameters of Electric Vehicles in the European Market. Energies. 2023; 16(7):2974. https://doi.org/10.3390/en16072974
Chicago/Turabian StyleMariasiu, Florin, Ioan Aurel Chereches, and Horia Raboca. 2023. "Statistical Analysis of the Interdependence between the Technical and Functional Parameters of Electric Vehicles in the European Market" Energies 16, no. 7: 2974. https://doi.org/10.3390/en16072974