# Transfer of Statistical Customer Data into Relevant Parameters for the Design of Vehicle Drive Systems

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Methods

#### 2.1. Database

^{2}. This conspicuity is also reflected in [5], where the driver behavior of the 34 participants is analyzed based on recorded real drives over a distance of around 35,000 km. The evaluation shows that the highest accelerations actually driven in the speed range up to approximately 100 kph are below 1.5 m/s

^{2}and at speeds of up to 140 kph are mostly not higher than approximately 1.0 m/s

^{2}. The limitation of the speed range to a maximum of 130 kph is derived from [6]. According to this report, the average top speed on German highways without speed limit is between 110 and 130 kph. Abroad, this value is lower due to speed limits. Based on these findings, accelerations between −2 and 2 m/s

^{2}and speeds of up to 130 kph can initially be assumed to be customer relevant. Consequently, this area can be identified as the focus of customer driving operations and used as the basis for a customer-specific drive system design.

#### 2.2. Comparison of the Data Bases and Derivation of Representative Time Series

_{i,j}| as absolute value of the cell-by-cell difference between time series cell

_{TS}and CLC data cell

_{CLC}is calculated according to Equation (1).

_{i,j}| divided by the number of all cells n in the above-mentioned area of greatest customer operation.

#### 2.3. Validation of the Purposed Method

#### 2.4. Time Frame-Based Analysis (TFBA)

_{1}and z

_{2}, which are identical with respect to their classic statistical properties such as the root mean square (rms) z

_{rms}, the minimum value z

_{min}and maximum value z

_{max}as well as their distribution functions.

_{1}and 𝛝

_{2}, which leads to different temperature gradients and thus, different thermal stresses on the components.

_{tf}with continuously increasing width are defined and shifted over the time series [9]. According to Equation (3), the maximum rms value is calculated for each time frame width.

_{tf}must be at least equal to the time step width Δt. Additionally, the overall time series length T must be extended to length t

_{total}to ensure considering sufficient values for the largest time frame τ

_{tf,max}at the last time step t = T. Finally, the time frame is moved over the whole time series from t = 0 to t = T.

_{1}and z

_{2}and each time frame width, which indicates that the respective rms value is demanded at least once for that frame.

_{1}and 𝛝

_{2}above, the comparison between the two signals at τ

_{tf}= 3 s indicates that the rms value z

_{1}is greater than z

_{2}. This leads to the envelope curve z

_{max}, which represents the overall highest value of all loads. These highest values indicate maximum thermal load and are therefore crucial for the system design.

#### 2.5. Frequency of Time Frames

_{1}and z

_{2}. First of all, the black graph shows that all rms values are positive for both loads, so, as expected, the frequency of the rms value is 100%. Additionally, the figure displays the curves for an exemplary threshold value of 60, so the curves show the rms values z

_{rms}≥ 60. In this case, z

_{rms,1}is greater than z

_{rms,2}, both in terms of duration and frequency. This not only means that the time frame τ

_{tf}

_{,1}for this specific rms value is longer, but also the frequency of occurrence in this load has a higher share in the overall process. This leads to z

_{1}being critical for the system design. With the help of these analyses, loads of different duration, intensity and characteristics can be compared and design-critical loads can be identified.

## 3. Conclusions and Future Work

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Conflicts of Interest

## References

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**Figure 4.**Comparison of two statistically equal loads. Modified from [9], Technische Universität Dresden, 2016.

**Figure 5.**Exemplary time-weighted continuous load curves. Modified from [9], Technische Universität Dresden, 2016.

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**MDPI and ACS Style**

Mieth, R.; Gauterin, F.; Pauli, F.; Kraus, H.
Transfer of Statistical Customer Data into Relevant Parameters for the Design of Vehicle Drive Systems. *Vehicles* **2022**, *4*, 137-144.
https://doi.org/10.3390/vehicles4010009

**AMA Style**

Mieth R, Gauterin F, Pauli F, Kraus H.
Transfer of Statistical Customer Data into Relevant Parameters for the Design of Vehicle Drive Systems. *Vehicles*. 2022; 4(1):137-144.
https://doi.org/10.3390/vehicles4010009

**Chicago/Turabian Style**

Mieth, Raphael, Frank Gauterin, Felix Pauli, and Harald Kraus.
2022. "Transfer of Statistical Customer Data into Relevant Parameters for the Design of Vehicle Drive Systems" *Vehicles* 4, no. 1: 137-144.
https://doi.org/10.3390/vehicles4010009