Representative cycles using Data Analytics Approach

Volvo Business Services AB / Datajobb / Göteborg
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Background of thesis project
With the continued development of Heavy duty trucks to increase efficiency a demand for increased optimization to make the vehicles perform on par with the costumers expectations. A continuous process of verification and validation is done before releasing vehicles into production and a crucial part of this is to have representative drive cycles.
Drive Cycles are a crucial part to verify functionalities of the heavy duty vehicles in situations that the costumers experience, hence the drive cycle should capture the typical costumers experiences with the vehicle. A case has been made if there is any implementation of methods that can possibly improve the correlation between internal duty cycles and costumer usage.

Suitable background
Master's education in Engineering Physics, Mathematics, Computer Science or equivalent.

Description of thesis work
The work would include to Establish, validate and compare different statistical methods to create synthetic drive cycles. Throughout the years these methods includes Monte Carlo, Random Forest, K-means Clustering and Support Vector Machines. The work would in the end be used to compare and determine if one of these methods can be used to improve the way drive cycles are used.
By using these methods a drive cycle can be created that should contain statistically significant similarities with what the costumers are experiencing. Hence, these cycles can be used within Simulations environments and test cycles.

Thesis Level: Master and/or Bachelor

Language:
English

Starting date:
January 2024.

Number of students:
One or two.

Tutor
Gabriel Angerd, Data Scientist

Så ansöker du
Sista dag att ansöka är 2023-10-31
Klicka på denna länk för att göra din ansökan

Omfattning
Detta är ett heltidsjobb.

Arbetsgivare
Volvo Business Services AB (org.nr 556029-5197)
405 08  GÖTEBORG

Arbetsplats
Group Trucks Technology

Jobbnummer
8145183

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