Automation of Energy Management Analysis in Automotive Engineering with Python

This paper presents the development and implementation of an automated application for the analysis of Energy Management tests in the automotive industry, using Python as the primary tool. The application automates the processing of data acquired from various test types, including Coast Down, Parasitic Losses, Full Load, Acceleration Metrics, and Fuel Consumption. By transforming data into dataframes and utilizing Python functions, the application achieves a significant reduction in the time required for data analysis, surpassing traditional methods reliant on Excel spreadsheets. The automation improves efficiency and enhances the precision of engineering calculations, making the process more dynamic and less prone to human errors. The results demonstrate a reduction of over 70% in data analysis time, with the potential for further improvements as all engineering calculations are fully transitioned to Python. This approach offers a competitive advantage for automotive companies, optimizing their processes and accelerating product development.

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Published
2024-11-27
DOI
https://doi.org/10.5753/latinoware.2024.245339
Primary Topic
Vehicle emissions and performance
Type
article
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article

Automation of Energy Management Analysis in Automotive Engineering with Python

Tiago Costa, Rafael Cardoso Carvalho, Larissa Pestana
1 links
Vehicle emissions and performance
article

Automation of Energy Management Analysis in Automotive Engineering with Python

Tiago Costa, Rafael Cardoso Carvalho, Larissa Pestana
article en

Abstract

This paper presents the development and implementation of an automated application for the analysis of Energy Management tests in the automotive industry, using Python as the primary tool. The application automates the processing of data acquired from various test types, including Coast Down, Parasitic Losses, Full Load, Acceleration Metrics, and Fuel Consumption. By transforming data into dataframes and utilizing Python functions, the application achieves a significant reduction in the time required for data analysis, surpassing traditional methods reliant on Excel spreadsheets. The automation improves efficiency and enhances the precision of engineering calculations, making the process more dynamic and less prone to human errors. The results demonstrate a reduction of over 70% in data analysis time, with the potential for further improvements as all engineering calculations are fully transitioned to Python. This approach offers a competitive advantage for automotive companies, optimizing their processes and accelerating product development.

Universidade Federal Rural de Pernambuco (BR)
Affordable and clean energy
Openalex Percentile: Top 77%
Vehicle emissions and performance
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Automation of Energy Management Analysis in Automotive Engineering with Python — Tiago Costa, Rafael Cardoso Carvalho, et al. · (2024) | TGRS Research Map | TGRS