Processed Data for Electric Vehicle Powertrain Efficiency: Integrating Driving Cycle Data and Electric Motor Efficiency Maps

Published: 3 August 2023| Version 1 | DOI: 10.17632/kbwr2z8r3y.1


This study focuses on using high-fidelity tracking (GPS) data obtained to design electric powertrains, mainly for retrofit applications. The tracking data was collected to study micro-mobility behaviour, particularly for minibus taxis. It also considered the impact of driving conditions, driving speed, and time of travel during the day on vehicle energy consumption. The data was recorded in trips to or from Bergzicht Taxi Rank in Stellenbosch, South Africa. This raw data can be found in the "Raw_tracking.csv" file, the dataset contains a comprehensive set of columns capturing various parameters related to a vehicle's performance and behaviour during its operation on these tracking routes. This enables engineers to determine the electric powertrain's expected efficiency, aiding in component selection and optimization for electric vehicles. Key steps in the method include: • Data manipulation: High-fidelity GPS tracking data is converted to the required torque and speed for the proposed electric drivetrain to replicate driver behaviour. • Electric motor efficiency map integration: The simulation process incorporates an electric motor efficiency map, ensuring a precise assessment of powertrain efficiency based on the specific characteristics and performance of the electric motor used. • Powertrain efficiency calculation: The overall powertrain efficiency of the proposed electric drivetrain is calculated and simulated based on the driving cycle data. The derived powertrain efficiency has broader applications beyond retrofit scenarios. It can be replicated and applied to design and develop new electric vehicles, providing insights into efficiency values and performance characteristics. Moreover, it supports strategic planning for electric infrastructure to encourage the widespread adoption of electric vehicles, thereby contributing to sustainable transportation solutions. The code provided "Drivetrain_efficiency_analysis.ipynb" simulates the data to generate the electric drive train efficiency by creating and plotting the driving cycle onto an electric motor efficiency map. This is used to generate the kinetic variables of the vehicle’s kinetic model namely the total resistive force, required motor torque required and required motor speed. The code enables the user to generate a specific motor efficiency map for the electric motor of the user's choosing. This will result in an output to generate an electric motor efficiency map on which the driving cycle data can be plotted. The motor efficiency is then used to determine the total drivetrain efficiency for a proposed electric powertrain for each driving cycle point. This analysed and processed data is found in the "Tracking_data_efficiecny.csv" file. In this data, a Bosch SMG180 electric motor is used to determine the efficiency. The "SMG180.csv" file contains the data points for the torque vs rpm curve of the motor.


Steps to reproduce

All the files and code files can be downloaded and run. If you want to adapt the data files to ensure your tracking data file is renamed to "Raw_tracking.csv" and change the column names to correspond to the functions within the code. You will require at minimum the following column names in your raw tracking data: "Velocity" "Slope Angle (rad)" "Acceleration"


Stellenbosch University


Transport, Automotive Engineering, Powertrain, Electric Vehicles, Electrical Mobility