Techno-economic cell selection for battery-electric long-haul trucks

To reach cost-parity with diesel trucks, battery-electric trucks require fast-chargeable lithium-ion cells with a high energy density and cycle life, at a low specific cost. However, cells generally excel at only a fraction of these characteristics. To help select the optimal cell, we have developed the techno-economic cell selection method.

This repository provides the source code to the method, which we use to select the optimal cell out of a database containing over 160 cells for a long-haul truck operating with a single driver in Germany. The detailed documentation can be found in the following publication: https://doi.org/10.1016/j.etran.2022.100225

Versions

To obtain the exact results shown in the publication, checkout the commit tagged as V0.

Since the initial publication (see commit V0), the following changes have been made:

1. We included the influence of calendaric aging in the battery life estimate. Because cell datasheets do not provide any information on a cell's calendar life, we used estimates available for different cell chemistries from literature.
2. A recent report showed that BET achieve a significantly lower energy consumption than the initial simulation, which used the average drag area and rolling friction coefficient of DT registered in 2019. Therefore, the best-in-class drag area and energy density are used to match the reported energy consumption better.
3. The initial publication used the average volumetric and gravimetric packaging efficiencies reported for passenger cars released between 2010 and 2019. However, for newer vehicle models, higher packaging efficiencies have been reported. To reflect the state-of-the-art, we updated the packaging efficiency for pouch and prismatic cells to those seen in the VW ID.3. The values for cylindrical cells have been scaled accordingly.
4. The cell database has been expanded.
5. The cell used in the VW ID.3 is used as the reference cell in both scenarios, to show the applicability of state-of-the-art, large-volume-production automotive-grade cells.
6. The cell to pack cost ratio was updated to reflect state of the art BEV battery packs
7. A differentiation of the packaging efficiency for different cell chemistries was implemented

Prerequisites

To run the code, you'll need the following python packages:

• pandas
• numpy
• scipy
• numba
• matplotlib

Running the Model/Code

The results and all figures are generated by executing the file main.py. The execution time on a 16GB RAM, 1.8GHz machine is less than 30 seconds.

Contributing

If you would like to contribute to this work or have any feedback, please do not hesitate to contact me at olaf.teichert@tum.de

Authors

Olaf Teichert, Steffen Link

License

This project is licensed under the LGPL License - see the LICENSE.md file for details