mattiasingelstrom/hd-ev-charging-profiles: Charging Demand Profiles for Heavy-Duty Electric Trucks — Skåne, Sweden
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| Formato: | Recurso digital |
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2026
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| _version_ | 1866901601104429056 |
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| author | Mattias Ingelström |
| author_facet | Mattias Ingelström |
| contents | <h2>Initial release — Reproducibility package</h2> <p>Hourly charging demand profiles for heavy-duty electric trucks in the Skåne region of southern Sweden, derived from 78 iterations of a MATSim agent-based transport simulation.</p> <h3>Contents</h3> <ul> <li><strong>Normalised profiles</strong> (fraction of daily energy, sum = 1.0) — reusable temporal shapes independent of fleet size</li> <li><strong>Scaled MW curves</strong> — pre-scaled to simulation-default fleet sizes</li> <li><strong>Absolute demand curves</strong> (kW) — with full percentile bands (P5–P95)</li> <li><strong>Battery-size breakdowns</strong> — profiles for five battery-capacity bins (0–200 through 500–600 kWh)</li> <li><strong>Self-contained Python plotting scripts</strong> — regenerate all figures or re-scale to custom scenarios</li> </ul> <h3>Charging scenarios</h3> <p>| Scenario | Description | |----------|-------------| | <strong>Public</strong> | En-route fast / opportunity charging (1,269 MWh/day, ~1,894 trucks) | | <strong>Depot</strong> | Optimised overnight charging with peak shaving (141.6 MWh/day, ~756 trucks) |</p> <h3>How to use</h3> <pre><code>pip install pandas numpy matplotlib python [plot_normalized_profiles.py](http://_vscodecontentref_/0) </code></pre> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18631297 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | mattiasingelstrom/hd-ev-charging-profiles: Charging Demand Profiles for Heavy-Duty Electric Trucks — Skåne, Sweden Mattias Ingelström <h2>Initial release — Reproducibility package</h2> <p>Hourly charging demand profiles for heavy-duty electric trucks in the Skåne region of southern Sweden, derived from 78 iterations of a MATSim agent-based transport simulation.</p> <h3>Contents</h3> <ul> <li><strong>Normalised profiles</strong> (fraction of daily energy, sum = 1.0) — reusable temporal shapes independent of fleet size</li> <li><strong>Scaled MW curves</strong> — pre-scaled to simulation-default fleet sizes</li> <li><strong>Absolute demand curves</strong> (kW) — with full percentile bands (P5–P95)</li> <li><strong>Battery-size breakdowns</strong> — profiles for five battery-capacity bins (0–200 through 500–600 kWh)</li> <li><strong>Self-contained Python plotting scripts</strong> — regenerate all figures or re-scale to custom scenarios</li> </ul> <h3>Charging scenarios</h3> <p>| Scenario | Description | |----------|-------------| | <strong>Public</strong> | En-route fast / opportunity charging (1,269 MWh/day, ~1,894 trucks) | | <strong>Depot</strong> | Optimised overnight charging with peak shaving (141.6 MWh/day, ~756 trucks) |</p> <h3>How to use</h3> <pre><code>pip install pandas numpy matplotlib python [plot_normalized_profiles.py](http://_vscodecontentref_/0) </code></pre> |
| title | mattiasingelstrom/hd-ev-charging-profiles: Charging Demand Profiles for Heavy-Duty Electric Trucks — Skåne, Sweden |
| url | https://doi.org/10.5281/zenodo.18631297 |