Optimizing EV truck fleet charging schedules on depot
As the commercial transport sector transitions toward electrification, the need for intelligent energy management tools becomes increasingly critical. This thesis presents a modular, datadriven optimization framework for scheduling electric truck fleet charging at a single depot. Developed alongside...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/444949 |
| Acceso en línea: | https://hdl.handle.net/2117/444949 |
| Access Level: | acceso embargado |
| Palabra clave: | Electric vehicles Mathematical optimization Battery charging stations (Electric vehicles) Vehicles elèctrics Optimització matemàtica Estacions de càrrega (Vehicles elèctrics) Àrees temàtiques de la UPC::Enginyeria elèctrica |
| Sumario: | As the commercial transport sector transitions toward electrification, the need for intelligent energy management tools becomes increasingly critical. This thesis presents a modular, datadriven optimization framework for scheduling electric truck fleet charging at a single depot. Developed alongside a partner job with Scania, the tool generates cost-minimizing charging plans by aligning operational constraints, such as truck availability, charger power limits, and State-of-Charge (SoC) requirements, with dynamic electricity prices from the Nord Pool dayahead market. The optimizer was implemented in Python using linear programming techniques (Pyomo + GLPK) and deployed entirely through Scania’s cloud-based CI/CD infrastructure to ensure automation and scalability. A detailed case study using real operational data from a Swedish Scania customer demonstrated substantial benefits: over 90% cost savings compared to flat-rate electricity contracts, improved km/SEK efficiency as routes lengthen, and a clear sensitivity to infrastructure limitations and fleet scale. Sensitivity analyses further quantified how small reductions in charger count or fleet expansions could lead to infeasibility, emphasizing the importance of strategic infrastructure planning. The tool is modular, requiring only standardized input files and no local computation, making it adaptable across clients and markets within the Nord Pool region. While the current version focuses on static, day-ahead planning for a single depot, proposed extensions include multidepot scheduling, battery degradation modelling, real-time re-optimization, and integration of telematics-based consumption forecasts. This thesis contributes both a practical tool for fleet electrification and a research foundation for future development. By combining operational realism with robust optimization, the methodology helps bridge the gap between sustainable transport goals and real-world implementation challenges. |
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