Thesis - Energy Mapping and Optimization for Electric Bus Fleets

Technology, Data & Digital · Data, AI & Analytics · Data Science · Machine Learning

In short

This thesis project, part of the ElbusTransTherm research project, focuses on energy mapping and optimization for electric bus fleets in Stockholm. You will work with real industry data from a public transport operator, applying data science and AI to analyze energy consumption and develop optimization strategies for HVAC and battery management.

Responsibilities

Develop a detailed empirical breakdown of fleet energy use (drivetrain, HVAC, auxiliaries) by time of day, season, and route.
Create a data-driven Sankey diagram with quantitative variance analysis identifying key energy drivers.
Choose one track: Passenger-Centric Climate Control (adaptive HVAC strategies), Integrated Thermal-Energy Management (linking battery health, powertrain, and HVAC), or Managing Operational Phases (analyzing energy consumption across different operational states).
Contribute to a reusable data pipeline and analysis methodology intended for operational deployment.

Requirements

Open to students across relevant fields: energy systems, mechanical/thermal engineering, data science, machine learning, electrical engineering, or applied mathematics.
Interest in modeling, optimization, or applied AI.
Ability to work with real-world data.
Must be a student.

Benefits

Industry data and placement.
Work directly with fleet telemetry from a commercial Stockholm public transport operator.
Collaboration with SL.
A shared cohort of four parallel theses across different operators.
Regular contact with peers and a joint results seminar.
Applied AI and data science experience.
Large-scale real data analysis and modeling.
Opportunity to work on an active, unresolved research area.
Outputs intended for operational use.
Academic rigour ensured through academic supervisor from KTH.

Skills

Data ScienceMachine LearningEnergy SystemsMechanical EngineeringThermal EngineeringElectrical EngineeringApplied MathematicsAI

#Energy mapping#electric bus fleet#Stockholm#Heltid#Trafikförvaltningen

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