MASTER THESIS PROPOSAL | Traton How Much Data Is Enough? Optimal data sources and sampling strategies for energy consumption estimation of connected electric vehicles Background Estimating how much energy an electric vehicle consumes and how its state of charge SoC evolves depends on the availability and quality of vehicle data, as well as the frequency at which it is collected. At lower sampling frequencies, fewer observations are available to capture changes in vehicle state, which can reduce estimation accuracy. Selecting suitable data sources and sampling frequencies is therefore central to developing reliable predictions. Even where European regulations or standards apply, access to relevant data may remain limited. Many OEMs share only a small portion of their proprietary telematics data, often at very low and irregular frequencies. External devices such as OBD dongles can provide additional or more frequent measurements, but they add hardware, installation, and maintenance costs. Collecting data more frequently can also increase the costs of transmitting, storing, and processing signals across a fleet. Problem statement The objective of this work is to evaluate how accurately vehicle energy consumption can be estimated from possibly sparse available data at different sampling frequencies. The study will quantify how estimation accuracy changes according to varying sampling frequency and availability of data. Resulting estimates will be compared against the vehicle’s measured energy consumption, which uses higher-frequency measured data. As a secondary consideration, the accuracy gains obtained by using more frequent data collection will be assessed to understand if the use of external measurement devices is justified. The expected outcome is an evidence-based analysis on suitable data sources and sampling frequencies for scalable connected-vehicle applications as well as a cost analysis of using external devices for increased accuracy. Research questions How accurately can energy consumption and SoC be estimated using only OEM data, without external devices? When is an external device justified? When does the gain in accuracy outweigh its cost? What are the required specifications that an external device must meet to allow effective data collection and sampling? How much does estimation performance improves with an external, tunable device compared to using OEMs’ existing solutions? Quantitively study the benefits of the different options. What is the best tradeoff between sampling frequency and estimation performance? What is the best strategy to support an economy of scale? How can an adaptive strategy be implemented? How well does it perform? Possible approaches The topic can be approached in a more theoretical or a more practical way, depending on the student’s interests: Signal processing: spectral content of driving and battery signals, Nyquist and aliasing, down sampling, reconstruction and irregular sampling, information-theoreti
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