Predicting Vehicle Usage Using Incremental Machine Learning
Examensarbete för masterexamen
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Bibliographical item details
|Type: ||Examensarbete för masterexamen|
|Title: ||Predicting Vehicle Usage Using Incremental Machine Learning|
|Authors: ||Lindroth, Tobias|
|Abstract: ||Today, there is an ongoing transition to more sustainable transportation, and an
essential part of this transition is the switch from combustion engine vehicles to
battery electric vehicles (BEVs). BEVs have many advantages from a sustainable
perspective, but issues such as limited driving range and long recharge times slows
down the transition from combustion engines. One way to mitigate these issues is by
performing battery thermal preconditioning, which increases the energy efficiency
of the battery. However, to optimally perform battery thermal preconditioning, the
vehicle usage pattern needs to be known, i.e., how and when the vehicle will be used.
This study attempts to predict the departure time and distance of upcoming drives
using different incremental machine learning models. Further, the study includes a
sensitivity analysis investigating how sensitive the performance of the battery thermal
preconditioning is to incorrect predictions.
The problem of predicting departure time and trip distance is approached in two
different ways. The first approach only considers the first drive each day, while
the second approach considers all drives that directly follows a charging session.
The incremental machine learning models are trained and evaluated on historical
driving data collected from a fleet of BEVs. Additionally, the prediction models
are extended to quantify the uncertainty of their predictions, which can be used as
guidance to whether the prediction should be used or dismissed.
The best-performing prediction models yield an aggregated mean absolute error of
2.6 hours when predicting departure time and 12.5 km when predicting trip distance.
However, for the considered temperatures and distances, the sensitivity analysis
shows that the battery thermal preconditioning requires more precise predictions.
The performance of the battery thermal preconditioning is sensitive, as the energy
that can be saved from accurate predictions is less than what may be lost by adapting
the preconditioning to incorrect predictions.|
|Keywords: ||Battery electric vehicles;Incremental machine learning;Uncertainty quantification;Sensitivity analysis;Time-to-leave;Trip distance|
|Issue Date: ||2022|
|Publisher: ||Chalmers tekniska högskola / Institutionen för data och informationsteknik|
|Collection:||Examensarbeten för masterexamen // Master Theses|
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