TY - GEN
T1 - Analysing the driving load on electric vehicles using unsupervised segmentation models as enabler to determine the time of battery replacement and assess driving mileage
AU - Nguyen, Tam T.
AU - Mrowca, Artur
AU - Moser, Barbara
AU - Jossen, Andreas
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/5/21
Y1 - 2018/5/21
N2 - The aim of this contribution is to study the driving load on electric vehicles through data mining methods. The battery pack is currently the major cost driver for these vehicles. Thus, battery durability is an important lever to economically establish electric mobility in the mass market. A segmentation modelling approach, based on unsupervised clustering algorithms, evaluates battery management system field data from BMW i3 vehicles such as state of charge, temperature and current in terms of their historical distribution. Clustering algorithms, due to their frequent use in the field of machine learning and information retrieval, are able to analyse big quantities of vehicle data with the aim to group vehicles with similar driving behaviour. The cluster analysis is further validated using the method of calculating respective silhouette coefficients to assess clustering performance and the influence of input parameters. The analysis of histograms concludes with the definition of the most common types of drivers worldwide. The vehicle clusters can further be correlated with battery ageing in order to find suitable 2nd life applications as part of stationary energy storage systems.
AB - The aim of this contribution is to study the driving load on electric vehicles through data mining methods. The battery pack is currently the major cost driver for these vehicles. Thus, battery durability is an important lever to economically establish electric mobility in the mass market. A segmentation modelling approach, based on unsupervised clustering algorithms, evaluates battery management system field data from BMW i3 vehicles such as state of charge, temperature and current in terms of their historical distribution. Clustering algorithms, due to their frequent use in the field of machine learning and information retrieval, are able to analyse big quantities of vehicle data with the aim to group vehicles with similar driving behaviour. The cluster analysis is further validated using the method of calculating respective silhouette coefficients to assess clustering performance and the influence of input parameters. The analysis of histograms concludes with the definition of the most common types of drivers worldwide. The vehicle clusters can further be correlated with battery ageing in order to find suitable 2nd life applications as part of stationary energy storage systems.
KW - automotive battery
KW - clustering
KW - electrical energy storage
KW - lithium-ion batteries
KW - silhouette coefficient
KW - unsupervised segmentation model
UR - http://www.scopus.com/inward/record.url?scp=85048540258&partnerID=8YFLogxK
U2 - 10.1109/EVER.2018.8362381
DO - 10.1109/EVER.2018.8362381
M3 - Conference contribution
AN - SCOPUS:85048540258
T3 - 2018 13th International Conference on Ecological Vehicles and Renewable Energies, EVER 2018
SP - 1
EP - 7
BT - 2018 13th International Conference on Ecological Vehicles and Renewable Energies, EVER 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 13th International Conference on Ecological Vehicles and Renewable Energies, EVER 2018
Y2 - 10 April 2018 through 12 April 2018
ER -
