TY - GEN
T1 - Databased Architecture Modeling for Battery Electric Vehicles
AU - Nicoletti, Lorenzo
AU - Schmid, Werner
AU - Lienkamp, Markus
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/9/10
Y1 - 2020/9/10
N2 - In recent years, the number of electric vehicles on the market has continuously increased. New regulations and the progressively critical effects of global warming contribute to the acceleration of this trend. Car manufacturers are obliged to redesign their fleet, gradually substituting the internal combustion vehicles with electrified vehicles. This is a complicated task, as electric powertrains still represent a new technology and no established vehicle architectures exist. To aid engineers in identifying feasible architectures in the early development phase, package tools can be employed. As in this phase, few parameters are known, such tools have to be based on empirical models. The data upon which empirical models are based must be updated cyclically. With each update, the models have to be manually recalculated, which is a time-consuming process. Therefore, this paper will propose new modeling that will enable the empirical models to update automatically. Firstly, the authors will describe the elements that make up an electric vehicle architecture. Subsequently, a database concept that enables the required data to be stored will be presented. Finally, this paper will describe the tool implementation.
AB - In recent years, the number of electric vehicles on the market has continuously increased. New regulations and the progressively critical effects of global warming contribute to the acceleration of this trend. Car manufacturers are obliged to redesign their fleet, gradually substituting the internal combustion vehicles with electrified vehicles. This is a complicated task, as electric powertrains still represent a new technology and no established vehicle architectures exist. To aid engineers in identifying feasible architectures in the early development phase, package tools can be employed. As in this phase, few parameters are known, such tools have to be based on empirical models. The data upon which empirical models are based must be updated cyclically. With each update, the models have to be manually recalculated, which is a time-consuming process. Therefore, this paper will propose new modeling that will enable the empirical models to update automatically. Firstly, the authors will describe the elements that make up an electric vehicle architecture. Subsequently, a database concept that enables the required data to be stored will be presented. Finally, this paper will describe the tool implementation.
KW - database
KW - electric vehicles
KW - empirical modeling
KW - vehicle architecture
UR - http://www.scopus.com/inward/record.url?scp=85085636877&partnerID=8YFLogxK
U2 - 10.1109/EVER48776.2020.9242995
DO - 10.1109/EVER48776.2020.9242995
M3 - Conference contribution
AN - SCOPUS:85085636877
T3 - 2020 15th International Conference on Ecological Vehicles and Renewable Energies, EVER 2020
BT - 2020 15th International Conference on Ecological Vehicles and Renewable Energies, EVER 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th International Conference on Ecological Vehicles and Renewable Energies, EVER 2020
Y2 - 10 September 2020 through 12 September 2020
ER -