TY - GEN
T1 - Concept for a Holistic Energy Management System for Battery Electric Vehicles Using Hybrid Genetic Algorithms
AU - Minnerup, Katharina
AU - Herrmann, Thomas
AU - Steinstraeter, Matthias
AU - Lienkamp, Markus
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - Due to the limited range of todays electric vehicles, it is important to lower the energy consumption for these vehicles. This can for example be achieved by employing an energy management system. Most research in this field focuses on strategies for individual components and only some literature exists on holistic energy management concepts. This paper presents an optimization-based holistic energy management system. The strategy is currently developed within a simulation environment and will, in the future, be adapted to a usage in existing vehicles. To demonstrate the feasibility of the concept, a hybrid genetic algorithm is implemented. By adjusting the velocity for each spatial discretization step and the air conditioning unit's power for each time step of a driving cycle, the total energy consumption, traveling time, and cabin temperature are optimized. The results show that the energy consumption can be considerably reduced, while keeping the driver comfort well within acceptable limits and the driving time constant.
AB - Due to the limited range of todays electric vehicles, it is important to lower the energy consumption for these vehicles. This can for example be achieved by employing an energy management system. Most research in this field focuses on strategies for individual components and only some literature exists on holistic energy management concepts. This paper presents an optimization-based holistic energy management system. The strategy is currently developed within a simulation environment and will, in the future, be adapted to a usage in existing vehicles. To demonstrate the feasibility of the concept, a hybrid genetic algorithm is implemented. By adjusting the velocity for each spatial discretization step and the air conditioning unit's power for each time step of a driving cycle, the total energy consumption, traveling time, and cabin temperature are optimized. The results show that the energy consumption can be considerably reduced, while keeping the driver comfort well within acceptable limits and the driving time constant.
KW - battery electric vehicle
KW - energy management system
KW - hybrid genetic algorithm
KW - multi-objective optimization
UR - http://www.scopus.com/inward/record.url?scp=85064894000&partnerID=8YFLogxK
U2 - 10.1109/VTCFall.2018.8690563
DO - 10.1109/VTCFall.2018.8690563
M3 - Conference contribution
AN - SCOPUS:85064894000
T3 - IEEE Vehicular Technology Conference
BT - 2018 IEEE 88th Vehicular Technology Conference, VTC-Fall 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 88th IEEE Vehicular Technology Conference, VTC-Fall 2018
Y2 - 27 August 2018 through 30 August 2018
ER -