Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | 2018 IEEE 88th Vehicular Technology Conference, VTC-Fall 2018 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781538663585 |
| DOIs | |
| State | Published - 2 Jul 2018 |
| Event | 88th IEEE Vehicular Technology Conference, VTC-Fall 2018 - Chicago, United States Duration: 27 Aug 2018 → 30 Aug 2018 |
Publication series
| Name | IEEE Vehicular Technology Conference |
|---|---|
| Volume | 2018-August |
| ISSN (Print) | 1550-2252 |
Conference
| Conference | 88th IEEE Vehicular Technology Conference, VTC-Fall 2018 |
|---|---|
| Country/Territory | United States |
| City | Chicago |
| Period | 27/08/18 → 30/08/18 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
Keywords
- battery electric vehicle
- energy management system
- hybrid genetic algorithm
- multi-objective optimization
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