Concept for a Holistic Energy Management System for Battery Electric Vehicles Using Hybrid Genetic Algorithms

Katharina Minnerup, Thomas Herrmann, Matthias Steinstraeter, Markus Lienkamp

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


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 languageEnglish
Title of host publication2018 IEEE 88th Vehicular Technology Conference, VTC-Fall 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538663585
StatePublished - 2 Jul 2018
Event88th IEEE Vehicular Technology Conference, VTC-Fall 2018 - Chicago, United States
Duration: 27 Aug 201830 Aug 2018

Publication series

NameIEEE Vehicular Technology Conference
ISSN (Print)1550-2252


Conference88th IEEE Vehicular Technology Conference, VTC-Fall 2018
Country/TerritoryUnited States


  • battery electric vehicle
  • energy management system
  • hybrid genetic algorithm
  • multi-objective optimization


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