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Hier finden Sie wissenschaftliche Publikationen aus den FraunhoferInstituten. Improvement of predictive energy efficiency optimization using long distance horizon estimation
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Postprint urn:nbn:de:0011n4644895 (455 KByte PDF) MD5 Fingerprint: 8cd5416b8f24cdcce53d0c9fab7b5d9c Created on: 24.10.2017 
 Institute of Electrical and Electronics Engineers IEEE; IEEE Intelligent Transportation Systems Society ITSS: 28th IEEE Intelligent Vehicles Symposium 2017 : June 1114, 2017, Redondo Beach, California, USA Piscataway, NJ: IEEE, 2017 ISBN: 9781509048045 ISBN: 9781509048038 ISBN: 9781509048052 pp.12491255 
 Intelligent Vehicles Symposium (IV) <28, 2017, Redondo Beach/Calif.> 

 English 
 Conference Paper, Electronic Publication 
 Fraunhofer IOSB () 
 improvement of predictive; energy efficiency; optimization using long distance; horizon estimation 
Abstract
Energy efficiency has become an important topic in trade, transportation and environment protection. Modern electric vehicles usually still have difficulties in reaching similar travel distances as combustion engine powered vehicles. While increasing the range of electric vehicles continues to be an active field of research, it is already possible to increase the energy efficiency by applying a more energy efficient driving behavior. A forwardbackward Dynamic Programming model predictive optimization approach is used to generate an energy efficient velocity and gear change trajectory. Due to the finite length of the computation horizon, common Dynamic Programming approaches sometimes have problems of choosing the optimal state at the end of the horizon. To address this problem, a method is presented that makes use of historic accumulated minimum costs to create a separate timeinvariant auxiliary horizon that grows during the journey. The auxiliary horizon is used to yield a better long range estimation of the optimal terminal behavior of the optimal trajectory within the regular horizon. While the proposed method can be applied to different types of optimization problems, the focus is on the predictive energy efficiency optimization of electric vehicles.