LEADER 01739nam  2200445z- 450 
001 9910576868503321
005 20231214133046.0
010   $a1000143200
035   $a(CKB)5860000000051233
035   $a(oapen)https://directory.doabooks.org/handle/20.500.12854/84371
035   $a(EXLCZ)995860000000051233
100   $a20202206d2022      |y             0
101 0 $aeng
135   $aurmn|---annan
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aStochastic Range Estimation Algorithms for Electric Vehicles using Data-Driven Learning Models
210   $aKarlsruhe$cKIT Scientific Publishing$d2022
215   $a1 electronic resource (192 p.)
225 1 $aKarlsruher Schriftenreihe Fahrzeugsystemtechnik
311   $a3-7315-1166-5 
330   $aThis work aims at improving the energy consumption forecast of electric vehicles by enhancing the prediction with a notion of uncertainty. The algorithm itself learns from driver and traffic data in a training set to generate accurate, driver-individual energy consumption forecasts.
606   $aMechanical engineering & materials$2bicssc
610   $aElektromobilität
610   $aVorhersagen
610   $aAlgorithmen
610   $aFahrzeugtechnik
610   $aEnergiemanagement
610   $aE-Mobility
610   $aForecasting
610   $aAlgorithms
610   $aVehicle Technology
610   $aEnergy Management
615  7$aMechanical engineering & materials
700   $aScheubner$b Stefan$4auth$01331144
906   $aBOOK
912   $a9910576868503321
996   $aStochastic Range Estimation Algorithms for Electric Vehicles using Data-Driven Learning Models$93040263
997   $aUNINA