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Dipl.-Ing. Kurt Kruppok Academic Assistant Institute of Energy Efficient Mobility (IEEM)

Dipl.-Ing. Kurt Kruppok Academic Assistant Institute of Energy Efficient Mobility (IEEM) University of Applied Science, Karlsruhe, Germany. Hongkong, December 17th, 2017. Do you trust your vehicle’s range prediction?. In case of battery electric vehicles:

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Dipl.-Ing. Kurt Kruppok Academic Assistant Institute of Energy Efficient Mobility (IEEM)

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  1. Dipl.-Ing. Kurt Kruppok Academic Assistant Institute of Energy Efficient Mobility (IEEM) University of Applied Science, Karlsruhe, Germany Hongkong, December 17th, 2017

  2. Do you trust your vehicle’s range prediction? • In case of battery electric vehicles: heating, ventilation, and air-conditioning (HVAC) system has a major impact on the vehicle’s range 87 km 122 km Δ17 km Δ34 km 70 km 88 km 28 km 22 km +6 km (+21,4%) -5 km (-22,7%) NEVVE 2017 || Dipl.-Ing. Kurt Kruppok

  3. Motivation • Model Overview • Methodology of Optimization • Simulation Results • Outlook • Global Research Goals Validation of a 5-Zone-Car-Cabin Model to Predict the Energy Saving Potentials of a Battery Electric Vehicle’s HVAC system NEVVE 2017 || Dipl.-Ing. Kurt Kruppok

  4. No trust in the battery electrical vehicle’s range  range anxiety is still widespread  An efficient use of the available energy is necessary.  Knowledge of the overall energy demand is required • Energy loads of propulsion and auxiliaries are route dependent  The heating, ventilation, and air-conditioning (HVAC) system can be the largest auxiliary load.  Range of BEVs significantly reduces when the HVAC system is activated during bad conditions Why is it necessary to know the conditions in the car cabin? NEVVE 2017 || Dipl.-Ing. Kurt Kruppok

  5. Temperature, Radiation and Velocity Influence the Cabin’s Condition Ambient temperature diffuse Radiation direct V2 V5 V4 Flow velocity V1 Passengers Initial temperature HVAC System Seat heater V3 NEVVE 2017 || Dipl.-Ing. Kurt Kruppok

  6. The 5 zones are coupled to each other and to various chassis parts Roof plate Roof panel Wind-shield V4 V3 V5 Rear window V1 Dashboard Parcel shelf Side-window Interior Side panel Side plate V2 Legend Convection Radiation Transmittance Temperature Measurement point Underbody paneling HV-battery System boundary Underbody panel Thermal capacity NEVVE 2017 || Dipl.-Ing. Kurt Kruppok

  7. Heating process without radiation and velocity • Cooling process without radiation and velocity • Heating process including sun radiation, without velocity • Heating process including sun radiation and velocity (real test drive) • Relevant Cases were Chosen forOptimizingthe Model NEVVE 2017 || Dipl.-Ing. Kurt Kruppok

  8. The 5-Zone-Car-Cabin Model was optimized in multiple iterations Compare model with measurement Optimizationprocess • Choose measurement step • including: • Ambient Temperature • (Case 1 + Case 2) • Sun radiation • (Case 3) • Flow velocity • (Case 8) Sensitivity analysis for each volume Weighting of factors on entire vehicle cabin 10 most relevant parameters of a single volume 10 most relevant parameters of entire car cabin Method of least square to optimize selected parameters R² < 0.95 R² ≥ 0.95 Evaluation of the model quality NEVVE 2017 || Dipl.-Ing. Kurt Kruppok

  9. Modelled Cooling and Heating Process Represent Reality Well NEVVE 2017 || Dipl.-Ing. Kurt Kruppok

  10. Internal sensorsfor solar radiationare not accurateenough  Impact ofsunhastobesplittedtothe relevant volumes  Further measurementsofsunintensity will bedone Investigations on Sun Intensity and Energy Demand are Necessary Modeling the thermal state of the car cabin Air conditioning of the vehicle cabin with regard to thermal comfort Energy forecast of the HVAC system Calculation of the energy saving potential NEVVE 2017 || Dipl.-Ing. Kurt Kruppok

  11. Driver enters desired destination and starts his trip • What is the Target of Our Entire Investigations? Requests route geometry information including elevation data and collects traffic congestions, road properties and weather conditions Route Generation Generates a velocity profile based on given data Motion profile Energy Calculation Energy demand of propulsion and ancillary units Energy Saving Potentials Route and environment dependent potentials to save energy Predictive Control Control auxiliaries and/or limit propulsion in order to save energy Driver reaches his desired destination NEVVE 2017 || Dipl.-Ing. Kurt Kruppok

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