HVAC system modeling for range prediction of electric vehicles

The HVAC system is considered as the largest auxiliary power load in electric vehicles (EV). Therefore, this paper presents a detailed modeling of an EV-based HVAC system to support a priori prediction of HVAC system energy consumption under consideration of the EV users thermal comfort. This prediction is integrated into a navigation system to allow the driver entering the preferred parameters of thermal comfort and advising the driver about the predicted overall energy consumption. The advice acceptance might increase the awareness of the driver regarding the potential saved energy and leads to an energy-efficient vehicle operation by extending the overall driving range.

[1]  Johan Driesen,et al.  An Availability Analysis and Energy Consumption Model for a Flemish Fleet of Electric Vehicles , 2011 .

[2]  Jorge Moreno,et al.  Energy-management system for a hybrid electric vehicle, using ultracapacitors and neural networks , 2006, IEEE Transactions on Industrial Electronics.

[3]  Hassan K. Khalil,et al.  Output feedback control of nonlinear systems using RBF neural networks , 2000, IEEE Trans. Neural Networks Learn. Syst..

[4]  Jun Liu,et al.  Effect of entropy change of lithium intercalation in cathodes and anodes on Li-ion battery thermal management , 2010 .

[5]  Stev Gerson,et al.  Optimized Energy Management and Components for Electric Vehicles under different Environmental Conditions , 2010 .

[6]  Jeffrey D. Spitler,et al.  The Residential Heat Balance Method for Heating and Cooling Load Calculations , 2005 .

[7]  R. Farrington,et al.  IMPACT OF VEHICLE AIR-CONDITIONING ON FUEL ECONOMY. TAILPIPE EMISSIONS, AND ELECTRIC VEHICLE RANGE: PREPRINT , 2000 .

[8]  Martin Fellendorf,et al.  Estimating energy consumption for routing algorithms , 2012, 2012 IEEE Intelligent Vehicles Symposium.

[9]  Giorgio Rizzoni,et al.  A-ECMS: An Adaptive Algorithm for Hybrid Electric Vehicle Energy Management , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[10]  Di Wu,et al.  Electric Energy and Power Consumption by Light-Duty Plug-In Electric Vehicles , 2011, IEEE Transactions on Power Systems.

[11]  Qi Cheng,et al.  A new eco-driving assistance system for a light vehicle: Energy management and speed optimization , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

[12]  S. Haykin.  Kalman Filtering and Neural Networks , 2001 .

[13]  Olivier Tremblay,et al.  Experimental validation of a battery dynamic model for EV applications , 2009 .

[14]  Cheng Deng,et al.  Electric vehicle’s energy consumption of car-following models , 2013 .

[15]  박희상,et al.  Air-conditioning system of electric vehicle , 2009 .

[16]  P. Van den Bossche,et al.  The Cell versus the System: Standardization challenges for electricity storage devices , 2009 .

[17]  Andrew G Simpson.  Parametric modelling of energy consumption in road vehicles , 2005 .

[18]  Bin Yao,et al.  High performance adaptive robust control of nonlinear systems: a general framework and new schemes , 1997, Proceedings of the 36th IEEE Conference on Decision and Control.

[19]  Ashwin Victor Samuel,et al.  Energy management system for a hybrid electric vehicle , 2015 .

[20]  João L. Afonso,et al.  Mobile geographic range prediction for electric vehicles , 2011 .

[21]  Mohammad Farrokhi,et al.  State-of-Charge Estimation for Lithium-Ion Batteries Using Neural Networks and EKF , 2010, IEEE Transactions on Industrial Electronics.

[22]  Okyay Kaynak,et al.  Robust and adaptive backstepping control for nonlinear systems using RBF neural networks , 2004, IEEE Transactions on Neural Networks.