Model-Predictive Energy Management for the Integration of Plug-In-Hybrid Electric Vehicles into Building Energy Systems

Paper #:
  • 2013-01-1443

  • 2013-04-08
Brendle, B., Hamacher, T., Lienkamp, M., Wilhelm, C. et al., "Model-Predictive Energy Management for the Integration of Plug-In-Hybrid Electric Vehicles into Building Energy Systems," SAE Technical Paper 2013-01-1443, 2013,
In current research projects such as "Vehicle to Grid" (V2G), "Vehicle to Building" (V2B) or "Vehicle to Home" (V2H), plug-in vehicles are integrated into stationary energy systems. V2B or V2H therefore stands for intelligent networking between vehicles and buildings. However, in these projects the objective is mostly from a pure electric point of view, to smooth the load profile on a household level by optimized charging and discharging of electric vehicles. In the present paper a small energy system of this kind, consisting of a building and a vehicle, is investigated from a holistic point of view. Thermal as well as electrical system components are taken into account and there is a focus on reduction of overall energy consumption and CO₂ emissions.A predictive energy management is presented that coordinates the integration of a plug-in hybrid electric vehicle into the energy systems of a building. System operation is optimized in terms of energy consumption and CO₂ emissions. A model predictive approach is applied to the charging phases of a plug-in hybrid electric vehicle as well as on the energy system of a building with integrated energy generation by a cogeneration unit and a photovoltaic system. In the present paper the energy-saving potential for different mobility scenarios that can be achieved through a holistic, integrative energy management is shown. The overall primary energy demand of the energy system as described is examined with a simulation model.The energy management contains, in a similar way to an MPC (Model Predictive Control) system, a model of the system dynamics. With this model, prediction of the energy process is conducted, based on a weather forecast and future mobility patterns. The future development of all relevant variables is thus predicted. Based on this, optimization of the operational management takes place. As part of this prediction process the best operation strategy for the manipulation of flexible system components is determined and selected.The energy management system optimizes and coordinates the use of components and the energy flows within the coupled energy system and involves the entire "well-to-wheel" chain for an ecological system operation.
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