Evaluation of the energy demand for air conditioning by means of weather and mobility data

Abstract

The goal of this paper is to develop a method for the assessment of the impact of the air conditioning system on the driving range of electric vehicles. The presented method considers real life driving behaviour and environmental conditions. Therefore recorded user data are analysed to create mobility profiles with respect to time, duration and average speed. A simulation-based energy analysis is performed using the mobility profiles, a thermal model of the vehicle cabin and climate data for specific locations. The required energy is quantified for five selected climatic zones in the course of the year. The developed method can also be used to evaluate the influence of specific measures on the energy demand with respect to real life driving behaviour and environmental conditions. 1 Motivation / Introduction Compared with conventional vehicles, electric vehicles have a reduced driving range due to the limited energy content of the traction battery. The driving range is reduced further by the energy consumption of auxiliary systems, especially the airconditioning system. The assessment of the impact on the driving range is complex and requires an extensive analysis of the environmental-specific energy flows and the individual driving behaviour. Previous studies ([1], [2]) predominantly use the standardized NEDC driving cycle to quantify the energy demand. This driving cycle was created for standardized comparison of vehicles energy demand but does not represent all user groups and their driving behaviour. The goal of this study is the development of a method to evaluate the impact of the air-conditioning the driving range, using customer driving data and environmental conditions. This method can be used for a user-group-specific evaluation of measures to increase driving range. It connects real life driving data with a thermodynamic model of the vehicle cabin. To do so the driving data are aggregated to create mobility profiles which are used as input parameters for energy analysis. 2 Method and modelling Firstly, the relevant factors influencing the energy demand have been identified in order to be able to determine the influence of the interior air conditioning on driving range subject to real mobility profiles and ambient conditions. Evaluation of the energy demand for air conditioning by means of weather and ... The relevant influencing factors are: solar radiation, ambient temperature and humidity (ambient conditions) component properties, air conditioning control system and coolant circuit: heating capacity, mass, thermal conductivity, heat transfer coefficient, transmission coefficient, absorption coefficient and projected surface (thermal cabin model) duration and time of parking the vehicle as well as of trips and average speed (mobility profile) 2.1 Method for combining the mobility profile and the vehicle cabin model As the range of battery electric vehicles is limited by the current low energy density of the energy storage device the total energy requirement of all trips between two charging processes is of great importance compared to conventional vehicles. [1,2] describe simulation models which are able to calculate the physical coherences for heat balance, coolant circuit and air conditioning control system by means of a speed profile with determined initial conditions. In order to be able to determine the energy demand between two charging processes a new simulation model is required which is able to process successive trips and intermediate parking periods depending on the time of day as input value. These input values shall be derived from recorded data based on existing customers’ mobility behaviour. In order to be able to calculate the energy demand detailed data of ambient air temperature, humidity and solar irradiance are required as a function of the time of day for the considered regions and seasons. The simulation model is expected to calculate the cabin air and component temperature as well as the required power for air conditioning. Figure 1 schematically shows the combination of a daily mobility pattern and climate data as input values for the thermal cabin model. Evaluation of the energy demand for air conditioning by means of weather and ... Figure 1: Methodological approach based on an exemplary day regarding mobility profile and ambient conditions Later on, this method is used to evaluate the influence of different measures on the energy demand for air conditioning. For the method to be applicable within the vehicle development process the simulation time has to be kept as short as possible. Therefore the simulation model has to be efficient and the extensive amount of recorded mobility data has to be aggregated to representative mobility patterns. The elements of the methodological approach are described in detail below. v