Browse Topic: Defoggers

Items (29)
Implementation of Reinforcement Learning on Air Source Heat Pump Defrost Control for Full Electric Vehicles2018-01-11934/3/2018
Air source heat pumps as the heating system for full electric vehicles are drawing more and more attention in recent years. Despite the high energy efficiency, frost accumulation on the heat pump evaporator is one of the major challenges associated with air source heat pumps. The evaporator needs to be actively defrosted periodically and heat pump heating will be interrupted during defrosting process. Proper defrost control is needed to obtain high average heat pump energy efficiency. In this paper, a new method for generating air source heat pump defrost control policy using reinforcement learning is introduced. This model-free method has several advantages. It can automatically generate optimal defrost control policy instead of requiring manually determination of the control policy parameters and logics. More measurement results can be incorporated into the defrost control policy without too many changes in the reinforcement learning algorithm so that the control policy can be better optimized under wider range of working conditions. The learning features also enable the controller to adapt to the system differences and changes which are impossible to predict a priori when designing defrost control policy. The algorithm was validated using experimentally obtained heating capacity and COP data in frost growth cycle of a heat pump under different conditions. The results showed that reinforcement learning can be used to generate defrost control policy that optimizes energy consumption for various working conditions.
Zhu, JingweiElbel, Stefan
Effects of the Glass and Body Heat Transfer Characteristics of an Electric Vehicle on its Energy Consumption and Cruising Distance2016-01-02604/5/2016
In order to develop various parts and components of electric vehicles, understanding the effects of their structures and thermal performance on the energy consumption and cruising distance is important. However, such essential and detailed information is generally not always available to suppliers of vehicle parts and components. This paper presents the development of a simple model of the energy consumption by an electric vehicle in order to roughly calculate the cruising performance based only on the published information to give to suppliers, who otherwise cannot obtain the necessary information. The method can calculate the cruising distance within an error of 4% compared to the published information. The effects of the glass and body heat transfer characteristics on the cruising performance in winter were considered as an example application of the proposed model. An anti-fog control method was modeled, where the relative humidity around the front windshield was assumed to be detected and the recirculation ratio of the ventilation was controlled in order to maintain defogging. The effect of the anti-fog control combined with the thermal insulation efficiency of the glass on the cruising performance was examined with the cruising distance estimation method. The results indicated that improving the thermal properties of the glass does not have much effect without the anti-fog control. Hence, anti-fog control enhances the effect of improving the thermal properties of the glass on the cruising distance.
Ozeki, YoshiichiNagano, HideakiKohri, Itsuhei
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