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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.

Increasing energy costs justify research on how to improve utilization of low-grade energy that is abundantly available as waste heat from many thermodynamic processes such as internal combustion engine cycles. One option is to directly generate cooling through absorption/adsorption or vapor jet ejector cycles. As in the case of power generation cycles, cooling cycle efficiencies would increase if the heat input were available at higher temperature. This paper assesses the feasibility of a novel idea that uses a vortex tube to increase the available temperature levels of low-grade heat sources. The desired temperature increase is achieved by sending a stream of vapor that was heated by the waste heat source through a vortex tube, which further elevates the temperature used in a heat driven ejector cooling cycle.