Heat Pump Systems with energy efficiency greater than 1 are mainly used in electric vehicles to increase vehicle mileage. However, the lower the ambient air temperature, the lower the saturation pressure of the low-pressure side refrigerant, making it impossible to use the Heat Pump System. This paper suggests a method of increasing the flow rate by recirculating the high-pressure side refrigerant to the low-pressure side using the Hot Gas Recycle System. Experiments are conducted to investigate the hot gas recycle mode in a EV6 car at various EXV steps, ambient air temperatures, and waste heats. The heating performance is increased by 63.2% when the refrigerant is recycled at 0C ambient air temperature. Because the saturation pressure of low-pressure side refrigerant increased, resulting in the higher mass flowrate

The next-generation electric vehicle will be applied with a next generation battery and autonomous driving system that generates high heat. However, since the proper operating temperature is low, it is essential to combine it with a refrigeration cycle for vehicle air conditioning. However, the existing cooling system is difficult to satisfy air conditioning and battery load at the same time, so to solve this problem, we developed a cascade cooling system using two vapor compression refrigeration systems connected by PIHX for the first time in the world. In this paper, the existing system and the cascade system were compared. Compared to the existing system, it was found that the cooling performance of the battery was improved while the cooling performance of the air conditioning was at the same level.

When adopting a deep learning model for embodied agents, it is required that the model structure be optimized for specific tasks and operational conditions. Such optimization can be static such as model compression or dynamic such as adaptive inference. Yet, these techniques have not been fully investigated for embodied control systems subject to time constraints, which necessitate sequential decision-making for multiple tasks, each with distinct inference latency limitations. In this paper, we present MoDeC, a time constraint-aware embodied control framework using the modular model adaptation. We formulate model adaptation to varying operational conditions on resource and time restrictions as dynamic routing on a modular network, incorporating these conditions as part of multi-task objectives. Our evaluation across several vision-based embodied environments demonstrates the robustness of MoDeC, showing that it outperforms other model adaptation methods in both performance and adherence to time constraints in robotic manipulation and autonomous driving applications.