Reinforcement Learning-Based Dynamic Power Management of a Battery-Powered System Supplying Multiple Active Modes

摘要

This paper addresses the problem of extending battery service lifetime in a portable electronic system while maintaining an acceptable performance degradation level. The proposed dynamic power management (DPM) framework is based on model-free reinforcement learning (RL) technique. In this DPM framework, the Power Manager (PM) adapts the system operating mode to the actual battery state of charge. It uses RL technique to accurately define the optimal battery voltage threshold value and use it to specify the system active mode. In addition, the PM automatically adjusts the power management policy by learning the optimal timeout value. Moreover, the SoC and latency tradeoffs can be precisely controlled based on a user-defined parameter. Experiments show that the proposed method outperforms existing methods by 35% in terms of saving battery service lifetime.