With the progress of power energy storage technologies in capacity, cycle life and reliability, data center can optimize its utilization of energy storage battery to reduce its Total Cost of Ownership (TCO). Data center can cut the peak and fill the valley of their power consumption graphs with proper management of battery charging and discharging, while maintaining uninterruptible power system (UPS) capacity. This paper studies the control technology of data center battery charging and discharging based on Deep Reinforcement Learning (DRL). According to the electricity price and status and cycle life of batteries, the appropriate time is selected to charge and discharge batteries in order to maximize the electricity bill savings. To achieve higher benefits, the system state, charging and discharging actions, reward function and a neural network structure were designed in detail. According to the simulation results, the proposed algorithm can infer the best savings strategy in both USA and Beijing electricity price systems. Compared with the baseline algorithm, the priority experience playback Deep Q-network (DQN) can increase the energy storage savings up to 47% and 55% with the electricity prices of the USA and Beijing respectively.