An Energy Consumption Optimization Method for Intelligent Transfer Robots in a Low-temperature Environment

Abstract

The functional design and performance parameters of existing intelligent transfer robots (ITRs) have met the operational requirements for most scenarios at present. However, under the operation condition of low temperatures, the battery discharge capacity of ITRs will significantly reduce, resulting in a decrease in the efficiency of ITRs. Aiming at this problem, this paper proposes an energy consumption optimization method for intelligent transfer robots in a low-temperature environment, which can make the battery discharge capacity meet the best performance by adjusting the motion parameters of ITRs. Firstly, the optimal output current is obtained by analyzing the output characteristics of the battery in a low-temperature environment, and it is measured that the energy consumption of ITRs mainly depends on the energy used in the moving process. Then, it is proved that the moving speed of the ITR has the greatest influence on the output current of the battery based on comparative experiments. Next, the LSTM prediction model and fuzzy PID control are used to regulate the moving speed of the ITR, so that the output current of the robot battery can meet the optimal output performance in a low-temperature environment. Finally, extensive comparative experiments show that the energy consumption optimization method proposed in this paper can effectively improve the discharge capacity of robot batteries in a low-temperature environment.