关键词: 自动导引车, 电量管理, 粒子群算法, 自动充电

Abstract: The battery of AGV will affect the efficiency of the production system. For example, if the system knows that the battery power of an AGV is not sufficient to perform a task, it will assign the task to another AGV or assign the AGV to a charging station. For the production workshop using AGV for material handling, one of the bottlenecks to increase production capacity is the available production time of AGV. The method of increasing production capacity by increasing the number of AGVs is very expensive, and the layout of the production workshop is mostly compact. Too many AGVs may cause the system to be more blocked and reduce production efficiency. In current industrial production applications, the battery is managed as follows: when the AGV continues to operate until its power level drops below a certain threshold, it goes to the charging station to fill it up to 100%. In this decision, if the system is in an idle period, that is, the AGV in the system is idle and the battery power does not reach the threshold, limited by the power management method, the AGV cannot effectively use the idle time to go to the charging station. If the system is in a busy period, that is, the AGV in the system is in a full load state, when the AGV power level drops below the threshold, the current position of the AGV may be accidentally far away from the nearest charging station, which will increase the time required to go to the charging station to charge and return to perform the remaining tasks, or at this time, the task volume in the system is large, and the AGV is performing an emergency order task, which cannot consume a lot of time to wait for full power before continuing to perform the remaining tasks. AGV can make a more sensible decision, that is, not fully charged, but it still can complete the task of emergency orders. After the system busy period, AGV can decide to continue charging.
Therefore, this paper studies how to deal with a sudden increase in capacity demand in the production workshop by changing the charging mode of AGV without changing the number of AGVs and improve the production efficiency of the workshop. A general AGV charging model is proposed. This model studies the most widely used lead-acid battery as the research object, and considers AGV in both full load and non-full load state, aiming at the AGV automatic charging mode lacking in previous studies. The model aims at the shortest total time for all tasks to complete, and makes decisions on the time, placement, and duration of AGV charging tasks in its task sequence. The AGV's automatic charging model has three decision problems to be solved: (1)Whether charging is required after the i task site. (2)Which charging station to go to be charged? (3)How long to be charged? There are two possible situations in the production that need to be considered at the same time: (1)The system is relatively busy, the AGV is in full load operation, and the tasks in all task sequences are continuously performed. (2)AGV is in a non-full load operation state, and there is idle time in the process of executing the task sequence. In order to solve the model, a heuristic algorithm (PSO-NCMO) combining particle swarm optimization algorithm and nonlinear constrained multivariable optimization algorithm is proposed, and the improved particle evolution framework is used to optimize the model.
The experimental data come from a real electrode production workshop of a new energy enterprise. The data of small, medium, and large AGV task scales in the workshop are selected for simulation experiments. By comparing the optimization results of the industrial benchmark method and the proposed PSO-NCMO algorithm, the results show that when the AGV is at full load, the PSO-NCMO algorithm reduces the total execution time of the task including charging by an average of 23.55% compared with the benchmark. When the AGV is at non-full load, the algorithm can prioritize the AGV to use the idle time to go to the charging station. When the initial power and minimum power limit of AGV are changed, AGV ends the task with as little residual power as possible, which shows that the charging model and algorithm have strong power management effect and have certain versatility. The charging time can be adjusted according to the actual needs of the enterprise. The limitation of the text is that it does not consider the faults and congestion during AGV driving. In the future, we will use deep learning technology to establish a prediction model during AGV driving, making the AGV power consumption and charging model more suitable for actual production.

Key words: AGV, battery management, particle swarm optimization, auto-charging