Research on Optimization of Urban Power Battery Recycling Networks Based on Uncertain Recycling Quality and Price

为了提升新能源汽车动力电池回收利用效率，完善城市动力电池回收网络。考虑回收质量与价格不确定性，以及政府补贴、“碳减排”影响，引入模糊参数，建立多节点、多周期不确定型动力电池回收网络选址模型。通过导入三角模糊数，将此模型转化为清晰等价类，最后以哈尔滨市动力电池回收网络优化为研究背景进行实例分析，采用多目标线性加权规划和编程进行计算求解，得出不确定条件下的优化结果，并根据结果绘制出哈尔滨动力电池回收网络示意图及地理分布。结论得出在回收质量和回收价格不确定的条件下，得到动力电池回收网络优化方法最优方案，提高废旧电池回收率和回收质量的同时，避免资源浪费及环境污染。并通过Pareto深度学习可以确定动力电池回收网络经济成本与环境成本合理权值。In order to improve the recycling efficiency of new energy vehicle power battery, the urban power battery recycling network is improved. Considering the uncertainty of recycling quality and price, as well as the influence of government subsidies and carbon emission reduction, a multi-node, multi-period uncertain power battery recycling network location model was established with fuzzy parameters. By importing triangular fuzzy numbers, this model was transformed into clear equivalence class. Finally, the optimization of Harbin power battery recovery network was analyzed as the research background, and the optimization results under uncertain conditions are obtained by using multi-objective linear weighted planning and programming. According to the results, it was shown that the schematic diagram and geographical distribution of Harbin power battery recovery network. The results show that under uncertain conditions of recycling quality and price, the optimal solution for optimizing the power battery recycling network can be obtained, which can improve the recycling rate and quality of waste batteries while avoiding resource waste and environmental pollution. And Pareto deep learning is used to determine the reasonable weights of the economic and environmental costs of power battery recycling network.