BattOpt：电动汽车电池回收的最优设施规划
BattOpt: Optimal Facility Planning for Electric Vehicle Battery Recycling
AI总结：
本文提出多阶段随机优化模型，将EverBatt转化为决策工具，结合计量经济学预测生成场景，得出最优电池回收投资可降22%制造成本、26%环境影响，算法求解速度提升14倍以上。
摘要：
电动汽车（EV）电池供应链将面临两大挑战：一是获取制造所需的稀缺且昂贵的矿物，二是处置具有危险性的退役电池。将回收技术整合到供应链中有望缓解这些问题，但电池市场参与者必须设计回收设施的投资计划。本文提出一种多阶段随机优化模型，用于计算最小成本的回收产能决策，其中退役电池被回收，回收得到的材料用于制造新电池。我们将电池回收成本与环境影响的领先评估框架EverBatt转化为用于确定最优投资策略的规范性决策工具。我们的模型是受线性约束的可分凹极小化问题，针对这类问题，我们描述了一种基于分段线性近似的有限收敛全局优化算法，该算法的求解速度比同类算法快14倍以上。我们提出了模型的等价重构形式，通过引入整数约束消除变量，大幅缩短了求解时间。为生成一组运营场景，我们利用最先进的计量经济学模型生成新电池需求、退役电池供应和材料成本的时间序列预测。我们应用该模型计算最优投资计划并量化最优决策的影响：最优投资可降低22%的电池制造成本和26%的环境影响。我们测量了不同回收技术的运营效益，并证明高不确定性会促使加大对回收产能的投资。最后，我们分析了政策工具的影响，以揭示产能补贴和生产积分的有效性方面的关键见解。
英文摘要：
The electric vehicle (EV) battery supply chain will face challenges in sourcing scarce and expensive minerals required for manufacturing and in disposing of hazardous retired batteries. Integrating recycling technology into the supply chain has the potential to alleviate these issues; however, players in the battery market must design investment plans for recycling facilities. In this paper, we propose a multistage stochastic optimization model for computing minimum cost recycling capacity decisions, in which retired batteries are recycled and recovered materials are used to manufacture new batteries. We transform EverBatt, a leading evaluation framework for battery recycling cost and environmental impact, into a prescriptive decision-making tool for determining optimal investment strategies. Our model is a separable concave minimization subject to linear constraints, a class for which we describe a finitely convergent global optimization algorithm based on piecewise linear approximation that solves up to 14x faster than comparable algorithms. We propose an equivalent reformulation of the model that eliminates variables by introducing integrality constraints, drastically reducing solve times. To produce a set of operational scenarios, we generate time-series projections for new battery demand, retired battery supply, and material costs, leveraging state-of-the-art econometric models. We apply our model to compute optimal investment plans and quantify the impact of optimal decision-making: optimal investment can reduce battery manufacturing costs by 22% and environmental impacts by 26%. We measure the operational benefits of different recycling technologies and demonstrate how high uncertainty motivates greater investment in recycling capacity. Finally, we analyze the impact of policy instruments to reveal key insights on the efficacy of capacity grants and production credits.
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