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Learning dependent subsidies for lithium-ion electric vehicle batteries

机译:依赖学习的锂离子电动汽车电池补贴

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摘要

Governments subsidize diffusion of a variety of energy technologies believed to provide social benefits. These subsidies are often based on the idea that stimulating learning and industry development will lower costs to make the technology competitive, after which point the subsidy can be removed. We investigate two questions related to the design of subsidy programs. One question is how net public investment changes with the time interval over which subsidies are reduced, i.e. semi-annually, annually, etc. Governments prefer to reduce subsidies more often to lower public costs, producers prefer longer time periods for a more stable investment environment The second question addressed is uncertainty in learning rates. Learning rates describe the fractional cost reduction per doubling of cumulative production; slower learning implies more government investment is needed to reach a cost target We investigate these questions via a case study of subsidizing electric vehicles (EV) in the United States. Given the importance of lithium battery cost in the price of an EV, we gather historical data to build an experience curve that describes cost reductions for lithium-ion vehicle batteries as a function of cumulative production. Our model assumes vehicle batteries experience the same learning as consumer electronics, yielding a learning rate of 22%. Using learning rates ranging from 9.5-22%, we estimate how much public subsidy would be needed to reach a battery cost target of $300/kWh battery. For a 9.5% learning rate, semi-annual, annual and biannual tapering costs a total of 24,27, and 34 billion USD respectively. For 22% learning, semi-annual, annual and biannual tapering costs a total of 2.1,2.3, and 2.6 billion USD respectively. While the tapering does affect program cost uncertainty in learning rate is the largest source of variability in program cost, highlighting the importance of finding realistic ranges for learning rates when planning technology subsidies.
机译:政府补贴各种能源技术的传播,这些技术被认为可以提供社会效益。这些补贴通常基于以下想法:刺激学习和行业发展将降低使技术具有竞争力的成本,此后可以取消补贴。我们研究了与补贴计划设计有关的两个问题。一个问题是净公共投资如何随着补贴减少的时间间隔(即半年一次,每年一次等)而变化。政府更倾向于减少补贴以降低公共成本,生产者更喜欢较长的时间来获得更稳定的投资环境解决的第二个问题是学习率的不确定性。学习率描述的是每增加一倍的累计产量所减少的部分成本;学习速度变慢意味着需要更多的政府投资才能实现成本目标。我们通过对美国补贴电动汽车(EV)的案例研究来调查这些问题。考虑到锂电池成本在电动汽车价格中的重要性,我们收集了历史数据以构建经验曲线,该曲线描述了锂离子汽车电池的成本降低与累计产量的关系。我们的模型假设车载电池的学习经历与消费电子产品相同,学习率达到22%。使用9.5-22%的学习率,我们估计要达到300美元/千瓦时电池的电池成本目标需要多少公共补贴。对于9.5%的学习率,每半年,每年和每两年逐渐减少的费用分别为24,27和340亿美元。对于22%的学习,半年,年度和半年缩减的总费用分别为2.1、2.3和26亿美元。缩减确实会影响计划成本,但是学习率的不确定性是计划成本变化的最大来源,这突出了在计划技术补贴时找到学习率的现实范围的重要性。

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