Data-Driven Recovery Potential Analysis and Modeling for Batteries Recovery Operations in Electric Bicycle Industry

Ping Zhang and Guangfu Liu

Discrete Dynamics in Nature and Society, 2018, vol. 2018, 1-18

Abstract:

To help the government manage waste lead-acid batteries in a more targeted and sustainable way, accurately forecasting the number of waste lead-acid batteries and analyzing their recovery potential play a key role. In China, electric bicycles are one of the most common means of transportation. As of the end of 2017, the social holding quantity of electric bicycles in China was over 250 million and that of electric tricycles was over 50 million. The quantity is equal to the total number of electric bicycles manufactured between 2011 and 2017. Currently, 90% of electric bicycles adopt lead-acid batteries as their power batteries. However, there are a few studies on the lead-acid batteries used in electric bicycles as power batteries. In this paper, we have selected lead-acid batteries used in electric bicycles as the subject of research as such kind of batteries enjoys the widest user base, the most single-battery consumption volume, and the strongest mobility. Based on the output and sales of electric bicycles, we have obtained the quantity of power lead-acid batteries. We have then estimated the annual waste quantity of lead-acid batteries used in electric bicycles in 2000-2022 using the “market supply A model” and the “Stanford Model”, respectively, and based on the proportion of raw materials contained in lead-acid batteries and the proportion between reclaimed and discarded lead-acid batteries, we have estimated the recovery potential of discarded lead-acid batteries in 2000-2022. We estimate that the lead-acid batteries used in electric bicycles only have great recovery potential and there are abundant potential resources for recovery. The research data and results can help decision-makers make more effective and more accurate management measures and policies.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnddns:6783190

DOI: 10.1155/2018/6783190

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