A Method for Draining Leftover Energy From Waste Lead–Acid Batteries Prior to Recycling

IF 9.9 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS IEEE Transactions on Industrial Informatics Pub Date : 2025-01-22 DOI:10.1109/TII.2025.3528547
Chien-Hsing Lee;Wen-Chi Wang;Joe-Air Jiang;Shih-Hsien Hsu;Chen-Wei Lee
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Abstract

In this study, a self-adaptive pulse discharge (SAPD) approach is developed and utilized to drain leftover energy from waste lead–acid batteries before entering the recycling process. This SAPD method was applied to find the optimal pulse frequency and duty cycle values for determining the discharge current from the batteries. Experiments were conducted using two parallel-connected Yuasa 12-V/6-Ah batteries. The energy recovered from the batteries was 54.7 kJ, and the corresponding recovery efficiency was 78.7%. The energy recovery efficiency could be improved to 80.9% by including three 15-min relaxation periods when the terminal voltage of the batteries reached the cutoff voltage of 10.5 V during discharge. The findings of this study could pave the way to a more sustainable future, even though major improvements in the recycling process are still required.
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一种回收前从废铅酸电池中提取剩余能量的方法
本研究开发了一种自适应脉冲放电(SAPD)方法,用于在废铅酸电池进入回收过程之前将剩余能量排出。该方法用于确定电池放电电流的最佳脉冲频率和占空比值。实验使用两个并联的Yuasa 12v / 6ah电池进行。电池回收能量为54.7 kJ,回收效率为78.7%。在放电过程中,当电池端电压达到10.5 V截止电压时,加入3次15 min的松弛期,能量回收率可提高到80.9%。这项研究的结果可以为更可持续的未来铺平道路,尽管回收过程仍然需要重大改进。
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来源期刊
IEEE Transactions on Industrial Informatics
IEEE Transactions on Industrial Informatics 工程技术-工程:工业
CiteScore
24.10
自引率
8.90%
发文量
1202
审稿时长
5.1 months
期刊介绍: The IEEE Transactions on Industrial Informatics is a multidisciplinary journal dedicated to publishing technical papers that connect theory with practical applications of informatics in industrial settings. It focuses on the utilization of information in intelligent, distributed, and agile industrial automation and control systems. The scope includes topics such as knowledge-based and AI-enhanced automation, intelligent computer control systems, flexible and collaborative manufacturing, industrial informatics in software-defined vehicles and robotics, computer vision, industrial cyber-physical and industrial IoT systems, real-time and networked embedded systems, security in industrial processes, industrial communications, systems interoperability, and human-machine interaction.
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