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Volume 43 Issue 9
Sep.  2025
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Article Contents
ZHAO Yuanhao, ZONG Yuhang, SUN Zhi, CAO Hongbin, YAN Wenyi, ZHAO He. Research progress on data-driven evaluation methods for recycling processes of spent lithium-ion batteries[J]. ENVIRONMENTAL ENGINEERING , 2025, 43(9): 183-197. doi: 10.13205/j.hjgc.202509019
Citation: ZHAO Yuanhao, ZONG Yuhang, SUN Zhi, CAO Hongbin, YAN Wenyi, ZHAO He. Research progress on data-driven evaluation methods for recycling processes of spent lithium-ion batteries[J]. ENVIRONMENTAL ENGINEERING , 2025, 43(9): 183-197. doi: 10.13205/j.hjgc.202509019

Research progress on data-driven evaluation methods for recycling processes of spent lithium-ion batteries

doi: 10.13205/j.hjgc.202509019
  • Received Date: 2024-10-17
    Available Online: 2025-11-05
  • Publish Date: 2025-09-01
  • With the rapid advancement of electric vehicles and energy storage systems, research on the recycling processes of spent lithium-ion batteries has become a critical focus for both academia and industry. Proper management of such processes is essential to address environmental challenges. This paper presents a comprehensive review of existing evaluation methods for the recycling of spent lithium-ion batteries, including life cycle assessment, techno-economic assessment, criticality assessment, material flow analysis, input-output analysis, best available technique evaluation, and the coupling of multiple methods. It evaluates the applicability, strengths, and limitations of each method while highlighting their differences in addressing battery chemistries, indicator selection, and model construction. Life cycle assessment is identified as the most widely applied and representative method due to its well-established evaluation framework and capacity to systematically assess environmental impacts. However, single-method approaches are insufficient to capture the intricate interdependencies among environmental, economic, and resource-related factors inherent in the recycling processes. Consequently, the integration of multiple evaluation methods has emerged as a significant research trend, allowing for a more comprehensive understanding of these multifaceted processes. Nevertheless, further advancements are required to enhance the coverage of multidimensional factors such as carbon emissions, resource utilization, environmental impacts, and economic benefits. The emergence of data-driven approaches, which incorporate machine learning techniques and big data analytics, offers new opportunities for addressing uncertainties, optimizing data processing, and improving model accuracy in the evaluation of recycling processes. This paper emphasizes the need for interdisciplinary methodologies and robust data frameworks to guide the sustainable development of the lithium-ion battery recycling industry. These findings can provide a scientific basis for optimizing recycling technologies, informing policy-making, and supporting the global transition to a circular economy.
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