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<p>With expanding application of decommissioned power batteries in echelon utilization and resource recovery, ensuring safe dismantling to avoid accidents such as thermal runaway has become crucial. This study proposes a knowledge-driven safety risk assessment method integrating social network analysis (SNA) and fault tree analysis (FTA). First, a safety risk factor identification network model using SNA to analyze complex interactions among uncertainty factors is constructed, serving as safety assessment data. Subsequently, an FTA-based safety risk assessment model is established based on identified safety risk factors, refining the knowledge-driven evaluation framework. To enhance the accuracy of the assessment model, Bayesian probabilities based on triangular fuzzy sets are employed to quantify the uncertainty in the dismantling process, using professional knowledge and data for reasonable probability estimation. Finally, using lithium iron phosphate battery echelon utilization as an example, our method identifies and prioritizes safety risk factors during dismantling. The model reveals a 0.66 probability for critical safety incidents and identifies three primary risk causation chains. Based on the findings, suggestions for preventing safety risks during the dismantling of power batteries are also provided.&#xa0;This study provides a theoretical foundation for quantitative decision-making in operational safety maintenance of decommissioned power battery production lines.</p>

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Knowledge-driven safety risk assessment for dismantling decommissioned power batteries

  • 期刊论文

  • 发表日期：

    2026年3月24日

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  • Jiawei Wan,
  • Yinghui Ren,
  • Xiaojun Zhuo,
  • Hongrui Zhao,
  • Miao Tian,
  • Wai Sze Yip

摘要

With expanding application of decommissioned power batteries in echelon utilization and resource recovery, ensuring safe dismantling to avoid accidents such as thermal runaway has become crucial. This study proposes a knowledge-driven safety risk assessment method integrating social network analysis (SNA) and fault tree analysis (FTA). First, a safety risk factor identification network model using SNA to analyze complex interactions among uncertainty factors is constructed, serving as safety assessment data. Subsequently, an FTA-based safety risk assessment model is established based on identified safety risk factors, refining the knowledge-driven evaluation framework. To enhance the accuracy of the assessment model, Bayesian probabilities based on triangular fuzzy sets are employed to quantify the uncertainty in the dismantling process, using professional knowledge and data for reasonable probability estimation. Finally, using lithium iron phosphate battery echelon utilization as an example, our method identifies and prioritizes safety risk factors during dismantling. The model reveals a 0.66 probability for critical safety incidents and identifies three primary risk causation chains. Based on the findings, suggestions for preventing safety risks during the dismantling of power batteries are also provided. This study provides a theoretical foundation for quantitative decision-making in operational safety maintenance of decommissioned power battery production lines.