Scholarlink

Energy storage systems are known as important features of connecting green energy resources to new power grids. Lithium-ion and solid-state batteries are commonly applauded because of their high-power density and efficiency. Of these, solid-state batteries have gained recent interest on the basis of their possible environmental and performance advantages. Nevertheless, the current research is affected by such pitfalls as high costs of manufacturing, risk of data variability, narrowness of the sphere of artificial intelligence (AI) use, recycling process inefficiencies, and the absence of assessments to consider the sustainability of materials. The use of sophisticated AI algorithms allows conducting a full life cycle analysis (LCA) of solid-state and advanced battery energy storage systems in this paper, which adds value to the paper. To overcome the drawbacks of the past, multi-dimensional LCA data is being trained with deep learning, ensemble, and reinforcement learning techniques that enhance the model of predicting environmental impact and optimize the recycling process. Inventory, emission, and material composition data are merged with AIs to increase accuracy and insightfulness. The environmental impact of solid-state batteries in terms of carbon footprint is evaluated, and the dramatic positive effects are put forward in contrast to other systems. The carbon footprint and resource utilization is minimized and the values range between 58.0 and 67.3 kg CO2-eq/kWh depending on the cathode mix. It proves that AI-based approaches have substantial predictive capability and low root mean square error. The sensitivity analysis indicates that the impact factors of importance are energy intensity and material costs. More environmental impact effects are reduced when greener materials like bio-polymer electrolyte and graphene are used. The hybrid recycling strategies are shown to be more efficient and less costly than conventional ones, thus enabled by optimized strategies, which align with the practice of the circular economy.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Life Cycle Assessment of Solid-State Batteries with AI-Enhanced Predictive and Recycling Models

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Abdul Samad Bin Shibghatullah,
  • Zahraa A. Jaaz,
  • Lateef Abd Zaid Qudr,
  • Safwan Nadweh,
  • Azmi Shawkat Abdulbaqi,
  • Reyad Omran Essa,
  • Ahmed Dheyaa Radhi,
  • Salwan S. Hatif,
  • Dima Haider Rasheed

摘要

Energy storage systems are known as important features of connecting green energy resources to new power grids. Lithium-ion and solid-state batteries are commonly applauded because of their high-power density and efficiency. Of these, solid-state batteries have gained recent interest on the basis of their possible environmental and performance advantages. Nevertheless, the current research is affected by such pitfalls as high costs of manufacturing, risk of data variability, narrowness of the sphere of artificial intelligence (AI) use, recycling process inefficiencies, and the absence of assessments to consider the sustainability of materials. The use of sophisticated AI algorithms allows conducting a full life cycle analysis (LCA) of solid-state and advanced battery energy storage systems in this paper, which adds value to the paper. To overcome the drawbacks of the past, multi-dimensional LCA data is being trained with deep learning, ensemble, and reinforcement learning techniques that enhance the model of predicting environmental impact and optimize the recycling process. Inventory, emission, and material composition data are merged with AIs to increase accuracy and insightfulness. The environmental impact of solid-state batteries in terms of carbon footprint is evaluated, and the dramatic positive effects are put forward in contrast to other systems. The carbon footprint and resource utilization is minimized and the values range between 58.0 and 67.3 kg CO2-eq/kWh depending on the cathode mix. It proves that AI-based approaches have substantial predictive capability and low root mean square error. The sensitivity analysis indicates that the impact factors of importance are energy intensity and material costs. More environmental impact effects are reduced when greener materials like bio-polymer electrolyte and graphene are used. The hybrid recycling strategies are shown to be more efficient and less costly than conventional ones, thus enabled by optimized strategies, which align with the practice of the circular economy.