English: Against the backdrop of global energy transition and the "dual-carbon" goals, electrochemical energy storage systems (e.g., lithium-ion batteries, fuel cells) have emerged as a core hub for building a zero-carbon power grid, making intelligent lifecycle management a critical research focus. This workshop highlights key technological breakthroughs, including multi-scale modeling and state estimation, lifespan prediction and fast-charging optimization, and digital twin-enabled fault diagnosis and safety management. It also explores system-level applications such as dynamic power allocation in hybrid energy storage systems, coordinated control of fuel cell subsystems, electric vehicle clusters participating in virtual power plant dispatch, multi-timescale optimization in microgrids, and machine vision-assisted retired battery sorting and repurposing. By integrating intelligent technologies like digital twins and deep learning, the workshop aims to optimize the entire "diagnosis-control-utilization" chain of energy storage systems, promoting the large-scale deployment of safe, cost-effective, and efficient storage solutions while enhancing the resilience of new power systems and accelerating the transition to clean energy.

中文: 在全球能源转型与"双碳"目标驱动下，电化学储能系统（如锂电池、燃料电池）作为零碳电网的核心枢纽，其智能化全生命周期管理成为关键研究方向。本次研讨会聚焦多尺度建模与状态估计、寿命预测与快充优化、数字孪生赋能的故障诊断与安全管控等关键技术突破，并探索复合储能动态功率分配、燃料电池协同控制、电动汽车集群参与虚拟电厂调度、微电网多时间尺度优化及机器视觉赋能的退役电池分选重组等系统级应用。通过融合数字孪生、深度学习等智能技术，实现储能系统"诊-控-用"全环节优化，推动安全、经济、高效的储能解决方案规模化部署，助力新型电力系统韧性提升与清洁能源加速替代。

• 中国科学技术大学知识表达与智能信息技术实验室 / Knowledge Representation and Intelligent Information Technology Laboratory, University of Science and Technology of China
• 中国自动化学会系统仿真专业委员会 / System Simulation Technical Committee, Chinese Association of Automation
• 中国仿真学会仿真技术应用专业委员会 / Simulation Technology Application Committee, Chinese Association for System Simulation