发表机构
Chalmers University of Technology; Uppsala University（查尔姆斯理工大学; 乌普萨拉大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出时间尺度感知的代理辅助框架，联合优化电池能量密度、快速充电与退化，经Sobol分析与Pareto优化，实现SOH损失降低99.31%等显著改进。
AI 中文摘要
电池单体设计必须在能量密度、快速充电和退化之间取得平衡，然而这些指标在不同时间尺度上演化，且联合优化成本高昂。我们开发了一个时间尺度感知的代理辅助框架，该框架同时评估初始寿命体积能量密度、10%至80%充电时间以及200次循环中的健康状态（SOH）损失。基于物理的模拟生成了1501个电池单体设计，从中获得1427个质量受控样本，涵盖12个制造相关参数。针对各目标的代理模型支持总阶Sobol分析，揭示了三个指标间不同的参数排名。随后，一种跨目标排名联合策略在进化Pareto优化之前保留对至少一个目标有影响的变量。使用原始基于物理的模型对优化候选方案进行重新评估，确认了在所有三个指标上均优于参考电池单体的设计。在这些联合改进的候选方案中，由不同设计实现的目标最优解可将SOH损失降低99.31%，体积能量密度提高12.49%，充电时间缩短28.73%。该框架使跨不同电化学时间尺度的Pareto探索在计算上变得可行，从而能够对电池单体设计进行系统的多目标优化。
英文摘要
Battery cell design must balance energy density, fast charging, and degradation, yet these metrics evolve over different time scales and are costly to optimize jointly. We develop a timescale-aware surrogate-assisted framework that evaluates beginning-of-life volumetric energy density and 10--80% charging time together with state-of-health (SOH) loss over 200 cycles. Physics-based simulations of 1501 cell designs generate 1427 quality-controlled samples across 12 manufacturing-relevant parameters. Objective-specific surrogates support total-order Sobol analysis, which reveals distinct parameter rankings across the three metrics. A cross-objective rank-union strategy then retains variables influential to at least one objective before evolutionary Pareto optimization. Re-evaluation of the optimized candidates using the original physics-based models confirms designs that outperform the reference cell in all three metrics. Among these jointly improving candidates, the objective-wise best solutions, attained by different designs, can reduce SOH loss by 99.31%, increase volumetric energy density by 12.49%, and shorten charging time by 28.73%. The framework makes Pareto exploration across disparate electrochemical timescales computationally tractable, thereby enabling systematic multi-objective optimization of battery cell design.
