This study proposes a method to predict the evolution of compression force during the degradation of a lithium-ion battery under packed conditions. The total compression force comprises irreversible and reversible forces. The former is estimated using a multivariate machine learning method, whereas the latter is estimated by combining machine learning and phenomenological modeling. For predicting the irreversible force, impedance-related features are extracted and their correlations with the evolution of the irreversible force are quantitatively analyzed using Grey relational analysis. Subsequently, features with high Grey relational grades are employed as representative health indicators for multivariate inputs of Gaussian process regression. For predicting the reversible force, the force evolution during the charge/discharge period is predicted using a phenomenological force model. The equivalent stiffness used in this model is separately estimated depending on the state of charge (SOC) to account for the inherent characteristics of phase transition and different degradation behaviors. The evolution of equivalent stiffness under high SOC shows nonlinearity but weak evolution characteristics, whereas those under low and medium SOCs show linearity but strong evolution characteristics. Finally, the proposed method is used to enable control and design for two potential applications: estimations of the state of health-dependent SOC and separator compression.

这项研究提出了一种预测在包装条件下锂离子电池降解过程中压缩力演变的方法。总压缩力包括不可逆和可逆力。前者是使用多元机器学习方法估算的，而后者是通过将机器学习和现象学建模相结合来估算的。为了预测不可逆力，提取了与阻抗相关的特征，并使用灰色关联分析定量分析了它们与不可逆力的演变之间的相关性。随后，具有高灰色关联度的特征被用作代表高斯过程回归的多元输入的健康指标。为了预测可逆力，使用现象学力模型预测充电/放电期间的力演化。该模型中使用的等效刚度是根据荷电状态（SOC）单独估算的，以考虑相变和不同退化行为的固有特性。高SOC下的等效刚度演化表现出非线性，但演化特征较弱；而中低SOC下的等效刚度演化表现出线性，但演化特征较强。最后，所提出的方法用于两种潜在应用的控制和设计：与健康相关的SOC状态估计和分离器压缩。根据荷电状态（SOC）分别估算此模型中使用的等效刚度，以考虑相变和不同退化行为的固有特性。高SOC下的等效刚度演化表现出非线性，但演化特征较弱；而中低SOC下的等效刚度演化表现出线性，但演化特征较强。最后，所提出的方法用于两种潜在应用的控制和设计：与健康相关的SOC状态估计和分离器压缩。该模型中使用的等效刚度是根据荷电状态（SOC）单独估算的，以考虑相变和不同退化行为的固有特性。高SOC下的等效刚度演化表现出非线性，但演化特征较弱；而中低SOC下的等效刚度演化表现出线性，但演化特征较强。最后，所提出的方法用于两种潜在应用的控制和设计：与健康相关的SOC状态估计和分离器压缩。