All-solid-state batteries (ASSBs) are considered to be the next generation of lithium-ion batteries. Physics-based models (PBMs) can effectively simulate the internal electrochemical reactions and provide critical internal states for battery management. In order to promote the onboard applications of PBMs for ASSBs, in this article, the parameter sensitivity of a typical PBM is analyzed, and a joint estimation method for states and parameters based on sigma-point Kalman filtering (SPKF) is proposed. First, to obtain accurate sensitivity analysis results, approaches from different principles, including local sensitivity, elementary effect test, and variance-based methods, are applied. Then, for the battery model based on partial differential equations, a nonlinear state-space model is constructed by using the finite-difference discretization method. Finally, the SPKF algorithm is employed to conduct the joint estimation of model parameters and lithium-ion concentrations. The results from constant current and dynamic cycles show that two parameters, namely maximum lithium-ion concentration and minimum lithium-ion concentration, have the most influence on the model results. The joint estimation method is validated in three different cases, and the mean absolute errors of the estimated voltage and state of charge (SOC) are below 2.1 mV and 1.5%, respectively.

中文翻译：

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全固态电池参数和状态的灵敏度分析和联合估计

全固态电池（ASSB）被认为是下一代锂离子电池。基于物理的模型 (PBM) 可以有效地模拟内部电化学反应，并为电池管理提供关键的内部状态。为了促进PBMs在ASSBs的车载应用，本文分析了典型PBMs的参数敏感性，提出了一种基于sigma-point Kalman滤波(SPKF)的状态参数联合估计方法。首先，为了获得准确的敏感性分析结果，应用了不同原理的方法，包括局部敏感性、基本效应检验和基于方差的方法。然后，针对基于偏微分方程的电池模型，利用有限差分离散化方法构建非线性状态空间模型。最后，采用SPKF算法对模型参数和锂离子浓度进行联合估计。恒流和动态循环的结果表明，最大锂离子浓度和最小锂离子浓度这两个参数对模型结果的影响最大。联合估计方法在三种不同情况下得到验证，估计电压和荷电状态 (SOC) 的平均绝对误差分别低于 2.1 mV 和 1.5%。