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Physics-based electrochemical models provide insight into the battery internal states and have shown great potential in battery design optimization and automotive and aerospace applications. However, the complexity of the electrochemical model makes it difficult to obtain parameter values accurately. In this study, a novel non-destructive parameter identification method is proposed to parameterize the most commonly used electrochemical pseudo-two-dimensional model. The whole identification process consists of three key steps. First, in order to find the optimal identification conditions, the sensitivity of model parameters is analyzed, and parameters are classified into three types according to their most sensitive conditions. Second, feasible initial guess values of these unknown parameters are obtained using a deep learning algorithm, which can not only help avoid the divergence problem of the identification …