Parameter estimation is of foundational importance for various model-based battery management tasks, including charging control, state-of-charge estimation and aging assessment. However, it remains a challenging issue as the existing methods generally depend on cumbersome and time-consuming procedures to extract battery parameters from data. Departing from the literature, this paper sets the unique aim of identifying all the parameters offline in a one-shot procedure, including the resistance and capacitance parameters and the parameters in the parameterized function mapping from the state-of-charge to the open-circuit voltage. Considering the well-known Thevenin’s battery model, the study begins with the parameter identifiability analysis, showing that all the parameters are locally identifiable. Then, it formulates the parameter identification problem in a prediction-error-minimization framework. As the non-convexity intrinsic to the problem may lead to physically meaningless estimates, two methods are developed to overcome this issue. The first one is to constrain the parameter search within a reasonable space by setting parameter bounds, and the other adopts regularization of the cost function using prior parameter guess. The proposed identifiability analysis and identification methods are extensively validated through simulations and experiments.

参数估算对于各种基于模型的电池管理任务至关重要，包括充电控制，充电状态估算和老化评估。但是，由于现有方法通常依赖繁琐且耗时的过程来从数据中提取电池参数，因此仍然是一个具有挑战性的问题。与文献不同的是，本文设定了唯一的目标，即可以一次性确定所有离线参数，包括电阻和电容参数以及参数化函数中从荷电状态到开路的映射中的参数电压。考虑到著名的戴维南电池模型，该研究从参数可识别性分析开始，表明所有参数都是本地可识别的。然后，它在预测误差最小化框架中提出了参数识别问题。由于问题固有的非凸性可能导致物理上无意义的估计，因此开发了两种方法来克服此问题。第一个方法是通过设置参数界限将参数搜索限制在合理的空间内，第二个方法是使用先验参数猜测对成本函数进行正则化。通过仿真和实验对提出的可识别性分析和识别方法进行了广泛的验证。另一个采用先验参数猜测对成本函数进行正则化。通过仿真和实验对提出的可识别性分析和识别方法进行了广泛的验证。另一个采用先验参数猜测对成本函数进行正则化。通过仿真和实验对提出的可识别性分析和识别方法进行了广泛的验证。