This article proposes a new equivalent circuit model for rechargeable batteries by modifying a double-capacitor model in the literature. It is known that the original model can address the rate capacity effect and energy recovery effect inherent to batteries better than other models. However, it is a purely linear model and includes no representation of a battery’s nonlinear phenomena. Hence, this article transforms the original model by introducing a nonlinear-mapping-based voltage source and a serial RC circuit. The modification is justified by an analogy with the single-particle model. Two off-line parameter estimation approaches, termed 1.0 and 2.0, are designed for the new model to deal with the scenarios of constant-current and variable-current charging/discharging, respectively. In particular, the 2.0 approach proposes the notion of Wiener system identification based on the maximum a posteriori estimation, which allows all the parameters to be estimated in one shot while overcoming the nonconvexity or local minima issue to obtain physically reasonable estimates. Extensive experimental evaluation shows that the proposed model offers excellent accuracy and predictive capability. A comparison against the Rint and Thevenin models further points to its superiority. With high fidelity and low mathematical complexity, this model is beneficial for various real-time battery management applications.

中文翻译：

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用于充电电池的非线性双电容器模型：建模，识别和验证

本文通过修改文献中的双电容器模型，提出了一种新的可充电电池等效电路模型。众所周知，与其他模型相比，原始模型可以更好地解决电池固有的倍率容量效应和能量回收效应。但是，它是纯线性模型，不包含电池非线性现象的表示。因此，本文通过引入基于非线性映射的电压源和串行RC电路来转换原始模型。通过与单粒子模型的类比可以证明这种修改是合理的。针对新模型设计了两种离线参数估计方法，分别称为1.0和2.0，以分别处理恒定电流和可变电流充电/放电的情况。特别是2。0方法提出了基于最大后验估计的Wiener系统识别的概念，该模型允许一口气估计所有参数，同时克服非凸性或局部极小问题以获得物理上合理的估计。大量的实验评估表明，所提出的模型具有出色的准确性和预测能力。与Rint和Thevenin模型的比较进一步指出了它的优越性。该模型具有高保真度和低数学复杂度，对于各种实时电池管理应用程序非常有用。大量的实验评估表明，所提出的模型具有出色的准确性和预测能力。与Rint和Thevenin模型的比较进一步指出了它的优越性。该模型具有高保真度和低数学复杂度，对于各种实时电池管理应用程序非常有用。大量的实验评估表明，所提出的模型具有出色的准确性和预测能力。与Rint和Thevenin模型的比较进一步指出了它的优越性。该模型具有高保真度和低数学复杂度，对于各种实时电池管理应用程序非常有用。