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This article introduces an efficient modeling approach based on the Wiener structure to reinforce the capacity of classical equivalent circuit models (ECMs) in capturing the nonlinearities of lithium-ion (Li-ion) batteries. The proposed block-oriented modeling architecture is composed of a simple linear ECM followed by a static output nonlinearity block, which helps achieving a superior nonlinear mapping property while maintaining the real-time efficiency. The observability of the established battery model is analytically proven. This article also introduces an efficient parameter estimator based on extended-kernel iterative recursive least squares algorithm for real-time estimation of the parameters of the proposed Wiener model. The proposed approach is applied for state-of-charge (SoC) estimation of 3.4-Ah 3.6-V nickel-manganese-cobalt-based Li-ion cells using the extended Kalman filter (EKF). The results show about 1.5% improvement in SoC estimation accuracy compared with the EKF algorithm based on the second-order ECM. A series of real-time tests are also carried out to demonstrate the computational efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Modular converters or reconfigurable battery energy storage systems are a promising approach to eliminate the dependence on the weakest element in previously hard-wired battery packs and to combine heterogeneous batteries (so-called mixed-battery systems). But their need for expensive sensors and complex monitoring as well as control subsystems hinders their progress. Estimating the parameters of each module can substantially reduce the number of required sensors and/or communication components. However, the existing estimation methods for cascaded modular circuits neglect important parameters such as the internal resistance of the battery, resulting in large systematic errors and bias. This article proposes an online estimator based on a dual Kalman filter (DKF) that exploits the slow dynamics of the battery compared to the load. The DKF algorithm estimates the open-circuit voltage (OCV) and internal resistance of each module by measuring only the output voltage and current of the system. Compared with the state-of-the-art, the proposed method is simpler and cheaper with only two sensors compared to $ \geq N + 2$ ($N$ is the number of modules). Furthermore, the proposed algorithm achieves a fast convergence through an optimal learning rate. Simulations and experimental results confirm the ability of the proposed approach, achieving <1.5% and <5% estimation error for OCV and the internal resistance, respectively. [ABSTRACT FROM AUTHOR]