Low-Cost Parameter Estimation Approach for Modular Converters and Reconfigurable Battery Systems Using Dual Kalman Filter

Abstract

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 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 paper 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 ≥ N + 2 (N is the number of modules). Furthermore, the proposed algorithm achieves a fast convergence through 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.