In this work, a moving horizon estimation (MHE)-based method is developed for estimating battery cells state in parallel-connected modules. Unlike conventional approaches, the proposed method acknowledges the impact of cell-to-cell (CtC) variations and heterogeneity propagation on module performance. A nonlinear observability analysis is performed to assess the feasibility of reconstructing individual cell states from module voltage and current measurements, considering interconnection resistance, state of charge (SOC)-dependent parameters, and different numbers of cells. The results indicate that states are distinguishable when the interconnection resistance is not null, and observability improves as the number of cells in parallel decreases. To the best of our knowledge, this is the first application of MHE in the context of battery modules, validated with real-world battery data. In contrast with conventional estimation methods, this study leverages MHE’s ability to handle equality constraints, allowing for the solution of Kirchhoff’s laws without complicating the module dynamics, maintaining the estimation accuracy. The proposed estimation algorithm demonstrates robustness against measurement noise and model uncertainties, with a maximum SOC error below 2.65%. Furthermore, the MHE results are compared against two widely used observers, the extended Kalman filter (EKF) and unscented Kalman filter (UKF), showing consistently higher estimation accuracy across all experimental conditions.
