In recent years, battery monitoring systems on cell level have been introduced. These architectures frequently employ current and voltage sensors at each battery cell. In this work, a novel model-based sensor data fusion method is presented, which combines the cell's individual current and voltage measurements of a battery module in order to reduce the measurement uncertainty of each value. Especially with distinct battery cell parameters, which typically arise with ongoing aging, the algorithm is superior to common approaches like averaging or total current sensing as it can handle uneven current and voltage distribution. A framework is proposed consisting of a weighted least squares regression algorithm in combination with dual Kalman filters for estimating the battery cells' states and parameters simultaneously. The method is validated with experiments and complementary simulations, in which a battery module, consisting of 12 cells, is cycled with specific current profiles. Reference model parameters are determined by supplementary characterization tests. It is shown that the sensor noise is reduced significantly with the presented algorithm, while no systematic error is induced.