The purpose of this project was to perform model-based diagnosis on Li-ion batteries using real-world data and sensor fusion algorithms. The data used in this project was collected and distributed by NASA and mainly consists of voltage and current measurements collected on numerous batteries that were repeatedly charged and discharged from their beginning of life, and until surpassing their end of life. The health of the batteries was decided by estimating their state of health through the increase in internal resistance. To validate the results, the increase in resistance was later compared with the decrease in charge capacity. Firstly an attempt was made to parameterize equivalent circuit models by fitting the model parameters to the data with a forgetting-factor recursive least squares filter, and a batch-wise recursive least squares filter. The forgetting-factor recursive least squares filter proved unreliable and unable to consistently be able to parameterize the models. The batch-wise recursive least squares filter was able to consistently parameterize the models providing a root mean squared error of around 0.35V in simulated voltage response tests. In total 20 equivalent circuit models were parameterized for four batteries. The models were then used in conjunction with a standard Kalman filter and an unscented Kalman filter to fur-ther estimate the increase in internal resistance of the batteries throughout their lifetime. In addition, the state of charge of the batteries was tracked through Coulomb counting and later attempted to refine via the Kalman filters. The results were partly successful as both the standard and unscented Kalman filters were able to track the state of health of the batteries, albeit to a varying degree. However, the Kalman filters were unable to improve the state of charge estimation, thus highlighting a limitation in their application. While the Kalman filters showcased limitations in tracking the state of charge, their effectiveness intracking the state of health thus proved that model-based approaches and sensorfusion algorithms can be utilized with both the standard Kalman filter and the unscented Kalman filter for meaningful health tracking.