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This thesis presents a novel electrochemical lithium-ion cell model, which can be used in battery control units. Based on classical single-particle approaches, a lumped-parameter nonlinear model is developed. It is able to predict accurately the terminal voltages for arbitrary loads, and even for potentiostatic operation. The key points are: (1) an incorporation of the electrolyte potential, (2) a modal decomposition of the partial differential equation of the liquid phase lithiumion concentration, (3) a correct handling of the SOC-dependent diffusivity in the insertion materials of both electrodes, and (4) a consideration of temperature-dependent kinetic processes. A combined parameter analysis and identification is successfully applied for the parameterization of the model. Using a Fisher-information matrix approach in combination with a sensitivity analysis, the identifiability of each parameter is estimated in dependence on the measurement information. Using this information, it is possible to choose a small number of relevant experiments, which are sufficient to fully parameterize the model. In order to determine the internal model states that account for the actual stored amount of charge an unscented Kalman Filter is employed. It allows for the distinction of electrode individual state of charges. This inherently discrete-time state estimation approach provides a decoupling of model and filter correction update and is therefore well suited for stiff differential equations. Besides, no Jacobians of the highly nonlinear state space battery model have to be computed. The UKF shows an extraordinary tolerance against parameter errors. This property makes it a promising candidate for the monitoring of cell aging effects. In a last step, the estimated states of the UKF are used to track cell degradation effects. By means of the cell model parameters that account for

A novel electrochemical battery model for state of charge and state of health estimation