This master's thesis addresses the development of a diagnostic tool for estimating the state of health of lithium-ion batteries based on open-circuit voltage analysis. The methodology is based on the processing of experimental measurements of battery cells operated under different conditions and on validation using simulations of an electrochemical DFN model in the open-source PyBaMM environment. The developed digital tool, implemented in the Python programming environment, enables decomposition of the open-circuit voltage curve into the potential contributions of the individual electrodes and analysis of characteristic changes in the voltage response. This makes it possible to identify key degradation indicators and distinguish the effects of loss of active material on the anode, loss of active material on the cathode, and loss of lithium inventory. The results provide a basis for a more accurate diagnostics of battery state of health, monitoring of performance fade, and prediction of remaining useful life.
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