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R. Morello, W. Zamboni, F. Baronti, R. D. Rienzo, R. Roncella, G. Spagnuolo, and R. Saletti, “Comparison of State and Parameter Estimators for Electric Vehicle Batteries,” in IECON 2015 - 41st Annu. Conf. IEEE Ind. Electron. Soc., 2015

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A Battery Management System (BMS) is needed to ensure a safe and effective operation of a Lithium-ion battery, especially in electric vehicle applications. An important function of a BMS is the reliable estimation of the battery state in a wide range of operating conditions. To this end, a BMS often uses an equivalent electrical model of the battery. Such a model is computationally affordable and can reproduce the battery behaviour in an accurate way, assuming that the model parameters are updated with the actual operating condition of the battery, namely its state-of-charge, temperature and ageing state. This paper compares the performance of two battery state and parameter estimation techniques, i.e., the Extended Kalman Filter and the classic Least Squares method in combination with the Mix algorithm. Compared to previous ones, this work focuses on the concurrent estimation of battery state and parameters using experimental data, measured on a Lithium-ion cell subject to a current profile significant for an electric vehicle application.