Fast Charging Control of Lithium-Ion Batteries: Effects of Input, Model, and Parameter Uncertainties

Paper in proceeding, 2022

The foundation of advanced battery management is computationally efficient control-oriented models that can capture the key battery characteristics. The selection of an appropriate battery model is usually focused on model order, whereas the effects of input and parameter uncertainties are often overlooked. This work aims to pinpoint the minimum model complexity for health-conscious fast charging control of lithiumion batteries in relation to sensor biases and parameter errors. Starting from a high-fidelity physics-based model that describes both the normal intercalation reaction and the dominant side reactions, Padé approximation and the finite volume method are employed for model simplification, with the number of control volumes as a tuning parameter. For given requirements on modeling accuracy, extensive model-based simulations are conducted to find the simplest models, based on which the effects of current sensor biases and parameter errors are systematically studied. The results show that relatively loworder models can be well qualified for the control of voltage, state of charge, and temperature. On the other hand, high-order models are necessary for health management, particularly during fast charging, and the choice of the safety margin should also take the current sensor biases into consideration. Furthermore, when the parameters have a certain extent of uncertainties, increasing the model order will not provide improvement in model accuracy.