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

Abstract

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, highorder 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.

Optimal Fast Charging of Lithium Ion Batteries: Between Model-Based and Data-Driven Methods

“…31 Critical to the success of these schemes is the the appropriateness of the model (if the model is too complex, then it adds complexity to the optimal control problem, if it is too simple then the predictions do not capture the key effects). The impact of this trade-off was explored in the sensitivity analysis of 32 which examined how changes in the model parameters, such as the number of active particles, affects its suitability for generating optimal trajectories. More recently, there has been a push toward going beyond the numerical analysis of these fast charging problems and, instead, develop an understanding of the solution structures by examining the optimality conditions.…”