Estimation of Potentials in Lithium-Ion Batteries Using Machine Learning Models

Abstract

Electrochemical mechanisms in lithium-ion batteries (LIBs) pose a significant challenge in deriving models that are highly accurate, have low computational complexity, and enable real-time state and parameter estimation. In this article, we propose a machine learning model as an important building block of a physics-based ANCF-e model that was recently proposed for LIBs. This machine learning model is used to estimate nonlinear potentials, including the open-circuit potential, electrolyte potential, and lithium-intercalation overpotential. Such an estimation is shown to result in a much smaller computational complexity and therefore can enable real-time state and parameter estimation. Three different machine learning architectures are explored, including multilayer perceptron, radial basis function (RBF)-based neural networks, and support vector machines. The training of these machine learning models is carried out using current profiles obtained with an electric vehicle model from driving cycles as inputs and ANCF-e model-based outputs. The underlying ANCF-e model is validated both through a high-fidelity numerical approach, including COMSOL and an experimental test using commercial LIBs. Both validations are carried out under both constant current discharging and dynamic load cycles. The resulting performance using these machine learning models is compared using different metrics, including estimation errors, convergence rates, training time, and computational time. The results indicate that an RBF-based neural network leads to better estimation of the underlying potentials in LIBs and that all machine learning models require a computational time that is 95% smaller than a physics-based approach for this estimation.