  Essential to various practical applications of lithium-ion batteries is the
availability of accurate equivalent circuit models. This paper presents a new
coupled electro-thermal model for batteries and studies how to extract it from
data. We consider the problem of maximum likelihood parameter estimation,
which, however, is nontrivial to solve as the model is nonlinear in both its
dynamics and measurement. We propose to leverage the Bayesian optimization
approach, owing to its machine learning-driven capability in handling complex
optimization problems and searching for global optima. To enhance the parameter
search efficiency, we dynamically narrow and refine the search space in
Bayesian optimization. The proposed system identification approach can
efficiently determine the parameters of the coupled electro-thermal model. It
is amenable to practical implementation, with few requirements on the
experiment, data types, and optimization setups, and well applicable to many
other battery models.
