  A data-driven solution is provided for the fast-charging problem of
lithium-ion batteries with multiple safety and aging constraints. The proposed
method optimizes the charging current based on the observed history of
measurable battery quantities, such as the input current, terminal voltage, and
temperature. The proposed method does not need any detailed battery model or
full-charging training episodes. The theoretical convergence is proven under
mild conditions and is validated numerically on several linear and nonlinear
battery models, including single-particle and equivalent-circuit models.
