  Fast charging of lithium-ion batteries has gained extensive research
interests, but most of existing methods are either based on simple rule-based
charging profiles or require explicit battery models that are non-trivial to
identify accurately. In this paper, instead of relying on parametric battery
models that are costly to derive and calibrate, we employ a novel data-enabled
predictive control (DeePC) paradigm to perform safe and optimal fast charging
for lithium-ion batteries. The developed DeePC methodology is based on
behavioral system theory and directly utilizes the input-output measurements
from the battery system to predict the future trajectory and compute the
optimal control policy. Constraints on input current and battery states are
incorporated in the DeePC formulation to ensure battery fast charging with safe
operations. Furthermore, we propose a principal component analysis based scheme
to reduce the dimension of the optimization variables in the DeePC algorithm,
which significantly enhances the computation efficiency without compromising
the charging performance. Numerical simulations are performed on a
high-fidelity battery simulator to validate the efficacy of the proposed fast
charging strategy.
