Beyond Traditional DoE: Deep Reinforcement Learning for Optimizing
  Experiments in Model Identification of Battery Dynamics
Gokhan Budan Francesca Damiani Can Kurtulus N. Kemal Ure
Model identification of battery dynamics is a central problem in energy
research; many energy management systems and design processes rely on accurate
battery models for efficiency optimization. The standard methodology for
battery modelling is traditional design of experiments (DoE), where the battery
dynamics are excited with many different current profiles and the measured
outputs are used to estimate the system dynamics. However, although it is
possible to obtain useful models with the traditional approach, the process is
time consuming and expensive because of the need to sweep many different
current-profile configurations. In the present work, a novel DoE approach is
developed based on deep reinforcement learning, which alters the configuration
of the experiments on the fly based on the statistics of past experiments.
Instead of sticking to a library of predefined current profiles, the proposed
approach modifies the current profiles dynamically by updating the output space
covered by past measurements, hence only the current profiles that are
informative for future experiments are applied. Simulations and real
experiments are used to show that the proposed approach gives models that are
as accurate as those obtained with traditional DoE but by using 85\% less
resources.