  - This work has been submitted to IFAC for possible publication - Models of
traction batteries are an essential tool throughout the development of
automotive drivetrains. Surprisingly, today's massively collected battery data
is not yet used for more accurate and reliable simulations. Primarily, the
non-uniform excitation during regular battery operations prevent a consequent
utilization of such measurements. Hence, there is a need for methods which
enable robust models based on large datasets. For that reason, a data-driven
error model is introduced enhancing an existing physically motivated model. A
neural network compensates the existing dynamic error and is further limited
based on a description of the underlying data. This paper tries to verify the
effectiveness and robustness of the general setup and additionally evaluates a
one-class support vector machine as the proposed model for the training data
distribution. Based on a five datasets it is shown, that gradually limiting the
data-driven error compensation outside the boundary leads to a similar
improvement and an increased overall robustness.
