  This paper proposes a way to augment the existing machine learning algorithm
applied to state-of-charge estimation by introducing a form of pulse injection
to the running battery cells. It is believed that the information contained in
the pulse responses can be interpreted by a machine learning algorithm whereas
other techniques are difficult to decode due to the nonlinearity. The
sensitivity analysis of the amplitude of the current pulse is given through
simulation, allowing the researchers to select the appropriate current level
with respect to the desired accuracy improvement. A multi-layer feedforward
neural networks is trained to acquire the nonlinear relationship between the
pulse train and the ground-truth SoC. The experimental data is trained and the
results are shown to be promising with less than 2\% SoC estimation error using
layer sizes in the range of 10 - 10,000 trained in 0 - 1 million epochs. The
testing procedure specifically designed for the proposed technique is explained
and provided. The implementation of the proposed strategy is also discussed.
The detailed system layout to perform the augmented SoC estimation integrated
in the existing active balancing hardware has also been given.
