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This paper proposes a way to augment the existing machine learning algorithmapplied to state-of-charge estimation by introducing a form of pulse injectionto the running battery cells. It is believed that the information contained inthe pulse responses can be interpreted by a machine learning algorithm whereasother techniques are difficult to decode due to the nonlinearity. Thesensitivity analysis of the amplitude of the current pulse is given throughsimulation, allowing the researchers to select the appropriate current levelwith respect to the desired accuracy improvement. A multi-layer feedforwardneural networks is trained to acquire the nonlinear relationship between thepulse train and the ground-truth SoC. The experimental data is trained and theresults are shown to be promising with less than 2\% SoC estimation error usinglayer sizes in the range of 10 - 10,000 trained in 0 - 1 million epochs. Thetesting procedure specifically designed for the proposed technique is explainedand provided. The implementation of the proposed strategy is also discussed.The detailed system layout to perform the augmented SoC estimation integratedin the existing active balancing hardware has also been given.

High-Fidelity State-of-Charge Estimation of Li-Ion Batteries Using Machine Learning