Prediction of battery system responses and capability for next few seconds can provide key information to use battery hardware effectively. The prediction performance will be much improved, when battery models can capture the real battery responses as accurate as possible. Equivalent circuit models (ECMs) have been used for control purpose due to their proper balance between computational efficiency and prediction accuracy. The limitations of ECMs can be efficiently compensated through real-time model parameter estimation. Further enhancement is possible by improving system observability and robustness, specifically effective under low temperature and aggressive driving. This paper proposes an approach to improve the robustness and accuracy in estimating parameters by reformulating ECMs with new parameters. The proposed approach can estimate battery parameters less sensitive to both external disturbance and possible model mismatch under various driving conditions.

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