The present work focuses on the state-of-charge (SOC) estimation of a lithium-ion battery in terms of a second-order extended Kalman filter (EKF). First, an equivalent circuit model is introduced to describe the performance of lithium-ion batteries. The model parameters are then identified through hybrid pulse power characterization experiments conducted over a wide range of temperatures (−10 to 55 • C). A two-dimensional mathematical relationship is established with respect to the SOC and temperature based on a dual-fifth polynomial expression. The main effects and sensitivities of the SOC and temperature on the parameters are analysed according to the principle of variance analysis and partial derivatives. An estimation algorithm is developed, which combines the two-dimensional parameter model and second-order EKF. Finally, the proposed approach is validated compared to other estimation schemes through discharge experiments under extreme temperatures and dynamic loading profiles, which yields experimental results that estimate the SOC with an absolute error of less than 4.5% under harsh conditions. This not only demonstrates that it can characterize dependency of the model parameters on the operating conditions and address the uncertainty of model parameters, but also verifies the advantage of present method at low temperatures especially at sub-zero temperatures.

In electric vehicles (EVs), owing to the necessity of large amounts of energy and power, lithium-ion batteries need to be used in series and parallel configurations. However, the performance of the battery pack is lower than that of any single cell within the pack because of the inconsistency among the packed cells. Herein, the inconsistent voltages of unpacked cells due to varying capacities during discharge are analyzed to provide mechanical reason for inconsistency of battery pack. In terms of dispersion and symmetry, the statistical characteristics of voltage distribution are described using Weibull parameters and is investigated using a numerical analysis of the characteristic voltage curve. The numerical analysis results agree well with the experimental and statistical ones, which confirms that voltage inconsistency originating from manufacturing processes is primarily related with capacity inconsistency and the features of the voltage curves. Furthermore, this numerical approach can provide not only significant theoretical insight into the formation and evaluation of voltage inconsistency; but also practical guidance for controlling the quality of cell production and state estimation for the battery pack due to its low computational cost. INDEX TERMSVoltage inconsistency, inconsistency model, weibull probability model, 4-d probability nephogram, dispersion and symmetry of voltage distribution.

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