The battery management system (BMS) in electric vehicles monitors the state of charge (SOC) and state of health (SOH) of lithium‐ion battery by controlling transient parameters such as voltage, current, and temperature prevents the battery from operating outside the optimal operating range. The main feature of the battery management system is the correct estimation of the SOC in the broad range of vehicle navigation. In this paper, to estimate real‐time of SOC in lithium‐ion batteries and overcome faults of Extended Kalman Filter (EKF), the Square‐Root Sigma Point Kalman Filter is applied on the basis of numerical approximations rather than analytical methods of EKF. For this purpose, the Hybrid Pulse Power Characterisation tests are combined with the non‐linear least square method that acquired the second‐order equivalent circuit model parameters. Then, the newly developed method is tested with an 18,650 cylindrical lithium‐ion battery with a nominal capacity of 2600 mAh in four different ambient temperatures. Finally, the accuracy and effectiveness of the two proposed methods are verified by comparing with results of pulse discharge and dynamic driving cycle tests. The comparison results indicate the error of the proposed algorithm is about 0.02 under the most test conditions.

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