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.

Improving the flooded-electrolyte batteries performance in fast charging and discharging processes has attracted many researchers. Due to electrochemical reactions during charging and discharging process in these batteries, many insoluble gas bubbles are produced within the electrolyte. These bubbles have a major effect on the performance of the electrodes and the rate of electrochemical reactions. On the other hand, the electrolyte flow rate plays an important role on the performance of the battery. In the present investigation, the effect of surface tension on the production of insoluble bubbles and velocity of electrolyte flow have been investigated experimentally when different types of surfactants are added to the electrolyte. In addition, a Particle Image Velocimetry setup has been used in order to measure the velocity of electrolyte flow and the behavior of bubbles. The results declared that the capacity of battery enhances about 16% and 10% by adding Triton X100 and SDS surfactants, respectively. Hence, the averaged electrolyte velocities during charging process reduces about 11% and 13% when Triton X100 and SDS, repectively; are used as surfactants. Also, the effect of adding different amounts of SDS surfactant on the mean diameter, rising velocity and production rates of bubbles have been illustrated comprehensively.

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