Improved extended Kalman filter for state of charge estimation of battery pack

• Use of a simple optimization algorithm for filtering cells and updating electrical model of aged cells.
• Adapting Kalman gain effect on state estimation.
• SOC estimation for battery pack using improved extended Kalman filter.
• Validation of the proposed method under different current profiles.

It is difficult to model the behavior of the battery pack accurately due to the electrochemical characteristics variations among cells of a battery pack. As a result, accurate state-of-charge (SOC) and state-of-health (SOH) estimation for the battery pack is a case provocation. The estimation process poses more challenges after substantial battery aging. This paper tries to estimate the SOC of a Li-ion battery pack for an electrical vehicle using improved extended Kalman filter (IEKF) which benefits from considering aging phenomenon in the electrical model of cells. In order to assemble a battery pack, we find cells with similar electrochemical characteristics. Model adaptive algorithm is applied on the corresponding cells of a string to minimize cell-to-cell variation's effect. During the operation, the values of electrical model of each cell are updated by the same algorithm to compensate aging effects on SOC estimation error. The mean value of updated cell's model is used for a single unit cell model of the pack used at IEKF to achieve more accurate SOC estimation. The algorithm's fast response and low computational burden, makes on-board estimation practical. The experimental results reveal that the proposed approach's SOC and voltage estimation errors do not exceed 1.5%.

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