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The determination of state-of-health (SOH) and state-of-charge (SOC) is challenging and remains as an active research area in academia and industry due to its importance for Li-ion battery applications. The estimation process poses more challenges after substantial battery aging. This paper presents an inline SOH and SOC estimation method for Li-ion battery packs, specifically for those based on LiFePO4 chemistry. This new hybridized SOC and SOH estimator can be used for battery packs. Inline estimated model parameters were used in a compounded SOC + SOH estimator consisting of the SOC calculation based on coulomb counting method as an expedient approach and an SOH observer using an extended Kalman filter (EKF) technique for calibrating the estimates from the coulomb counting method. The algorithm's low SOC and SOH estimation error, fast response time, and less-demanding computational requirement make it practical for on-board estimations. The simulation and experimental results, along with the test bed structure, are presented to validate the proposed methodology on a single cell and a 3S1P LiFePO4 battery pack.

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%.

A battery management system needs to have an accurate inline estimation of SOC for each individual cell in the battery pack. This estimation process poses challenges after substantial battery aging. This article presents a novel method based on model adaptive extended Kalman filter (MAEKF) to estimate SOC for Li-ion batteries. Sensitivity analysis of the electrical model verifies that the accuracy of SOC estimated by EKF is sensitive to resistors used in the cell's electrical model. In order to get the best estimation, values of resistors are obtained in an optimization process in the MAEKF. This method uses the fact of two sudden changes in the cell's voltage derivative with respect to time while discharging current is constant. These two points are assumed as reference points in which their SOC can be determined from cell's chemistry. The optimization algorithm uses the derivative of the cell's measured terminal voltage to allocate SOC of 92% and 15% for two reference points in the Vcell equation and updates cell's electrical model. The algorithm's process is fast and computationally inexpensive, making on-board estimation practical. The obtained results demonstrate that by using this method the estimated SOC error for aged Li-ion cells does not exceed 4%.