Frontiers in Energy Research (Jan 2023)
Robust hybrid estimator for the state of charge of a lithium-ion battery
Abstract
The use of batteries for diverse energy storage applications is increasing, primarily because of their high energy density, and lithium-ion batteries (LiBs) are of particular significance in this regard. However, designing estimators that are robust to compute the state of charge (SOC) of these batteries in the presence of disturbance signals arising from different battery types remains a challenge. Hence, this paper presents a hybrid estimator that combines the extended Kalman filter (EKF) and sliding mode observer (SMO) via a switching function and tracking closed loop to achieve the qualities of noise cancellation and disturbance rejection. Hybridization was carried out in such a way that the inactive observer tracks the output of the used observer, simultaneously feeding back a zero-sum signal to the input gain of the used observer. The results obtained show that noise filtering is preserved at a convergence time of .01 s. Also, the state of charge estimation interval improves greatly from a range of [1, .93] and [.94, .84] obtained from the extended Kalman filter and sliding mode observer, respectively, to a range of [1, 0], in spite of the added disturbance signals from a lithium–nickel (INR 18650) battery type.
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