A second-order discrete-time sliding mode observer (DSMO)-based method is proposed to estimate the state of charge (SOC) of a Li-ion battery. Unlike the first-order sliding mode approach, the proposed method eliminates the chattering phenomenon in SOC estimation. Further, a battery model with a dynamic resistance is also proposed to improve the accuracy of the battery model. Similar to actual battery behavior, the resistance parameters in this model are changed by both the magnitude of the discharge current and the SOC level. Validation of the dynamic resistance model is performed through pulse current discharge tests at two different SOC levels. Our experimental results show that the proposed estimation method not only enhances the estimation accuracy but also eliminates the chattering phenomenon. The SOC estimation performance of the second-order DSMO is compared with that of the first-order DSMO.

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In a practical holographic data storage system, the reconstruction process for a data page should account for the processing time as well as the bit error rate (BER) performance. To improve both aspects, we introduce two-dimensional (2D) partial response maximum likelihood composed of a 2D partial response (PR) target including diagonal elements and a 2D soft output Viterbi algorithm (SOVA) with a variable reliability factor. The 2D SOVA performs two one-dimensional (1D) SOVAs in structural accordance with the 2D PR target where extrinsic information uses the expected value calculated on a synchronization pattern. Finally, the 2D SOVA exports a weighted average using the reliability factor that is updated similarly as the optimization scheme for each page. The simulation results show that the proposed method has superior BER performance, despite using only two 1D SOVAs as compared with the modified 2D SOVA composed of four 1D SOVAs.

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