Christoph Unterrieder

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We present a novel iterative algorithm for approximating the linear least squares solution with low complexity. After a motivation of the algorithm we discuss the algorithm's properties including its complexity, and we present theoretical results as well as simulation based performance results. We describe the analysis of its convergence behavior and show(More)
Recently, we proposed approximate least squares (ALS), a low complexity approach to solve the linear least squares problem. In this work we present the step-adaptive linear least squares (SALS) algorithm, an extension of the ALS approach that significantly reduces its approximation error. We theoretically motivate the extension of the algorithm, and(More)
—A reliable knowledge of cell parameters like the state-of-charge (SoC) is essential for the optimization of battery-powered applications. Usually, during relaxation (the phase of no or low loads) the SoC is determined based on the measurement of the battery's electro-motive force (EMF). To obtain a reliable measurment, it is required that the battery(More)
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