A method is proposed for processing a measurement signal, in particular for a steering system. The method comprises the following steps: A measured variable is acquired based on the measurement signal, wherein the measured variable comprises items of information about a physical variable, and wherein the measured variable is a superposition of the actual value of the physical variable and the measurement noise. Filter parameters of a filter are ascertained based on the measured variable and a mathematical model of the measurement noise. The measurement signal is filtered by means of the filter, whereby an estimated value of the physical variable is obtained, wherein the filter has the ascertained filter parameters. The filter parameters are ascertained in such a way that a deviation between the estimated value of the physical variable and the actual value of the physical variable is approximated and minimized. Furthermore, a control unit for a steering system, a steering system, a computer program, and a computer-readable data carrier are disclosed.

Disclosed is a method and a battery management system for estimating the parameters of an equivalent circuit model for a battery. The equivalent circuit model includes a first resistor, a second resistor connected in series to the first resistor and a capacitor connected in parallel to the second resistor. The method according to an embodiment of the present disclosure individually estimates the resistance of the first resistor and the resistance of the second resistor based on a first number of terminal voltages and a first number of currents measured in a sequential order at each time step in a sliding time window having a predefined size, and stores data indicating the estimated results in the memory.

Disclosed is a method and a battery management system for estimating the parameters of an equivalent circuit model for a battery. The equivalent circuit model includes a first resistor, a second resistor connected in series to the first resistor and a capacitor connected in parallel to the second resistor. The method according to an embodiment of the present disclosure individually estimates the resistance of the first resistor and the resistance of the second resistor based on a first number of terminal voltages and a first number of currents measured in a sequential order at each time step in a sliding time window having a predefined size, and stores data indicating the estimated results in the memory.

An apparatus for reducing belt slip of a vehicle, in which the vehicle includes an engine as a vibration source and an electric motor connected to the engine through a belt to transmit torque of the engine, includes: a signal generator configured to generate a reference signal with a frequency corresponding to vibration of the engine; an adaptive filter configured to calculate a filter coefficient to remove an error value between a rotational speed of the engine and a rotational speed of the electric motor and apply the filter coefficient to the reference signal to generate a reference torque signal; and a torque compensator configured to generate a belt slip compensation torque signal by changing an amplitude of the reference torque signal, and apply the belt slip compensation torque signal to determine a final torque command of the electric motor.

An adaptation hardware accelerator comprises a calculation unit to receive inputs at predefined time interval(s) that correspond to a calculation iteration, the inputs associated with adaptive filters having taps, and determine correlation and cross-correlation data based thereon for a given iteration. The correlation data comprises a correlation matrix. Determining the matrix comprises determining submatrices in an upper triangular portion and a diagonal portion of the matrix. The accelerator comprises an adaptation core unit to determine adaptive weights associated with the adaptive filters, respectively, based on an adaptive algorithm, utilizing the correlation and cross correlation data. The accelerator unit comprises a convergence detector unit to determine a convergence parameter; and a controller to generate an iteration signal for each time interval based on the parameter. The iteration signal communicates to continue or conclude; the conclusion indicates determination of a final value of adaptive weights by the core unit.

An adaptation hardware accelerator comprises a calculation unit configured to receive a plurality of inputs at one or more predefined time intervals, wherein each time interval corresponds to a calculation iteration, the plurality of inputs being associated with a plurality of adaptive filters each having a plurality of taps, and determine a correlation data and a cross-correlation data based thereon for a given calculation iteration. The correlation data comprises a correlation matrix comprising a plurality of sub-matrices, wherein determining the correlation matrix comprises determining only the submatrices in an upper triangular portion and a diagonal portion of the correlation matrix. Further, the adaptation hardware accelerator comprises an adaptation core unit configured to determine a plurality of adaptive weights associated with the plurality of adaptive filters, respectively, based on an optimized RLS based adaptive algorithm, by utilizing the correlation data and the cross correlation data. In addition, the hardware accelerator unit comprises a convergence detector unit configured to determine a convergence parameter; and a controller configured to generate an iteration signal for each of the predefined time intervals based on the convergence parameter. The iteration signal communicates to the calculation unit and the adaptation core unit to continue with a next calculation iteration or to conclude, wherein the conclusion indicates a determination of a final value of the plurality of the adaptive weights by the adaptation core unit.