Examples described herein provide a computer-implemented method that includes calculating, by a processing device, a motor acceleration error based at least in part on a motor torque and a motor speed. The method further includes calculating, by the processing device, a regression fit line based at least in part on the motor acceleration error. The method further includes identifying, by the processing device, a zero point using the regression fit line. The method further includes comparing, by the processing device, the zero point to a datum reference to determine a difference. The method further includes integrating, by the processing device, the difference to determine the lash angle. The method further includes controlling, by the processing device, the motor based at least in part on the lash angle.

Examples described herein provide a computer-implemented method that includes calculating, by a processing device, a motor acceleration error based at least in part on a motor torque and a motor speed. The method further includes calculating, by the processing device, a regression fit line based at least in part on the motor acceleration error. The method further includes identifying, by the processing device, a zero point using the regression fit line. The method further includes comparing, by the processing device, the zero point to a datum reference to determine a difference. The method further includes integrating, by the processing device, the difference to determine the lash angle. The method further includes controlling, by the processing device, the motor based at least in part on the lash angle.

A controller discharges a traction battery according to power limits defined by output that is indicative of a state of charge of the traction battery and is updated by a bar delta filter according to a balancing current. The balancing current is associated with each cell of a string of series connected cells of the traction battery and represents a deviation of an estimated current through the cell from a total current of the string.

A torque map generation system includes a motor, an inverter that drives the motor, a controller that controls the inverter, a torque sensor coupled to the motor, a power analyzer coupled to the torque sensor and a torque map generator that measures a current vector value of the motor by switching a MTPA (Maximum Torque Per Ampere) method and a square wave method based on a voltage utilization ratio of the inverter, wherein the torque map generator utilizes a measurement result by the MTPA method when the torque map generator uses the square wave method.

A torque map generation system includes a motor, an inverter that drives the motor, a controller that controls the inverter, a torque sensor coupled to the motor, a power analyzer coupled to the torque sensor and a torque map generator that measures a current vector value of the motor by switching a MTPA (Maximum Torque Per Ampere) method and a square wave method based on a voltage utilization ratio of the inverter, wherein the torque map generator utilizes a measurement result by the MTPA method when the torque map generator uses the square wave method.

A dynamic safe state control circuit is disclosed that controls an electrical motor based on vehicle speed. A microcontroller or other processing device is configured to control an inverter system of an electrical motor. The dynamic safe state control circuit is configured to receive a first signal that corresponds to a speed of the electric motor. The circuit is configured to activate any one of a plurality of safe states in the inverter system based on the first signal and in response to a malfunction in the microcontroller.

In various embodiments, methods, systems, and vehicle apparatuses are provided. A method for implementing torque control using a Neural Network (NN) for a torque prediction model to receive a set of measured vehicle operating inputs associated with torque prediction; substituting a set of multiple independent variables into the torque prediction model so that the NN is then taking the form of a simplified pseudo-NN that contains a reduced variable set of one independent variable; processing, the set of measured vehicle operating inputs by the pseudo-NN based on the NN prediction model by using only one independent variable in a pseudo-NN's simplified mathematical expression; and solving for at least one root of the pseudo-NN's simplified mathematical expression by obtaining a root value without having to rely on an inversion operation of a mathematical expression that consists of an entire set of independent variables.

A method comprises determining a first pressure increase in an electrochemical energy storage unit based on a first repetition rate, detecting that the first pressure increase has exceeded a first threshold value, determining a second pressure increase in the energy storage unit based on a second repetition rate, the second repetition rate being greater than the first repetition rate, detecting that the second pressure increase exceeds a second threshold value, and outputting a signal to a control unit based on detecting that the second pressure increase has exceeded the second threshold value.

Device for determining the angular position of a rotor of a rotary electric machine on the basis of signals delivered by a plurality of position sensors, including a circuit producing a control loop for estimating position of the rotor, delivering at output a signal representative of the position, and a circuit for dynamic normalization by the amplitude of the first harmonic of each signal originating from a position sensor. The circuit receives as input each signal originating from a position sensor, and at least one image of the signal representative of the position of the rotor and is configured to demodulate each signal by the image of the signal, determine, at the end of this demodulation, amplitude of the first harmonic of this signal originating from a position sensor, and normalize each signal by dividing it by the amplitude of the first harmonic of the previously determined signal.

Device for determining the angular position of a rotor of a rotary electric machine on the basis of signals delivered by a plurality of position sensors, including a circuit producing a control loop for estimating position of the rotor, delivering at output a signal representative of the position, and a circuit for dynamic normalization by the amplitude of the first harmonic of each signal originating from a position sensor. The circuit receives as input each signal originating from a position sensor, and at least one image of the signal representative of the position of the rotor and is configured to demodulate each signal by the image of the signal, determine, at the end of this demodulation, amplitude of the first harmonic of this signal originating from a position sensor, and normalize each signal by dividing it by the amplitude of the first harmonic of the previously determined signal.