Real-time machine learning and physics-based hybrid approach to perform eLSD torque estimation

A vehicle, comprising:

• a plurality of sensors each configured to generate vehicle data;
• an electronic limited-slip differential (eLSD);
• a controller in communication with the plurality of sensors and the eLSD, wherein the controller is programmed to:
• receive the vehicle data in real-time, wherein the vehicle data includes a torque request;
• determine a preliminary eLSD clutch torque using a neural network and the vehicle data;
• determine clutch torque bounds of the eLSD using a physics-based model;
• determine whether the preliminary eLSD clutch torque is outside the clutch torque bounds of the eLSD;
• in response to determining that the preliminary eLSD clutch torque is outside the clutch torque bounds of the eLSD, adjust the preliminary eLSD clutch torque using the clutch torque bounds to determine a final clutch torque of the eLSD;
• and
• command, in real-time, the eLSD to apply the final clutch torque to a clutch of the eLSD.

A tangible, non-transitory, machine-readable medium, comprising machine-readable instructions, that when executed by a processor, cause the processor to

• receive vehicle data in real-time, wherein the vehicle data includes a torque request;
• determine a preliminary electronic limited-slip differential (eLSD) clutch torque of an eLSD using a neural network and the vehicle data;
• determine clutch torque bounds of the eLSD using a physics-based model;
• determine whether the preliminary eLSD clutch torque is outside the clutch torque bounds of the eLSD;
• in response to determining that the preliminary eLSD clutch torque is outside the clutch torque bounds of the eLSD, adjust the preliminary eLSD clutch torque using the clutch torque bounds to determine a final clutch torque of the eLSD;
• and
• command, in real-time, the eLSD to apply the final clutch torque to a clutch of the eLSD.