Method for adaptative real-time optimization of a power or torque split in a vehicle

A method for adaptive real-time optimization of a power or torque split in a vehicle having a first propulsion source, a second propulsion source and a reinforcement-learning-based split controller for controlling a power or torque distribution between the first and second propulsion sources, the method comprises:

• initializing an optimal power or torque split matrix and a learning feedback variable of the reinforcement-learning-based split controller,
• obtaining a vehicle power setpoint or torque setpoint from a vehicle driver or a vehicle autonomous driving system;
• calculating a first optimal power or torque setpoint for the first propulsion source and a second optimal power or torque setpoint for the second propulsion source based on a power split value derived from optimal power or torque split matrix and the vehicle power setpoint or torque setpoint,
• controlling the first propulsion source using the first optimal power or torque setpoint, and controlling the second propulsion source using the second optimal power or torque setpoint,
• using a machine learning algorithm of the reinforcement-learning-based split controller for calculating a learning feedback variable based on measured or estimated power or torque output feedback, wherein the learning feedback variable represents an optimization criteria,
• updating the optimal power or torque split matrix when the calculated learning feedback variable represents an improvement in terms of the optimization criteria over the previous learning feedback variable.