Abstract: The disclosed computer-implemented method optimizes thermal control of a vehicle motor, the vehicle including a cooling device including an actuator varying cooling capacity, the method including training a reinforcement learning algorithm including the iterative steps: 1) determining an action to control an actuator by applying a control function to a current state of the thermal system, and implementing the action; 2) determining a modified state of the thermal system after implementing the action; 3) calculating, by implementing a thermodynamic reward function of the motor, a reward value based on the modified state of the thermal system, and the action; 4) updating a function for estimating thermal performance based on the current state of the thermal system, the modified state of the thermal system, the action and the reward; and 5) modifying the control function based on the update of the function for estimating thermal performance.

Abstract: The subject matter of the present invention relates to trained machine-learning models (300), methods (200, 400) and apparatuses (500) allowing a future resonant frequency of a catalyst for selective reduction of nitrogen oxides (SCR) to be predicted, the resonant frequency being representative of a concentration of a reducing agent within the SCR. The SCR forms part of a system for after-treatment of a flow of exhaust gases of an internal combustion engine with which a motor vehicle is provided. The general principle of the invention is based on the observation of correlations between the resonant frequency of an SCR and the concentration of ammonia present within the SCR. This observation led the inventor to envision using machine learning to create a trained machine-learning model in order to predict the resonant frequency of an SCR.

Abstract: The disclosed computer-implemented method optimizes thermal control of a vehicle motor, the vehicle including a cooling device including an actuator varying cooling capacity, the method including training a reinforcement learning algorithm including the iterative steps: 1) determining an action to control an actuator by applying a control function to a current state of the thermal system, and implementing the action; 2) determining a modified state of the thermal system after implementing the action; 3) calculating, by implementing a thermodynamic reward function of the motor, a reward value based on the modified state of the thermal system, and the action; 4) updating a function for estimating thermal performance based on the current state of the thermal system, the modified state of the thermal system, the action and the reward; and 5) modifying the control function based on the update of the function for estimating thermal performance.

Abstract: The subject matter of the present invention relates to trained machine-learning models (300), methods (200, 400) and apparatuses (500) allowing a future resonant frequency of a catalyst for selective reduction of nitrogen oxides (SCR) to be predicted, the resonant frequency being representative of a concentration of a reducing agent within the SCR. The SCR forms part of a system for after-treatment of a flow of exhaust gases of an internal combustion engine with which a motor vehicle is provided. The general principle of the invention is based on the observation of correlations between the resonant frequency of an SCR and the concentration of ammonia present within the SCR. This observation led the inventor to envision using machine learning to create a trained machine-learning model in order to predict the resonant frequency of an SCR.