Abstract: A hybrid electric vehicle (HEV) includes an internal combustion engine and a motor/generator configured to drive the engine or be driven by the engine to generate electricity to charge a high voltage (HV) battery and/or drive an electric traction motor. The HEV includes an exhaust system, a HV system electrically coupling the HV battery and the electric traction motors, and a control system including a controller for monitoring an underhood environment of the HEV and performing a cooling operation to cool the exhaust system. The controller is programmed to determine if a temperature of the underhood environment, the internal combustion engine, and/or the exhaust system is above a predetermined threshold indicating a potential degradation of HV system performance, and operate the internal combustion engine in fuel shut-off to generate a flow of air to the exhaust system for cooling of the exhaust system to thereby cool the HV system.

Abstract: A hybrid electric vehicle (HEV) includes an internal combustion engine and a motor/generator configured to drive the engine or be driven by the engine to generate electricity to charge a high voltage (HV) battery and/or drive an electric traction motor. The HEV includes an exhaust system, a HV system electrically coupling the HV battery and the electric traction motors, and a control system including a controller for monitoring an underhood environment of the HEV and performing a cooling operation to cool the exhaust system. The controller is programmed to determine if a temperature of the underhood environment, the internal combustion engine, and/or the exhaust system is above a predetermined threshold indicating a potential degradation of HV system performance, and operate the internal combustion engine in fuel shut-off to generate a flow of air to the exhaust system for cooling of the exhaust system to thereby cool the HV system.

Abstract: An intelligent battery charge depletion system for an electrified powertrain of a range-extended electrified vehicle (REEV) determines a modified state of charge (SOC) setpoint based on an estimated road load and an estimated gross combined vehicle weight (GCCW) of the REEV. The modified SOC setpoint is different than a charge sustaining SOC setpoint and is for a battery system having an SOC and that is configured to power an electric motor of the electrified powertrain. The estimated road load is for a road segment that the REEV is traversing. The system also controls the electric motor based on the modified SOC setpoint and the battery system SOC to maintain a torque reserve that the electric motor and the battery system can use when needed; and controls an engine of the electrified powertrain to selectively recharge the battery system.

Abstract: The present invention is directed to machine-learning based methods and systems related to dynamically inserting items multimedia content into media broadcasts. By using machine-learning based models, the performance of different items of multimedia content with different audiences can be automatically simulated, resulting in recommendations for where, when and how to optimally distribute those items of multimedia content. The multimedia content can be distributed by dynamically integrating that multimedia content into a streaming video feed. The reaction of an audience to the multimedia content is then automatically monitored, collected, and analyzed using machine-learning techniques, allowing the reaction of the audience to the multimedia content to be automatically determined. This reaction can then be input back into the machine-learning based simulator, further refining future predictions for the performance of items of multimedia content with audiences.

Abstract: The present invention is directed to machine-learning based methods and systems related to dynamically inserting items multimedia content into media broadcasts. By using machine-learning based models, the performance of different items of multimedia content with different audiences can be automatically simulated, resulting in recommendations for where, when and how to optimally distribute those items of multimedia content. The multimedia content can be distributed by dynamically integrating that multimedia content into a streaming video feed. The reaction of an audience to the multimedia content is then automatically monitored, collected, and analyzed using machine-learning techniques, allowing the reaction of the audience to the multimedia content to be automatically determined. This reaction can then be input back into the machine-learning based simulator, further refining future predictions for the performance of items of multimedia content with audiences.