Cummins Inc.

Siemens Aktiengesellschaft

Shashank Tamaskar

Kartavya Neema

David J Stroh

Heiko Claussen

Govindarajan Kothandaraman

Anthony Kyle Perfetto

Carlos Alcides Lana

Juan L Aparicio Ojea

Chengtao Wen

Eugen Solowjow

Vikas Narang

Yash Shahapurkar

Ines Ugalde Diaz

Wei Xi Xia

One embodiment is a system comprising an engine including a dedicated EGR cylinder configured to provide EGR to the engine via an EGR loop, a non-dedicated cylinder, a plurality of injectors structured to inject fuel into the dedicated EGR cylinder and the non-dedicated EGR cylinder, and an electronic control system operatively coupled with the fueling system and the ignition system. The electronic control system is configured to evaluate engine operating parameters including an engine load and an engine speed. The electronic control system is responsive to variation of the engine operating parameters to control operation of the fueling system to vary combustion in the at least one dedicated cylinder between rich of stoichiometric and stoichiometric.

Techniques for improving fuel economy in dedicated EGR engines

One embodiment is a system comprising an engine including a dedicated EGR cylinder configured to provide EGR to the engine via an EGR loop, a non-dedicated cylinder, a plurality of injectors, an ignition system including a plurality of spark plugs, an intake throttle, and an electronic control system. The electronic control system is configured to control combustion during transient operation of the engine by determining one or more combustion control parameters compensating for variation of one or more of inert matter, unburned air and unburned fuel in EGR output by the dedicated EGR cylinder during transient operation of the engine, and an effect of the EGR loop on inert matter, unburned air and unburned fuel provided to the plurality of cylinders, and controlling operation of at least one of the throttle, the ignition system and the plurality of injectors in response to at least one of the one or more combustion control parameters.

Techniques for transient estimation and compensation of control parameters for dedicated EGR engines

An engine system includes an air handling control unit which controls a plurality of air handling actuators responsible for maintaining flow of air and exhaust gas within the engine system. The engine system has a plurality of sensors whose sensor signals at least partially define a current state of the engine system. The air handling control unit includes a controller which controls the air handling actuators of the engine system as well as a processing unit coupled to the sensors and the controller. The processing unit includes an agent which learns a policy function that is trained to process the current state, determines a control signal to send to the controller by using the policy function after receiving the current state as an input, and outputs the control signal to the controller. Then, the agent receives a next state and a reward value from the processing unit and updates the policy function using a policy evaluation algorithm and a policy improvement algorithm based on the received reward value. Subsequently, the controller controls the air handling actuators in response to receiving the control signal. In one aspect of the embodiment, the control signal is a command signal for the air handling actuators.

Deep reinforcement learning for air handling control

An engine system includes an air handling and fuel system whose states are managed by a reference managing unit. The engine system has a plurality of sensors whose sensor signals at least partially define a current state of the engine system. The reference managing unit includes a controller which controls the air handling and fuel system of the engine system as well as a processing unit coupled to the sensors and the controller. The processing unit includes an agent which learns a policy function that is trained to process the current state, determines air handling references and fuel system references by using the policy function after receiving the current state as an input, and outputs the air handling references and fuel system references to the controller. Then, the agent receives a next state and a reward value from the processing unit and updates the policy function using a policy evaluation algorithm and a policy improvement algorithm based on the received reward value. Subsequently, the controller controls the air handling and fuel system of the engine in response to receiving the air handling references and the fuel system references.

Deep reinforcement learning for air handling and fuel system referencing

Growing community of inventors

West Lafayette, IN, United States of America

Shashank Tamaskar