Lambda virtual sensor systems and methods for a combustion engine

1. A system comprising

an engine control system comprising a processor configured to:

receive, via a first sensor, a signal representative of at least one of a manifold pressure, a manifold temperature, or a manifold mass flow rate of a manifold;

derive, via a manifold model and the first sensor signal, an oxygen concentration measurement at a first manifold section of the manifold; and

apply the gas concentration measurement during operations of an engine, wherein the manifold is fluidly coupled to the engine.

2. The system of claim 1 , wherein the manifold model comprises a physics-based mass flow model accounting for a compressor bypass recirculating flow through the manifold, and wherein the manifold comprises a charging manifold fluidly coupled to an inlet of the engine and configured to provide fuel and an oxidant to the inlet.

3. The system of claim 1 , wherein the oxygen concentration measurement comprises a lambda measurement

λ

2

⁢

⁢

s

=

1

-

c

g

⁢

⁢

2

⁢

⁢

s

c

g

⁢

⁢

2

⁢

⁢

s

⁢

AFR

s

,

wherein c g2s is representative of the gas concentration measurement derived via the manifold model and the first sensor signal and wherein AFR s is representative of the stoichiometric air to fuel ratio (AFR) of the engine.

4. The system of claim 1 , wherein the processor is configured to execute a Kalman filter to derive one or more estimated states when deriving, via the manifold model, the oxygen concentration measurement, and wherein the one or more estimated states are applied for controlling the engine.

5. The system of claim 4 , wherein the one or more estimated states comprise the gas concentration measurement, a pressure, an engine speed, a turbocharger speed, or a combination thereof.

6. A tangible, non-transitory computer readable medium storing code configured to:

receive, via a first sensor, a signal representative of at least one of a manifold pressure, a manifold temperature, or a manifold mass flow rate of a manifold;

derive, via a manifold model and the first sensor signal, a gas concentration measurement at a first manifold section of the manifold; and

apply the oxygen concentration measurement during operations of an engine, wherein the manifold is fluidly coupled to the engine.

7. The tangible, non-transitory computer readable medium of claim 6 , wherein the oxygen concentration measurement comprises a lambda measurement.

8. The tangible, non-transitory computer readable medium of claim 6 , wherein the code configured to derive, via the manifold model, the gas concentration measurement, comprises code configured to execute a Kalman filter to derive one or more estimated states.

9. The tangible, non-transitory computer readable medium of claim 8 , comprising code configured to recursively apply an engine output y to derive the one or more estimated states, wherein the engine output y comprises at least one of an intake manifold pressure, an engine speed, a generator electrical power, or a combination thereof, and wherein the engine is mechanically coupled to an electrical generator to produce the generator electrical power during operations.

10. The tangible, non-transitory computer readable medium of claim 8 , wherein the Kalman filter is configured to derive a control input u, and wherein applying the oxygen concentration measurement during operations of an engine comprises applying the control input u, to control the engine during operations.

11. A method, comprising:

receiving, via a first sensor, a signal representative of at least one of a manifold pressure, a manifold temperature, or a manifold mass flow rate of a manifold;

deriving, via a manifold model and the first sensor signal, a gas concentration measurement at a first manifold section of the manifold; and

applying the gas concentration measurement during operations of an engine, wherein the manifold is fluidly coupled to the engine.

12. The method of claim 11 , wherein the manifold model comprises a physics-based mass flow model accounting for a compressor bypass recirculating flow through the manifold.

13. The method of claim 12 , wherein the manifold comprises a charging manifold fluidly coupled to an inlet of the engine and configured to provide fuel and an oxidant to the inlet.

14. The method of claim 11 , wherein the oxygen concentration measurement comprises a lambda measurement.

15. The method of claim 14 , wherein the lambda measurement comprises a

λ

2

⁢

⁢

s

=

1

-

c

g

⁢

⁢

2

⁢

⁢

s

c

g

⁢

⁢

2

⁢

⁢

s

⁢

AFR

s

,

wherein c g2s is representative of the gas concentration measurement derived via the manifold model and the first sensor signal and wherein AFR s is representative of the stoichiometric air to fuel ratio (AFR) of the engine.

16. The method of claim 11 , wherein deriving, via the manifold model, the oxygen concentration measurement, comprises executing a Kalman filter to derive one or more estimated states.

17. The method of claim 16 , wherein the Kalman filter comprises an Extended Kalman Filter (EKF) configured to analyze non-linear data.

18. The method of claim 17 , wherein the EKF is configured to derive a control input u comprising a desired gas flow, a throttle valve control input, a bypass valve control input, or a combination thereof, and wherein applying the oxygen concentration measurement during operations of an engine comprises applying the control input u, to control the engine during operations.

19. The method of claim 17 , wherein the EKF is configured to recursively apply an engine output y to derive the one or more estimated states, wherein the engine output y comprises at least one of an intake manifold pressure, an engine speed, a generator electrical power, or a combination thereof, and wherein the engine is mechanically coupled to an electrical generator to produce the generator electrical power during operations.