학술논문

Accelerometer-Based Robust Estimation of In-Cylinder Pressure for Cycle-to-Cycle Combustion Control
Document Type
Periodical
Source
IEEE Transactions on Instrumentation and Measurement IEEE Trans. Instrum. Meas. Instrumentation and Measurement, IEEE Transactions on. 72:1-13 2023
Subject
Power, Energy and Industry Applications
Components, Circuits, Devices and Systems
Combustion
Engines
Estimation
Accelerometers
Vibrations
Pressure measurement
Signal processing algorithms
Accelerometer
CA50
engine combustion estimation
engine control
in-cylinder pressure
Language
ISSN
0018-9456
1557-9662
Abstract
This article develops a new approach for the estimation of in-cylinder pressure and combustion variables for cycle-to-cycle combustion control in diesel engines. Such combustion control can lead to enhancement of engine performance and efficiency as well as prevention of combustion failures in UAV diesel engines. In-cylinder pressure and combustion variables are estimated using measurements from a nonintrusive accelerometer located on the engine block. The new estimation approach is based on separating the combustion component of the acceleration signal from the noncombustion component. A time-varying first-order differential equation model relating the combustion components of acceleration and pressure is then utilized. The proposed method is evaluated using the experimental data from a turbocharged high-speed diesel engine. The robustness of the proposed estimation algorithm is demonstrated by evaluating it with 85 experimental datasets of many different operating conditions involving both single and double (pilot and main) injections. In-cylinder pressure and combustion variables, such as cumulative heat release (CHR) and crank angle of 50% CHR (CA50), are estimated, with CA50 being a key variable needed for closed-loop cycle-to-cycle combustion control. Experimental results show that the CA50 value is estimated accurately with a root mean squared error (RMSE) of 1.45° in single-injection datasets involving 40 different operating conditions and an RMSE of 3.96° in double-injection datasets involving 45 different operating conditions.