Portable In-Cylinder Pressure Measurement and Signal Processing System for Real-Time Combustion Analysis and Engine Control

The present paper proposes an optimized algorithm for minimizing the amount of data processed in order to maintain all critical information from the in-cylinder pressure sensor but with minimum computational cost. The algorithm uses singular value decomposition (SVD) for reducing the number of samples and pivoting QR decomposition to identify the optimal sampling locations. Experimental data from four engines with different combustion modes, namely Spark ignition (SI), Compression ignition (CI), turbulent jet ignition (TJI), and dual fuel ignition (DFCI), was used to validate the algorithm. The impact of the different sampling methodologies on different metrics for engine performance has been addressed and studied showing negligible information loss when reducing to 25 samples per cycle the acquisition buffer size.

Section: Methods Descriptionmentioning

confidence: 97%

In-cylinder pressure smart sampling for efficient data management

Plá

Morena

Bares

et al. 2022

“…In order to guarantee cycle-to-cycle control at an engine speed of 2000 rpm, equivalent to 60 ms per combustion cycle, the proposed learning algorithm was integrated into the ECU without communication overhead. 40 Finally, the control parameters of the ECU were modified through a host computer with a LabVIEW interface. Figure 1 shows the setup and instrumentation of the experimental engine test cell.…”

Section: Methodsmentioning

confidence: 99%

Reinforcement learning applied to dilute combustion control for increased fuel efficiency

Maldonado,

Kaul,

Schuman

et al. 2024

To reduce the modeling burden for control of spark-ignition engines, reinforcement learning (RL) has been applied to solve the dilute combustion limit problem. Q-learning was used to identify an optimal control policy to adjust the fuel injection quantity in each combustion cycle. A physics-based model was used to determine the relevant states of the system used for training the control policy in a data-efficient manner. The cost function was chosen such that high cycle-to-cycle variability (CCV) at the dilute limit was minimized while maintaining stoichiometric combustion as much as possible. Experimental results demonstrated a reduction of CCV after the training period with slightly lean combustion, contributing to a net increase in fuel conversion efficiency of 1.33%. To ensure stoichiometric combustion for three-way catalyst compatibility, a second feedback loop based on an exhaust oxygen sensor was incorporated into the fuel quantity controller using a slow proportional-integral (PI) controller. The closed-loop experiments showed that both feedback loops can cooperate effectively, maintaining stoichiometric combustion while reducing combustion CCV and increasing fuel conversion efficiency by 1.09%. Finally, a modified cost function was proposed to ensure stoichiometric combustion with a single controller. In addition, the learning period was shortened by half to evaluate the RL algorithm performance on limited training time. Experimental results showed that the modified cost function could achieve the desired CCV targets, however, the learning time was reduced by half and the fuel conversion efficiency increased only by 0.30%.

“…Cycle-to-cycle results from the analysis are transmitted over Ethernet to the RPECS where the engine control strategy is executed. Further details on the in-cylinder pressure signal processing system for engine control has been discussed by Luo et al 17 Finally, measurements, as well as commands issued by RPECS, are sent through CAN bus to the AVL-PUMA system that controls the AC-Dynamometer. Here, data are recorded at 50 Hz, which suffices for capturing cycle-to-cycle phenomena.…”

Section: Methodsmentioning

confidence: 99%

Learning reference governor for cycle-to-cycle combustion control with misfire avoidance in spark-ignition engines at high exhaust gas recirculation–diluted conditions

Maldonado

Kolmanovsky

et al. 2020

Cycle-to-cycle feedback control is employed to achieve optimal combustion phasing while maintaining high levels of exhaust gas recirculation by adjusting the spark advance and the exhaust gas recirculation valve position. The control development is based on a control-oriented model that captures the effects of throttle position, exhaust gas recirculation valve position, and spark timing on the combustion phasing. Under the assumption that in-cylinder pressure information is available, an adaptive extended Kalman filter approach is used to estimate the exhaust gas recirculation rate into the intake manifold based on combustion phasing measurements. The estimation algorithm is adaptive since the cycle-to-cycle combustion variability (output covariance) is not known a priori and changes with operating conditions. A linear quadratic regulator controller is designed to maintain optimal combustion phasing while maximizing exhaust gas recirculation levels during load transients coming from throttle tip-in and tip-out commands from the driver. During throttle tip-outs, however, a combination of a high exhaust gas recirculation rate and an overly advanced spark, product of the dynamic response of the system, generates a sequence of misfire events. In this work, an explicit reference governor is used as an add-on scheme to the closed-loop system in order to avoid the violation of the misfire limit. The reference governor is enhanced with model-free learning which enables it to avoid misfires after a learning phase. Experimental results are reported which illustrate the potential of the proposed control strategy for achieving an optimal combustion process during highly diluted conditions for improving fuel efficiency.