Modelling and estimation of combustion variability for fast light-off of diesel aftertreatment

Maldonado, Bryan P.; Bieniek, Mitchell; Hoard, John; Stefanopoulou, Anna G.; Fulton, Brien; Nieuwstadt, Michiel Van

doi:10.1504/ijpt.2020.108423

Cited by 4 publications

(4 citation statements)

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“…These issues may become more pronounced for future medium-duty SI gaseous and low-carbon fuels with reduced flame speeds. Moreover, similar stability limits have been found in diesel combustion during cold starts, 11,12 diesel combustion in aerial powertrains, 13 spark-assisted compression ignition, 14 and gasoline compression ignition. 15 Thus, a robust and adaptable control strategy for minimizing combustion CCV can have wide applications within the transportation sector to improve vehicle efficiencies.…”

Section: Introductionsupporting

confidence: 72%

Reinforcement learning applied to dilute combustion control for increased fuel efficiency

Maldonado,

Kaul,

Schuman

et al. 2024

International Journal of Engine Research

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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%.

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Section: Introductionsupporting

confidence: 72%

Reinforcement learning applied to dilute combustion control for increased fuel efficiency

Maldonado,

Kaul,

Schuman

et al. 2024

International Journal of Engine Research

View full text Add to dashboard Cite

show abstract

“…This variance estimation can be achieved by either an infinite impulse response (IIR) filter, as previously done by Akhlaghi et al, 29 or by a finite impulse response (FIR) filter as originally suggested by Mehra. Given that the system will be tested during throttle tip-ins and tip-outs, the following minimum variance unbiased estimator for σ CA 50 2 [ k ] (realized by FIR filters) was shown to have better transient properties than equivalent IIR filters 30…”

Section: State Estimation and Ca50 Filteringmentioning

confidence: 99%

“…Recall that if Z ~ N ( 0 , S E 2 ) , then the probability Pr [ | Z | ≤ 3 . 3 SE ] ≈ 99 . 9 % . Here, SE denotes the standard error of the mean estimate, which can be computed by the following unbiased estimator 30 where Γ ( · ) if the Gamma function. Therefore, if | Z k | ≤ 3 . 3 S E k − 1 then the variance estimator updates normally; otherwise, the estimator stops updating and outputs the last stored value, that is, S k 2 = S k − 1 2 .…”

Section: Adaptive Extended Kalman Filter Modification During Transientsmentioning

confidence: 99%

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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

International Journal of Engine Research

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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.

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Artificial-intelligence-based prediction and control of combustion instabilities in spark-ignition engines

Maldonado

Stefanopoulou

Kaul

2022

Artificial Intelligence and Data Driven Optimization of Internal Combustion Engines

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Modelling and estimation of combustion variability for fast light-off of diesel aftertreatment

Cited by 4 publications

References 0 publications

Reinforcement learning applied to dilute combustion control for increased fuel efficiency

Reinforcement learning applied to dilute combustion control for increased fuel efficiency

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

Artificial-intelligence-based prediction and control of combustion instabilities in spark-ignition engines

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