Data-driven Modeling and Predictive Control of Maximum Pressure Rise Rate in RCCI Engines

Basina, L. N. Aditya; Irdmousa, Behrouz Khoshbakht; Velni, Javad Mohammadpour; Borhan, Hoseinali; Naber, Jeffrey; Shahbakhti, Mahdi

doi:10.1109/ccta41146.2020.9206358

Cited by 14 publications

(23 citation statements)

References 13 publications

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“…This paper builds upon our prior experience for the design and implementation of model-based ICE controllers [2,4,5,7,21,22,46,. In this review paper, we searched for relevant papers in the field of MPC and ICE using the Scopus, Web of Science, IEEE Xplore, ScienceDirect, Springer, SAE, ASME, Wiley, Taylor & Francis, arXiv, and SAGE databases.…”

Section: Scope Of the Papermentioning

confidence: 99%

“…MPC has been used in ICE control for a wide variety of ICE control problems. Among these control problems, MPC has been used widely for the control of fuel consumption [28,30,[33][34][35][36]52,54,[135][136][137], combustion phasing [21,22,27,28,[37][38][39]52,54,108,129,130,138], cyclic variability [54,130], torque and load (IMEP) [22,27,28,33,[37][38][39][40][41][42]52,54,108,129,130,[136][137][138], idle speed [25,36,43,139,140], airpath (P man , EGR)…”

Section: Mpc Applications In Icesmentioning

confidence: 99%

“…MPC applications are not limited to a specific engine type, and, due to the capability of MPC, they have been implemented in various types of engines. MPC has been implemented for different ICE combustion modes, including Compression Ignition Combustion (CI) [26,[28][29][30][31]34,35,40,45,[47][48][49][50]53,135,137,139,[141][142][143]145,147,148,150], Spark Ignition (SI) [25,33,36,[41][42][43]51,52,54,136,138,140,144], and Low Temperature Combustion (LTC) [21,22,27,[37][38][39]108,129,130].…”

Section: Mpc Applications In Icesmentioning

confidence: 99%

“…However, systematic optimization methods based on a model developed and identified using experimental results are ideal ways to tackle this problem. Several model-based controllers have been used in engine feedback control to address this, such as the Linear Quadratic Regulator (LQR) controller [17], Linear Quadratic Gaussian (LQG) controller [18], Sliding Model Controller (SMC) [5,19,20], Adaptive [20], and Model Predictive Control (MPC) controller [21,22]. Among these mode-based controllers, MPC is one of the most promising controllers that can deal with the highly constrained nonlinear system of ICEs.…”

Section: Introduction 1progress In Engine Controlmentioning

confidence: 99%

“…MPC can provide an optimal real-time solution for meeting multi-objective goals while addressing system and operational constraints. New variants of MPC utilize optimization solvers and packages that are suitable for the real-time operation of time-critical systems [21,22].…”

Section: Introduction 1progress In Engine Controlmentioning

confidence: 99%

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Model Predictive Control of Internal Combustion Engines: A Review and Future Directions

et al. 2021

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An internal combustion engine (ICE) is a highly nonlinear dynamic and complex engineering system whose operation is constrained by operational limits, including emissions, noise, peak in-cylinder pressure, combustion stability, and actuator constraints. To optimize today’s ICEs, seven to ten control actuators and 10–20 feedback sensors are often used, depending on the engine applications and target emission regulations. This requires extensive engine experimentation to calibrate the engine control module (ECM), which is both cumbersome and costly. Despite these efforts, optimal operation, particularly during engine transients and to meet real driving emission (RDE) targets for broad engine speed and load conditions, has still not been obtained. Methods of model predictive control (MPC) have shown promising results for real-time multi-objective optimal control of constrained multi-variable nonlinear systems, including ICEs. This paper reviews the application of MPC for ICEs and analyzes the recent developments in MPC that can be utilized in ECMs. ICE control and calibration can be enhanced by taking advantage of the recent developments in the field of Artificial Intelligence (AI) in applying Machine Learning (ML) to large-scale engine data. Recent developments in the field of ML-MPC are investigated, and promising methods for ICE control applications are identified in this paper.

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