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.

Mitigating CO₂ emissions from long-haul commercial trucking is a major challenge that must be addressed to achieve substantial reductions in greenhouse gas (GHG) emissions from the transportation sector. Extensive recent research and development programs have shown how significant near-term reductions in GHGs from commercial vehicles can be achieved by combining technological advances. This paper reviews progress in technology for engine efficiency improvements, vehicle resistance and drag reductions, and the introduction of hybrid electric powertrains in long-haul trucks. The results of vehicle demonstration projects by major vehicle manufacturers have shown peak brake thermal efficiency of 55% in heavy-duty diesel engines and have demonstrated freight efficiency improvements of 150% relative to a 2009 baseline in North America. These improvements have been achieved by combining multiple incremental improvements in both engine and vehicle technologies. Powertrain electrification through hybridization has been shown to offer some potential reductions in fuel consumption. These potential benefits depend on the vehicle use, the details of the powertrain design, and the duty cycle. To date, most papers have focused on standard drive cycles, leaving a research gap in how hybrid electric powertrains would be designed to minimize fuel consumption over real-world drive cycles, which are essential for a reliable powertrain design. The results of this paper suggest that there is no “one-size-fits-all” solution to reduce the GHGs in long-haul trucking, and a combination of technologies is required to provide an optimum solution for each application.

Among different types of low temperature combustion (LTC) regimes, eactively controlled compression ignition (RCCI) has received a lot of attention as a promising advanced combustion engine technology with high indicated thermal efficiency and low nitrogen oxides ( ${\mathrm{NO}}_{\mathrm{x}}$ ) and particulate matter (PM) emissions. In this study, an RCCI engine for the purpose of fuel economy investigation is incorporated in series hybrid electric vehicle (SHEV) architecture, which allows the engine to run completely in the narrow RCCI mode for common driving cycles. Three different types of energy management control (EMC) strategies are designed and implemented to achieve the best fuel economy. The EMC strategies encompass rule-based control (RBC), offline, and online optimal controllers, including dynamic programing (DP) and model predictive control (MPC), respectively. The simulation results show a 13.1% to 14.2% fuel economy saving by using an RCCI engine over a modern spark ignition (SI) engine in SHEV for different driving cycles. This fuel economy saving is reduced to 3% in comparison with a modern compression ignition (CI) engine, while ${\mathrm{NO}}_{\mathrm{x}}$ emissions are significantly lower. Simulation results show that the RCCI engine offers more fuel economy improvement in more aggressive driving cycles (e.g., US06), compared to less aggressive driving cycles (e.g., UDDS). In addition, the MPC results show that sub-optimal fuel economy is achieved by predicting the vehicle speed profile for a time horizon of 70 s.

In cold temperatures, vehicles idle more, have high cold-start emissions including greenhouse gases, and have less effective exhaust filtration systems, which can cause up to ten-fold more harmful vehicular emissions. Only a few vehicle technologies have been tested for emissions below −7 °C (20 °F). Four-hundred-million people living in cities with sub-zero temperatures may be impacted. We conducted a scoping review to identify the existing knowledge about air-pollution-related health outcomes in a cold climate, and pinpoint any research gaps. Of 1019 papers identified, 76 were selected for review. The papers described short-term health impacts associated with air pollutants. However, most papers removed the possible direct effect of temperature on pollution and health by adjusting for temperature. Only eight papers formally explored the modifying effect of temperatures. Five studies identified how extreme cold and warm temperatures aggravated mortality/morbidity associated with ozone, particles, and carbon-monoxide. The other three found no health associations with tested pollutants and temperature. Additionally, in most papers, emissions could not be attributed solely to traffic. In conclusion, evidence on the relationship between cold temperatures, traffic-related pollution, and related health outcomes is lacking. Therefore, targeted research is required to guide vehicle regulations, assess extreme weather-related risks in the context of climate change, and inform public health interventions.

The standards for emissions from diesel engines are becoming more stringent and accurate emission modeling is crucial in order to control the engine to meet these standards. Soot emissions are formed through a complex process and are challenging to model. A comprehensive analysis of diesel engine soot emissions modeling for control applications is presented in this paper. Physical, black-box, and gray-box models are developed for soot emissions prediction. Additionally, different feature sets based on the least absolute shrinkage and selection operator (LASSO) feature selection method and physical knowledge are examined to develop computationally efficient soot models with good precision. The physical model is a virtual engine modeled in GT-Power software that is parameterized using a portion of experimental data. Different machine learning methods, including Regression Tree (RT), Ensemble of Regression Trees (ERT), Support Vector Machines (SVM), Gaussian Process Regression (GPR), Artificial Neural Network (ANN), and Bayesian Neural Network (BNN) are used to develop the black-box models. The gray-box models include a combination of the physical and black-box models. A total of five feature sets and eight different machine learning methods are tested. An analysis of the accuracy, training time and test time of the models is performed using the K-means clustering algorithm. It provides a systematic way for categorizing the feature sets and methods based on their performance and selecting the best method for a specific application. According to the analysis, the black-box model consisting of GPR and feature selection by LASSO shows the best performance with test $R^2$ of 0.96. The best gray-box model consists of SVM-based method with physical insight feature set along with LASSO for feature selection with test $R^2$ of 0.97.