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

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.

Material Application Methodologies for Winter Road Mobility: A Renewed Perspective

Winter road maintenance operations, commonly known as snow/ice control operations, are one of the most critical functions of state, provincial and local transportation agencies in cold regions. These operations aim to provide safety and mobility by timely and effective application of materials and plowing. The most common materials used are salt (sodium chloride, solid and brine), magnesium chloride-based, and calcium chloride-based deicers, agro-based additives and blends, and abrasives. In practice, the specific choice and application method and rate of these materials dependent on temperature, precipitation type, level of service goals, budget, and environmental sustainability. Best practices of material application are designed to apply the right type and amount of materials in the right place at the right time. This review presents the literature review and agency interviews that were conducted to assemble the information about the use of materials, including types of materials, application tactics, application rates, and application equipment.

Before-and-After Empirical Bayes Evaluation of Achieving Bare Pavement using Anti-Icing on Urban Roads

Canadian municipalities are increasingly choosing to achieve bare pavement (BP) for snow and ice control during fall/winter seasons. When a snowstorm event is forecasted, one strategy is to apply anti-icing chemicals to the pavement surface to prevent the snow and ice from forming a bond with the road surface. Such an approach facilitates a more efficient plowing operation and reduces the amount of deicing chemicals needed to achieve BP. This study assesses the safety performance of achieving BP using anti-icing compared with the traditional reactive winter road maintenance (WRM) approach on urban roads using the before-and-after Empirical Bayes technique. Results suggest that achieving BP significantly reduces all collision types and severities on midblocks with a reduction value in the range of 13.7% to 19.7%. Attaining BP at intersections was found to be very effective in reducing injury collisions with an estimated reduction of 12.5%. When sites were grouped based on a WRM priority-basis, it was found that anti-icing was effective for reducing the majority of collision types and severities at the different priority levels with reductions ranging from 8.7% to 49.83% on midblocks and between 5.37% and 13% at intersections. All reductions were statistically significant. The monetary benefits of the reductions in property-damage only and nonfatal injury collisions were estimated at 60 million Canadian dollars using a 1.92% interest rate and a 2-year service life. These findings provide unequivocal evidence that achieving BP using anti-icing can lead to significant societal safety benefits that economically translate to huge collision cost savings.