Control and machine learning for internal combustion engines and their exhaust aftertreatment systems

该研究计划的短期和中期目标是开发方法，以重型发动机为重点，减少使用无碳或可再生燃料的内燃机产生的二氧化碳和其他有害排放。加拿大和许多其他国家计划到2050年将温室气体排放减少到零。这给运输部门带来了巨大的变化和创新。北美的重型8类卡车运输了60%的货运吨公里，产生了超过75%的公路货运二氧化碳排放量。减少这些运输卡车二氧化碳排放的一个有希望的方法是通过最大化热效率和使用可再生燃料或零碳燃料(如氢)来减少燃料使用量。这项工作的影响是迅速减少加拿大这些卡车的二氧化碳排放。例如，在艾伯塔省的卡车上使用蓝色氢气时，重型货运卡车使用40：60%的氢/柴油双燃料(能源分配)可减少32%的生命周期二氧化碳，预计到2039年将减少150万吨/年的二氧化碳排放。目前的内燃机标定方法已经变得如此复杂，以至于行业强烈要求在基于模型的控制中嵌入系统的知识和约束。创新的发动机概念，如H2或H2/柴油双燃料，可以快速减少二氧化碳，但由于当前控制/校准方法的复杂性和成本，以满足实际驾驶排放(RDE)要求，正在推迟进入市场。这项提议的重点将是开发机器学习控制的方法及其在重型货运卡车上对H2/柴油双燃料、100%H2燃料和其他可再生燃料的应用，以快速减少二氧化碳。需要对燃烧和排气后处理系统，包括传感器/执行器有详细的物理了解。这被结合到适合于基于模型的控制的仿真模型中。例如，要减少使用柴油/氢气或100%氢气的货运卡车中的二氧化碳，需要利用对爆震、预燃、NOx和颗粒排放的了解来制定控制策略。长期目标是，拟议的研究将在以下方面取得突破性进展：(1)发动机燃烧的循环内控制，使先进的燃烧成为可能；(2)将机器学习与模型预测控制相结合的新方法，用于复杂的受限工程系统；(3)机器学习--用于健壮且易于校准的发动机控制，使用碳中性燃料将燃料和排放降至最低；以及(4)培训高素质的人员在H2经济中工作。这将产生长期影响，使加拿大在复杂的工程技术方面保持竞争力，例如交通运输，这些技术需要控制以实现最佳性能。

The short to medium term objective of this research program is to develop methods to reduce CO2 and other harmful emissions from internal combustion engines using carbon free or renewable fuels with the focus on heavy duty engines. Canada and many other countries, plan to reduce greenhouse gas emissions to zero by 2050. This is causing tremendous changes and innovation in the transportation sector. Heavy Duty Class 8 trucks in North America move 60% freight ton-km and produce over 75% of the CO2 emissions of road freight. One promising way to reduce CO2 emissions in these transport trucks is to decrease fuel usage by maximizing the thermal efficiency and using renewable fuels or zero carbon fuels such as hydrogen (H2). The impact of this work is to quickly reduce CO2 emissions from these trucks in Canada. For example, a H2/Diesel dual fuel 40:60% (energy split) in heavy duty freight trucks reduces the life cycle CO2 32% when using blue H2 for trucks in Alberta with a projected reduction of 1.5 megatonnes of CO2/year by 2039. Current Internal Combustion Engine calibration methods have become so complex there is strong industry demand to embed knowledge and constraints of the system in a model based control. Innovative engine concepts such as H2 or H2/Diesel dual fuel, which can quickly reduce CO2, are being delayed into the market due to the complexity and cost of current control/calibration methods to meet Real Driving Emission (RDE) requirements. The focus of this proposal will be the development of methods and their application of Machine Learning control to H2/Diesel dual fuel, 100% H2 fuel and other renewable fuels in heavy duty freight trucks to quickly reduce CO2. A detailed physical understanding of the combustion and exhaust aftertreatment system including the sensors/actuators is needed. This is incorporated into a simulation model suitable for model based control. For example, to decrease CO2 in freight trucks using Diesel/H2 or 100% H2 requires using an understanding of knock, preignition, NOx and particulates emissions to develop control strategies. The long term objective is that the proposed research will lead to ground breaking advances in (1) In-cycle control for engine combustion enabling advanced combustion; (2) new methods of combining Machine Learning with Model Predictive Control for complex constrained engineering systems; (3) Machine Learning-Control for robust and easy to calibrate engine control that minimizes fuel and emissions using carbon neutral fuels; and (4) training of highly qualified people to work in the H2 economy. This will have a long term impact to allow Canada to remain competitive in complex engineering technologies, such as transportation, that require control for optimal performance.