Diesel engine combustion and emission formation is highly nonlinear and thus creates a challenge related to engine diagnostics and engine control with emission feedback. This paper presents a novel methodology to address the challenge and develop virtual sensing models for engine exhaust emission. These models are capable of predicting transient emissions accurately and are computationally efficient for control and optimization studies. The emission models developed in this paper belong to the family of hierarchical models, namely the “neuro-fuzzy model tree.” The approach is based on divide-and-conquer strategy, i.e., to divide a complex problem into multiple simpler subproblems, which can then be identified using a simpler class of models. Advanced experimental setup incorporating a medium duty diesel engine is used to generate training data. Fast emission analyzers for soot and NOx provide instantaneous engine-out emissions. Finally, the engine-in-the-loop is used to validate the models for predicting transient particulate mass and NOx.

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This paper proposes a self-learning approach to develop optimal power management with multiple objectives, e.g. to minimize fuel consumption and transient engine-out NOx and particulate matter emission for a series hydraulic hybrid vehicle. Addressing multiple objectives is particularly relevant in the case of a diesel powered hydraulic hybrid since it has been shown that managing engine transients can significantly reduce real-world emissions. The problem is formulated as an infinite time horizon stochastic sequential decision making/markovian problem. The problem is computationally intractable by conventional Dynamic programming due to large number of states and complex modeling issues. Therefore, the paper proposes an online self-learning neural controller based on the fundamental principles of Neuro-Dynamic Programming (NDP) and reinforcement learning. The controller learns from its interactions with the environment and improves its performance over time. The controller tries to minimize multiple objectives and continues to evolve until a global solution is achieved. The control law is a stationary full state feedback based on 5 states and can be directly implemented. The controller performance is then evaluated in the Engine-in-the-Loop (EIL) facility.

This paper presents the modeling and control of an opposed piston (OP) engine in a novel hybrid architecture. The OP engine was selected for this work due to the inherent thermody-namic benefits and the balanced nature of the engine. The typical geartrain required on an OP engine was exchanged for two electric motors, significantly reducing friction and decoupling the crankshafts. By using the motors to control the crankshaft motion profiles, this configuration introduces capabilities to dynamically vary compression ratio, combustion volume, and scavenging dynamics. To realize these opportunities, a model of the system capturing the instantaneous engine dynamics is essential along with methodology to regulate the crankshaft’s rotational dynamics utilizing the electric motors. The modeling presented here couples a 1D model capturing the gas exchange process during scavenging and a 0D model of the crankshaft dynamics and the heat release profile due to combustion. With the use of this model, a linear quadratic controller with reference feedforward was designed to track the crankshaft motion trajectory. Experimental results are used to validate the model and controller performance. These results highlight the sensitivity to model uncertainty at points with high cylinder pressure, leading to large differences in control input near minimum volume. The proposed controller is, however, still able to maintain tracking error for crankshaft position below ± 1 degree.

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The paper describes the approach, addresses integration challenges and discusses capabilities of the Hybrid Powertrain-in-the-Loop (H-PIL) facility for the series/hydrostatic hydraulic hybrid system. We describe the simulation of the open-loop and closed-loop hydraulic hybrid systems in H-PIL and its use for concurrent engineering and development of advanced supervisory strategies. The configuration of the hydraulic-hybrid system and details of the hydraulic circuit developed for the H-PIL integration are presented. Next, software and hardware interfaces between the real components and virtual systems are developed, and special attention is given to linking component-level controllers and system-level supervisory control. The H-PIL setup allows imposing realistic dynamic loads on hydraulic pump/motors and accumulator based on vehicle driving schedule. Application of fast analyzers allows characterization of the impact of dynamic interactions in the propulsion system on engine-out emissions. Therefore, the H-PIL facility allows optimization of the hybrid system for both high-efficiency and low emissions. The impetus is provided by previous work showing that more than half of the soot emissions from a conventional diesel powertrain over the urban driving schedule can be attributed to transients. The setup includes a 6.4L V-8 International diesel engine, highly dynamic dynamometer, Radial piston pump/motors supplied by Bosch-Rexroth and dSPACE real-time environment with in-house developed simulation of the virtual vehicle.

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Properties of alternative fuels, such as cetane number, aromatic content, bulk modulus, etc., can vary significantly; thus, a detailed investigation is conducted to assess the impacts on engine combustion, efficiency, and emissions during steady-state and transient engine conditions. Insights can be used to maximize the benefits of alternative fuels or to avoid potential problems that stem from differing ignition and combustion characteristics of particular fuels. In this work, biodiesel (20%, 50%, and 100% biodiesel blends), jet fuel JP8, and synthetic jet fuel S8 are compared to diesel #2 (ULSD). Comparisons are made on ignition, combustion, particulate matter, particulate size spectra, and NO X emission at steady state under harmonized operating conditions, that is, with equalized fuel energy input. All experiments are conducted using the same baseline calibration originally developed for diesel fuel. Ignition delay and combustion phasing trends suggest the need for possible adaptation of ECU calibration. The impact of alternative fuels on particulate emission depended strongly on fuel aromatic content and oxygenation. NO X emission was correlated to multiple physical and chemical fuel properties. While the significance of different properties on NO X emission varied with engine condition, the CA50 location was strongly correlated at all conditions. This paper also includes detailed insight into particulate spectra obtained for all six fuels. Transient engine operation over a driving schedule was characterized with the engine-in-the-loop setup. The use of alternative fuels caused more aggressive cyber-driver behavior as a reaction to their lower energy densities. Different fueling histories were recorded with the alternative fuels and led to marked changes in instantaneous emissions traces. In particular, spikes of particulate emission in the exhaust that typically occur at accelerator tipin were reduced with biodiesel, while extended high load operation was observed in order to follow the demanded vehicle velocity trace and led to higher NO. Cumulative results over the complete FTP 75 driving schedule indicate that transient engine operation reduces the particulate matter benefits of the alternative fuels. JP8 and S8 show slight NO emission benefits with biodiesel showing slightly worse NO emission when compared to steady state. The more aggressive driver behavior led to worse fuel economy.