Development of a model-based transient calibration process for diesel engine electronic control module tables – Part 1: data requirements, processing, and analysis

This is the first part of a study investigating a model-based transient calibration process for diesel engines. The motivation is to populate hundreds of parameters (which can be calibrated) in a methodical and optimum manner by using model-based optimization in conjunction with the manual process so that, relative to the manual process used by itself, a significant improvement in transient emissions and fuel consumption and a sizable reduction in calibration time and test cell requirements is achieved. Empirical transient modelling and optimization has been addressed in the second part of this work, while the required data for model training and generalization are the focus of the current work. Transient and steady-state data from a turbocharged multicylinder diesel engine have been examined from a model training perspective. A single-cylinder engine with external air-handling has been used to expand the steady-state data to encompass transient parameter space. Based on comparative model performance and differences in the non-parametric space, primarily driven by a high engine difference between exhaust and intake manifold pressures (ΔP) during transients, it has been recommended that transient emission models should be trained with transient training data. It has been shown that electronic control module (ECM) estimates of transient charge flow and the exhaust gas recirculation (EGR) fraction cannot be accurate at the high engine ΔP frequently encountered during transient operation, and that such estimates do not account for cylinder-to-cylinder variation. The effects of high engine ΔP must therefore be incorporated empirically by using transient data generated from a spectrum of transient calibrations. Specific recommendations on how to choose such calibrations, how many data to acquire, and how to specify transient segments for data acquisition have been made. Methods to process transient data to account for transport delays and sensor lags have been developed. The processed data have then been visualized using statistical means to understand transient emission formation. Two modes of transient opacity formation have been observed and described. The first mode is driven by high engine ΔP and low fresh air flowrates, while the second mode is driven by high engine ΔP and high EGR flowrates. The EGR fraction is inaccurately estimated at both modes, while EGR distribution has been shown to be present but unaccounted for by the ECM. The two modes and associated phenomena are essential to understanding why transient emission models are calibration dependent and furthermore how to choose training data that will result in good model generalization.

A New Reactor Concept for Efficient Solar-Thermochemical Fuel Production

We describe and analyze the efficiency of a new solar-thermochemical reactor concept, which employs a moving packed bed of reactive particles produce of H2 or CO from solar energy and H2O or CO2. The packed bed reactor incorporates several features essential to achieving high efficiency: spatial separation of pressures, temperature, and reaction products in the reactor; solid–solid sensible heat recovery between reaction steps; continuous on-sun operation; and direct solar illumination of the working material. Our efficiency analysis includes material thermodynamics and a detailed accounting of energy losses, and demonstrates that vacuum pumping, made possible by the innovative pressure separation approach in our reactor, has a decisive efficiency advantage over inert gas sweeping. We show that in a fully developed system, using CeO2 as a reactive material, the conversion efficiency of solar energy into H2 and CO at the design point can exceed 30%. The reactor operational flexibility makes it suitable for a wide range of operating conditions, allowing for high efficiency on an annual average basis. The mixture of H2 and CO, known as synthesis gas, is not only usable as a fuel but is also a universal starting point for the production of synthetic fuels compatible with the existing energy infrastructure. This would make it possible to replace petroleum derivatives used in transportation in the U.S., by using less than 0.7% of the U.S. land area, a roughly two orders of magnitude improvement over mature biofuel approaches. In addition, the packed bed reactor design is flexible and can be adapted to new, better performing reactive materials.

Thermal Energy Storage for Solar Power Production

Solar energy is the most abundant persistent energy resource. It is also an intermittent one available for only a fraction of each day while the demand for electric power never ceases. To produce a significant amount of power at the utility scale, electricity generated from solar energy must be dispatchable and able to be supplied in response to variations in demand. This requires energy storage that serves to decouple the intermittent solar resource from the load and enables around-the-clock power production from solar energy. Practically, solar energy storage technologies must be efficient as any energy loss results in an increase in the amount of required collection hardware, the largest cost in a solar electric power system. Storing solar energy as heat has been shown to be an efficient, scalable, and relatively low-cost approach to providing dispatchable solar electricity. Concentrating solar power systems that include thermal energy storage (TES) use mirrors to focus sunlight onto a heat exchanger where it is converted to thermal energy that is carried away by a heat transfer fluid and used to drive a conventional thermal power cycle (e.g., steam power plant), or stored for later use. Several approaches to TES have been developed and can generally be categorized as either thermophysical (wherein energy is stored in a hot fluid or solid medium or by causing a phase change that can later be reversed to release heat) or thermochemical (in which energy is stored in chemical bonds requiring two or more reversible chemical reactions).

Development of Dynamic Constraint Models for a Model Based Transient Calibration Process

Model based calibration has gained popularity in recent years as a method to optimize increasingly complex engine systems. However virtually all model based techniques are applied to steady state calibration. Transient calibration is by and large an emerging technology. An important piece of any transient calibration process is the ability to constrain the optimizer to treat the problem as a dynamic one and not as a quasi-static process. The optimized air-handling parameters corresponding to any instant of time must be achievable in a transient sense; this in turn depends on the trajectory of the same parameters over previous time instances. In this work dynamic constraint models have been proposed to translate commanded to actually achieved air-handling parameters. These models enable the optimization to be realistic in a transient sense. The air handling system has been treated as a linear second order system with PD control. Parameters for this second order system have been extracted from real transient data. The model has been shown to be the best choice relative to a list of appropriate candidates such as neural networks and first order models. The selected second order model was used in conjunction with transient emission models to predict emissions over the FTP cycle. It has been shown that emission predictions based on air-handing parameters predicted by the dynamic constraint model do not differ significantly from corresponding emissions based on measured air-handling parameters.

Development of a model-based transient calibration process for diesel engine electronic control module tables – Part 2: modelling and optimization

This is the second part of a study investigating a model-based transient calibration process for diesel engines. The first part addressed the data requirements and data processing required for empirical transient emission and torque models. The current work focuses on modelling and optimization. The unexpected result of this investigation is that when trained on transient data, simple regression models perform better than more powerful methods such as neural networks or localized regression. This result has been attributed to extrapolation over data that have estimated rather than measured transient air-handling parameters. The challenges of detecting and preventing extrapolation using statistical methods that work well with steady-state data have been explained. The concept of constraining the distribution of statistical leverage relative to the distribution of the starting solution to prevent extrapolation during the optimization process has been proposed and demonstrated. Separate from the issue of extrapolation is preventing the search from being quasi-static. Second-order linear dynamic constraint models have been proposed to prevent the search from returning solutions that are feasible if each point were run at steady state, but which are unrealistic in a transient sense. Dynamic constraint models translate commanded parameters to actually achieved parameters that then feed into the transient emission and torque models. Combined model inaccuracies have been used to adjust the optimized solutions. To frame the optimization problem within reasonable dimensionality, the coefficients of commanded surfaces that approximate engine tables are adjusted during search iterations, each of which involves simulating the entire transient cycle. The resulting strategy, different from the corresponding manual calibration strategy and resulting in lower emissions and efficiency, is intended to improve rather than replace the manual calibration process.