Data driven techniques for on-board performance estimation and prediction in vehicular applications.

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Energy management for vehicular electric power systems

The electric energy consumption in passenger vehicles is rapidly increasing. To limit the associated increase in fuel consumption, an energy management system has been developed. This system exploits the fact that the losses in the internal combustion engine vary with the operating point, and uses the possibility to temporarily store electric energy in a battery, such that electric energy is only produced at moments when it is cheap to generate. To come to a practically applicable solution, a vehicle model is derived, containing only the component characteristics relevant for this application. The energy management problem is formulated as an optimization problem. The fuel consumption over a driving cycle is minimized, while respecting physical limitations of the components and maintaining an acceptable energy level of the battery. Several optimization methods are studied to come to a solution. The dynamic optimization problem is solved using Dynamic Programming. After rewriting it as a static optimization problem and approximating the cost function by a quadratic function, the problem is solved using Quadratic Programming, which requires less computation time. A real-time implementable strategy has been derived from the Quadratic Programming problem, that does not require a prediction of the future driving cycle. This strategy compares the current cost of producing electric energy with the estimated average cost. By adapting the average cost based on the energy level of the battery, it is ensured that the battery energy level will remain around the desired value. Simulations show that a fuel reduction up till 2% can be obtained on a conventional vehicle without major hardware changes. Higher reductions are possible on the exhaust emissions. To predict and explain the amount of fuel reduction that can be obtained with a given vehicle configuration, a set of engineering rules is derived based on typical component characteristics. Their results correspond reasonably well with the simulations. An advanced power net topology is studied which contains both a battery and an ultracapacitor that are connected by a DC-DC converter and a switch. Because of the increased complexity, this system is modeled using linear and piecewise linear approximations of the component characteristics, such that the energy management problem can be casted as a Linear Programming problem. The discrete switch makes it a Mixed Integer Linear Programming Problem. A realtime strategy, similar to the strategy for the conventional power net, has been derived. The addition fuel reduction that is obtained with the dual storage power net is small, because the maximum profit that can be obtained with an ideal lossless battery is not much higher than with a normal battery. Subsequently, hybrid electric vehicles are studied that use an Integrated Starter Generator which can be used for generating electric power and for vehicle propulsion. Several conSummary figurations are studied with respect to their potential fuel reduction. Configurations that enable start-stop operation of the engine obtain a much higher fuel reduction, up to 40%. The controllers are tested in real-time on a Hardware-in-the-Loop environment, where a vehicle simulation model is combined with existing electric components. It is shown that the electric power setpoints provided by an energy management strategy can be realized in practice

Powertrain Architectures and Technologies for New Emission and Fuel Consumption Standards

New powertrain design is highly influenced by CO2 and pollutant limits defined by legislations, the demand of fuel economy in for real conditions, high performances and acceptable cost. To reach the requirements coming from both end-users and legislations, several powertrain architectures and engine technologies are possible (e.g. SI or CI engines), with many new technologies, new fuels, and different degree of electrification. The benefits and costs given by the possible architectures and technology mix must be accurately evaluated by means of objective procedures and tools in order to choose among the best alternatives. This work presents a basic design methodology and a comparison at concept level of the main powertrain architectures and technologies that are currently being developed, considering technical benefits and their cost effectiveness. The analysis is carried out on the basis of studies from the technical literature, integrating missing data with evaluations performed by means of powertrain-vehicle simplified models, considering the most important powertrain architectures. Technology pathways for passenger cars up to 2025 and beyond have been defined. After that, with support of more detailed models and experimentations, the investigation has been focused on the more promising technologies to improve internal combustion engine, such as: water injection, low temperature combustions and heat recovery systems

Real-time Optimal Energy Management System for Plug-in Hybrid Electric Vehicles

Air pollution and rising fuel costs are becoming increasingly important concerns for the transportation industry. Hybrid electric vehicles (HEVs) are seen as a solution to these problems as they off er lower emissions and better fuel economy compared to conventional internal combustion engine vehicles. A typical HEV powertrain consists of an internal combustion engine, an electric motor/generator, and a power storage device (usually a battery). Another type of HEV is the plug-in hybrid electric vehicle (PHEV), which is conceptually similar to the fully electric vehicle. The battery in a PHEV is designed to be fully charged using a conventional home electric plug or a charging station. As such, the vehicle can travel further in full-electric mode, which greatly improves the fuel economy of PHEVs compared to HEVs. In this study, an optimal energy management system (EMS) for a PHEV is designed to minimize fuel consumption by considering engine emissions reduction. This is achieved by using the model predictive control (MPC) approach. MPC is an optimal model-based approach that can accommodate the many constraints involved in the design of EMSs, and is suitable for real-time implementations. The design and real-time implementation of such a control approach involves control-oriented modeling, controller design (including high-level and low-level controllers), and control scheme performance evaluation. All of these issues will be addressed in this thesis. A control-relevant parameter estimation (CRPE) approach is used to make the control-oriented model more accurate. This improves the EMS performance, while maintaining its real-time implementation capability. To reduce the computational complexity, the standard MPC controller is replaced by its explicit form. The explicit model predictive controller (eMPC) achieves the same performance as the implicit MPC, but requires less computational effort, which leads to a fast and reliable implementation. The performance of the control scheme is evaluated through different stages of model-in-the-loop (MIL) simulations with an equation-based and validated high-fidelity simulation model of a PHEV powertrain. Finally, the CRPE-eMPC EMS is validated through a hardware-in-the-loop (HIL) test. HIL simulation shows that the proposed EMS can be implemented to a commercial control hardware in real time and results in promising fuel economy figures and emissions performance, while maintaining vehicle drivability

Energy and Emissions Conscious Optimal Following for Automated Vehicles with Diesel Powertrains

The emerging application of autonomous driving provides the benefit of eliminating the driver from the control loop, which offers opportunities for safety, energy saving and green house gas emissions reduction by adjusting the speed trajectory. The technological advances in sensing and computing make it realistic for the vehicle to obtain a preview information of its surrounding environment, and optimize its speed trajectory accordingly using predictive planning methods. Conventional speed following algorithms usually adopt an energy-centric perspective and improve fuel economy by means of reducing the power loss due to braking and operating the engine at its high fuel efficiency region. This could be a problem for diesel-powered vehicles, which rely on catalytic aftertreatment system to reduce overall emissions, as reduction efficiency drops significantly with a cold catalyst that would result from a smoother speed profile. In this work, control and constrained optimization techniques are deployed to understand the potential for and achieve concurrent reduction of fuel consumption and emissions. Trade-offs between fuel consumption and emissions are shown using results from a single objective optimal planning problem when the calculation is performed offline assuming full knowledge of the whole cycle. Results indicate a low aftertreatment temperature when energy-centric objectives are used, and this motivates the inclusion of temperature performance metric inside the optimization problem. An online optimal speed planner is then designed for concurrent treatment of energy and emissions, with a limited but accurate preview information. An objective function comprising an energy conscious term and an emissions conscious term is proposed based on its effectiveness of 1) concurrent reduction of fuel and emissions, 2) flexible balancing between the emphasis on fuel saving or emissions reduction based on performance requirements and 3) low computational complexity and ease of numerical treatment. Simulation results of the online optimal speed planner over multiple drive cycles are presented, and for the vehicle simulated in this work, concurrent reduction of fuel and emissions is demonstrated using a specific powertrain, when allowing flexible modification of the drive cycle. Hardware-in-the-loop experiment is also performed over the Federal Test Procedure (FTP) drive cycle, and shows up to 15% reduction in fuel consumption and 70% reduction in NOx emissions when allowing a flexible following distance. Finally, the stringent requirement of accurate preview information is relaxed by designing a robust re-formulation of the energy and emissions conscious speed planner. Improved fuel economy and emissions are shown while satisfying the constraints even in the presence of perturbations in the preview information. A Gaussian mixture regression-based speed prediction is applied to test the performance of the speed following strategy without assuming knowledge of the preview information. A performance degradation is observed in simulation results when using the predicted velocity compared with an accurate preview, but the speed planner preserves the capability to improve fuel and tailpipe emissions performance compared with a non-optimal controller.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/170004/1/huangchu_1.pd

A Study on the Effects of Combined Diesel-Hydrogen Combustion on Diesel Engines using Experimental and Simulation Techniques

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonWith the increasingly stringent regulations and laws being put in place worldwide with regards to a cleaner and a safer environment, the modern diesel engine has scope to be improved upon to help meet these new standards set for the betterment of our cities. There are many current modes of alternative transport, with diesel-hydrogen combustion being a transitional solution from fossil fuels to hydrogen powered vehicles. The main objective of this research effort was to investigate the effects of intake air enrichment with hydrogen on the performance, combustion, and emissions of a diesel engine. The secondary aim was to design and optimise accurate engine models which can replicate real world experiments and conditions. This becomes increasingly useful in the modern era of engine testing and development as it allows for more manufacturers to test and optimise new combustion methods, without the need for a physical engine, to meet the ever-tightening emissions legislations. Therefore, the accuracy of the models produced could pave the way for more simulations to be carried out via manufacturers with more confidence. The experimental tests were carried out on a 2.0 litre Ford High Speed Direct Injection (HSDI) diesel engine. the engine was tested at various conditions mimicking light- and medium-duty diesel engines. Hydrogen was used via a bottle with the composition of the gas replicating exhaust gas reformed intake air. The percentage of the hydrogen and the start of injection for diesel were altered for the tests. The simulations were carried out on a replicated four-cylinder 2.0 litre Ford HSDI diesel engine on Ricardo Wave® and a single-cylinder DI diesel engine modelled based on a small Yanmar L70N diesel engine. The experimental operating parameters were used in the simulations to measure the level of accuracy achieved with the models on the software. The experimental results showed that with hydrogen enrichment of the intake air, the CO and smoke emissions were reduced significantly, however NOx emissions were found to have increased at certain conditions. The simulations for the multi-cylinder diesel engine showed great promise with an average of 95% accuracy across the operating conditions and emissions measured. The single-cylinder diesel engine displayed low levels of Total Hydrocarbons (THC), Carbon Monoxide (CO), with a slight increase in Oxides of Nitrogen (NOx) emissions but did show high levels of accuracy against literature and other experimental work based on similar operating conditions. Although there is an abundance of literature currently investigating the effects of hydrogen enrichment of the intake air, the new contributions to knowledge of this research is the comparison between simulated and experimental work of transitional combustion methods such as this. This research is believed to help aid the industry in testing and optimising of simulated engine models for a more reliable manufacturing process

Analysis and Control of Multimode Combustion Switching Sequence.

Highly dilute, low temperature combustion technologies, such as homogeneous charge compression ignition (HCCI), show significant improvements in internal combustion engine fuel efficiency and engine-out NOx emissions. These improvements, however, occur at limited operating range and conventional spark ignition (SI) combustion is still required to fulfill the driver's high torque demands. In consequence, such multimode engines involve discrete switches between the two distinct combustion modes. Such switches unfortunately require a finite amount of time, during which they exhibit penalties in efficiency. Along with its challenges, the design of such a novel system offers new degrees of freedom in terms of engine and aftertreatment specifications. Prior assessments of this technology were based on optimistic assumptions and neglected switching dynamics. Furthermore, emissions and driveability were not fully addressed. To this end, a comprehensive simulation framework, which accounts for above-mentioned penalties and incorporates interactions between multimode engine, driveline, and three-way catalyst (TWC), has been developed. Experimental data was used to parameterize a novel mode switch model, formulated as finite-state machine. This model was combined with supervisory controller designs, which made the switching decision. The associated drive cycle results were analyzed and it was seen that mode switches have significant influence on overall fuel economy, and the issue of drivability needs to be addressed within the supervisory strategy. After expanding the analysis to address emissions assuming a TWC, it was shown that, in practice, HCCI operation requires the depletion of the TWC's oxygen storage capacity (OSC). For large OSCs the resulting lean-rich cycling nullifies HCCI's original efficiency benefits. In addition, future emissions standards are still unlikely to be fulfilled, deeming a system consisting of such a multimode engine and TWC with generous OSC unfavorable. In view of these difficulties, the modeling framework was extended to a mild hybrid electric vehicle (HEV) allowing a prolonged operation in HCCI mode with associated fuel economy benefits during city driving. Further analysis on how to reduce NOx while maintaining fuel economy resulted in a counterintuitive suggestion. It was deemed beneficial to constrain the HCCI operation to a small region, exhibiting lowest NOx, while reducing instead of increasing the OSC.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116660/1/snuesch_1.pd

Development of predictive energy management strategies for hybrid electric vehicles supported by connectivity

Nowadays, the spreading of the air pollution crisis enhanced by greenhouse gases emission is leading to the worsening of the global warming. In this context, the transportation sector plays a vital role, since it is responsible for a large part of carbon dioxide production. In order to address these issues, the present thesis deals with the development of advanced control strategies for the energy efficiency optimization of plug-in hybrid electric vehicles (PHEVs), supported by the prediction of future working conditions of the powertrain. In particular, a Dynamic Programming algorithm has been developed for the combined optimization of vehicle energy and battery thermal management. At this aim, the battery temperature and the battery cooling circuit control signal have been considered as an additional state and control variables, respectively. Moreover, an adaptive equivalent consumption minimization strategy (A-ECMS) has been modified to handle zero-emission zones, where engine propulsion is not allowed. Navigation data represent an essential element in the achievement of these tasks. With this aim, a novel simulation and testing environment has been developed during the PhD research activity, as an effective tool to retrieve routing information from map service providers via vehicle-to-everything connectivity. Comparisons between the developed and the reference strategies are made, as well, in order to assess their impact on the vehicle energy consumption. All the activities presented in this doctoral dissertation have been carried out at the Green Mobility Research Lab} (GMRL), a research center resulting from the partnership between the University of Bologna and FEV Italia s.r.l., which represents the industrial partner of the research project

The impact of in-cylinder charge motion on lean limit extension and in-pre-chamber mixture preparation in a homogeneous ultra-lean engine

The perpetual desire to conserve fuel is driving strong demand for increased efficiency in spark ignited (SI) engines. A method being increasingly explored to accomplish this goal is lean combustion. Homogeneous ultra-lean combustion with λ > 1.6 has demonstrated the ability to both increase thermal efficiency and significantly reduce engine-out nitrogen oxides (NOx) emissions due to the colder cylinder temperatures innate to combustion with high levels of dilution. The major limitation in developing lean and ultra-lean combustion systems is the less favorable ignition quality of the mixture. This has necessitated the development of higher energy ignition sources. A pre-chamber combustor application known as jet ignition is one such technology, having been researched extensively. Differing types and magnitudes of charge motion are incorporated in SI engines to aid with mixture preparation. The influence of charge motion over lean SI combustion however is less well understood. Additionally, charge motion introduced in the main combustion chamber has the potential to translate to the pre-chamber, thereby affecting pre-chamber mixing and combustion. The effect of charge motion on mixing and combustion comprehensively throughout the engine cycle is unknown and has not been investigated. This study seeks to evaluate the impact of charge motion on mixture preparation and combustion processes in a jet ignition engine. Experimental engine testing is undertaken to quantify the impact of differing levels and types of induced charge motion on pre-chamber and main chamber combustion. An analysis of high speed pressure data from the pre-chamber provides insight into how charge motion affects pre-chamber combustion stability, and how instabilities cascade to the main chamber combustion event. A set of simulations, matched to experimental engine results, is used to develop an understanding of charge motion influence over the complexities of in-pre-chamber phenomena that are not easily observed experimentally. From the synthesis of these data sets, a clear understanding of the role that charge motion plays in homogeneous highly dilute jet ignition engines emerges. This study quantifies the impact that charge motion has on lean limit extension and engine efficiency, identifies optimal charge motion type, and provides a roadmap for engine system optimization

Low Complexity Model Predictive Control of a Diesel Engine Airpath.

The diesel air path (DAP) system has been traditionally challenging to control due to its highly coupled nonlinear behavior and the need for constraints to be considered for driveability and emissions. An advanced control technology, model predictive control (MPC), has been viewed as a way to handle these challenges, however, current MPC strategies for the DAP are still limited due to the very limited computational resources in engine control units (ECU). A low complexity MPC controller for the DAP system is developed in this dissertation where, by "low complexity," it is meant that the MPC controller achieves tracking and constraint enforcement objectives and can be executed on a modern ECU within 200 microseconds, a computation budget set by Toyota Motor Corporation. First, an explicit MPC design is developed for the DAP. Compared to previous explicit MPC examples for the DAP, a significant reduction in computational complexity is achieved. This complexity reduction is accomplished through, first, a novel strategy of intermittent constraint enforcement. Then, through a novel strategy of gain scheduling explicit MPC, the memory usage of the controller is further reduced and closed-loop tracking performance is improved. Finally, a robust version of the MPC design is developed which is able to enforce constraints in the presence of disturbances without a significant increase in computational complexity compared to non-robust MPC. The ability of the controller to track set-points and enforce constraints is demonstrated in both simulations and experiments. A number of theoretical results pertaining to the gain scheduling strategy is also developed. Second, a nonlinear MPC (NMPC) strategy for the DAP is developed. Through various innovations, a NMPC controller for the DAP is constructed that is not necessarily any more computationally complex than linear explicit MPC and is characterized by a very streamlined process for implementation and calibration. A significant reduction in computational complexity is achieved through the novel combination of Kantorovich's method and constrained NMPC. Zero-offset steady state tracking is achieved through a novel NMPC problem formulation, rate-based NMPC. A comparison of various NMPC strategies and developments is presented illustrating how a low complexity NMPC strategy can be achieved.PhDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120832/1/huxuli_1.pd