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

Supervisory control for emission compliance of heavy-duty vehicles

Heavy freight trucks globally contribute to a significant proportion of transport-related air pollution. The dominant air pollutants from heavy freight trucks with diesel engine and exhaust aftertreatment system (EATS) are CO2, hydrocarbons (HC), CO, particulate matter (PM), NOX (NO and NO2), and NH3. The greenhouse gas emission legislation limits the amount of CO2 emission, and Euro VI emission legislation limits the other dominant air pollutants. Emission legislation is gradually becoming more and more stringent to reach the long term goal of near-zero-emission. Several parties are working together to reduce the emissions, keeping both the short and long term goal in mind. Any step which results in ICE downsizing contributes to the reduction of all dominant emissions. But, with size and type of the ICE decided, there is a trade-off between NOX emission and other emissions: reduced NOX emission means reduced fuel efficiency (i.e. increased CO2, PM, and HC emissions). It is a challenge to fulfil the current emission legislation—especially real-driving NOX emission legislation—with existing control functionalities in the engine management system (EMS). However, better control of NOX emission is possible by exploiting predictive driving information and considering the coupling between the engine system and EATS. This work pursues this idea and concludes that fulfilling real-driving NOX emission legislation is possible, considering the coupling between the engine system and EATS while using predictive information. The work provides a mathematical formulation of the concept and then develops, evaluates, and implements an engine-EATS supervisor which optimizes total fuel consumption and fulfils both the world harmonized transient cycle (WHTC) based and real-driving NOX emission legislation. The developed supervisor is a distributed economic nonlinear model predictive controller (E-NMPC). This work develops and analyzes two different versions of the distributed E-NMPC based supervisory control algorithm. The more efficient one of the two is again compared for three variants. Considering the computation time of the three algorithms and processing speed of the existing EMS, one algorithm is selected for implementation. The supervisor performs much better compared to a baseline controller (optimized offline). Simulation results show that the supervisory controller has 1.7% less total fuel consumption and 88.4% less NH3 slip, compared to the baseline controller, to achieve the same real-driving NOX emission

Model predictive emissions control of a diesel engine airpath: Design and experimental evaluation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163480/2/rnc5188.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163480/1/rnc5188_am.pd

Model Predictive Control of Modern High-Degree-of-Freedom Turbocharged Spark Ignited Engines with External Cooled EGR

The efficiency of modern downsized SI engines has been significantly improved using cooled Low-Pressure Exhaust Gas Recirculation, Turbocharging and Variable Valve Timing actuation. Control of these sub-systems is challenging due to their inter-dependence and the increased number of actuators associated with engine control. Much research has been done on developing algorithms which improve the transient turbocharged engine response without affecting fuel-economy. With the addition of newer technologies like external cooled EGR the control complexity has increased exponentially. This research proposes a methodology to evaluate the ability of a Model Predictive Controller to coordinate engine and air-path actuators simultaneously. A semi-physical engine model has been developed and analyzed for non-linearity. The computational burden of implementing this control law has been addressed by utilizing a semi-physical engine system model and basic analytical differentiation. The resulting linearization process requires less than 10% of the time required for widely used numerical linearization approach. Based on this approach a Nonlinear MPC-Quadratic Program has been formulated and solved with preliminary validation applied to a 1D Engine model followed by implementation on an experimental rapid prototyping control system. The MPC based control demonstrates the ability to co-ordinate different engine and air-path actuators simultaneously for torque-tracking with minimal constraint violation. Avenues for further improvement have been identified and discussed

Explicit Nonlinear Model Predictive Control of the Air Path of a Turbocharged Spark-Ignited Engine

International audiencePollutant emissions and fuel economy objectives have led car manufacturers to develop innovative and more sophisticated engine layouts. In order to reduce time-to-market and development costs, recent research has investigated the idea of a quasi-systematic engine control development approach. Model based approaches might not be the only possibility but they are clearly predetermined to considerably reduce test bench tuning work requirements. In this paper, we present the synthesis of a physics-based nonlinear model predictive control law especially designed for powertrain control. A binary search tree is used to ensure real-time implementation of the explicit form of the control law, computed by solving the associated multi-parametric nonlinear problem

Flexible and robust control of heavy duty diesel engine airpath using data driven disturbance observers and GPR models

Diesel engine airpath control is crucial for modern engine development due to increasingly stringent emission regulations. This thesis aims to develop and validate a exible and robust control approach to this problem for speci cally heavy-duty engines. It focuses on estimation and control algorithms that are implementable to the current and next generation commercial electronic control units (ECU). To this end, targeting the control units in service, a data driven disturbance observer (DOB) is developed and applied for mass air ow (MAF) and manifold absolute pressure (MAP) tracking control via exhaust gas recirculation (EGR) valve and variable geometry turbine (VGT) vane. Its performance bene ts are demonstrated on the physical engine model for concept evaluation. The proposed DOB integrated with a discrete-time sliding mode controller is applied to the serial level engine control unit. Real engine performance is validated with the legal emission test cycle (WHTC - World Harmonized Transient Cycle) for heavy-duty engines and comparison with a commercially available controller is performed, and far better tracking results are obtained. Further studies are conducted in order to utilize capabilities of the next generation control units. Gaussian process regression (GPR) models are popular in automotive industry especially for emissions modeling but have not found widespread applications in airpath control yet. This thesis presents a GPR modeling of diesel engine airpath components as well as controller designs and their applications based on the developed models. Proposed GPR based feedforward and feedback controllers are validated with available physical engine models and the results have been very promisin

Optimization and control of a dual-loop EGR system in a modern diesel engine

Focusing on the author's research aspects, the intelligent optimization algorithm and advanced control methods of the diesel engine's air path have been proposed in this work. In addition, the simulation platform and the HIL test platform are established for research activities on engine optimization and control. In this thesis, it presents an intelligent transient calibration method using the chaos-enhanced accelerated particle swarm optimization (CAPSO) algorithm. It is a model-based optimization approach. The test results show that the proposed method could locate the global optimal results of the controller parameters within good speed under various working conditions. The engine dynamic response is improved and a measurable drop of engine fuel consumption is acquired. The model predictive control (MPC) is selected for the controllers of DLEGR and VGT in the air-path of a diesel engine. Two MPC-based controllers are developed in this work, they are categorized into linear MPC and nonlinear MPC. Compared with conventional PIO controller, the MPC-based controllers show better reference trajectory tracking performance. Besides, an improvement of the engine fuel economy is obtained. The HIL test indicates the two controllers could be implemented on the real engine

A STUDY OF MODEL-BASED CONTROL STRATEGY FOR A GASOLINE TURBOCHARGED DIRECT INJECTION SPARK IGNITED ENGINE

To meet increasingly stringent fuel economy and emissions legislation, more advanced technologies have been added to spark-ignition (SI) engines, thus exponentially increase the complexity and calibration work of traditional map-based engine control. To achieve better engine performance without introducing significant calibration efforts and make the developed control system easily adapt to future engines upgrades and designs, this research proposes a model-based optimal control system for cycle-by-cycle Gasoline Turbocharged Direct Injection (GTDI) SI engine control, which aims to deliver the requested torque output and operate the engine to achieve the best achievable fuel economy and minimum emission under wide range of engine operating conditions. This research develops a model-based ignition timing prediction strategy for combustion phasing (crank angle of fifty percent of the fuel burned, CA50) control. A control-oriented combustion model is developed to predict burn duration from ignition timing to CA50. Using the predicted burn duration, the ignition timing needed for the upcoming cycle to track optimal target CA50 is calculated by a dynamic ignition timing prediction algorithm. A Recursive-Least-Square (RLS) with Variable Forgetting Factor (VFF) based adaptation algorithm is proposed to handle operating-point-dependent model errors caused by inherent errors resulting from modeling assumptions and limited calibration points, which helps to ensure the proper performance of model-based ignition timing prediction strategy throughout the entire engine lifetime. Using the adaptive combustion model, an Adaptive Extended Kalman Filter (AEKF) based CA50 observer is developed to provide filtered CA50 estimation from cyclic variations for the closed-loop combustion phasing control. An economic nonlinear model predictive controller (E-NMPC) based GTDI SI engine control system is developed to simultaneously achieve three objectives: tracking the requested net indicated mean effective pressure (IMEPn), minimizing the SFC, and reducing NOx emissions. The developed E-NMPC engine control system can achieve the above objectives by controlling throttle position, IVC timing, CA50, exhaust valve opening (EVO) timing, and wastegate position at the same time without violating engine operating constraints. A control-oriented engine model is developed and integrated into the E-NMPC to predict future engine behaviors. A high-fidelity 1-D GT-POWER engine model is developed and used as the plant model to tune and validate the developed control system. The performance of the entire model-based engine control system is examined through the software-in-the-loop (SIL) simulation using on-road vehicle test data

Evaporator Modeling and an Optimal Control Strategy Development of an Organic Rankine Cycle Waste Heat Recovery System for a Heavy Duty Diesel Engine Application

The Organic Rankine Cycle (ORC) has proven to be a promising technology for Waste Heat Recovery (WHR) systems in heavy duty diesel engine applications. However, due to the highly transient heat source, controlling the working fluid flow through the ORC system and maximizing the heat recovery is a challenge for real time application. To that end, this research resulted in the following main developments. The first new development is in the area of heat exchanger modeling. The heat exchanger is a key component within the WHR system and it governs the dynamics of the complete ORC system. The heat exchanger model is extended using a thermal image data to improve its phase length prediction capability. It’s shown that the new identified empirical equations help improve the phase length estimation by 43% over a set of transient experiments. As a result, the model can be used to develop an improved control oriented moving boundary model and to provide insights into evaporator design. The second new development is the advancement of the control design of an ORC system. With advanced knowledge of the heat source dynamics, there is potential to enhance power optimization from the WHR system through predictive optimal control. The proposed approach in this this dissertation is a look-ahead control strategy where, the future vehicle speed is predicted utilizing road topography and V2V connectivity. The forecasted vehicle speed is utilized to predict the engine speed and torque, which facilitates estimation of the engine exhaust conditions used in the ORC control model. In the simulation study, a reference tracking controller is designed based on the Model Predictive Control (MPC) methodology. Two variants of Non-linear MPC (NMPC) are evaluated: an NMPC with look-ahead exhaust conditions and a baseline NMPC without the knowledge of future exhaust conditions. Simulation results show no particular improvement to working fluid superheat tracking at the evaporator outlet via the look-ahead strategy for a drive cycle. However, the look-ahead control strategy does provide a substantial reduction in system control effort via dampening the heavily transient working fluid pump actuation, enhancing pump longevity, health, and reducing pump power consumption. This reduction in pump actuation helps the NMPC with preview to maintain the superheat lower than the NMPC without this feature for certain frequency of the exhaust conditions. Overall, NMPC with preview feature can help reduce parasitic losses, like pump power and improve power generation. The third development addresses the modeling errors and measurement inaccuracies for NMPC implementation. NMPC is inherently a state feedback system and for that reason an Extended Kalman Filter (EKF) is used to estimate unmeasurable states inside the ORC evaporators based on exhaust gas and working fluid temperatures. Since it is not realistic to expect that the system model will perfectly describe the behavior of the evaporator dynamics in all operating conditions, the estimator is therefore augmented with a disturbance model for offset free MPC tracking. Simulation study shows that the augmented system is perfectly capable of discarding the model errors and rejecting the measurement inaccuracies. Moreover, experimental validation confirms that no steady state error is observed during online implementation of the augmented EKF. Finally, experimental validation of the designed NMPC control strategy was conducted. The performance of the NMPC was evaluated on a heavily transient drive cycle, as well as on a sinusoidal generated heat signals. Both experimental and simulated sinusoidal exhaust condition shows that evaporator under consideration inherently helps attenuate the fluctuating exhaust conditions due to its thermal inertia especially for heat signals of shorter time periods. However for slow changing exhaust conditions, a slower rate of change of working fluid flow helps in inhibiting temperature overshoot at the evaporator outlet