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

Explicit-Ready Nonlinear Model Predictive Control for Turbocharged Spark-Ignited Engine

International audienceThe trend to reduce the engine size in automotive industry is motivated by more restrictive pollutant emissions standards. That is why engine technical definitions have become more and more complicated. The control challenge has also grown since engines are now considered as highly nonlinear multi-input multi-output systems with saturated actuators. In this context, the need for model-based control laws is bigger than ever. In this study we propose a nonlinear model predictive control strategy based on a physical engine model. Moreover, we also underline the benefit of using a thermodynamic engine term in the objective function. Finally, the design and calibration choices consciously fulfill the criterions of the use of an explicit approach for the real time implementation

Nonlinear Model Predictive Control of the Air Path of a Turbocharged Gasoline Engine Using Laguerre Functions

International audienceObjectives in terms of pollutant emissions and fuel consumption reduction have led car manufacturers to enhance the technical definitions of combustion engines. The latter should now be considered as multiple-input multiple-output nonlinear systems with saturated actuators. This considerably increases the challenge regarding the development of optimal control laws under the constraints of constant cost reductions in the automotive industry. In the present paper, the use of a nonlinear model predictive control (NMPC) scheme is studied for the air path control of a turbocharged gasoline engine. Specifically, a zero dimension physics-based model is combined with parameterization of the future control trajectory. The use of Laguerre polynomials is shown to increase flexibility for the future control trajectory at no cost in computational requirements. This increase in flexibility leads to an improvement of the transient response of the closed-loop with respect to traditional approaches. This practical application shows that this approach makes it easier to fine-tune the NMPC scheme when dealing with engine air path control

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

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

A Challenging Future for the IC Engine: New Technologies and the Control Role

[FR] Un challenge pour le futur du moteur a` combustion interne : nouvelles technologies et ro¿le du contro¿le moteur ¿ Les nouvelles normes sur les e¿missions, en particulier le CO2, pourraient re¿duire l¿utilisation du moteur a` combustion interne pour les ve¿hicules. Cet article pre¿sente une revue de diffe¿rentes technologies en cours de de¿veloppement afin de respecter ces normes, depuis de nouveaux concepts de combustion jusqu¿a` des syste`mes avance¿s de suralimentation ou de post-traitement. La plupart de ces technologies demande un contro¿le pre¿cis des conditions de fonctionnement et impose souvent de fortes contraintes lors de l¿inte¿gration des syste`mes. Dans ce contexte et en profitant des dernie`res avance¿es dans les mode`les, les me¿thodes et les capteurs, le contro¿le moteur jouera un ro¿le clef dans la mise en œuvre et le de¿veloppement de la prochaine ge¿ne¿ration de moteurs. De l¿avis des auteurs, le moteur a` combustion interne restera la technologie dominante pour les ve¿hicules des prochaines de¿cennies.[EN] New regulations on pollutants and, specially, on CO2 emissions could restrict the use of the internal combustion engine in automotive applications. This paper presents a review of different technologies under development for meeting such regulations, ranging from new combustion concepts to advanced boosting methods and after-treatment systems. Many of them need an accurate control of the operating conditions and, in many cases, they impose demanding requirements at a system integration level. In this framework, engine control disciplines will be key for the implementation and development of the next generation engines, taking profit of recent advancements in models, methods and sensors. According to authors¿ opinion, the internal combustion engine will still be the dominant technology in automotive applications for the next decades.F. Payri; Luján, JM.; Guardiola, C.; Pla Moreno, B. (2015). A Challenging Future for the IC Engine: New Technologies and the Control Role. Oil & Gas Science and Technology ¿ Revue d¿IFP Energies nouvelles. 70(1):15-30. doi:10.2516/ogst/2014002S153070

A Study Model Predictive Control for Spark Ignition Engine Management and Testing

Pressure to improve spark-ignition (SI) engine fuel economy has driven thedevelopment and integration of many control actuators, creating complex controlsystems. Integration of a high number of control actuators into traditional map basedcontrollers creates tremendous challenges since each actuator exponentially increasescalibration time and investment. Model Predictive Control (MPC) strategies have thepotential to better manage this high complexity since they provide near-optimal controlactions based on system models. This research work focuses on investigating somepractical issues of applying MPC with SI engine control and testing.Starting from one dimensional combustion phasing control using spark timing(SPKT), this dissertation discusses challenges of computing the optimal control actionswith complex engine models. A nonlinear optimization is formulated to compute thedesired spark timing in real time, while considering knock and combustion variationconstraints. Three numerical approaches are proposed to directly utilize complex high-fidelity combustion models to find the optimal SPKT. A model based combustionphasing estimator that considers the influence of cycle-by-cycle combustion variations isalso integrated into the control system, making feedback and adaption functions possible.An MPC based engine management system with a higher number of controldimensions is also investigated. The control objective is manipulating throttle, externalEGR valve and SPKT to provide demanded torque (IMEP) output with minimum fuelconsumption. A cascaded control structure is introduced to simplify the formulation and solution of the MPC problem that solves for desired control actions. Sequential quadratic programming (SQP) MPC is applied to solve the nonlinear optimization problem in real time. A real-time linearization technique is used to formulate the sub-QP problems with the complex high dimensional engine system. Techniques to simplify the formulation of SQP and improve its convergence performance are also discussed in the context of tracking MPC. Strategies to accelerate online quadratic programming (QP) are explored. It is proposed to use pattern recognition techniques to “warm-start” active set QP algorithms for general linear MPC applications. The proposed linear time varying (LTV) MPC is used in Engine-in-Loop (EIL) testing to mimic the pedal actuations of human drivers who foresee the incoming traffic conditions. For SQP applications, the MPC is initialized with optimal control actions predicted by an ANN. Both proposed MPC methods significantly reduce execution time with minimal additional memory requirement

A novel fuzzy logic variable geometry turbocharger and exhaust gas recirculation control scheme for optimizing the performance and emissions of a diesel engine

Variable geometry turbocharger and exhaust gas recirculation valves are widely installed on diesel engines to allow optimized control of intake air mass flow and exhaust gas recirculation ratio. The positions of variable geometry turbocharger vanes and exhaust gas recirculation valve are predominantly regulated by dual-loop proportional–integral–derivative controllers to achieve predefined set-points of intake air pressure and exhaust gas recirculation mass flow. The set-points are determined by extensive mapping of the intake air pressure and exhaust gas recirculation mass flow against various engine speeds and loads concerning engine performance and emissions. However, due to the inherent nonlinearities of diesel engines and the strong interferences between variable geometry turbocharger and exhaust gas recirculation, an extensive map of gains for the P, I, and D terms of the proportional–integral–derivative controllers is required to achieve desired control performance. The present simulation study proposes a novel fuzzy logic control scheme to determine appropriate positions of variable geometry turbocharger vanes and exhaust gas recirculation valve in real-time. Once determined, the actual positions of the vanes and valve are regulated by two local proportional–integral–derivative controllers. The fuzzy logic control rules are derived based on an understanding of the interactions among the variable geometry turbocharger, exhaust gas recirculation, and diesel engine. The results obtained from an experimentally validated one-dimensional transient diesel engine model showed that the proposed fuzzy logic control scheme is capable of efficiently optimizing variable geometry turbocharger and exhaust gas recirculation positions under transient engine operating conditions in real-time. Compared to the baseline proportional–integral–derivative controllers approach, both engine’s efficiency and total turbo efficiency have been improved by the proposed fuzzy logic control scheme while NOx and soot emissions have been significantly reduced by 34% and 82%, respectively

Investigation Into Advanced Architecture and Strategies For Turbocharged Compressed Natural Gas Heavy Duty SI-engine

CNG is at present retaining a growing interest as a factual alternative to traditional fuel for SI engine thanks to its high potentials in reducing the engine-out emissions. Increasing thrust into the exploitation of NG in the transport field is in fact produced by the even more stringent emission regulations which are being introduced into the worldwide scenario. Specific attention is also to be devoted to heavy duty engines given the high impact they retain due to the diesel oil exploitation and to the PM emissions, the latter issue assessing for the need to shift towards alternative fuels such as natural gas. A thorough control of the air-to-fuel ratio appears to be mandatory in spark ignition CNG engines in order to meet the even more stringent thresholds set by the current regulations. The accuracy of the air/fuel mixture highly depends on the injection system dynamic behavior and to its coupling to the engine fluid-dynamic. The amount of injected fuel should in fact be properly targeted by the ECU basing on the estimation of the induced air and accounting for the embedded closed-loop strategies. Still, these latter are normally derived from engine-base routines and totally ignore the injection system dynamics. Thus, a sound investigation into the mixing process can only be achieved provided that a proper analysis of the injection rail and of the injectors is carried out. The first part of the present work carries out a numerical investigation into the fluid dynamic behavior of a commercial CNG injection system by means of a 0D-1D code. The research has been focused on defining the set of parameters to be precisely reproduced in the 0D-1D simulation so as to match the injection system experimental behavior. Specific attention has been paid to the one component which significantly contributes to fully defining its dynamic response, i.e. the pressure reducing valve. The pressure reducer is made up of various elements that retain diverse weights on the valve behavior and should consequently be differently addressed to. A refined model of the pressure reducer has hence been proposed and the model has been calibrated, tested and run under various operating conditions so as to assess for the set-up validity. Comparisons have been carried out on steady state points as well as through out a vehicle driving cycle and the model capability to properly reproduce the real system characteristic has been investigated into. The proposed valve model has proved to consistently replicate the injection system response for different speed and load conditions. A few methodological indications concerning modeling aspects of a pressure regulator can be drawn from the present study. The model has been run in a predictive mode so as to inquiry into the response of the system to fast transient operations, both in terms of speed and load. The model outputs have highlighted mismatches between the ECU target mass and the actually injected one and have hinted at the need for dedicated and refined control strategies capable of preventing anomalies in the mixture formation and hence in the engine functioning. The second part of the present work aims at deeply investigating into the potentials of a heavy duty engine running on CNG and equipped with two different injection systems, an advanced SP one and a prototype MP one. The considered 7.8 liter engine was designed and produced to implement a Sigle-Point (SP) strategy and has hence been modified to run with a dedicated Multi-Point (MP) system so as to take advantage of its flexibility in terms of control strategies. More specifically, a thorough comparison between the experimental performances of the engine equipped with the two injection systems has been carried out at steady state as well as at transient operations. Better performances in terms of cycle-to-cycle variability were proved for the MP system despite poorer mixture homogeneity. Attention has also been paid to the different engine control strategies to be eventually adopted in compliance with the constraints set by the two different layouts. A 0D-1D model has also been built and validated on the experimental data set to be hence exploited for investigating into different strategies both for the SP and for the MP layout. An extensive simulation has been carried out on the effects of the injection phasing on the SP system performance referring to the engine power output and to the air-to-fuel ratio homogeneity amongst the cylinders. Finally, as far as the MP injection system is concerned, the innovative fire-skipping (DSF) or cylinder deactivation has been considered and deployed by means of the numerical model, assessing for an overall decrease in the fuel consumption of 12% at part load operations

Real-time predictive control for SI engines using linear parameter-varying models

As a response to the ever more stringent emission standards, automotive engines have become more complex with more actuators. The traditional approach of using many single-input single output controllers has become more difficult to design, due to complex system interactions and constraints. Model predictive control offers an attractive solution to this problem because of its ability to handle multi-input multi-output systems with constraints on inputs and outputs. The application of model based predictive control to automotive engines is explored below and a multivariable engine torque and air-fuel ratio controller is described using a quasi-LPV model predictive control methodology. Compared with the traditional approach of using SISO controllers to control air fuel ratio and torque separately, an advantage is that the interactions between the air and fuel paths are handled explicitly. Furthermore, the quasi-LPV model-based approach is capable of capturing the model nonlinearities within a tractable linear structure, and it has the potential of handling hard actuator constraints. The control design approach was applied to a 2010 Chevy Equinox with a 2.4L gasoline engine and simulation results are presented. Since computational complexity has been the main limiting factor for fast real time applications of MPC, we present various simplifications to reduce computational requirements. A benchmark comparison of estimated computational speed is included