Your Selections

Pekar, Jaroslav
Show Only

Collections

File Formats

Content Types

Dates

Sectors

Topics

Authors

Publishers

Affiliations

Events

   This content is not included in your SAE MOBILUS subscription, or you are not logged in.

Model Predictive Control of an Air Path System for Multi-Mode Operation in a Diesel Engine

Garrett Advancing Motion-Paul Dickinson, Jaroslav Pekar, MinSeok Ko
Hyundai Motor Group-Buomsik Shin, Yohan Chi, Minsu Kim
  • Technical Paper
  • 2020-01-0269
To be published on 2020-04-14 by SAE International in United States
A supervisory model predictive control system is developed for the air system of diesel engine. The diesel air system is complicated, composing of many components and actuators, with significant nonlinear behavior. Furthermore, the engine usually often operates in various modes, for example to activate catalyst regeneration like LNT or DPF. Model predictive control (MPC) is based on a dynamical model of the controlled system and it features predicted actuator path optimization. MPC has been previously successfully applied to the diesel air path control problem, however, most of these applications were developed for a single operating mode (often called normal operating mode) which has only one set of high-level set point values. In reality, each engine operating mode requires a different set of set point maps in order to meet the various system requirements such as, HP-EGR modes for cold start purposes, heat-up modes for after-treatment conditioning, rich operation for catalyst purging and normal modes. Air mass and its composition requirement are heavily depending on each specific mode. This large array of mode specific set points…
   This content is not included in your SAE MOBILUS subscription, or you are not logged in.

On the Robustness of Adaptive Nonlinear Model Predictive Cruise Control

Ford Motor Company-John Michelini, Steven Szwabowski, Shankar Mohan, Dimitar Filev
Honeywell Automotive Software-Ondrej Santin, Jaroslav Beran, Ondřej Mikuláš, Jaroslav Pekar
Published 2018-04-03 by SAE International in United States
In order to improve the vehicle’s fuel economy while in cruise, the Model Predictive Control (MPC) technology has been adopted utilizing the road grade preview information and allowance of the vehicle speed variation. In this paper, a focus is on robustness study of delivered fuel economy benefit of Adaptive Nonlinear Model Predictive Controller (ANLMPC) reported earlier in the literature to several noise factors, e.g. vehicle weight, fuel type etc. Further, the vehicle position is obtained via GPS with finite precision and source of road grade preview might be inaccurate. The effect of inaccurate information of the road grade preview on the fuel economy benefits is studied and a remedy to it is established. It is shown that the effect of scale and value bias error in the road grade preview can be eliminated by the on-line adaptation of the model parameters performed by the constrained Recursive Least Squares (RLS) method and the estimation of the additive acceleration by the Extended Kalman Filter (EKF). The effect of phase error in road grade preview is eliminated by…
This content contains downloadable datasets
Annotation ability available
   This content is not included in your SAE MOBILUS subscription, or you are not logged in.

Evaluation of Model Predictive and Conventional Method Based Hybrid Electric Vehicle Supervisory Controllers

Honeywell Automotive Software-Ondřej Mikuláš, Jaroslav Pekar
Honeywell Turbo India Pvt.Ltd-Malay Maniar
Published 2017-03-28 by SAE International in United States
Increasingly strict CO2 and emissions norms are pushing the automotive industry towards increasing adoption of Hybrid Electric Vehicle (HEV) technology. HEVs are complex hardware systems which are often controlled by software that is complex to maintain, time-consuming to calibrate, and not always guaranteed to deliver optimal fuel economy. Hence, coordinated, systematic control of different subsystems of HEV is an attractive proposition. In this paper, Model Predictive Control (MPC) and Equivalent Consumption Minimization Strategy (ECMS) based supervisory controllers have been developed to coordinate the power split between the two prime movers of an HEV – internal combustion engine and electric motor. A dynamical physics based HEV model has been developed for simulation of the system behavior. A cost function has been formulated to improve fuel economy and battery life. The dynamical structure of HEV along with its I/O, constraints, set points, operating points, etc. has been framed into the MPC controller that has been realized using Honeywell OnRAMP® Design Suite. Similarly, fuel and electricity consumption and efficiency models, and constraints have been framed into the ECMS…
This content contains downloadable datasets
Annotation ability available
   This content is not included in your SAE MOBILUS subscription, or you are not logged in.

Adaptive Nonlinear Model Predictive Cruise Controller: Trailer Tow Use Case

Ford Motor Company-John Michelini, Junbo Jing, Steve Szwabowski, Dimitar Filev
Honeywell Automotive Software-Ondrej Santin, Jaroslav Beran, Jaroslav Pekar
Published 2017-03-28 by SAE International in United States
Conventional cruise control systems in automotive applications are usually designed to maintain the constant speed of the vehicle based on the desired set-point. It has been shown that fuel economy while in cruise control can be improved using advanced control methods namely adopting the Model Predictive Control (MPC) technology utilizing the road grade preview information and allowance of the vehicle speed variation. This paper is focused on the extension of the Adaptive Nonlinear Model Predictive Controller (ANLMPC) reported earlier by application to the trailer tow use-case. As the connected trailer changes the aerodynamic drag and the overall vehicle mass, it may lead to the undesired downshifts for the conventional cruise controller introducing the fuel economy losses. In this work, the ANLMPC concept is extended to avoid downshifts by translating the downshift conditions to the constraints of the underlying optimization problem to be solved. To deal with significant noise factors, e.g., overall vehicle mass, change of aerodynamic drag, actual weather conditions, fuel type, the on-line adaptation of the parameters is performed by the constrained Recursive Least…
This content contains downloadable datasets
Annotation ability available
   This content is not included in your SAE MOBILUS subscription, or you are not logged in.

NO2/NOx Ratio and NH3 Storage Estimation of Automotive SCR Multi-Brick Systems

Honeywell Automotive Software-Jiri Figura, Jaroslav Pekar, Pavel Krejza, David Mracek
Renault SA-Dirk von Wissel, Tianran Zhang
Published 2017-03-28 by SAE International in United States
Many control approaches for selective catalytic reduction (SCR) systems require knowledge of ammonia storage (NH3 storage) to dose urea accurately. Currently there are no technologies to directly measure internal NH3 storage in a vehicle, so it can only be inferred from hardware sensors located upstream, downstream, or in the catalyst. This paper describes an application of extended Kalman filter (EKF) state estimator used as a virtual sensor for urea injection control of a multi-brick aftertreatment system. The proposed estimator combines mean-value physics-based models of combined SCR and diesel particulate filter (SCR/DPF), SCR and clean-up catalyst (CUC). It uses hardware sensors at the inlet and outlet of the aftertreatment system, and includes no sensors between the catalysts. Performance of the proposed estimator was validated in simulations against a high-fidelity model of the aftertreatment system. The algorithm provides accurate estimates of the dominant gaseous species NOx and NH3 as well as NH3 storage for a feedback model predictive control (MPC) control of urea injection. Moreover, the algorithm is able to estimate upstream NO2/NOx ratio from provided constant…
This content contains downloadable datasets
Annotation ability available
   This content is not included in your SAE MOBILUS subscription, or you are not logged in.

Cascade MPC Approach to Automotive SCR Multi-Brick Systems

Honeywell Automotive Software-Pavel Krejza, Jaroslav Pekar, Jiri Figura, Lukas Lansky
Renault SA-Dirk von Wissel, Tianran Zhang
Published 2017-03-28 by SAE International in United States
The paper provides an overview of a developed methodology and a toolchain for modeling and control of a complex aftertreatment system for passenger cars. The primary objective of this work is to show how the use of this methodology allows to streamline the development process and to reduce the development time thanks to a model based semi-automatic control design methodology combined with piece-wise optimal control. Major improvements in passenger car tailpipe NOx removal need to be achieved to fulfil the upcoming post EURO 6 norms and Real Driving Emissions (RDE) limits. Multi-brick systems employing combinations of multiple Selective Catalytic Reduction (SCR) catalysts with an Ammonia Oxidation Catalysts, known also as Ammonia Clean-Up Catalyst (CUC), are proposed to cover operation over a wide temperature range. However, control of multi-brick systems is complex due to lack of available sensors in the production configurations. Advanced control and inferential sensing techniques can address this complexity, making the control design task more straight forward and less error prone when compared to traditional control design approach. This paper shows an application…
Annotation ability available
   This content is not included in your SAE MOBILUS subscription, or you are not logged in.

Vehicle Powertrain Thermal Management System Using Model Predictive Control

SAE International Journal of Materials and Manufacturing

Ford Motor Company-Amey Y. Karnik, Phillip Bonkoski, Mrdjan Jankovic
Honeywell Automotive Software-Adrian Fuxman, Jaroslav Pekar
  • Journal Article
  • 2016-01-0215
Published 2016-04-05 by SAE International in United States
An advanced powertrain cooling system with appropriate control strategy and active actuators allows greater flexibility in managing engine temperatures and operating near constraints. An organized controls development process is necessary to allow comparison of multiple configurations to select the best way forward. In this work, we formulate, calibrate and validate a Model Predictive Controller (MPC) for temperature regulation and constraint handling in an advanced cooling system. A model-based development process was followed; where the system model was used to develop and calibrate a gain scheduled linear MPC. The implementation of MPC for continuous systems and the modification related to implementing switching systems has been described.Multiple hardware configurations were compared with their corresponding control system in simulations. The system level requirements were translated into MPC calibration parameters for consistent comparison between multiple configurations. Some of the configurations were then evaluated via hardware in the loop testing prior to evaluation in a vehicle. The paper works through key steps related to the development of MPC for system level evaluation of actuators for powertrain thermal management.
Annotation ability available
   This content is not included in your SAE MOBILUS subscription, or you are not logged in.

Cruise Controller with Fuel Optimization Based on Adaptive Nonlinear Predictive Control

SAE International Journal of Passenger Cars - Electronic and Electrical Systems

Ford Motor Company-Anthony D'Amato, Engin Ozatay, John Michelini, Steven Szwabowski, Dimitar Filev
Honeywell Automotive Software-Ondrej Santin, Jaroslav Pekar, Jaroslav Beran
  • Journal Article
  • 2016-01-0155
Published 2016-04-05 by SAE International in United States
Automotive cruise control systems are used to automatically maintain the speed of a vehicle at a desired speed set-point. It has been shown that fuel economy while in cruise control can be improved using advanced control methods. The objective of this paper is to validate an Adaptive Nonlinear Model Predictive Controller (ANLMPC) implemented in a vehicle equiped with standard production Powertrain Control Module (PCM). Application and analysis of Model Predictive Control utilizing road grade preview information has been reported by many authors, namely for commercial vehicles. The authors reported simulations and application of linear and nonlinear MPC based on models with fixed parameters, which may lead to inaccurate results in the real world driving conditions. The significant noise factors are namely vehicle mass, actual weather conditions, fuel type, etc. In the ANLMPC approach, the vehicle and fuel model parameters are adapted automatically, so accuracy of the prediction is ensured. The adaptation is implemented by a Recursive Least Square (RLS) algorithm and the numerical robustness is improved by adopting Bierman’s implementation with exponential/directional forgetting, and with…
Annotation ability available
   This content is not included in your SAE MOBILUS subscription, or you are not logged in.

Automotive Selective Catalytic Reduction System Model-Based Estimators for On-ECU Implementation: A Brief Overview

Cummins Inc.-Nassim Khaled, Sriram Srinivasan
Honeywell Automotive Software-Jiri Figura, Dejan Kihas, Jaroslav Pekar, Michael Uchanski
Published 2016-04-05 by SAE International in United States
The amount of ammonia stored on the walls of the catalyst (or ammonia storage) is a parameter with significant impact on NOx reduction efficiency and undesired ammonia slip of Selective Catalytic Reduction catalysts. This makes the ammonia storage interesting for utilization in urea injection control. However, ammonia storage is not directly measurable onboard vehicles, it can only be estimated. Model-based online estimation requires models that are capable of capturing the main phenomena of the SCR and at the same time can be computed onboard vehicle. While the modeling of SCR and model-based control is well present in the literature, it is apparent that few attempts of implementing the models on production ECUs were published. This paper reviews literature on ammonia storage, outlet NH3 and NOx concentration estimation in SCR and SCR/DPF systems-including the estimation of NOx sensor cross-sensitive to NH3-in order to present the state of the art. The discussion includes applications of estimators (virtual sensors), accuracy of estimation, required inputs, computational demands, robustness and sources of error.
Annotation ability available
   This content is not included in your SAE MOBILUS subscription, or you are not logged in.

Multivariable Control of Dual Loop EGR Diesel Engine with a Variable Geometry Turbo

Cummins Inc.-Nassim Khaled, Michael Cunningham
Honeywell Automotive Software-Jaroslav Pekar, Adrian Fuxman, Ondrej Santin
Published 2014-04-01 by SAE International in United States
In this paper we consider the issues facing the design of a practical multivariable controller for a diesel engine with dual exhaust gas recirculation (EGR) loops. This engine architecture requires the control of two EGR valves (high pressure and low pressure), an exhaust throttle (ET) and a variable geometry turbocharger (VGT). A systematic approach suitable for production-intent air handling control using Model Predictive Control (MPC) for diesel engines is proposed. Furthermore, the tuning process of the proposed design is outlined. Experimental results for the performance of the proposed design are implemented on a 2.8L light duty diesel engine. Transient data over an LA-4 cycle for the closed loop performance of the controller are included to prove the effectiveness of the proposed design process. The MPC implementation process took a total of 10 days from the start of the data collection to build a calibrated engine model all the way through the calibration of the controller over the transient drive cycle.
Annotation ability available