Two-Level Nonlinear Model Predictive Control for Lean NOx Trap Regenerations

2010 ◽  
Vol 132 (4) ◽  
Author(s):  
Ming-Feng Hsieh ◽  
Junmin Wang ◽  
Marcello Canova
Keyword(s):  
Diesel Engine ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Nox Reduction ◽  
Nonlinear Model Predictive Control ◽  
Lean Nox Trap ◽  
Nox Trap ◽  
Profile Control ◽  
Lean Nox

This paper describes a two-level nonlinear model predictive control (NMPC) scheme for diesel engine lean NOx trap (LNT) regeneration control. Based on the physical insights into the LNT operational characteristics, a two-level NMPC architecture with the higher-level for the regeneration timing control and the lower-level for the regeneration air to fuel ratio profile control is proposed. A physically based and experimentally validated nonlinear LNT dynamic model is employed to construct the NMPC control algorithms. The control objective is to minimize the fuel penalty induced by LNT regenerations while keeping the tailpipe NOx emissions below the regulations. Based on the physical insights into the LNT system dynamics, different choices of cost function were examined in terms of the impacts on fuel penalty and tailpipe NOx slip amount. The designed control system was evaluated on an experimentally validated vehicle simulator, cX-Emissions, with a 1.9 l diesel engine model through the FTP75 driving cycle. Compared with a conventional LNT control strategy, 31.9% of regeneration fuel penalty reduction was observed during a single regeneration. For the entire cold-start FTP75 test cycle, a 28.1% of tailpipe NOx reduction and 40.9% of fuel penalty reduction were achieved.

Improving the NOx reduction performance of an Euro VI d SCR System in real-world condition via nonlinear model predictive control

2021 ◽  
pp. 146808742110662
Author(s):  
Alberto Petrillo ◽  
Maria Vittoria Prati ◽  
Stefania Santini ◽  
Francesco Tufano
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Real World ◽  
Nox Reduction ◽  
Nonlinear Model Predictive Control ◽  
Feed Ratio ◽  
Real World Data ◽  
Ammonia Slip ◽  
Scr System

This paper deals with the possibility of improving the urea dosage control for the Selective Catalytic Reduction Systems (SCR) of an Euro VI d diesel light commercial vehicle in order to increase [Formula: see text] after-treatment reduction performance. To this aim, first, we assess the effective emissions abatement performance for the appraised diesel vehicle via real-world experimental campaign, carried out according to the Real Driving Emissions (RDE) tests on urban, extra-urban and motorway road sections in Naples, Italy. Based on these real-world data, we derive a parameterized control-oriented model for the SCR system which is, then, exploited for the designing of an alternative urea injection logic which could be able to maximize the [Formula: see text] reduction efficiency while minimizing tailpipe ammonia slip. Specifically, the optimal AdBlue injection rate is designed according to a Nonlinear Model Predictive Control Approach which allows obtaining a proper trade-off between the [Formula: see text] abatement and the urea overdosing problem. The effectiveness of the proposed controller is evaluated by comparing the performance assessed for the appraised SCR system during the experimental tests with the ones achievable if the Euro VI diesel would be equipped with the proposed control strategy. Numerical simulation discloses the effectiveness of the NMPC controller in ensuring improved [Formula: see text] reduction with performance complying with the emissions norms, main in avoiding excessive ammonia slip and in guaranteeing a reduced feed ratio w.r.t. to the standard industrial SCR controller mounted on the vehicle.

Nonlinear Model Predictive Control Strategies for a Parallel Evaporator Diesel Engine Waste Heat Recovery System

2016 ◽  
Author(s):  
Adamu Yebi ◽  
Bin Xu ◽  
Xiaobing Liu ◽  
John Shutty ◽  
Paul Anschel ◽  
...  
Keyword(s):  
Diesel Engine ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Pid Control ◽  
Heat Recovery ◽  
Waste Heat Recovery ◽  
Waste Heat ◽  
Nonlinear Model Predictive Control ◽  
Working Fluid

This paper discusses the control challenges of a parallel evaporator organic Rankine cycle (ORC) waste heat recovery (WHR) system for a diesel engine. A nonlinear model predictive control (NMPC) is proposed to regulate the mixed working fluid outlet temperature of both evaporators, ensuring efficient and safe ORC system operation. The NMPC is designed using a reduced order control model of the moving boundary heat exchanger system. In the NMPC formulation, the temperature difference between evaporator outlets is penalized so that the mixed temperature can be controlled smoothly without exceeding maximum or minimum working fluid temperature limits in either evaporator. The NMPC performance is demonstrated in simulation over an experimentally validated, high fidelity, physics based ORC plant model. NMPC performance is further validated through comparison with a classical PID control for selected high load and low load engine operating conditions. Compared to PID control, NMPC provides significantly improved performance in terms of control response time, overshoot, and temperature regulation.

A Data-Driven Model Predictive Control Approach to Lean NOx Trap Regeneration

2018 ◽  
Vol 141 (1) ◽  
Author(s):  
Milad Karimshoushtari ◽  
Carlo Novara
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Prediction Models ◽  
Low Complexity ◽  
Data Driven ◽  
Lean Nox Trap ◽  
Nox Trap ◽  
Control Approach ◽  
Highly Nonlinear ◽  
Lean Nox

Lean NOx trap (LNT) is one of the most effective after-treatment technologies used to reduce NOx emissions of diesel engines. One relevant problem in this context is LNT regeneration timing control. This problem is indeed difficult due to the fact that LNTs are highly nonlinear systems, involving complex physical/chemical processes, that are hard to model. In this paper, a novel approach for regeneration timing of LNTs is proposed, allowing us to overcome these issues. This approach, named data-driven model predictive control (D2-MPC), does not require a physical model of the engine/trap system but is based on low-complexity polynomial prediction models, directly identified from data. The regeneration timing is computed through an optimization algorithm, which uses the identified models to predict the LNT behavior. Two D2-MPC strategies are proposed, and tested in a co-simulation study, where the plant is represented by a detailed LNT model, built using the well-known commercial tool AMEsim, and the controller is implemented in matlab/simulink.

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