Optimal Transient Control Trajectories in Diesel–Electric Systems—Part I: Modeling, Problem Formulation, and Engine Properties

2014 ◽  
Vol 137 (2) ◽  
Author(s):  
Martin Sivertsson ◽  
Lars Eriksson
Keyword(s):  
Optimal Control ◽  
Output Power ◽  
Control Strategies ◽  
Problem Formulation ◽  
Free Variable ◽  
Mean Value ◽  
Engine Model ◽  
Transient Control ◽  
Time Optimal ◽  
Power And Energy

A nonlinear four state-three input mean value engine model (MVEM), incorporating the important turbocharger dynamics, is used to study optimal control of a diesel–electric powertrain during transients. The optimization is conducted for the two criteria, minimum time and fuel, where both engine speed and engine power are considered free variables in the optimization. First, steps from idle to a target power are studied and for steps to higher powers the controls for both criteria follow a similar structure, dictated by the maximum torque line and the smoke-limiter. The end operating point, and how it is approached is, however, different. Then, the power transients are extended to driving missions, defined as, that a certain power has to be met as well as a certain energy has to be produced. This is done both with fixed output profiles and with the output power being a free variable. The time optimal control follows the fixed output profile even when the output power is free. These solutions are found to be almost fuel optimal despite being substantially faster than the minimum fuel solution with variable output power. The discussed control strategies are also seen to hold for sequences of power and energy steps.

scholarly journals A novel predictive semi-physical feed-forward turbocharging system transient control strategy based on mean-value turbocharger model

2016 ◽  
Vol 18 (8) ◽  
pp. 765-775 ◽  
Author(s):  
Huayin Tang ◽  
Colin Copeland ◽  
Sam Akehurst ◽  
Chris Brace ◽  
Peter Davies ◽  
...  
Keyword(s):  
Control Strategy ◽  
Control Strategies ◽  
Mean Value ◽  
Variable Geometry ◽  
Gasoline Engines ◽  
Engine Model ◽  
Turbocharging System ◽  
Transient Control ◽  
Variable Geometry Turbine ◽  
The One

Variable geometry turbine is a technology that has been proven on diesel engines. However, despite the potential to further improve gasoline engines’ fuel economy and transient response using variable geometry turbine, controlling the variable geometry turbine during transients is challenging due to its highly non-linear behaviours especially on gasoline applications. After comparing three potential turbocharger transient control strategies, the one that predicts the turbine performances for a range of possible variable geometry turbine settings in advance was developed and validated using a high-fidelity engine model. The proposed control strategy is able to capture the complex transient behaviours and achieve the optimum variable geometry turbine trajectories. This improved the turbocharger response time by more than 14% compared with a conventional proportional–integral–derivative controller, which cannot achieve target turbocharge speed in all cases. Furthermore, the calibration effort required can be significantly reduced, offering significant benefits for powertrain developers. It is expected that the structure of this transient control strategy can also be applied to complex air-path systems.

scholarly journals Sub-Optimal Control of Autonomous Wheel Loader With Approximate Dynamic Programming

2019 ◽  
Author(s):  
Tohid Sardarmehni ◽  
Xingyong Song
Keyword(s):  
Optimal Control ◽  
Dynamic Programming ◽  
Approximate Dynamic Programming ◽  
A Priori ◽  
Optimal Path ◽  
Mean Value ◽  
Wheel Loader ◽  
Engine Model ◽  
Control Objective ◽  
Wheel Loaders

Abstract Optimal control of wheel loaders in short loading cycles is studied in this paper. For modeling the wheel loader, the data from a validated diesel engine model is used to find a control oriented mean value engine model. The driveline is modeled as a switched system with three constant gear ratios (modes) of −60 for backwarding, 60 for forwarding, and zero for stopping. With these three modes, the sequence of active modes in a short loading cycle is fixed as backwarding, stopping, forwarding, and stopping. For the control part, it is assumed that the optimal path is known a priori. Given the mode sequence, the control objective is finding the optimal switching time instants between the modes while the wheel loader tracks the optimal path. To solve the optimal control problem, approximate dynamic programming is used. Simulation results are provided to show the effectiveness of the solution.

scholarly journals Research on Key Parameters Operation Range of Central Air Conditioning Based on Binary K-Means and Apriori Algorithm

Energies ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 102 ◽  
Author(s):  
Liangwen Yan ◽  
Fengfeng Qian ◽  
Wei Li
Keyword(s):  
Optimal Control ◽  
Temperature Difference ◽  
Air Conditioning ◽  
Control Strategies ◽  
Cooling Water ◽  
Apriori Algorithm ◽  
The Third ◽  
Time Optimal ◽  
Central Air Conditioning ◽  
Energy Saving Control

As the energy-saving control of central air conditioning has been widely applied in modern architecture, research of real-time optimal control based on historical data and identification of its optimal control strategies are of great importance for reducing energy wasting of buildings. However, due to the property of easily falling into local optimum, conventional k-means approach cannot achieve the goal of real-time optimal control, we therefore propose an innovative binary k-means clustering algorithm which is used to adjust the target value of temperature difference (TD) in the control system of chilled water and cooling water of central air conditioning system (CACS). Thanks to the clustering control, among the 304 test data, the coefficient of performance (COP) of 211 sets of data, which accounted for 69.41%, are higher than those of the traditional control method. In the simulation system, the COP of 191 sets of data, which accounted for 62.83%, are higher than those of traditional control methods, achieving better energy efficiency. To achieve the goal of identify potential energy-saving control strategies, the Apriori algorithm is proposed to correlate the key parameters and energy consumption efficiency of the CACS. The results show when the chilled water temperature difference (CWTD) > 2.0 °C and the cooling water temperature difference (COWTD) > 2.4 °C, some rules are discovered as follows: 1. The probability of a larger system COP will increase if the CWTD is set lower than the third quartile value or the COWTD is set lower than the first quartile value. 2. The probability of a larger system COP will also increase if the CTWD is set lower than the first quartile and the COWTD is set between the first and the third quartile. These underlying regularity is useful for technicians to adjust the control parameters of the equipment, to improve energy efficiency and to reduce energy consumption.

scholarly journals IN-CYLINDER MASS FLOW ESTIMATION AND MANIFOLD PRESSURE DYNAMICS FOR STATE PREDICTION IN SI ENGINES

2014 ◽  
Vol 54 (3) ◽  
pp. 240-247 ◽  
Author(s):  
Wojnar Sławomir ◽  
Boris Rohal-Ilkiv ◽  
Peter Šimončic ◽  
Marek Honek ◽  
Csambál Jozef
Keyword(s):  
Control Strategies ◽  
Estimation Method ◽  
Mean Value ◽  
Intake Manifold ◽  
Flow Estimation ◽  
Engine Model ◽  
Pressure Dynamics ◽  
Estimation And Control ◽  
Cylinder Flow ◽  
And Control

The aim of this paper is to present a simple model of the intake manifold dynamics of a spark ignition (SI) engine and its possible application for estimation and control purposes. We focus on pressure dynamics, which may be regarded as the foundation for estimating future states and for designing model predictive control strategies suitable for maintaining the desired air fuel ratio (AFR). The flow rate measured at the inlet of the intake manifold and the in-cylinder flow estimation are considered as parts of the proposed model. In-cylinder flow estimation is crucial for engine control, where an accurate amount of aspired air forms the basis for computing the manipulated variables. The solutions presented here are based on the mean value engine model (MVEM) approach, using the speed-density method. The proposed in-cylinder flow estimation method is compared to measured values in an experimental setting, while one-step-ahead prediction is illustrated using simulation results.

Optimal Control of Complex Air-Path Systems for Advanced Diesel Engines

2009 ◽  
Author(s):  
Fengjun Yan ◽  
Benjamin Haber ◽  
Junmin Wang
Keyword(s):  
Optimal Control ◽  
Diesel Engines ◽  
Linear Quadratic Regulator ◽  
Mean Value ◽  
Diffusion Combustion ◽  
Linear Quadratic ◽  
Control Approach ◽  
Engine Model ◽  
Path System ◽  
Linearized Model

This paper describes a linear optimal control approach for advanced diesel engines equipped with complex air-path systems including a dual-loop exhaust gas recirculation (EGR) and a two-stage turbocharger. Such complex air-path systems are instrumental to achieve smooth and stable transient operation of diesel engines running advanced multiple combustion modes such as low temperature diffusion combustion (LTDC) and homogeneous charge compression ignition (HCCI). A mean-value engine model was developed to capture the main dynamics of the advanced air-path system. A linear quadratic regulator (LQR) optimal controller was designed based on a linearized model at a fixed operating point. Simulation results using a high-fidelity detailed GT-Power engine model show the effectiveness of the controller.

Transient Control of Combustion Phasing and Lambda in a Six-Cylinder Port-Injected Natural-Gas Engine

2010 ◽  
Vol 132 (9) ◽  
Author(s):  
Mehrzad Kaiadi ◽  
Magnus Lewander ◽  
Patrick Borgqvist ◽  
Per Tunestal ◽  
Bengt Johansson
Keyword(s):  
Natural Gas ◽  
Control Strategies ◽  
Maximum Load ◽  
Spark Ignition Engines ◽  
Heavy Duty ◽  
Gas Engine ◽  
Natural Gas Engine ◽  
Engine Model ◽  
Transient Control ◽  
Cylinder Port

Fuel economy and emissions are the two central parameters in heavy duty engines. High exhaust gas recirculation rates combined with turbocharging has been identified as a promising way to increase the maximum load and efficiency of heavy duty spark ignition engines. With stoichiometric conditions, a three way catalyst can be used, which keeps the regulated emissions at very low levels. The Lambda window, which results in very low emissions, is very narrow. This issue is more complex with transient operation, resulting in losing brake efficiency and also catalyst converting efficiency. This paper presents different control strategies to maximize the reliability for maintaining efficiency and emissions levels under transient conditions. Different controllers are developed and tested successfully on a heavy duty six-cylinder port injected natural gas engine. Model predictive control was used to control lambda, which was modeled using system identification. Furthermore, a proportional integral regulator combined with a feedforward map for obtaining maximum brake torque timing was applied. The results show that excellent steady-state and transient performance can be achieved.

Determining Model Accuracy Requirements for Automotive Engine Coldstart Hydrocarbon Emissions Control

2012 ◽  
Vol 134 (5) ◽  
Author(s):  
Nasser L. Azad ◽  
Pannag R. Sanketi ◽  
J. Karl Hedrick
Keyword(s):  
Optimal Control ◽  
Sensitivity Analysis ◽  
Real Time ◽  
Minimum Principle ◽  
Optimal Solution ◽  
Control Performance ◽  
Automotive Engine ◽  
Engine Model ◽  
Time Optimal ◽  
Hc Emissions

In this work, a systematic method is introduced to determine the required accuracy of an automotive engine model used for real-time optimal control of coldstart hydrocarbon (HC) emissions. The engine model structure and development are briefly explained and the model predictions versus experimental results are presented. The control design problem is represented with a dynamic optimization formulation on the basis of the engine model and solved using the Pontryagin’s minimum principle (PMP). To relate the level of plant/model mismatch and the control performance degradation in practice, a sensitivity analysis using a computationally efficient method is employed. In this way, the sensitivities or the effects of small parameter variations on the optimal solution, which is the minimum of cumulative tailpipe HC emissions over the coldstart period, are calculated. There is a good agreement between the sensitivity analysis results and the experimental data. The sensitivities indicate the directions of the subsequent parameter estimation and model improvement tasks to enhance the control-relevant accuracy, and thus, the control performance. Furthermore, they provide some insights to simplify the engine model, which is critical for real-time implementation of the coldstart optimal control system.

scholarly journals Intelligent Control/Operational Strategies in WWTPs through an Integrated Q-Learning Algorithm with ASM2d-Guided Reward

Water ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 927 ◽  
Author(s):  
Jiwei Pang ◽  
Shanshan Yang ◽  
Lei He ◽  
Yidi Chen ◽  
Nanqi Ren
Keyword(s):  
Optimal Control ◽  
Wastewater Treatment ◽  
Wastewater Treatment Plant ◽  
Learning Algorithm ◽  
Control Strategies ◽  
Treatment Plant ◽  
Model Verification ◽  
Operational Strategies ◽  
Q Learning ◽  
Time Optimal

The operation of a wastewater treatment plant (WWTP) is a typical complex control problem, with nonlinear dynamics and coupling effects among the variables, which renders the implementation of real-time optimal control an enormous challenge. In this study, a Q-learning algorithm with activated sludge model No. 2d-guided (ASM2d-guided) reward setting (an integrated ASM2d-QL algorithm) is proposed, and the widely applied anaerobic-anoxic-oxic (AAO) system is chosen as the research paradigm. The integrated ASM2d-QL algorithms equipped with a self-learning mechanism are derived for optimizing the control strategies (hydraulic retention time (HRT) and internal recycling ratio (IRR)) of the AAO system. To optimize the control strategies of the AAO system under varying influent loads, Q matrixes were built for both HRTs and IRR optimization through the pair of <max reward-action> based on the integrated ASM2d-QL algorithm. 8 days of actual influent qualities of a certain municipal AAO wastewater treatment plant in June were arbitrarily chosen as the influent concentrations for model verification. Good agreement between the values of the model simulations and experimental results indicated that this proposed integrated ASM2d-QL algorithm performed properly and successfully realized intelligent modeling and stable optimal control strategies under fluctuating influent loads during wastewater treatment.

Sign in / Sign up

Export Citation Format

Share Document