System Identification and Control Design for Internal Combustion Engine Variable Valve Timing Systems PDF Download
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Author: Zhen Ren
Publisher:
ISBN: 9781267073075
Category : Automatic timers
Languages : en
Pages : 128
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Book Description
Author: Zhen Ren
Publisher:
ISBN: 9781267073075
Category : Automatic timers
Languages : en
Pages : 128
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Book Description
Author: Lino Guzzella
Publisher: Springer Science & Business Media
ISBN: 3662080036
Category : Technology & Engineering
Languages : en
Pages : 303
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Book Description
Internal combustion engines still have a potential for substantial improvements, particularly with regard to fuel efficiency and environmental compatibility. These goals can be achieved with help of control systems. Modeling and Control of Internal Combustion Engines (ICE) addresses these issues by offering an introduction to cost-effective model-based control system design for ICE. The primary emphasis is put on the ICE and its auxiliary devices. Mathematical models for these processes are developed in the text and selected feedforward and feedback control problems are discussed. The appendix contains a summary of the most important controller analysis and design methods, and a case study that analyzes a simplified idle-speed control problem. The book is written for students interested in the design of classical and novel ICE control systems.
Author: Hsien-Hsin Liao
Publisher: Stanford University
ISBN:
Category :
Languages : en
Pages : 201
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Book Description
There has been an enormous global research effort to alleviate the current and projected environmental consequences incurred by internal combustion (IC) engines, the dominant propulsion systems in ground vehicles. Two technologies have the potential to improve the efficiency and emissions of IC engines in the near future: variable valve actuation (VVA) and homogeneous charge compression ignition (HCCI). IC engines equipped with VVA systems are proven to show better performance by adjusting the valve lift and timing appropriately. An electro-hydraulic valve system (EHVS) is a type of VVA system that possesses full flexibility, i.e., the ability to change the valve lift and timing independently and continuously, making it an ideal rapid prototyping tool in a research environment. Unfortunately, an EHVS typically shows a significant response time delay that limits the achievable closed-loop bandwidth and, as a result, shows poor tracking performance. In this thesis, a control framework that includes system identification, feedback control design, and repetitive control design is presented. The combined control law shows excellent performance with a root-mean-square tracking error below 40 [Mu]m over a maximum valve lift of 4 mm. A stability analysis is also provided to show that the mean tracking error converges to zero asymptotically with the combined control law. HCCI, the other technology presented in this thesis, is a combustion strategy initiated by compressing a homogeneous air-fuel mixture to auto-ignition, therefore, ignition occurs at multiple points inside the cylinder without noticeable flame propagation. The result is rapid combustion with low peak in-cylinder temperature, which gives HCCI improved efficiency and reduces NOx formation. To initiate HCCI with a typical compression ratio, the sensible energy of the mixture needs to be high compared to a spark ignited (SI) strategy. One approach to achieve this, called recompression HCCI, is by closing the exhaust valve early to trap a portion of the exhaust gas in the cylinder. Unlike a SI or Diesel strategy, HCCI lacks an explicit combustion trigger, as autoignition is governed by chemical kinetics. Therefore, the thermo-chemical conditions of the air-fuel mixture need to be carefully controlled for HCCI to occur at the desired timing. Compounding this challenge in recompression HCCI is the re-utilization of the exhaust gas which creates cycle-to-cycle coupling. Furthermore, the coupling characteristics can change drastically around different operating points, making combustion timing control difficult across a wide range of conditions. In this thesis, a graphical analysis examines the in-cylinder temperature dynamics of recompression HCCI and reveals three qualitative types of temperature dynamics. With this insight, a switching linear model is formulated by combining three linear models: one for each of the three types of temperature dynamics. A switching controller that is composed of three local linear feedback controllers can then be designed based on the switching model. This switching model/control formulation is tested on an experimental HCCI testbed and shows good performance in controlling the combustion timing across a wide range. A semi-definite program is formulated to find a Lyapunov function for the switching model/control framework and shows that it is stable. As HCCI is dictated by the in-cylinder thermo-chemical conditions, there are further concerns about the robustness of HCCI, i.e., the boundedness of the thermo-chemical conditions with uncertainty existing in the ambient conditions and in the engine's own characteristics due to aging. To assess HCCI's robustness, this thesis presents a linear parameter varying (LPV) model that captures the dynamics of recompression HCCI and possesses an elegant model structure that is more amenable to analysis. Based on this model, a recursive algorithm using convex optimization is formulated to generate analytical statements about the boundedness of the in-cylinder thermo-chemical conditions. The bounds generated by the algorithm are also shown to relate well to the data from the experimental testbed.
Author: Andrew P. White
Publisher: Springer Science & Business Media
ISBN: 1447150406
Category : Technology & Engineering
Languages : en
Pages : 118
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Book Description
The subject of this brief is the application of linear parameter-varying (LPV) control to a class of dynamic systems to provide a systematic synthesis of gain-scheduling controllers with guaranteed stability and performance. An important step in LPV control design, which is not well covered in the present literature, is the selection of weighting functions. The proper selection of weighting functions tunes the controller to obtain the desired closed-loop response. The selection of appropriate weighting functions is difficult and sometimes appears arbitrary. In this brief, gain-scheduling control with engineering applications is covered in detail, including the LPV modeling, the control problem formulation, and the weighting function optimization. In addition, an iterative algorithm for obtaining optimal output weighting functions with respect to the H2 norm bound is presented in this brief. Using this algorithm, the selection of appropriate weighting functions becomes an automatic process. The LPV design and control synthesis procedures in this brief are illustrated using: • air-to-fuel ratio control for port-fuel-injection engines; • variable valve timing control; and • application to a vibration control problem. After reading this brief, the reader will be able to apply its concepts to design gain-scheduling controllers for their own engineering applications. This brief provides detailed step-by-step LPV modeling and control design strategies along with an automatic weight-selection algorithm so that engineers can apply state-of-the-art LPV control synthesis to solve their own engineering problems. In addition, this brief should serve as a bridge between the H-infinity and H2 control theory and the real-world application of gain-scheduling control.
Author: Philip Hugh Goyns
Publisher:
ISBN:
Category : Internal combustion engines
Languages : en
Pages : 246
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Book Description
Author: Amir-Mohammad Shamekhi
Publisher: CRC Press
ISBN: 1000838579
Category : Technology & Engineering
Languages : en
Pages : 214
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Book Description
This book presents a step-by-step guide to the engine control system design, providing case studies and a thorough analysis of the modeling process using machine learning, and model predictive control (MPC). Covering advanced processes alongside the theoretical foundation, MPC enables engineers to improve performance in both hybrid and non-hybrid vehicles. Control system improvement is one of the major priorities for engineers seeking to enhance an engine. Often possible on a low budget, substantial improvements can be made by applying cutting-edge methods, such as artificial intelligence when modeling engine control system designs and using MPC. This book presents approaches to control system improvement at mid, low, and high levels of control. Beginning with the model-in-the-loop hierarchical control design of ported fuel injection SI engines, this book focuses on optimal control of both transient and steady state and also discusses hardware-in-the-loop. The chapter on low-level control discusses adaptive MPC and adaptive variable functioning, as well as designing a fuel injection feed-forward controller. At mid-level control, engine calibration maps are discussed, with consideration of constraints such as limits on pollutant emissions. Finally, the high-level control methodology is discussed in detail in relation to transient torque control of SI engines. This comprehensive yet clear guide to control system improvement is an essential read for any engineer working in automotive engineering and engine control system design.
Author: Theodore Taylor
Publisher:
ISBN:
Category :
Languages : en
Pages : 78
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Book Description
Author: William E. Tourdot
Publisher:
ISBN:
Category :
Languages : en
Pages : 334
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Book Description
Author: Jian Tang
Publisher:
ISBN:
Category : Electronic dissertations
Languages : en
Pages : 0
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Book Description
This dissertation focuses on predicting the system responses and using them to improve the automotive system performance based on the data-driven based algorithms. Two applications included are multivariable borderline knock prediction and control and tire-road friction coefficient estimation. Internal combustion engines are core components of traditional and hybrid passenger vehicles and also widely used for off road applications. When the combustion is limited by the engine knock, it is desired to operate it as close to its borderline knock limit as possible to optimize combustion efficiency. Traditionally, this limit is detected by sweeping tests of related control parameters, which is expensive and time-consuming; and also, the detected borderline knock limit often is relatively conservative. When more advanced control parameters (subsystems) are added, these sweeping tests lead to tremendous higher test cost. An intelligent and efficient way to predict borderline knock without detailed knowledge of combustion dynamics is proposed. This supervised-learning based Bayesian optimization method is assisted by a surrogate model trained based on the system statistic properties. A two-control-parameter (spark timing and intake valve timing) case is demonstrated for optimizing two competing objectives (knock intensity (KI) and fuel economy).A complete borderline knock control structure is proposed and divided into three parts. The first part is about offline training with necessary modifications of the Bayesian optimization algorithm. Engine tests are conducted under two different operational conditions to obtain knock borderline limit, indicating the proposed algorithm is able to reduce required experimental budget (cost and time) significantly. The predicted mean Pareto front and its variance can be used to find the optimum control parameters at borderline knock limit for the best fuel economy possible. Smooth response surfaces of surrogate models can also be used as the initial model to be updated in real-time. The second part is an online updating process, based on the offline-trained surrogate model, using modified likelihood ratio controller. Principal component analysis indicated that spark timing is the most sensitive factor affecting the Pareto front. A two-buffer design was proposed to update the surrogate model under different rates so that both short-term compensation for environment changes and long-term for slow engine aging effect are covered. Both simulation and engine test results indicate that the proposed control strategy is able to update the machine-learned surrogate models in real-time, which outperforms the conventional knock control strategy and offline-trained knock limit, and especially reduces the conservativeness of borderline knock control significantly. Finally, to reduce cycle-to-cycle combustion variations, a real-time cycle-wised knock compensation scheme is developed based on the measured exhaust temperature when the engine is operated close to its knock borderline. To make model-based control possible, ?-Markov COVER (COVariance Equivalent Realization) system identification was used to obtain a linearized engine exhaust temperature model from change of spark timing to associated variations of exhaust temperature and knock intensity (KI). Accordingly, a Linear-Quadratic-Gaussian (LQG) controller is designed to minimizing the KI fluctuations based on change (?) of exhaust temperature. For the entire control architecture, results of three test scenarios indicated that the spark timing can be further advanced while maintaining the same knock intensity level due to reduced knock combustion variations.For the vehicle dynamics research, estimation of tire-road friction coefficient is very important due to new active safety control systems, especially for autonomous vehicles that rely on the accurate estimation of road surface conditions to find vehicle operational boundary and achieve the best performance possible. Several cause- and effect-based methods were proposed with their own limitations. A new evaluation criterion associated with slip-ratio is found based on CarSim simulation data on different road conditions; and strong correlation between proposed criterion and tire-road friction under different road surface conditions is observed. Note that the data-driven based method proposed in this dissertation only utilizes the statistic information from existing production vehicle sensors without increasing hardware cost. A computational cheap black-box model of proposed criterion and tire-road friction can be obtained and augmented with the existing dual-Kalman filter estimation algorithm, which improves tire-road friction estimation.
Author: Daniel Alberer
Publisher: Springer Science & Business Media
ISBN: 1447122208
Category : Technology & Engineering
Languages : en
Pages : 360
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Book Description
Increasing complexity and performance and reliability expectations make modeling of automotive system both more difficult and more urgent. Automotive control has slowly evolved from an add-on to classical engine and vehicle design to a key technology to enforce consumption, pollution and safety limits. Modeling, however, is still mainly based on classical methods, even though much progress has been done in the identification community to speed it up and improve it. This book, the product of a workshop of representatives of different communities, offers an insight on how to close the gap and exploit this progress for the next generations of vehicles.
