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2014 | Buch

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Optimization and Optimal Control in Automotive Systems

herausgegeben von: Harald Waschl, Ilya Kolmanovsky, Maarten Steinbuch, Luigi del Re

Verlag: Springer International Publishing

Buchreihe : Lecture Notes in Control and Information Science

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

This book demonstrates the use of the optimization techniques that are becoming essential to meet the increasing stringency and variety of requirements for automotive systems. It shows the reader how to move away from earlier approaches, based on some degree of heuristics, to the use of more and more common systematic methods. Even systematic methods can be developed and applied in a large number of forms so the text collects contributions from across the theory, methods and real-world automotive applications of optimization.

Greater fuel economy, significant reductions in permissible emissions, new drivability requirements and the generally increasing complexity of automotive systems are among the criteria that the contributing authors set themselves to meet. In many cases multiple and often conflicting requirements give rise to multi-objective constrained optimization problems which are also considered. Some of these problems fall into the domain of the traditional multi-disciplinary optimization applied to system, sub-system or component design parameters and is performed based on system models; others require applications of optimization directly to experimental systems to determine either optimal calibration or the optimal control trajectory/control law.

Optimization and Optimal Control in Automotive Systems reflects the state-of-the-art in and promotes a comprehensive approach to optimization in automotive systems by addressing its different facets, by discussing basic methods and showing practical approaches and specific applications of optimization to design and control problems for automotive systems. The book will be of interest both to academic researchers, either studying optimization or who have links with the automotive industry and to industrially-based engineers and automotive designers.

Inhaltsverzeichnis

Frontmatter

Optimization Methods

Frontmatter

Chapter 1. Trajectory Optimization: A Survey

Abstract

A survey of numerical methods for trajectory optimization. The goal of this survey is to describe typical methods that have been developed over the years for optimal trajectory generation. In addition, this survey describes modern software tools that have been developed for solving trajectory optimization problems. Finally, a discussion is given on how to choose a method.

Anil V. Rao

Chapter 2. Extremum Seeking Methods for Online Automotive Calibration

Abstract

The automotive calibration process is becoming increasingly difficult as the degrees of freedom in modern engines rises with the number of actuators. This is coupled with the desire to utilise alternative fuels to gasoline and diesel for the promise of lower \(\mathrm {CO}_2\) levels in transportation. However, the range of fuel blends also leads to variability in the combustion properties, requiring additional sensing and calibration effort for the engine control unit (ECU). Shifting some of the calibration effort online whereby the engine controller adjusts its operation to account for the current operating conditions may be an effective alternative if the performance of the controller can be guaranteed within some performance characteristics. This tutorial chapter summarises recent developments in extremum seeking control, and investigates the potential of these methods to address some of the complexity in developing fuel-flexible controllers for automotive powertrains.

Chris Manzie, Will Moase, Rohan Shekhar, Alireza Mohammadi, Dragan Nesic, Ying Tan

Chapter 3. Model Predictive Control of Autonomous Vehicles

Abstract

The control of autonomous vehicles is a challenging task that requires advanced control schemes. Nonlinear Model Predictive Control (NMPC) and Moving Horizon Estimation (MHE) are optimization-based control and estimation techniques that are able to deal with highly nonlinear, constrained, unstable and fast dynamic systems. In this chapter, these techniques are detailed, a descriptive nonlinear model is derived and the performance of the proposed control scheme is demonstrated in simulations of an obstacle avoidance scenario on a low-fricion icy road.

Mario Zanon, Janick V. Frasch, Milan Vukov, Sebastian Sager, Moritz Diehl

Chapter 4. Approximate Solution of HJBE and Optimal Control in Internal Combustion Engines

Abstract

Optimal control problems naturally arise in several kinds of applications, including automotive systems. Unfortunately, the solution of such problems—which hinges upon a partial differential differential equation, the so-called Hamilton-Jacobi-Bellman (HJB) pde—might be hard or even impossible to determine in practice. Herein, introducing the notion of Dynamic Value function, we propose a novel technique that consists in the immersion of the given model into an extended state-space in which the solution may be defined in a constructive manner. This leads to a dynamic control law that approximates the optimal policy. The proposed approach is validated by means of a case study arising from the field of combustion engines, namely optimal control of the torque and the speed of a test bench.

Mario Sassano, Alessandro Astolfi

Inter and Intra Vehicle System Optimization

Frontmatter

Chapter 5. Intelligent Speed Advising Based on Cooperative Traffic Scenario Determination

Abstract

A novel system for safe speed recommendation, based on a cooperative method for vehicular density estimation and on the intelligent determination of the traffic scenario, is presented.

Rodrigo H. Ordóñez-Hurtado, Wynita M. Griggs, Kay Massow, Robert N. Shorten

Chapter 6. Driver Control and Trajectory Optimization Applied to Lane Change Maneuver

Abstract

The problem of driver control and trajectory optimization for the lane change maneuver is approached through the application of the same design and simulation tools used in the development of modern automobiles. The vehicle model and driver control algorithms are combined with a genetic algorithm for trajectory optimization to determine an optimal path for achieving an objective measure of maximum speed. Results are compared with subjective results from a professional driver using a new driver-in-the-loop system. The conclusion is integrated objective-subjective simulation methods can be used earlier in the design to improve vehicle handling performance.

Patrick J. McNally

Chapter 7. Real-Time Near-Optimal Feedback Control of Aggressive Vehicle Maneuvers

Abstract

Optimal control theory Patrick J. can be used to generate aggressive maneuvers for vehicles under a variety of conditions using minimal assumptions. Although optimal control provides a very powerful framework for generating aggressive maneuvers utilizing fully nonlinear vehicle and tire models, its use in practice is hindered by the lack of guarantees of convergence, and by the typically long time to generate a solution, which makes this approach unsuitable for real-time implementation unless the problem obeys certain convexity and/or linearity properties. In this chapter, we investigate the use of statistical interpolation (e.g., kriging) in order to synthesize on-the-fly near-optimal feedback control laws from pre-computed optimal solutions. We apply this methodology to the challenging scenario of generating a minimum-time yaw rotation maneuver of a speeding vehicle in order to change its posture prior to a collision with another vehicle, in an effort to remedy the effects of a head-on collision. It is shown that this approach offers a potentially appealing option for real-time, near-optimal, robust trajectory generation.

Panagiotis Tsiotras, Ricardo Sanz Diaz

Chapter 8. Applications of Computational Optimal Control to Vehicle Dynamics

Abstract

Modern vehicle dynamic control systems are based on new types of actuators, such as active steering and active differentials, in order to improve the overall handling performance including stability, responsiveness, and agility. Numerical techniques of off-line optimization of vehicle dynamics control variables can conveniently be used to facilitate decisions on optimal actuator configurations and provide guidance for design of realistic, on-line controllers. This chapter overviews the previous authors’ results of assessment of various vehicle dynamics actuator configurations based on application of a back propagation through time (BPTT) conjugate gradient optimization algorithm. It is then focused on detailed optimization of active front and rear steering control variables for various maneuvers and design specifications, where a nonlinear programming-based optimization tool is used.

Joško Deur, Mirko Corić, Josip Kasać, Francis Assadian, Davor Hrovat

Chapter 9. Stochastic Fuel Efficient Optimal Control of Vehicle Speed

Abstract

Stochastic dynamic programming (SDP) is applied to generate control policies that adjust vehicle speed to improve average fuel economy without degrading, significantly, the average travel speed. The SDP policies take into account statistical patterns in traffic speed and road topography. Specific problems of fuel efficient in-traffic driving and fuel efficient lead vehicle following are considered, and it is shown how these problems can be treated within an SDP framework. Simulation results are summarized to quantify fuel economy improvements, and experimental results are reported for the fuel efficient lead vehicle following case. The properties of vehicle speed trajectories induced by SDP policies are examined.

Kevin McDonough, Ilya Kolmanovsky, Dimitar Filev, Steve Szwabowski, Diana Yanakiev, John Michelini

Chapter 10. Predictive Cooperative Adaptive Cruise Control: Fuel Consumption Benefits and Implementability

Abstract

Impressive improvements of efficiency and safety of vehicles have been achieved over the last decade, but increasing traffic density and drivers’ age accentuate the need of further improvements. The contributions summarized in this chapter argue that a substantial additional fuel benefit can be achieved by extending the well introduced Adaptive Cruise Control in a predictive sense, e.g. taking into account a predicted behavior of other traffic components. This chapter starts by discussing results on the potential benefits in the ideal case (full information, no limits on computing power) and then examines how much of the potential benefits is retained if approximate solutions are used to cope with a realistic situation, with limited information and computing power. Two setups are considered: vehicles exchanging a small set of simple data over a V2V link and the case of mixed traffic, in which some vehicles will not provide any information, but the information must be obtained by a probabilistic estimator. The outcome of these considerations is that the approach is able to provide—statistically—a substantial fuel consumption benefit without affecting negatively the driveability or the driver comfort like other methods, e.g. platooning, would.

Dominik Lang, Thomas Stanger, Roman Schmied, Luigi del Re

Powertrain Optimization

Frontmatter

Chapter 11. Topology Optimization of Hybrid Power Trains

Abstract

Topology optimization methods for continuum systems (structural topology, shape, material) are well-established. However, these methods do not apply to non-continuum or dynamic systems with discrete components with unique characteristics as with hybrid vehicles. This chapter examines the power train topology and control design optimization problem at vehicle system level. The design space related to power train and control system optimization level is rapidly increasing with new developments in power train, auxiliary technologies, system architectures (topologies) and cyber-physical systems. The multi-objective, mixed or hybrid (continuous/discrete time) character on both coupled levels of the problem requires relative long computation time. Therefore, it requires a bi-level (nested) or simultaneous system design approach. Since, sequential or iterative design procedures fail to prove system-level optimality. In this chapter, some illustrative examples are discussed related to nested control and design optimization problems related to continuous/stepped-gear transmission shifting, power split control and/or in combination with topology optimization.

Theo Hofman, Maarten Steinbuch

Chapter 12. Model-Based Optimal Energy Management Strategies for Hybrid Electric Vehicles

Abstract

Methods from optimal control theory have been used since the past decade to design model-based energy management strategies for hybrid electric vehicles (HEVs). These strategies are usually designed as solutions to a finite-time horizon, constrained optimal control problem that guarantees optimality upon perfect knowledge of the driving cycle. Properly adapted these strategies can be used for real-time implementation (without knowledge of the future driving mission) at the cost of either high (sometime prohibitive) computational burden or high memory requirement to store high-dimensional off-line generated look-up tables. These issues have motivated the research reported in this chapter. We propose to address the optimal energy management problem over an infinite time horizon by formulating the problem as a nonlinear, nonquadratic optimization problem. An analytical supervisory controller is designed that ensures stability, optimality with respect to fuel consumption, ease of implementation in real-time application, fast execution and low control parameter sensitivity. The approach generates a drive cycle independent control law without requiring discounted cost or shortest path stochastic dynamic programming introduced in the prior literature.

Simona Onori

Chapter 13. Optimal Energy Management of Automotive Battery Systems Including Thermal Dynamics and Aging

Abstract

Hybrid-electric vehicles (HEV) has been the subject of intensive research as a field of application of optimal control in the past decade. In particular, researchers have proven that energy management (or supervisory control) can be effectively designed using optimal control-based techniques (Guzzella and Sciarretta, Vehicle Propulsion Systems. Introduction to Modeling and Optimization. Springer, Berlin, 2013. Such methods have been applied to charge-sustaining hybrids implementing various architecture, as well as, more recently, to plug-in hybrids (Stockar et al. IEEE Trans Vehr Technol, 60(7):2949–2962, 2011; Sivertsson 2012). Plug-in hybrids (PHEV) are characterized by much higher battery capacities and energies than charge-sustaining hybrids, thus the proper description of battery behavior plays an even more fundamental role in energy management design.

Antonio Sciarretta, Domenico di Domenico, Philippe Pognant-Gros, Gianluca Zito

Chapter 14. Optimal Control of Diesel Engines with Waste Heat Recovery System

Abstract

This study presents an integrated energy and emission management strategy for a Euro-VI diesel engine with Waste Heat Recovery (WHR) system. This Integrated Powertrain Control (IPC) strategy optimizes the \(\mathrm {CO_2}\)-\(\mathrm {NO_x}\) trade-off by minimizing the operational costs associated with fuel and AdBlue consumption, while satisfying tailpipe emission constraints. The main contribution of this work is that the optimal solution is determined numerically for a given cycle and is compared with a real-time implementable strategy. Also, the WHR dynamics are explicitly included in the control design. In a simulation study, the potential of this IPC strategy is demonstrated over the World Harmonized Transient Cycle. It is shown that the real-time strategy can be applied with negligible loss of optimality. Using IPC, an additional 3.5 % \(\mathrm {CO_2}\) reduction is achieved, while complying with the \(\mathrm {NO_{x}}\) emission limit, when compared to a baseline strategy.

Frank Willems, M. C. F. Donkers, Frank Kupper

Optimization of the Engine Operation

Frontmatter

Chapter 15. Learning Based Approaches to Engine Mapping and Calibration Optimization

Abstract

In this chapter we consider a class of optimization problems arising in the process of automotive engine mapping and calibration. Fast optimization algorithms applicable to high fidelity simulation models or experimental engines can reduce the time, effort and costs required for calibration. Our approach to these problems is based on iterations between local model identification and calibration parameter (set-points and actuator settings) improvements based on the learned surrogate model. Several approaches to algorithm implementation are considered. In the first approach, the surrogate model is defined in a linear incremental form and its identification reduces to Jacobian Learning. Then quadratic programming is applied to adjust the calibration parameters. In the second approach, we consider a predictor-corrector algorithm that estimates the change in the minimizer based on changing operating conditions before correcting it. Case studies are described that illustrate the applications of algorithms.

Dimitar Filev, Yan Wang, Ilya Kolmanovsky

Chapter 16. Online Design of Experiments in the Relevant Output Range

Abstract

Nonlinear system identification requires informative data obtained from experiments in order to parameterise a model of the underlying process. As an example for the automotive industry, good models for NO\(_x\) and smoke emissions are required to effectively calibrate modern combustion engines. With continuously increasing complexity in terms of the number of variation channels available in the engines the experimental effort provides a growing challenge for efficient calibration. Design of Experiments (DoE) refers to optimal excitation of the system in order to maximise the knowledge gained for a process under investigation from a limited amount of measurements. We introduce a methodology that omits measurements unrelevant for calibration via pre-specification of restrictions on the inputs as well as the outputs. Output restriction to a certain target region is obtained via a supervising online model that is trained during the workflow. The distribution of input samples obtained via this method is non-uniform over the pre-image of the target region. The effectiveness of this concept is demonstrated for the modeling of NO\(_x\) and smoke emissions of a diesel engine.

Nico Didcock, Andreas Rainer, Stefan Jakubek

Chapter 17. Optimal Control of HCCI

Abstract

HCCI (Homogeneous Charge Compression Ignition) is a very control-intensive combustion concept which has been studied for over a decade because of its favorable combination of high efficiency and low emissions. Various optimal control methods have been applied to HCCI and this chapter gives an overview of them. Optimal control of HCCI can be divided into model based and non-model based where MPC is an example of model based and extremum seeking control is an example of non-model based control. The model-based methods can be divided based on whether they use physics based or black box models. Finally a division can be made based on whether the control aims for optimal set-point tracking of e.g. combustion timing or whether it attempts to optimize an overall design criterion such as fuel consumption. This chapter presents and characterizes a number of published methods for optimal HCCI control and characterizes them according to the above criteria.

Per Tunestål

Chapter 18. Optimal Lifting and Path Profiles for a Wheel Loader Considering Engine and Turbo Limitations

Abstract

Time and fuel optimal control of an articulated wheel loader is studied during the lift and transport sections of the short loading cycle. A wheel loader model is developed including engine (with turbo dynamics), torque converter, transmission and vehicle kinematics, lifting hydraulics and articulated steering. The modeling is performed with the aim to use the models for formulating and solving optimal control problems. The considered problem is the lift and transport section of the wheel loader that operates in the short loading cycle, with several different load receiver positions, while the considered criteria are minimum time and minimum fuel. The problem is separated into four phases to avoid solving a mixed integer problem imposed by the gearshifting discontinuities. Furthermore, two different load lifting patterns are studied one with the lifting free and one with the lifting performed only in the last 30 % of the transport. The results show that the optimal paths to the load receiver are identical for both minimum time and minimum fuel cycles and do not change when the loading lifting pattern is altered. A power break-down during the wheel loader operation is presented for the selected cycles of normal and delayed lifting where it is shown that the cycle time remains almost unchanged when lifting is delayed while the fuel consumption slightly decreases in minimum time transients.

Vaheed Nezhadali, Lars Eriksson

Backmatter

Titel: Optimization and Optimal Control in Automotive Systems
herausgegeben von: Harald Waschl
Ilya Kolmanovsky
Maarten Steinbuch
Luigi del Re
Verlag: Springer International Publishing
Electronic ISBN: 978-3-319-05371-4
Print ISBN: 978-3-319-05370-7
DOI: https://doi.org/10.1007/978-3-319-05371-4