A tutorial account of variable structure control with sliding mode is presented. The purpose is to introduce in a concise manner the fundamental theory, main results, and practical applications of this powerful control system design approach. This approach is particularly attractive for the control of nonlinear systems. Prominent characteristics such as invariance, robustness, order reduction, and control chattering are discussed in detail. Methods for coping with chattering are presented. Both linear and nonlinear systems are considered. Future research areas are suggested and an extensive list of references is included. >

Variable structure control: a survey

A comparative study of equivalent circuit models for Li-ion batteries

Hybrid vehicle techniques have been widely studied recently because of their potential to significantly improve the fuel economy and drivability of future ground vehicles. Due to the dual-power-source nature of these vehicles, control strategies based on engineering intuition frequently fail to fully explore the potential of these advanced vehicles. In this paper, we present a procedure for the design of a near-optimal power management strategy. The design procedure starts by defining a cost function, such as minimizing a combination of fuel consumption and selected emission species over a driving cycle. Dynamic programming (DP) is then utilized to find the optimal control actions including the gear-shifting sequence and the power split between the engine and motor while subject to a battery SOC-sustaining constraint. Through analysis of the behavior of DP control actions, near-optimal rules are extracted, which, unlike DP control signals, are implementable. The performance of this power management control strategy is studied by using the hybrid vehicle model HE-VESIM developed at the Automotive Research Center of the University of Michigan. A tradeoff study between fuel economy and emissions was performed. It was found that significant emission reduction could be achieved at the expense of a small increase in fuel consumption.

/pdf/power-management-strategy-for-a-parallel-hybrid-electric-44jddwnj1p.pdf

Power management strategy for a parallel hybrid electric truck

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from field measurements, experiments and large-scale simulations at multiple spatiotemporal scales. Machine learning offers a wealth of techniques to extract information from data that could be translated into knowledge about the underlying fluid mechanics. Moreover, machine learning algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of machine learning for fluid mechanics. It outlines fundamental machine learning methodologies and discusses their uses for understanding, modeling, optimizing, and controlling fluid flows. The strengths and limitations of these methods are addressed from the perspective of scientific inquiry that considers data as an inherent part of modeling, experimentation, and simulation. Machine learning provides a powerful information processing framework that can enrich, and possibly even transform, current lines of fluid mechanics research and industrial applications.

Machine Learning for Fluid Mechanics

This tutorial introduces the CMA Evolution Strategy (ES), where CMA stands for Covariance Matrix Adaptation. The CMA-ES is a stochastic, or randomized, method for real-parameter (continuous domain) optimization of non-linear, non-convex functions. We try to motivate and derive the algorithm from intuitive concepts and from requirements of non-linear, non-convex search in continuous domain.

/pdf/the-cma-evolution-strategy-a-tutorial-209ln9p7fz.pdf

The CMA Evolution Strategy: A Tutorial

Internal combustion engines (ICE) still have potential for substantial improvements, particularly with regard to fuel efficiency and environmental compatibility. In order to fully exploit the remaining margins, increasingly sophisticated control systems have to be applied. This book offers 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 and solutions for selected feedforward and feedback control-problems are presented. The discussions concerning pollutant emissions and fuel economy of ICE in automotive applications constantly intensified since the first edition of this book was published. Concerns about the air quality, the limited resources of fossil fuels and the detrimental effects of greenhouse gases exceedingly spurred the interest of both the industry and academia in further improvements. The most important changes and additions included in this second edition are: - restructured and slightly extended section on superchargers; - short subsection on rotational oscillations and their treatment on engine test-benches; - complete section on modeling, detection, and control of engine knock; - improved physical and chemical model for the three-way catalytic converter; - new methodology for the design of an air-to-fuel ratio controller; - short introduction to thermodynamic engine-cycle calculation and corresponding control-oriented aspects. (orig.)

Introduction to Modeling and Control of Internal Combustion Engine Systems

Introduction.- Vehicle Energy and Fuel Consumption - Basic Concepts.- IC-Engine-Based Propulsion Systems.- Models of Fuel-Cell Propulsion Systems.- Supervisory Control Algorithms.- Appendix I - Case Studies.- Appendix II - Optimal Control Theory.

Vehicle Propulsion Systems: Introduction to Modeling and Optimization

Global optimization techniques, such as dynamic programming, serve mainly to evaluate the potential fuel economy of a given powertrain configuration. Unless the future driving conditions can be predicted during real-time operation but the results obtained using this noncausal approach establish a benchmark for evaluating the optimality of realizable control strategies. Real-time controllers must be simple in order to be implementable with limited computation and memory resources. Moreover, manual tuning of control parameters should be avoided. This article has analyzed two approaches, namely, feedback controllers and ECMS. Both of these approaches can lead to system behavior that is close to optimal, with feedback controllers based on dynamic programming. Additional challenges stem from the need to apply optimal energy-management controllers to advanced HEV architectures, such as combined and plug-in HEVs, as well as to optimization problems that include performance indices in addition to fuel economy, such as pollutant emissions, driveability, and thermal comfort

Control of hybrid electric vehicles

In this paper, a model-based strategy for the real-time load control of parallel hybrid vehicles is presented. The aim is to develop a fuel-optimal control which is not relying on the a priori knowledge of the future driving conditions (global optimal control), but only upon the current system operation. The methodology developed is valid for those problem that are characterized by hard constraints on the state-battery state-of-charge (SOC) in this application-and by an arc cost-fuel consumption rate-which is not an explicit function of the state. A suboptimal control is found with a proper definition of a cost function to be minimized at each time instant. The "instantaneous" cost function includes the fuel energy and the electrical energy, the latter related to the state constraints. In order to weight the two forms of energy, a new definition of the equivalence factors has been derived. The strategy has been applied to the "Hyper" prototype of DaimlerChrysler, obtained from the hybridization of the Mercedes A-Class. Simulation results illustrate the potential of the proposed control in terms of fuel economy and in keeping the deviations of SOC at a low level.

Lino Guzzella

Papers

Introduction to Modeling and Control of Internal Combustion Engine Systems

Vehicle Propulsion Systems: Introduction to Modeling and Optimization

Control of hybrid electric vehicles

Optimal control of parallel hybrid electric vehicles

Vehicle Propulsion Systems