Lino Guzzella

Control of hybrid electric vehicles

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 diesel engines

This article is intended to give control engineers an overview of models and controls of diesel engines. The main emphasis is on the engines torque generation, including all necessary ancillary devices (turbocharger, injection-system, etc.), pollutant emission and model-based controls. The paper gives a brief introduction of the basic working principles and the salient features of diesel engines and their main differences to Otto (gasoline or spark-ignited) engines are shown. The most important control tasks are then identified and their implications on engine performance are analyzed. An overview of the current state-of-the-art in industrial diesel control applications is given. It also discusses models for the simulation of transient macroscopic effects, and how these models can be simplified to be useful for controller synthesis. Finally. an outlook on possible future control issues and their role in diesel engine evolution is presented.

Unified modeling of hybrid electric vehicle drivetrains

Hybridizing automotive drivetrains, or using more than one type of energy converter, is considered an important step toward very low pollutant emission and high fuel economy. The automotive industry and governments in the United States, Europe, and Japan have formed strategic initiatives with the aim of cooperating in the development of new vehicle technologies. Efforts to meet fuel economy and exhaust emission targets have initiated major advances in hybrid drivetrain system components, including: high-efficiency high-specific power electric motors and controllers; load-leveling devices such as ultracapacitors and fly-wheels; hydrogen and direct-methanol fuel cells; direct injection diesel and Otto cycle engines; and advanced batteries. The design of hybrid electric vehicles is an excellent example of the need for mechatronic system analysis and design methods. If one is to fully realize the potential of using these technologies, a complete vehicle system approach for component selection and optimization over typical driving situations is required. The control problems that arise in connection with hybrid power trains are significant and pose additional challenges to power-train control engineers. The principal aim of the paper is to propose a framework for the analysis, design, and control of optimum hybrid vehicles within the context of energy and power flow analysis. The approaches and results presented in the paper are one step toward the development of a complete toolbox for the analysis and design of hybrid vehicles.

A generic dynamic programming Matlab function

This paper introduces a generic dynamic programming function for Matlab. This function solves discretetime optimal-control problems using Bellmans dynamic programming algorithm. The function is implemented such that the user only needs to provide the objective function and the model equations. The function includes several options for solving optimal-control problems. The model equations can include several state variables and input variables. Furthermore, the model equations can be time-variant and include time-variant state and input constraints. The syntax of the function is explained using two examples. The first is the well-known Lotka-Volterra fishery problem and the second is a parallel hybrid-electric vehicle optimization problem.

Energy-Optimal Control of Plug-in Hybrid Electric Vehicles for Real-World Driving Cycles

Plug-in hybrid electric vehicles (PHEVs) are currently recognized as a promising solution for reducing fuel consumption and emissions due to the ability of storing energy through direct connection to the electric grid. Such benefits can be achieved only with a supervisory energy management strategy that optimizes the energy utilization of the vehicle. This control problem is particularly challenging for PHEVs due to the possibility of depleting the battery during usage and the vehicle-to-grid interaction during recharge. This paper proposes a model-based control approach for PHEV energy management that is based on minimizing the overall CO2 emissions produced-directly and indirectly-from vehicle utilization. A supervisory energy manager is formulated as a global optimal control problem and then cast into a local problem by applying the Pontryagins minimum principle. The proposed controller is implemented in an energy-based simulator of a prototype PHEV and validated on experimental data. A simulation study is conducted to calibrate the control parameters and to investigate the influence of vehicle usage conditions, environmental factors, and geographic scenarios on the PHEV performance using a large database of regulatory and “real-world” driving profiles.

Model-Based Control of the VGT and EGR in a Turbocharged Common-Rail Diesel Engine: Theory and Passenger Car Implementation

In this article model-based controller design techniques are investigated for the transient operation of a common-rail diesel engine in order to optimize driveability and to reduce soot emissions. The computer-aided design has benefits in reducing controller calibration time. This paper presents a nonlinear control concept for the coordinated control of the exhaust gas recirculation (EGR) valve and the variable geometry turbocharger (VGT) in a common-rail diesel engine. The overall controller structure is set up to regulate the total cylinder air-charge with a desired fresh air-charge amount by means of controlling the intake manifold pressure and estimating the fresh air-charge inducted into the cylinders. During varying engine operating conditions the two control loops are coordinated by a compensation of the EGR valve action through the VGT controller. A nonlinear exhaust pressure controller is designed to regulate the estimated turbocharger power which compensates all EGR valve actions and results in the desired turbocharger power management. The VGT and EGR controllers, the cylinder air-charge observer and the turbocharger identification algorithm are developed based on a nonlinear diesel engine model. The benefits of the coordinated controller structure are demonstrated with transient engine measurements in a passenger car.

Predictive Reference Signal Generator for Hybrid Electric Vehicles

A novel model-based and predictive energy supervisory controller for hybrid electric vehicles (HEVs) is presented. Its objective is to minimize the fuel consumption (FC) of HEVs using only the information on the current state of charge (SoC) of the battery and data available from a standard onboard navigation system. This objective is achieved using a predictive reference signal generator (pRSG) in combination with a nonpredictive reference tracking controller for the battery SoC. The pRSG computes the desired battery SoC trajectory as a function of vehicle position such that the recuperated energy is maximized despite the constraints on the battery SoC. To compute the SoC reference trajectory, only the topographic profile of the future road segments and the corresponding average traveling speeds must be known. Simulation results of the proposed predictive strategy show substantial improvements in fuel economy in hilly driving profiles, compared with nonpredictive strategies. A parallel HEV is analyzed in this paper. However, the proposed method is independent of the powertrain topology. Therefore, the method is applicable to all types of HEVs.

CAE tools for quasi-static modeling and optimization of hybrid powertrains

Hybrid vehicles offer larger flexibility than conventional powertrains and, therefore, opportunities for improved fuel economy, but they need systematic design and optimization procedures to realize that potential. Especially choosing the best system structure, parametrization, and supervisory control algorithms is not trivial. This paper presents a tool which supports these tasks and which is based on a somewhat unusual system description. With this approach, fast simulations over entire test cycles are achieved on standard computer platforms. To demonstrate the benefits of the proposed tool, three case studies are shown, one including experimental data.

Battery State-of-Health Perceptive Energy Management for Hybrid Electric Vehicles

This paper presents a causal optimal control-based energy management strategy for a parallel hybrid electric vehicle (HEV). This strategy not only seeks to minimize fuel consumption while maintaining the state-of-charge of the battery within reasonable bounds but to minimize wear of the battery by penalizing the instantaneous battery usage with respect to its relative impact on battery life as well. This impact is derived by means of a control-oriented state-of-health model. The results indicate that the proposed causal strategy effectively reduces battery wear with only a relatively small penalty on fuel consumption. Ultimately, in terms of cost of fuel and battery replacements, the total cost of ownership over the entire life of the vehicle is significantly reduced.

Optimal Hybridization in Two Parallel Hybrid Electric Vehicles using Dynamic Programming

Abstract This study explores different hybridization ratios of two types of parallel hybrid electric vehicles, a torque assist parallel hybrid and a full parallel hybrid, with equal power-to-weight ratio. The powertrain consist of an internal combustion engine, an electric motor, and a NiMH battery. The different hybridization ratios are compared by their optimal fuel consumption for eight different drive cycles. The optimal fuel consumption is determined using dynamic programming for each of the different hybridization ratios. In the full parallel hybrid the engine and motor can be decoupled while in the torque assist hybrid the engine and motor are always mechanically connected. Results show that there are not only lower fuel consumption for the full hybrid but the need for hybridization is lower than in the torque assist hybrid for all eight cycles. The hybridization ratio where a full hybrid have the same fuel consumption as the optimal torque assist hybrid can differ as much as 51%.