Time-delay systems: an overview of some recent advances and open problems

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

Survey Constructive nonlinear control: a historical perspective

The supervisory control strategy of a hybrid vehicle coordinates the operation of vehicle sub-systems to achieve performance targets such as maximizing fuel economy and reducing exhaust emissions. This high-level control problem is commonly referred as the power management problem. In the past, many supervisory control strategies were developed on the basis of a few pre-defined driving cycles, using intuition and heuristics. The resulting control strategy was often inherently cycle-beating and lacked a guaranteed level of optimality. In this study, the power management problem is tackled from a stochastic viewpoint. An infinite-horizon stochastic dynamic optimization problem is formulated. The power demand from the driver is modeled as a random Markov process. The optimal control strategy is then obtained by using stochastic dynamic programming (SDP). The obtained control law is in the form of a stationary full-state feedback and can be directly implemented. Simulation results over standard driving cycles and random driving cycles are presented to demonstrate the effectiveness of the proposed stochastic approach. It was found that the obtained SDP control algorithm outperforms a sub-optimal rule-based control strategy trained from deterministic DP results.

/pdf/a-stochastic-control-strategy-for-hybrid-electric-vehicles-55whtvcnmb.pdf

A stochastic control strategy for hybrid electric vehicles

Hybrid electric vehicles (HEVs) have demonstrated the capability to improve fuel economy and emissions. The plug-in HEV (PHEV), utilizing more battery power, has become a more attractive upgrade of the HEV. The charge-depletion mode is more appropriate for the power management of PHEVs, i.e., the state of charge (SOC) is expected to drop to a low threshold when the vehicle reaches the trip destination. Trip information has so far been considered as future information for vehicle operation and is thus not available a priori. This situation can be changed by the recent advancement in intelligent transportation systems (ITSs) based on the use of on-board global positioning systems (GPSs), geographical information systems (GISs), and advanced traffic flow modeling techniques. In this paper, a new approach to optimal power management of PHEVs in the charge-depletion mode is proposed with driving cycle modeling based on the historic traffic information. A dynamic programming (DP) algorithm is applied to reinforce the charge-depletion control such that the SOC drops to a specific terminal value at the end of the driving cycle. The vehicle model was based on a hybrid electric sport utility vehicle (SUV). Only fuel consumption is considered for the current stage of the study. A simulation study was conducted for several standard driving cycles and two trip models using the proposed method, and the results showed significant improvement in fuel economy compared with a rule-based control and a depletion sustenance control for most cases. Furthermore, the results showed much better consistency in fuel economy compared with rule-based and depletion sustenance control.

Trip-Based Optimal Power Management of Plug-in Hybrid Electric Vehicles

Variable-geometry turbochargers (VGTs) are employed in high-end diesel engines. These VGTs also help in controlling the trade-offs in emissions performance. Exhaust gas recirculation (EGR) is used to dilute the combustion mixture, resulting in lower peak combustion temperatures and a lower oxygen concentration and hence lower NOx emissions. In this article, we compare some of the control methodologies previously presented and some not yet presented to evaluate their benefits experimentally. We do not include any new theory. Rather we refer to other sources for the development of the controllers evaluated. We present an objective comparison of advanced control methodologies on a complex industrial problem with widespread applications. The control methodologies discussed are essentially system based, i.e., the initial controller is developed on an engine model.

EGR-VGT control schemes: experimental comparison for a high-speed diesel engine

Decentralized and Multivariable Designs for EGR-VGT Control of a Diesel Engine

Preserving Stability/Performance when Facing an Unknown Time-Delay

Stringent emission regulations combined with customer demands for improved fuel economy and performance have forced the automotive industry to consider more advanced powertrain configurations than standard port-fuel injected gasoline engines. Modern state-of-the-art powertrain systems may combine several power sources (internal combustion engines, electric motors, fuel cells, etc.) and various exhaust aftertreatment devices (catalytic converters, lean NOx traps, particulate filters, etc.) in addition to conventional engine subsystems such as turbochargers and exhaust gas recirculation. The determination of the way in which these systems need to be operated to meet driver's torque demand, performance and fuel economy expectations while satisfying federal emission regulations is a complex and a multiobjective optimal control problem. This paper reviews some of the approaches to this problem in the context of two case studies.

M. J. van Nieuwstadt

Papers

EGR-VGT control schemes: experimental comparison for a high-speed diesel engine

Decentralized and Multivariable Designs for EGR-VGT Control of a Diesel Engine

Preserving Stability/Performance when Facing an Unknown Time-Delay

Optimization of complex powertrain systems for fuel economy and emissions

Coordinated EGR-VGT Control for Diesel Engines: an Experimental Comparison