Dominik Karbowski

“Fair” Comparison of Powertrain Configurations for Plug-In Hybrid Operation Using Global Optimization

Plug-in Hybrid Electric Vehicles (PHEVs) use electric energy from the grid rather than fuel energy for most short trips, therefore drastically reducing fuel consumption. Different configurations can be used for PHEVs. In this study, the parallel pre-transmission, series, and power-split configurations were compared by using global optimization. The latter allows a fair comparison among different powertrains. Each vehicle was operated optimally to ensure that the results would not be biased by non-optimally tuned or designed controllers. All vehicles were sized to have a similar allelectric range (AER), performance, and towing capacity. Several driving cycles and distances were used. The advantages of each powertrain are discussed.

Modeling the Hybridization of a Class 8 Line-Haul Truck

Hybrid electric vehicles have demonstrated their ability to significantly reduce fuel consumption for several medium- and heavy-duty applications. In this paper we analyze the impact on fuel economy of the hybridization of a tractor-trailer. The study is done in PSAT (Powertrain System Analysis Toolkit), which is a modeling and simulation toolkit for light- and heavy-duty vehicles developed by Argonne National Laboratory. Two hybrid configurations are taken into account, each one of them associated with a level of hybridization. That increases the braking energy recuperation rates. We first analyze the benefits of the two hybrid configurations on standard cycles. We then compare fuel economy results from a short standard highway cycle with a longer cruising scenario to illustrate the sensitivity of the benefits to the drive cycle. Finally, using simulation involving a grade scenario of periodical hills that we designed for this project, we show hybridization can be beneficial on hilly terrain.

Instantaneously Optimized Controller for a Multimode Hybrid Electric Vehicle

A multimode transmission combines several power-split modes and possibly several fixed gear modes, thanks to complex arrangements of planetary gearsets, clutches and electric motors. Coupled to a battery, it can be used in a highly flexible hybrid configuration, which is especially practical for larger cars. The Chevrolet Tahoe Hybrid is the first light-duty vehicle featuring such a system. This paper introduces the use of a high-level vehicle controller based on instantaneous optimization to select the most appropriate mode for minimizing fuel consumption under a broad range of vehicle operating conditions. The control uses partial optimization: the engine ON/OFF and the battery power demand regulating the battery state-of-charge are decided by a rulebased logic; the transmission mode as well as the operating points are chosen by an instantaneous optimization module that aims at minimizing the fuel consumption at each time step. The controller is then implemented in a Simulink/Stateflow controller that can be used in Argonnes PSAT (Powertrain System Analysis Toolkit), a forward-looking powertrain simulation toolkit with dynamic plant models. As a result, the controller described in this paper is realistically implementable on an actual vehicle. Simulation results show the mode use and describe the practical operations of the system.

Route-Based Online Energy Management of a PHEV and Sensitivity to Trip Prediction

In this paper, we present a method of optimizing the energy management of a plug-in hybrid electric vehicle (PHEV) using GIS-assisted stochastic trip prediction. A process was developed to synthesize speed profiles through a combination of Markov chains and information from a geographical information system (GIS) about the future route. In a potential real-world scenario, the future trip (speed, grade, stops, etc.) can be estimated, but not deterministically known. The stochastic trip prediction process models such uncertainty. The route-based energy management presented in this paper uses the Pontryagin Minimum Principle (PMP). A PMP strategy was implemented in a Simulink controller for a model of Prius-like PHEV and compared to a baseline strategy using Autonomie, an automotive modeling environment. An itinerary was defined, and several speed profiles were synthesized. It was then possible to evaluate the sensitivity of PMP tuning to the speed profile, providing insights about the applicability of PMP control in real-world situations.

Electric Drive Vehicle Development and Evaluation Using System Simulation

Abstract To reduce development time and introduce technologies faster to the market, many companies have been moving to Model-based System Engineering (MBSE). In MBSE, the development process centers around a multi-physics model of the complete system being developed, from requirements to design, implementation and test. Engineers can avoid a generation of system design processes based on hand coding, and use graphical models to design, analyze, and implement the software that determines system performance and behavior. This paper describes the process implemented in Autonomie, a Plug-and-Play Software Environment, to design and evaluate electric drive powertrain and component technologies in a multi-physics environment. We will discuss best practices and provide examples of the different steps of the V-diagram including model-in-the-loop, software-in-the-loop and component-in-the-loop simulation.

Using trip information for PHEV fuel consumption minimization

When driven past their all-electric range, plug-in hybrid vehicles (PHEVs) must use their engines. Numerous theoretical studies showed that the conventional control strategy, i.e. all-electric mode followed by a charge-sustaining mode, is not the most energy-efficient control strategy. Better strategies require knowledge of the trip ahead. In this paper, we present a method of predicting a trip for a given itinerary (vehicle speed, stop time, and grade) defined by using a geographical information system (GIS). For each segment of the itinerary, a vehicle speed profile is generated through a Markov process, defined by transition probabilities extracted from a large database of real-world trip records. Ten trip predictions are then generated from a single itinerary for evaluation of an optimal control strategy for a short-range power-split PHEV by using Autonomie, a powertrain modeling environment. The baseline controller uses rules and optimal operating point look-up tables when in charge-sustaining mode. The optimal controller uses the Pontryagins Minimization Principle (PMP), the performance of which heavily depends on the choice of one scalar parameter, the equivalence factor. Finally, we demonstrate the fuel-saving potential of the PMP controller, using the aforementioned trip predictions.

Impact of Advanced Technologies on Medium-Duty Trucks Fuel Efficiency

Rising fuel costs, increased regulations, and heightened customer sensitivity to energy efficiency has prompted the evaluation of numerous powertrain technology improvements to introduce into production. The actual impact of such technologies can differ broadly, depending on the technology or application. To evaluate the fuel consumption impact, various baseline vehicles have been created and simulated by using Argonne National Laboratorys vehicle modeling and simulation tool, the Powertrain Systems Analysis Toolkit (PSAT). This paper provides a quantitative evaluation of several technologies or combinations of technologies. First, we assess the impact of single technologies, including vehicle/chassis characteristics, such as weight, aerodynamics, or rolling resistance. Next, we consider advanced powertrain technologies, ranging from dieselization to transmissions with a higher gear number, and hybridization. Finally, we examine the effects of combining each technology, such as aerodynamics and tire improvements.

Maximization of Platoon Formation Through Centralized Routing and Departure Time Coordination

Platooning allows vehicles to travel with a small intervehicle distance in a coordinated fashion because of vehicle-to-vehicle connectivity. When applied at a larger scale, platooning creates significant opportunities for energy savings because of reduced aerodynamic drag, as well as increased road capacity and a reduction in congestion resulting from shorter vehicle headways. These potential savings are maximized, however, if platooning-capable vehicles spend most of their travel time within platoons. Ad hoc platoon formation may not ensure a high rate of platoon driving. This paper considers the problem of central coordination of platooning-capable vehicles. Coordination of their routes and departure times can maximize the fuel savings afforded by platooning vehicles. The resulting problem is a combinatorial optimization problem that considers the platoon coordination and vehicle routing problems simultaneously. The methodology is demonstrated through evaluation of the benefits of a coordinated solution and comparison with the uncoordinated case when platoons form only in an ad hoc manner. The coordinated and uncoordinated scenarios are compared on a grid network with various assumptions about demand and the time vehicles are willing to wait.

Assessing the energy impact of traffic management and vehicle hybridisation

We provide a review of methodologies previously used to evaluate impacts of transportation systems and changes in transportation infrastructure on energy consumption. We present a new framework that allows estimating the energy impacts of managed traffic lanes in the context of vehicle automation. The presented framework relies on two major components, an integrated transportation system simulator and a powertrain simulator. For the transportation system simulator we propose using integrated transportation system simulator POLARIS. For the powertrain simulator we use AUTONOMIE, a tool funded by the US Department of Energy. Both tools are developed at Argonne National Laboratory. We demonstrate our approach by modelling a transportation corridor along a major highway. Two scenarios are considered, unmanaged, when both trucks and cars use all the lanes of the highway and managed, under which one of the highway lanes is a dedicated lane for truck traffic and trucks are forming platoons using adaptive cruise control technology. We provide the numerical results of the experiment at the end of the paper. We also present the impact of vehicle hybridisation combined with automation on the energy consumption.