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In order to reduce fuel consumption, companies have been looking at hybridizing vehicles. So far, two main hybridization options have been considered: electric and hydraulic hybrids. Because of light duty vehicle operating conditions and the high energy density of batteries, electric hybrids are being widely used for cars. However, companies are still evaluating both hybridization options for medium and heavy duty vehicles. Trucks generally demand very large regenerative power and frequent stop-and-go. In that situation, hydraulic systems could offer an advantage over electric drive systems because the hydraulic motor and accumulator can handle high power with small volume capacity. This study compares the fuel displacement of class 6 trucks using a hydraulic system compared to conventional and hybrid electric vehicles. The paper will describe the component technology and sizes of each powertrain as well as their overall vehicle level control strategies.

This paper presents a unified modeling environment to simulate vehicle driving and powertrain operations within the context of the surrounding environment, including interactions between vehicles and between vehicles and the road. The goal of this framework is to facilitate the analysis of the energy impacts of vehicle connectivity and automation, as well as the development of eco-driving algorithms. Connectivity and automation indeed provide the potential to use information about the environment and future driving to minimize energy consumption. To achieve this goal, the designers of eco-driving control strategies need to simulate a wide range of driving situations, including the interactions with other vehicles and the infrastructure in a closed-loop fashion.

The Prius - a power-split hybrid electric vehicle from Toyota - has become synonymous with the word “Hybrid.” As of October 2010, two million of these vehicles had been sold worldwide, including one million vehicles purchased in the United States. In 2004, the second generation of the vehicle, the Prius MY04, enhanced the performance of the components with advanced technologies, such as a new magnetic array in the rotors. However, the third generation of the vehicle, the Prius MY10, features a remarkable change of the configuration - an additional reduction gear has been added between the motor and the output of the transmission [1]. In addition, a change in the energy management strategy has been found by analyzing the results of a number of tests performed at Argonne National Laboratory's Advanced Powertrain Research Facility (ARRF).

The first commercially available plug-in hybrid electric vehicle (PHEV), the General Motors (GM) Volt, was introduced into the market in mid-December 2010. The Volt uses a series-split powertrain architecture, which provides benefits over the series architecture that typically has been considered for use in electric-range extended vehicles (EREVs). A specialized EREV powertrain, called the Voltec, drives the Volt through its entire range of speed and acceleration with battery power alone and within the limit of battery energy, thereby displacing more fuel with electricity than a PHEV, which characteristically blends electric and engine power together during driving. This paper assesses the benefits and drawbacks of these two different plug-in hybrid electric architectures (series versus series-split) by comparing component sizes, system efficiency, and fuel consumption over urban and highway drive cycles.

The control analysis and model validation of a 2014 BMW i3-Range Extender (REX) was conducted based on the test data in this study. The vehicle testing was performed on a chassis dynamometer set within a thermal chamber at the Advanced Powertrain Research Facility at Argonne National Laboratory. The BMW i3-REX is a series-type plug-in hybrid range extended vehicle which consists of a 0.65L in-line 2-cylinder range-extending engine with a 26.6kW generator, 125kW permanent magnet synchronous AC motor, and 18.8kWh lithium-ion battery. Both component and vehicle model including thermal aspects, were developed based on the test data. For example, the engine fuel consumption rate, battery resistance, or cabin HVAC energy consumption are affected by the temperature. Second, the vehicle-level control strategy was analyzed at normal temperature conditions (22°C ambient temperature). The analysis focuses on the engine on/off strategy, battery SOC balancing, and engine operating conditions.

This paper includes analysis results for the control strategy of the Peugeot 3008 Hybrid4, a diesel-electric hybrid vehicle, under different thermal conditions. The analysis was based on testing results obtained under the different thermal conditions in the Advanced Powertrain Research Facility (APRF) at Argonne National Laboratory (ANL). The objectives were to determine the principal concepts of the control strategy for the vehicle at a supervisory level, and to understand the overall system behavior based on the concepts. Control principles for complex systems are generally designed to maximize the performance, and it is a serious challenge to determine these principles without detailed information about the systems. By analyzing the test results obtained in various driving conditions with the Peugeot 3008 Hybrid4, we tried to figure out the supervisory control strategy.

This paper presents the vehicle model development and validation process for the Ford Focus battery electric vehicles (BEVs) using Autonomie and test results from Advanced Powertrain Research Facility in Argonne National Laboratory. The parameters or characteristic values for the important components such as the electric machine and battery pack system are estimated through analyzing the test data of the multi cycle test (MCT) procedure under the standard ambient condition. A novel process was used to import vehicle test data into Autonomie. Through this process, a complete vehicle model of the Ford Focus BEV is developed and validated under ambient temperature for different drive cycles (UDDS, HWFET, US06 and Steady-State). The simulation results of the developed vehicle model show coincident results with the test data within 0.5% ∼ 4% discrepancies for electrical consumption.

Their easy availability, lower well-to-wheel emissions, and relative ease of use with existing engine technologies have made ethanol and ethanol-gasoline blends a viable alternative to gasoline for use in spark-ignition (SI) engines. The lower energy density of ethanol and ethanol-gasoline blends, however, results in higher volumetric fuel consumption compared with gasoline. Also, the higher latent heat of vaporization can result in cold-start issues with higher-level ethanol blends. On the other hand, a higher octane number, which indicates resistance to knock and potentially enables more optimal combustion phasing, results in better engine efficiency, especially at higher loads. This paper compares the fuel consumption and emissions of two ethanol blends (E50 and E85) with those for gasoline when used in conventional (non-hybrid) and power-split-type plug-in hybrid electric vehicles (PHEVs).

Fuel cell vehicles are undergoing extensive research and development because of their potential for high efficiency and low emissions. Because fuel cell vehicles remain expensive and there is limited demand for hydrogen at present, very few fueling stations are being built. To try to accelerate the development of a hydrogen economy, some original equipment manufacturers in the automotive industry have been working on a hydrogen-fueled internal combustion engine (ICE) as an intermediate step. This paper compares the fuel economy potential of hydrogen powertrains to conventional gasoline vehicles. Several timeframes are considered: 2010, 2015, 2030, and 2045. To address the technology status uncertainty, a triangular distribution approach was implemented for each component technology. The fuel consumption and cost of five powertrain configurations will be discussed and compared with the conventional counterpart.

In recent studies, optimal control has shown promise as a strategy for enhancing the energy efficiency of connected autonomous vehicles. To maximize optimization performance, it is important to accurately predict constraints, especially separation from a vehicle in front. This paper proposes a novel prediction method for forecasting the trajectory of the nearest preceding car. The proposed predictor is designed to produce short to medium-length speed trajectories using a locally weighted polynomial regression algorithm. The polynomial coefficients are trained by using two types of information: (1) vehicle-to-vehicle (V2V) messages transmitted by multiple preceding vehicles and (2) vehicle-to-infrastructure (V2I) information broadcast by roadside equipment. The predictor’s performance was tested in a multi-vehicle traffic simulation platform, RoadRunner, previously developed by Argonne National Laboratory.

Plug-in Hybrid Electric Vehicles (PHEVs) have demonstrated the potential to provide significant reduction in fuel use across a wide range of dynamometer test driving cycles. Companies and research organizations are involved in numerous research activities related to PHEVs. One of the current unknowns is the impact of driving behavior and standard test procedure on the true benefits of PHEVs from a worldwide perspective. To address this issue, five different PHEV powertrain configurations (input split, parallel, series, series-output split and series-parallel), implemented on vehicles with different all-electric ranges (AERs), were analyzed on three different standard cycles (i.e., Urban Dynamometer Driving Schedule, Highway Fuel Economy Test, and New European Driving Cycle). Component sizes, manufacturing cost, and fuel consumption were analyzed for a midsize car in model year 2020 through the use of vehicle system simulations.

This study presents a process to quantify the fuel saving potential of electrified powertrains for medium and heavy duty vehicles. For this study, equivalent vehicles with electrified powertrains are designed with the underlying principle of not compromising on cargo carrying capacity or performance. Several performance characteristics, that are relevant for all types of medium and heavy duty vehicles, were identified for benchmarking based on the feedback from the industry. Start-stop hybrids, parallel pre-transmission hybrids, plug-in hybrids, and battery electric vehicles are the technology choices in this study. This paper uses one vehicle as an example, explains the component sizing process followed for each powertrain, and examines each powertrain’s fuel saving potential. The process put forth in this paper can be used for evaluating vehicles that belong to all medium and heavy duty classes.

This paper introduces an eco-driving highway cruising algorithm based on optimal control theory that is applied to a conventionally-powered connected and automated vehicle. Thanks to connectivity to the cloud and/or to infrastructure, speed limit and slope along the future route can be known with accuracy. This can in turn be used to compute the control variable trajectory that will minimize energy consumption without significantly impacting travel time. Automated driving is necessary to the implementation of this concept, because the chosen control variables (e.g., torque and gear) impact vehicle speed. An optimal control problem is built up where quadratic models are used for the powertrain. The optimization is solved by applying Pontryagin’s minimum principle, which reduces the problem to the minimization of a cost function with parameters called co-states.

The establishment of Corporate Average Fuel Economy (CAFE) standards by the Energy Policy and Conservation Act (EPCA) of 1975 marked a pivotal moment in the automotive industry's pursuit of greater fuel efficiency. The responsibility for the development and enforcement of these standards was assigned to the U.S. Department of Transportation (DOT), with the National Highway Traffic Safety Administration (NHTSA) assuming a critical role in their oversight and implementation. In collaboration with Argonne National Laboratory (Argonne), supported by the U.S. Department of Energy (DOE), significant strides have been made in advancing fuel efficiency through the development of Autonomie, a leading full-vehicle simulation tool. Through an Inter-Agency Agreement between the DOE Argonne Site Office and Argonne, comprehensive full-vehicle simulations are conducted to support NHTSA's CAFE rulemaking processes.

Near-term advances in spark ignition (SI) engine technology (e.g., variable value lift [VVL], gasoline direct injection [GDI], cylinder deactivation, turbo downsizing) for passenger vehicles hold promise of delivering significant fuel savings for vehicles of the immediate future. Similarly, trends in transmissions indicate higher (8-speed, 9-speed) gear numbers, higher spans, and a focus on downspeeding to improve engine efficiency. Dual-clutch transmissions, which exhibit higher efficiency in lower gears, than the traditional automatics, and are being introduced in the light-duty vehicle segment worldwide. Another development requiring low investment and delivering immediate benefits has been the adaptation of start-stop (micro hybrids or idle engine stop technology) technology in vehicles today.

Connectivity and automation are increasingly being developed for cars and trucks, aiming to provide better safety and better driving experience. As these technologies mature and reach higher adoption rates, they will also have an impact on the energy consumption: Connected and Automated Vehicles (CAVs) may drive more smoothly, stop less often, and move at faster speeds, thanks to overall improvements to traffic flows. These potential impacts are not well studied, and any existing studies tend to focus solely on conventional engine-powered cars, leaving aside electrified vehicles such as Hybrid Electric Vehicles (HEVs) and Battery Electric Vehicles (BEVs). This work intends to address this issue by analyzing the energy impact of various CAV scenarios on different types of electric vehicles using high-fidelity models. The vehicles-all midsize, one HEV, one BEV, and a conventional-are modeled in Autonomie, a high-fidelity, forward-looking vehicle simulation tool.

In support of the U.S Department of Energy's Vehicle Technologies Program, numerous vehicle technology combinations have been simulated using Autonomie. Argonne National Laboratory (Argonne) designed and wrote the Autonomie modeling software to serve as a single tool that could be used to meet the requirements of automotive engineering throughout the development process, from modeling to control, offering the ability to quickly compare the performance and fuel efficiency of numerous powertrain configurations. For this study, a multitude of vehicle technology combinations were simulated for many different vehicles classes and configurations, which included conventional, power split hybrid electric vehicle (HEV), power split plug-in hybrid electric vehicle (PHEV), extended-range EV (E-REV)-capability PHEV, series fuel cell, and battery 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 rule-based 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.

This paper describes the integration of a Modal Energy and Emissions Model (MEEM) into a hybrid-electric vehicle simulation model, the PNGV System Analytic Toolkits (PSAT). PSAT is a forward-looking computer simulation model for advanced-technology vehicles. MEEM is a vehicle fuel-consumption and emissions model developed by one of the authors for internal-combustion-engine (ICE) -powered vehicles. MEEM engine simulation module uses a power-demand physical model based on a parameterized analytical representation of engine fuel and emissions production. One major advantage of MEEM is that it does not rely on steady-state engine maps, which are usually not available for most production vehicles; rather, it depends on a list of engine parameters that are calibrated based on regular vehicle dynamometer testing. The integrated PSAT-MEEM model can be used effectively to predict fuel consumption and emissions of various ICE-powered vehicles with both conventional and hybrid power trains.

This paper presents the validation of an entire vehicle model of the Honda Accord Plug-in Hybrid Electric Vehicle (PHEV), which has a new powertrain system that can be driven in both series and parallel hybrid drive using a clutch, including thermal aspects. The Accord PHEV is a series-parallel PHEV with about 21 km of all-electric range and no multi-speed gearbox. Vehicle testing was performed at Argonne’s Advanced Powertrain Research Facility on a chassis dynamometer set in a thermal chamber. First, components (engine, battery, motors and wheels) were modeled using the test data and publicly available assumptions. This includes calibration of the thermal aspects, such as engine efficiency as a function of coolant temperature. In the second phase, the vehicle-level control strategy, especially the energy management, was analyzed in normal conditions in both charge-depleting and charge-sustaining modes.