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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Integrated Optimization of Powertrain Energy Management and Vehicle Motion Control for Autonomous Hybrid Electric Vehicles

Hybrid Electric Vehicles (HEVs) and autonomous vehicles have been widely studied recently for on-road transportation. In the study of autonomous HEVs, the control of the vehicle's external dynamics and powertrain dynamics are often treated separately. Optimizing these two problems together can significantly improve fuel economy. In this paper, an autonomous HEV following a leader is considered. First, the augmented model to integrate the abovementioned dynamics is presented. Second, the optimization problem is defined to find the optimum fuel consumption of the follower in pursuit of a leader in a drive cycle. A customized control strategy based on Approximate Dynamic Programming (ADP) is then explored in which the optimal cost-to-go at each time step is approximated using neural networks. Also, the accuracy of the optimization solution is enhanced by applying the concept of the reachable sets. At last, three case studies show that the examined integrated control strategy outperforms the one with the separated optimization method by an additional 7.4%, 4.6%, and 11.8% improvement in fuel consumption, respectively.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1997

Hybrid Turbine Electric Vehicle

Hybrid electric power trains may revolutionize today's ground passenger vehicles by significantly improving fuel economy and decreasing emissions. The NASA Lewis Research Center is working with industry, universities, and Government to develop and demonstrate a hybrid electric vehicle. Our partners include Bowling Green State University, the Cleveland Regional Transit Authority, Lincoln Electric Motor Division, the State of Ohio's Department of Development, and Teledyne Ryan Aeronautical. The vehicle will be a heavy class urban transit bus offering double the fuel economy of today's buses and emissions that are reduced to 1/10th of the Environmental Protection Agency's standards. At the heart of the vehicle's drive train is a natural-gas-fueled engine. Initially, a small automotive engine will be tested as a baseline. This will be followed by the introduction of an advanced gas turbine developed from an aircraft jet engine. The engine turns a high-speed generator, producing electricity. Power from both the generator and an onboard energy storage system is then provided to a variable-speed electric motor attached to the rear drive axle. An intelligent power-control system determines the most efficient operation of the engine and energy storage system.

Viterna, Larry A.↗
NASA NTRS1992

Electric and hybrid electric vehicle study utilizing a time-stepping simulation

The applicability of NASA's advanced power technologies to electric and hybrid vehicles was assessed using a time-stepping computer simulation to model electric and hybrid vehicles operating over the Federal Urban Driving Schedule (FUDS). Both the energy and power demands of the FUDS were taken into account and vehicle economy, range, and performance were addressed simultaneously. Results indicate that a hybrid electric vehicle (HEV) configured with a flywheel buffer energy storage device and a free-piston Stirling convertor fulfills the emissions, fuel economy, range, and performance requirements that would make it acceptable to the consumer. It is noted that an assessment to determine which of the candidate technologies are suited for the HEV application has yet to be made. A proper assessment should take into account the fuel economy and range, along with the driveability and total emissions produced.

Schreiber, Jeffrey G.↗
DOE OSTI2020

Vehicle Platooning: An Energy Consumption Perspective

Urban traffic congestion is a chronic problem faced by many cities in the US and worldwide. It results in inefficient infrastructure use as well as increased vehicle fuel consumption and emission levels. Excessive fuel consumptions add extra costs to commuters as well as transportation businesses. Consuming less fuel and thus reducing costs by a single percentage digit can have a significant impact on the balance sheet as well as the protection of the environment. Researchers have developed, and continue to develop, tools and systems to optimize the operations of fleets as well as engines in order to burn less fuel and therefore generate less CO2 emissions. Platooning is one such tool that attempts to maintain relatively small distances (i.e. pre-determined time gap) between consecutive vehicles. It has the potential to increase the capacity of the road as well as reduce the consumed fuel. In this paper, we use a fuel consumption model for internal combustion light-duty vehicles, electric vehicles, hybrid electric vehicles, buses and trucks in order to determine and quantify the effects of platooning on a fleet fuel consumption. The results suggest that a reduction of up to 3%, 3.5%, 4.5 %, 10%, and 15% in fuel consumption can be achieved for internal combustion engine vehicles, hybrid electric vehicles, electric vehicles, buses and trucks.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Downloadable Dynamometer Database (D3): Public Test Data on Advanced-Technology Vehicles

Access to high-quality, independent vehicle test data is critical to advancing energy-efficient transportation research. The Downloadable Dynamometer Database (D3) is a public repository of dynamometer test data on advanced-technology vehicles, generated at the Advanced Mobility Technology Laboratory (AMTL) at Argonne National Laboratory and hosted by the Transportation and Power Systems Division. The database has been made available to support researchers, students, and professionals engaged in energy-efficient vehicle research, development, and education. A wide range of vehicle categories has been tested (i.e., alternative fuel vehicles, conventional gasoline and diesel vehicles, all-electric vehicles, hybrid electric vehicles, and plug-in hybrid electric vehicles), as well as various drive cycles and test conditions documented in the accompanying D3 user presentation. Stakeholders can select a vehicle type, identify a vehicle of interest, and download the associated test data for use in their own analyses. Data downloaded from D3 must be accompanied by the required attribution: "This data is from the Downloadable Dynamometer Database and was generated at the Advanced Mobility Technology Laboratory (AMTL) at Argonne National Laboratory." These data are critical to vehicle modeling, validation, technology assessment, and educational use.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1985

Power Train for Hybrid Electric Vehicle

Power train of hybrid electric vehicle enables internal-combustion engine and electric motor to operate near same constant speed except when off or idling.

Vivian, H. C.↗
DOE OSTI2023

Power management for hybrid electric vehicles

A system and method for power management of hybrid electric vehicles is provided. In some implementations, a plug-in series hybrid electric vehicle may include an engine, a motor/generator (MG), a traction motor, an energy storage device, and a controller. The controller is coupled to the engine and the MG to control operation of the engine and the MG such that a state-of-charge (SOC) of the energy storage device tracks a dynamic reference SOC profile during a trip and an average engine power (AEP) is maintained above a threshold. In some instances, maintaining AEP above a threshold supports emission control of the vehicle.

Le, Dat Duc↗
DOE OSTI2023

Hybrid electric vehicle and method of operating engine of the same

A hybrid electric vehicle (HEV) for multiple operation modes includes: a gasoline diffusion flame (GDF) combustion engine configured to perform gasoline diffusion flame combustion; a motor-generator operatively connected to the GDF combustion engine and configured to selectively drive the HEV with electric power of a battery or generate electric power to charge the battery; and a multi-mode controller including a processor and configured to receive operating conditions of the GDF combustion engine and the motor-generator and define a plurality of mode operating regions based on the received operating conditions. In particular, the plurality of mode operating regions includes: an electric vehicle (EV) only mode operating region, a GDF mode operating region where the GDF combustion engine operates and drives the HEV while the motor-generator stops, and a GDF+EV mode operating region where the motor-generator assists the operation of the GDF combustion engine to drive the HEV.

Bourcier, Mark↗
DOE OSTI2021

Co-Optimization Scheme for Hybrid Electric Vehicles Powertrain and Exhaust Emission Control System Using Future Speed Prediction

Hybrid electric vehicles (HEVs) have been an effective solution for improved vehicle fuel efficiency and reduced emission pollution. In this paper, a co-optimization scheme is proposed to optimize fuel efficiency for HEVs. Here, the proposed optimization scheme uses obtainable future speed prediction as the basis to optimally tune control parameters for the existing powertrain control system. The ramp-up time of the catalyst temperature to reach its light-off level in the exhaust emission system is also considered as an additional optimization constraint to reduce emission. The Toyota Prius Hybrid Simulink model which is an integrated model for a powertrain and exhaust emission system is validated using real data from several real driving cycle scenarios. Then, to simplify the formulation of the proposed algorithm, the model for the optimization for powertrain and exhaust emission systems is represented by a set of equivalent neural network (NN) models learned using the data generated from the well-validated Toyota Prius Hybrid Simulink model. Using NN models, a co-optimization algorithm is established that provides an optimal tuning of some fuel-sensitive powertrain control parameters using future speed prediction, leading to a novel co-optimization algorithm, achieving on average a further 9.22% fuel savings for the Toyota Prius Hybrid Simulink model.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

China's plug-in hybrid electric vehicle transition: An operational carbon perspective

Assessing the emissions of plug-in hybrid electric vehicle (PHEV) operations is crucial for accelerating the carbon–neutral transition in the passenger car sector. This study is the first to adopt a bottom-up model to measure the real-world energy use and carbon dioxide emissions of China’s top twenty selling PHEV models across different regions from 2020 to 2022. The results indicate that (1) the actual electricity intensity of the best-selling PHEV models (20.2–38.2 kWh/100 km) was 30–40 % higher than the New European Driving Cycle values, and the actual gasoline intensity (4.7–23.5 L/100 km) was 3–6 times greater than the New European Driving Cycle values. (2) The overall energy use of the best-selling models varied among different regions, and the energy use from 2020 to 2022 in Southern China was double that Northern China and the Yangtze River Middle Reach. (3) The top-selling models emitted 4.7 megatons of carbon dioxide nationwide from 2020 to 2022, with 1.9 megatons released by electricity consumption and 2.8 megatons released by gasoline combustion. Furthermore, targeted policy implications for expediting the carbon–neutral transition within the passenger car sector are proposed. In essence, this study explores and compares benchmark data at both the national and regional levels, along with performance metrics associated with PHEV operations. The main objective is to aid nationwide decarbonization efforts, focusing on carbon reduction and promoting the rapid transition of road transportation toward a net-zero carbon future.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Developing a Hybrid Electric Vehicle Eco-Cooperative Adaptive Cruise Control System at Signalized Intersections.

This study develops an eco-driving strategy for hybrid electric vehicles (HEVs) in the vicinity of signalized intersections, entitled HEV Eco-Cooperative Adaptive Cruise Control at Intersections (Eco-CACC-I). The proposed system computes real-time, energy-optimized vehicle trajectories using HEV vehicle dynamics and energy consumption models. In the proposed system, a simple HEV energy model is used to compute the instantaneous fuel consumption. This HEV energy model is selected since it is general, transferable, and can be easily used to compute instantaneous energy consumption levels for HEVs without the additional input of vehicle engine data or complicated power control strategies. In addition, a vehicle dynamics model is used to capture the relationship between speed, acceleration level, and tractive/resistance forces on vehicles. The energy-optimum problem is formulated as an optimization problem with constraints, which is solved using a moving-horizon dynamic programming approach. The proposed HEV Eco-CACC-I system was tested to evaluate its performance for various speed limits, roadway grades, and signal timings. Lastly, the proposed HEV controller was implemented in a microscopic traffic simulation software to test its network-wide performance. The test results from an arterial corridor with three signalized intersections demonstrate that the proposed system can effectively reduce stop-and-go traffic in the vicinity of signalized intersections producing savings of 7.4% in energy consumption, 5.8% in traffic delay and 23% vehicle stops, respectively.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

A Multirange Vehicle Speed Prediction With Application to Model Predictive Control-Based Integrated Power and Thermal Management of Connected Hybrid Electric Vehicles

Abstract Connectivity and automated driving technologies have opened up new research directions in the energy management of vehicles which exploit look-ahead preview and enhance the situational awareness. Despite this advancement, the vehicle speed preview that can be obtained from vehicle-to-vehicle/infrastructure (V2V/I) communications is often limited to a relatively short time-horizon. The vehicular energy systems, specifically those of the electrified vehicles, consist of multiple interacting power and thermal subsystems that respond over different time-scales. Consequently, their optimal energy management can greatly benefit from long-term speed prediction beyond that available through V2V/I communications. Accurately extending the look-ahead preview, on the other hand, is fundamentally challenging due to the dynamic nature of the traffic environment. To address this challenge, we propose a data-driven multirange vehicle speed prediction strategy for arterial corridors with signalized intersections, providing the vehicle speed preview for three different ranges, i.e., short-, medium-, and long-range. The short-range preview is obtained by V2V/I communications. The medium-range preview is realized using a neural network (NN), while the long-range preview is predicted based on a Bayesian network (BN). The predictions are updated in real-time based on the current state of traffic and incorporated into a multihorizon model predictive control (MH-MPC) for integrated power and thermal management (iPTM) of connected vehicles. The results of design and evaluation of the performance of the proposed data-informed MH-MPC for iPTM of connected hybrid electric vehicles (HEVs) using traffic data for real-world city driving are reported.

Automation & Control Systems↗
DOE OSTI2021

Development and Evaluation of Velocity Predictive Optimal Energy Management Strategies in Intelligent and Connected Hybrid Electric Vehicles

In this study, a thorough and definitive evaluation of Predictive Optimal Energy Management Strategy (POEMS) applications in connected vehicles using 10 to 20 s predicted velocity is conducted for a Hybrid Electric Vehicle (HEV). The presented methodology includes synchronous datasets gathered in Fort Collins, Colorado using a test vehicle equipped with sensors to measure ego vehicle position and motion and that of surrounding objects as well as receive Infrastructure to Vehicle (I2V) information. These datasets are utilized to compare the effect of different signal categories on prediction fidelity for different prediction horizons within a POEMS framework. Multiple artificial intelligence (AI) and machine learning (ML) algorithms use the collected data to output future vehicle velocity prediction models. The effects of different combinations of signals and different models on prediction fidelity in various prediction windows are explored. All of these combinations are ultimately addressed where the rubber meets the road: fuel economy (FE) enabled from POEMS. FE optimization is performed using Model Predictive Control (MPC) with a Dynamic Programming (DP) optimizer. FE improvements from MPC control at various prediction time horizons are compared to that of full-cycle DP. All FE results are determined using high-fidelity simulations of an Autonomie 2010 Toyota Prius model. The full-cycle DP POEMS provides the theoretical upper limit on fuel economy (FE) improvement achievable with POEMS but is not currently practical for real-world implementation. Perfect prediction MPC (PP-MPC) represents the upper limit of FE improvement practically achievable with POEMS. Real-Prediction MPC (RP-MPC) can provide nearly equivalent FE improvement when used with high-fidelity predictions. Constant-Velocity MPC (CV-MPC) uses a constant speed prediction and serves as a “null” POEMS. Results showed that RP-MPC, enabled by high-fidelity ego future speed prediction, led to significant FE improvement over baseline nearly matching that of PP-MPC.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Co-Optimization of Velocity and Charge-Depletion for Plug-In Hybrid Electric Vehicles: Accounting for Acceleration and Jerk Constraints

Abstract Recent advances in vehicle connectivity and automation technologies promote advanced control algorithms that co-optimize the longitudinal dynamics and powertrain operation of hybrid electric vehicles. Typically, a sequential optimization with the vehicle dynamics optimized followed by powertrain optimization is adopted to manage a number of complexities such as the inherent mixed-integer nature of the hybrid powertrain, the numerous state and control variables, the differing time scales of vehicle and powertrain subsystems, time-varying state constraints, and large horizon lengths. Instead, we solve the offline optimization problem in a centralize manner assuming exact knowledge of the lead vehicle's position over the entire trip by applying a discrete-time single shooting-based numerical approach, Discrete Mixed-Integer Shooting (DMIS), including a linearly increasing computational complexity to the problem horizon. In particular, the hierarchical problem structure is exploited to decompose the computationally intensive Hamiltonian minimization step into a set of low-dimensional optimizations. DMIS allows us to compute the direct fuel minimization problem including the vehicle and powertrain dynamics in a centralized manner to its full horizon while systematically tuning weighting factors that penalize passenger discomfort. For the first time, this study reveals that practically implemented sequential optimization exhibits similar fuel optimality as co-optimization when a certain level of passenger comfort is required.

Automation & Control Systems↗
DOE OSTI2021

A Component-Sizing Methodology for a Hybrid Electric Vehicle Using an Optimization Algorithm

Many leading companies in the automotive industry have been putting tremendous effort into developing new powertrains and technologies to make their products more energy efficient. Evaluating the fuel economy benefit of a new technology in specific powertrain systems is straightforward; and, in an early concept phase, obtaining a projection of energy efficiency benefits from new technologies is extremely useful. However, when carmakers consider new technology or powertrain configurations, they must deal with a trade-off problem involving factors such as energy efficiency and performance, because of the complexities of sizing a vehicle’s powertrain components, which directly affect its energy efficiency and dynamic performance. As powertrains of modern vehicles become more complicated, even more effort is required to design the size of each component. This study presents a component-sizing process based on the forward-looking vehicle simulator “Autonomie” and the optimization algorithm “POUNDERS”; the supervisory control strategy based on Pontryagin’s Minimum Principle (PMP) assures sufficient computational system efficiency. We tested the process by applying it to a single power-split hybrid electric vehicle to determine optimal values of gear ratios and each component size, where we defined the optimization problem as minimizing energy consumption when the vehicle’s dynamic performance is given as a performance constraint. The suggested sizing process will be helpful in determining optimal component sizes for vehicle powertrain to maximize fuel efficiency while dynamic performance is satisfied. Indeed, this process does not require the engineer’s intuition or rules based on heuristics required in the rule-based process.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS2018

NASA Electric Aircraft Testbed Single-Aisle Transport Air Vehicle Hybrid Electric Tail-Cone Thruster Powertrain Configuration and Test Results

A key technical challenge is to establish a viable concept for a MW-class hybrid gas-electric propulsion system for a commercial transport aircraft. This includes developing aircraft propulsion system conceptual designs, integrating sub-systems, high efficiency/power density electric machines, flight-weight power system and electronics, and enabling materials in high voltage insulation, high frequency soft magnetics, and conductors. The primary benefit of this research is to diversify the current turbofan propulsion options to include hybrid electric propulsion elements that reduce energy usage, emissions, and noise.

Dyson, Rodger↗
DOE OSTI2022

Ecological Adaptive Cruise Control of Plug-In Hybrid Electric Vehicle With Connected Infrastructure and On-Road Experiments

Abstract This paper examines both mathematical formulation and practical implementation of an ecological adaptive cruise controller (ECO-ACC) with connected infrastructure. Human errors are typical sources of accidents in urban driving, which can be remedied by rigorous control theories. Designing an ECO-ACC is, therefore, a classical research problem to improve safety and energy efficiency. We add two main contributions to the literature. First, we propose a mathematical framework of an online ECO-ACC for plug-in hybrid electric vehicle (PHEV). Second, we demonstrate ECO-ACC in a real world, which includes other human drivers and uncertain traffic signals on a 2.6 (km) length of the corridor with eight signalized intersections in Southern California. The demonstration results show, on average, 30.98% of energy efficiency improvement and 8.51% additional travel time.

Automation & Control Systems↗