NASA's Open Source Electrified Aircraft Propulsion Modeling and Simulation Tools
Overview of NASA’s Open Source Electrified Aircraft Propulsion Modeling and Simulation Tools.
Electrified powertrain modeling↗
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Evaluating Connected and Automated Vehicles in Co-Simulation Environment of Traffic Microsimulation and Vehicle Dynamics
Connected and automated vehicles (CAVs) have the potential to improve many aspects of the current transportation systems such as safety, mobility, and energy efficiency. In order to evaluate the benefits and impacts of a CAV, the CAV control algorithm is typically implemented on vehicles simulated in a traffic microsimulation environment. However, traffic microsimulation usually lacks detailed vehicle and powertrain dynamics, making it challenging to fully understand how a CAV control algorithm will perform and respond on an actual vehicle. Whether the same benefits measured in the simulation will also be observed in real-world remains an open question. One potential approach to fill in this gap is to conduct a co-simulation of traffic microsimulation with detailed vehicle and powertrain dynamics models, often developed in MATLAB Simulink. However, current microsimulation tools such as VISSIM and SUMO do not have a ready-to-use interface for co-simulation with vehicle dynamics and Simulink. Also, even if such an interface exists, it will be tool-specific, making it challenging to shift from one tool to another or test CAV controls in different tools. There are needs for tool-agnostic co-simulation as different microsimulation tools have their pros and cons, and researchers often need to use different tools based on the purposes of the simulation, project needs, and applications. In this work, Flexible Interface for X-in-the-loop Simulation (FIXS) is developed that can support the co-simulation of microsimulation, CAV control algorithm, and vehicle dynamics model in Simulink. Enabled by the FIXS, the benefit and performance of a CAV control algorithm can be better understood with the consideration of vehicle responses and dynamics. The connection to VISSIM and SUMO is handled internally by the interface, and users can easily switch tools by changing a configuration file. The co-simulation capability is demonstrated for a VISSIM eco-approach and departure CAV scenario and a SUMO cooperative merging scenario for both a passenger CAV and a class 8 heavy-duty connected and automated trucks.
Shao, Yunli↗
Validation and Calibration of Energy Models with Real Vehicle Data from Chassis Dynamometer Experiments
Accurate estimation of vehicle fuel consumption typically requires detailed modeling of complex internal powertrain dynamics, often resulting in computationally intensive simulations. However, many transportation applications-such as traffic flow modeling, optimization, and control-require simplified models that are fast, interpretable, and easy to implement, while still maintaining fidelity to physical energy behavior. This work builds upon a recently developed model reduction pipeline that derives physics-like energy models from high-fidelity Autonomie vehicle simulations. These reduced models preserve essential vehicle dynamics, enabling realistic fuel consumption estimation with minimal computational overhead. While the reduced models have demonstrated strong agreement with their Autonomie counterparts, previous validation efforts have been confined to simulation environments. This study extends the validation by comparing the reduced energy model's outputs against real-world vehicle data. Focusing on the MidSUV category, we tune the baseline Autonomie model to closely replicate the characteristics of a Toyota RAV4. We then assess the accuracy of the resulting reduced model in estimating fuel consumption under actual drive conditions. Our findings suggest that, when the reference Autonomie model is properly calibrated, the simplified model produced by the reduction pipeline can provide reliable, semi-principled fuel rate estimates suitable for large-scale transportation applications.
42 ENGINEERING↗
Energy Consumption and Cost Reduction of Future Light-Duty Vehicles through Advanced Vehicle Technologies: A Modeling Simulation Study Through 2050
The U.S. Department of Energy’s (DOE’s) Vehicle Technologies Office (VTO) and Hydrogen and Fuel Cell Technologies Office (HFTO) aim to develop sustainable, affordable and efficient technologies for transportation of goods and people. Translating investments in advanced transportation component technologies and powertrains to estimate vehicle-level fuel savings potential is critical for understanding DOE’s impact. In this work, we simulated technologies funded by VTO and HFTO for light duty vehicles. The simulations were performed across: Multiple powertrain configurations (i.e., conventional, power-split, extended-range electric vehicle, battery electric drive, and fuel-cell vehicles), Vehicle classes (i.e., compact car, midsize car, small sport utility vehicle [SUV], midsize SUV, and pickup trucks); and Fuels (i.e., gasoline, diesel, hydrogen, and battery electricity). These various technologies are assessed for six different timeframes: laboratory years 2015, 2020, 2025, 2030, and 2045. A delay of 5 years is assumed between laboratory year and model year (year technology is introduced into production). Finally, uncertainties are included for both technology performance and cost aspects by considering two cases: Low case, aligned with DOE technology manager estimates of expected original equipment manufacturer (OEM) improvements based on regulations, business as usual; and High case, aligned with aggressive technology advancements based on R&D targets developed through support by VTO & HFTO. These scenarios are not intended as predictions of future performances. The energy and cost impact of different technologies were estimated using Autonomie (www.autonomie.net), Argonne vehicle system simulation tool. Autonomie is a state-of-the-art vehicle system simulation tool used to assess the energy consumption, performance and cost of multiple advanced vehicle technologies across classes (from light to heavy duty), powertrains (from conventional to HEVs, FCEVs, PHEVs and BEVs), components and control strategies. Autonomie is packaged with a complete set of vehicle models for a wide range of vehicle classes, powertrain configurations and component technologies, including vehicle level and component level controls. These controls were developed and calibrated using dynamometer test data. Autonomie has been used to support a wide range of studies including analyzing various component technologies, sizing powertrains components for different vehicle requirements, comparing the benefits of powertrain configurations, optimizing both heuristic and route based vehicle energy control and predicting transportation energy use when paired with a traffic modeling tool such as POLARIS. This report documents the assumptions and estimates the vehicle-level energy consumption benefits and associated technology costs for the various types of light duty vehicles. All details of vehicle assumptions and simulation results are available in the spreadsheets accompanying this report.
33 ADVANCED PROPULSION SYSTEMS↗
Automobile and Technology Lifecycle-Based Assignment (ATLAS) v2.0.12
ATLAS is a comprehensive vehicle transaction and technology adoption microsimulator. ATLAS evolves the fleet mix of individual households by simulating the transaction (vehicle addition, disposal, and replacement) and choice (vehicle type, vintage, powertrain, and tenure) decisions in response to the co-evolving demographics, land use, and vehicle technology simulations. Different from the existing vehicle models that are either static or aggregated (e.g. stock model), ATLAS is fully disaggregated and dynamic following a sequential and circumstantial decision-making trajectory. This fine-grained approach not only enhances the realism of the simulation but also provides a nuanced understanding of the dynamics inherent in vehicle fleet evolution. ATLAS outputs are fully compatible with subsequent agent-based transportation modeling system and can enable distributional effect analysis regarding the fleet turnover among heterogeneous populations. ATLAS expands the typical new sale focused vehicle choice modeling to including used vehicle transactions that are of increasing interests to understanding the vehicle adoption behavior among lower income households.
Jin, Ling↗
Assessing the National Off-Cycle Benefits of 2-Layer HVAC Technology Using Dynamometer Testing and a National Simulation Framework
Some CO2-reducing technologies have real-world benefits not captured by regulatory testing methods. This paper documents a two-layer heating, ventilation, and air-conditioning (HVAC) system that facilitates faster engine warmup through strategic increased air recirculation. The performance of this technology was assessed on a 2020 Hyundai Sonata. Empirical performance of the technology was obtained through dynamometer tests at Argonne National Laboratory. Performance of the vehicle across multiple cycles and cell ambient temperatures with the two-layer technology active and inactive indicated fuel consumption reduction in nearly all cases. A thermally sensitive powertrain model, the National Renewable Energy Laboratory's FASTSim Hot, was calibrated and validated against vehicle testing data. The developed model included the engine, cabin, and HVAC system controls. Validation of component thermal models and engine efficiency ensured accurate thermal dynamics, fuel consumption, and two-layer benefit. The real-world benefit of the two-layer technology was calculated by simulating the validated powertrain model across a representative test matrix comparing performance with and without the two-layer system. Simulation across the test matrix revealed a real-world representative benefit of 0.0835%. Analysis of test matrix results at the regional level revealed the most benefit in cold climates and rural regions. Mean results across cycle length sensitivity simulations revealed a larger real-world benefit of 0.0872%. These benefit values can be considered a more accurate assessment of real-world technology performance. Future work is planned to explore the requisite number of drive cycles to ensure the full technology benefit is captured.
2-layer↗
LOCOMOTIVES - Comprehensive Impact and Cost Assessment Framework of Carbon Lowering Approaches for the US Rail Freight System
The goal of this project is to develop a tool to aid railroads and other stakeholders assess and approach the decarbonization of freight rail operations by identifying new, viable low-carbon energy storage and conversion systems for future locomotive systems and how they should be deployed on the existing US freight rail network. In the first quarter, the project focused on collecting data, establishing a simulation workflow, and engaging industry through the creation of the Industry Advisory Board (IAB). In the second quarter, the project focused on selecting fuel pathways and powertrain technologies, setting performance targets, conducting a techno-economic analyses, and developing the simulation framework that would serve as the backbone of the future toolhead. The third quarter involved developing an industry-oriented interactive dashboard powered by a five-step sequential framework, as well as holding industry advisory board meetings as per the initial technology-to-market plan. In the remaining project quarters, the NUFRIEND dashboard were fine-tuned with the help of IAB member feedback and in-depth scenario analyses were conducted to support the techno-economic analysis of energy sources. Additionally, dashboard documentation, project insights, and open-source code on GitHub were prepared and released. Throughout the project, the team completed testing and analysis of all model components, integrated all initial test scenarios, and conducted stakeholder engagement. Lower-carbon drop-in fuels can be deployed as admixtures and are considered uniform across the network at a desired penetration rate, while hydrogen and battery-electric technology deployment poses a more complex problem as they require significant investments to be made in the siting of refueling/charging facilities and the replacement of locomotive fleets. Thus, strategies for locating and sizing refueling/charging facilities on a railroad’s network to meet their energy demands were developed to inform deployment decisions. To address this challenge, the Northwestern University Freight Rail Infrastructure & Energy Network Decarbonization (NUFRIEND) framework presents a five-step sequential framework to select O-D paths, locate facilities, reroute flows, size facilities, and evaluate the deployment for alternative energy sources that require locomotive powertrains to be converted and new refueling infrastructure to be deployed. The NUFRIEND Framework is an industry-oriented tool for simulating the deployment of new energy technologies across the US freight rail network. The framework provides a comprehensive network-level optimization and scenario simulation tool for decarbonizing the freight rail sector, addressing the uncertainties surrounding technological developments by supporting sensitivity analyses for different operational and technological parameters through a transparent and flexible input module. It offers practical alternatives to diesel locomotives and can be applied for any railroad considering the specific network structure and freight demand, outputting evaluation metrics for the associated emissions and costs relative to diesel operations. A number of relevant simulation scenarios were run and analyzed for key insights on the value of different alternative technologies for freight rail decarbonization. The project developments and findings have been presented at numerous conferences and events.
08 HYDROGEN↗
A Detailed Vehicle Modeling & Simulation Study Quantifying Energy Consumption and Cost Reduction of Advanced Vehicle Technologies Through 2050
The U.S. Department of Energy (DOE) Vehicle Technologies Office (VTO) and Hydrogen and Fuel Cell Technologies Office (HFTO) aim to develop sustainable, affordable, and efficient technologies for transportation of goods and people. Translating investments in advanced transportation component technologies and powertrains to estimate vehicle-level fuel savings potential is critical for understanding DOE’s impact. In this work, we simulated technologies funded by VTO and HFTO for light duty vehicles. The simulations were performed across: (1) Multiple powertrain configurations (conventional, power-split, extended-range electric vehicle, battery electric drive, and fuel-cell vehicles); (2) Vehicle classes (compact car, midsize car, small sport utility vehicle [SUV], midsize SUV, and pickup trucks); and (3) Fuels (gasoline, diesel, natural gas, hydrogen, and battery electricity). These various technologies are assessed for six different timeframes: laboratory years 2015 (reference), 2020, 2025, 2030, and 2045. A delay of five years is assumed between laboratory year and model year (the year the technology is introduced into production).
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
An analytical study of electric vehicle handling dynamics
Hypothetical electric vehicle configurations were studied by applying available analytical methods. Elementary linearized models were used in addition to a highly sophisticated vehicle dynamics computer simulation technique. Physical properties of specific EV's were defined for various battery and powertrain packaging approaches applied to a range of weight distribution and inertial properties which characterize a generic class of EV's. Computer simulations of structured maneuvers were performed for predicting handling qualities in the normal driving range and during various extreme conditions related to accident avoidance. Results indicate that an EV with forward weight bias will possess handling qualities superior to a comparable EV that is rear-heavy or equally balanced. The importance of properly matching tires, suspension systems, and brake system front/rear torque proportioning to a given EV configuration during the design stage is demonstrated.
Greene, J. E.↗
Future Battery Material Demand Analysis Based on U.S. Department of Energy R&D Targets
The U.S. Department of Energy’s Vehicle Technologies Office (VTO) supports research, development, and deployment of efficient, sustainable transportation technologies that will improve energy efficiency and fuel economy, and enable America to use less petroleum. To accelerate the development and adoption of new technologies, VTO has developed specific targets for a wide range of powertrain components, including the energy storage system. In this study, we use Autonomie, Argonne National Laboratory’s (Argonne’s) vehicle system simulation tool to evaluate future energy storage requirements (power, energy, etc.) for different vehicle classes, powertrains, component technologies and timeframes. BatPac, Argonne’s tool dedicated to energy storage pack design and costs, is then used to quantify the materials required for each pack. Market penetrations are then used to estimate the overall material demand worldwide and in the United States, with or without recycling. The results demonstrate that the positive impact of VTO research and development will lead to significant reduction in material compared to business-as-usual due to new anode and cathode designs, along with acceleration in battery cell chemistry penetrations. In terms of material demands, it is observed that lithium demand reaches about 80,000 tons (by a factor of 42–45), nickel demand reaches about 500,000 tons (by a factor of 47–56), manganese demand reaches about 30,000–50,000 tons (by a factor of 20–34), and cobalt demand reaches about 30,000 tons (by a factor of 13–28) in the future by 2050. The individual material demand per unit energy, however, decreases significantly in the future due to advances in VTO research and development activities. The increase in battery material demands is mostly driven by increased electrified vehicle fleet penetration in the markets.
25 ENERGY STORAGE↗
Preliminary Dynamic Modeling of the Quarter-Scale Distributed Electric Propulsion Aircraft
This paper describes the early-stage modeling of a quarter-scale distributed electric propulsion aircraft, based on the SUbsonic Single Aft eNgine (SUSAN) Electrofan, a transformative concept aircraft for which a model exists. The full-scale 180 passenger SUSAN concept has a single turbofan engine in the tail that both produces thrust and provides electrical power to 16 electric fans distributed across the wings utilizing a series/parallel hybrid architecture. The quarter-scale version would have an internal combustion piston engine to provide power to 17 electric fans–16 on the wings, one in the tail–in a series hybrid configuration, and no vertical or horizontal stabilizers. It would also have a reduced flight envelope in terms of both altitude and speed. The initial modeling approach is to scale down the original airframe model to capture the dynamic behavior of a much smaller aircraft, albeit with an empennage. The original powertrain model is then replaced with one representing the physical components of that of the quarter-scale vehicle. This powertrain model is suitable for control design and analysis, and the fully integrated, although preliminary, aircraft model allows flight simulator testing and evaluation. Results from simulations are presented.
Electrified powertrain↗
Harnessing the Digital Transformation for Development of Electrified Aircraft Propulsion Control Systems
Hybrid electric aircraft propulsion is an emerging technology that presents a variety of potential benefits along with technical integration challenges. Developing these new propulsion architectures with their complex control systems, and ultimately proving their benefit, is a multistep process that goes from concept, to analysis, to dynamic simulation, to hardware in the loop testing, to full scale testing and beyond. This effort is being revolutionized and indeed enabled by new digital tools that support the increasing technology readiness level throughout the maturation process. NASA has developed and made available a suite of digital tools that ease the path from concept to implementation. The three packages are the Toolbox for the Modeling and Analysis of Thermodynamic Systems (T-MATS), the Electrical Modeling and Thermal Analysis Toolbox (EMTAT), and the Thermal Systems Analysis Toolbox (TSAT). These tools are interactive, complementary, and compatible with each other. T-MATS is a modular thermodynamic modeling framework designed for creating custom component level models of jet engines. EMTAT is a modeling framework used to simulate a variety of power electronic devices, using both physics-based and power flow calculations. TSAT is a framework for modeling and analysis of dynamic heat transfer. These packages all consist of graphical, drag-and-drop, parameterizable building blocks representing various components of the system to be modeled, e.g., compressors, turbines, motors, energy storage devices, etc. They are designed to enable the user to model and simulate the end-to-end dynamic operation of a hybrid powertrain at the timescale of the turbomachinery, capturing mechanical, electrical, and thermal interactions. This paper will describe through multiple examples how these tools have been used successfully in several of the early stages of hybrid electric propulsion system development, from the initial system modeling to real-time interactive pilot-in-the-loop simulation to physical hardware-in-the-loop testing, each step bringing the technology closer to fruition.
Hybrid electric propulsion↗
Fault Detection and Performance Monitoring of Propellers in Electric UAV
Unmanned aerial vehicles (UAVs) are used in various industries such as agriculture and logistics, to name but a few, where their applications are beyond basic mapping, surveillance, and photography. In near future, UAVs are expected to be used in package delivery service and larger electric vertical takeoff and landing vehicles will be employed for urban air mobility applications (air taxi). Thus, several electric propulsion systems will enter the low-altitude airspace with frequent take offs and landings. To achieve state-of-the-art safety standards under such high traffic density, UAVs will require in-time fault detection and performance monitoring of critical powertrain components. This work focuses on propeller blade performance and damage detection in electric UAVs. Propellers are the fastest moving component in an UAV; even a minor defect in the propeller blades could cause performance deterioration, with consequent challenges in flying through the planned trajectory or adhere to the safety requirements of the operation. Monitoring and updating aerodynamic efficiency of each rotor would therefore enable the detection of off-nominal propeller conditions thus magnifying the state-awareness of powertrain monitoring systems based on the acquired electrical signals. We use an extended Kalman filter-based parameter estimation algorithm that incorporates time history responses from UAV powertrain in conjunction with a full powertrain system model. Propeller fault detection is achieved by incorporating the aerodynamic parameters of propeller into the powertrain model. The proposed technique is successfully validated with numerical simulations.
Unmanned aerial vehicles↗
Model Based Validation of Intelligent Powertrain Strategies for Connected and Automated Vehicles
Systems incorporating Vehicle to Everything (V2X) and conventional cellular based communication in vehicles can significantly help improve energy consumption via a combination of intelligent powertrain control strategies, smarter routing algorithms and driving in such a way as to minimize fuel economy and the emission of carbon dioxide, known as "eco-driving." In projects led by the Southwest Research Institute (SwRI), large-scale traffic simulations are created to model real-world scenarios with dynamic behavior that is reactive to imposed changes. Coupled with high fidelity powertrain models, the closed loop framework enables research and development of such Connected and Automated Vehicle (CAV) enabled technologies at scale. This paper will discuss a traffic system simulation environment that was built based on the High Street urban corridor in Columbus, Ohio. Eco-driving strategies were tested at scale on a variety of powertrain platforms – internal combustion engines, hybrid electric and fully electric vehicles. Furthermore, the paper will focus on hybrid electric powertrain modeling along with details on how the powertrain model was leveraged to develop a sophisticated clustering scheme to help down-select speed traces from large scale simulation studies for validation on vehicle dynamometer. Nominal energy consumption improvement around 12% was observed with good match between simulation studies and vehicle testing.
33 ADVANCED PROPULSION SYSTEMS↗
Revolutionary Vertical Lift Technology (RVLT) Side-By-Side Hybrid Concept Vehicle Powertrain Dynamic Model
The Side-by-Side (SBS) Hybrid is one of several Revolutionary Vertical Lift Technology (RVLT) concept aircraft identified by NASA to investigate Urban Air Mobility (UAM) requirements. This paper presents a dynamic model of the SBS Hybrid powertrain built using the Toolbox for the Modeling and Analysis of Thermodynamic Systems (T-MATS) and the Electrical Modeling and Thermal Analysis Toolbox (EMTAT). The model consists of the rotors, electrical power system, and turboshaft engines connected through freewheeling clutches, gearboxes, and multiple shafts. This research effort models the complex behavior of the powertrain, including the operation of the freewheeling clutches and electrical power system at the simulation time scale of the shaft dynamics. Several simulations highlight the key features present in the model and demonstrate its operation.
Powertrain↗
Revolutionary Vertical Lift Technology (RVLT) Side-By-Side Hybrid Concept Vehicle Powertrain Dynamic Model
The Side-by-Side (SBS) Hybrid is one of several Revolutionary Vertical Lift Technology (RVLT) concept aircraft identified by NASA to investigate Urban Air Mobility (UAM) requirements. This paper presents a dynamic model of the SBS Hybrid powertrain built using the Toolbox for the Modeling and Analysis of Thermodynamic Systems (T-MATS) and the Electrical Modeling and Thermal Analysis Toolbox (EMTAT). The model consists of the rotors, electrical power system, and turboshaft engines connected through freewheeling clutches, gearboxes, and multiple shafts. This research effort models the complex behavior of the powertrain, including the operation of the freewheeling clutches and electrical power system at the simulation time scale of the shaft dynamics. Several simulations highlight the key features present in the model and demonstrate its operation.
powertrain↗
Future Cost Benefits Analysis for Electrified Vehicles from Advances Due to U.S. Department of Energy Targets
The U.S. Department of Energy’s Vehicle Technologies Office (DOE-VTO) supports research and development (R&D), as well as deployment of efficient and sustainable transportation technologies, that will improve energy efficiency and fuel economy and enable America to use less petroleum. To accelerate the creation and adoption of new technologies, DOE-VTO has developed specific targets for a wide range of powertrain technologies (e.g., engine, battery, electric machine, lightweighting, etc.). This paper quantifies the impact of VTO R&D on vehicle energy consumption and cost compared to expected historical improvements across vehicle classes, powertrains, component technologies and timeframes. We have implemented a large scale simulation process to develop and simulate tens of thousands of vehicles on U.S. standard driving cycles using Autonomie, a vehicle simulation tool developed by Argonne National Laboratory. Results demonstrate significant additional reductions in both cost and energy consumption due to the existence of VTO R&D targets compared to predicted historical trends. It is observed that, over time, the fuel consumption of different electrified vehicles is expected to decrease by 40–50% and a reduction of 45–55% for vehicle manufacturing costs owing to significant improvements through various VTO R&D targets.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Piloted Evaluation of a Fault Recovery System for an Aircraft with Distributed Electric Propulsion
Electrified aircraft powertrains contain multiple tightly coupled subsystems, making them much more complex than traditional aircraft propulsion systems, both in terms of integration and control. Electrification enables aircraft to have multiple distributed thrust-producing fans that the flight control system can utilize for enhanced maneuverability, further increasing the control complexity. The SUbsonic Single Aft eNgine (SUSAN) Electrofan is a NASA concept aircraft that leverages this technology. SUSAN is a series/parallel partial hybrid electric single-aisle transport aircraft that takes advantage of its electrified powertrain to provide fuel burn and emissions benefits when compared to the state-of-the-art. Achieving these benefits requires an appropriately designed control architecture that coordinates the various powertrain and flight control subsystems. As such, the SUSAN aircraft is designed with a high level of automation, allowing it to properly manage coupled subsystems and react rapidly to failures and anomalies. To do this effectively, algorithms that perform component health management, fault detection, isolation, and accommodation, and continuous optimization, must be developed, tested, validated, and implemented. This paper describes a piloted evaluation of such an algorithm in scenarios with multiple fan failures, performed in a flight simulator, demonstrating failure recovery and continued safe operation up to the limits of the powertrain. These scenarios are subsequently related to certification requirements.
Electrified Aircraft Propulsion↗