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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 1,603 records · Page 89
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

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↗
DOE OSTI2024

Terrain-Relative Navigation with Neuro-Inspired Elevation Encoding

Terrain-relative navigation (TRN) encompasses a wide variety of algorithms that perform localization with respect to the terrain below a flying vehicle. In traditional approaches, measurements of the terrain are matched to a map carried onboard. This work presents a terrain-relative navigation filter with a position measurement inspired by neural activity associated with positioning in nature. Here, the filter is shown to produce accurate position measurements that outperform popular optimization and template matching methods given poor prior knowledge of the position. The proposed method is also better-suited to distributed implementation than optimization-based methods.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Cooperative Routing for an Air–Ground Vehicle Team—Exact Algorithm, Transformation Method, and Heuristics

This paper considers a cooperative vehicle routing problem for an intelligence, surveillance, and reconnaissance mission in the presence of communication constraints between the vehicles. The proposed framework uses a ground vehicle and an unmanned aerial vehicle (UAV) that travel cooperatively and visit a set of targets while satisfying the communication constraints. The problem is formulated as a mixed-integer linear program, and a branch-and-cut algorithm is developed to solve the problem to optimality. Furthermore, a transformation method and a heuristic are also developed for the problem. The effectiveness of all the algorithms is corroborated through extensive computational experiments on several randomly generated instances. Furthermore, this paper is motivated by an intelligence, surveillance, and reconnaissance mission involving a single unmanned aerial vehicle (UAV) and a ground vehicle, where the vehicles must coordinate their activity in the presence of communication constraints. The combination of a small UAV and a ground vehicle is an ideal platform for such missions, since small UAVs can fly at low altitudes and can avoid obstacles or threats that would be problematic for the ground vehicle alone. This paper addresses the coordinated routing problem involving these two vehicles and presents an algorithm to obtain an optimal solution for this problem, fast heuristics to obtain good feasible solutions, and also a transformation method to transform any instance of this cooperative vehicle routing problem to an instance of the one-in-a-set traveling salesman problem.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Single-Phase Jet Impingement Cooling for a Power-Dense Silicon Carbide Power Module

The adoption of silicon carbide (SiC) devices in the electric vehicle (EV) industry is increasing due to their superior performance over silicon devices. SiC devices enable further miniaturization of EV inverters, increasing their power density, which results in thermal management challenges. Here in this paper, the limits of single-phase jet impingement cooling are explored for an automotive SiC power module. We propose embedding pin fins in the direct-bonded-copper (DBC) substrate of the power module package using laser powder bed fusion additive manufacturing. The thermalhydraulic performance of the DBC-embedded pin fins is compared against folded fins that are directly soldered to the DBC substrate. A heat conduction analysis was conducted on a SiC package to determine the target heat transfer coefficient (HTC) for the heat sink. A water-ethylene glycol (WEG) jet impingement on the proposed concepts was studied using unit-cell models to achieve the target HTC. The studied designs put emphasis on the reliability and manufacturability requirements of the automotive industry. The thermal performance of DBC-embedded pin fins outperformed the DBC-soldered folded fins. The performance of the DBC-embedded pin fins is benchmarked against WEG-based cooling systems of commercial EVs. With the proposed cooling solution, we have shown a pathway of reducing the specific thermal resistance by 75% compared to BMW i3 thermal management system without any penalty on pressure drop or parasitic power.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2014

A Consumer-Oriented Control Framework for Performance Analysis in Hybrid Electric Vehicles

Hybrid electric vehicles (HEVs) have attracted considerable attention due to their potential to reduce fuel consumption and emissions. Our objective with this paper is to enhance our understanding of the associated tradeoffs among the HEV subsystems, e.g., the engine, the motor, and the battery, and investigate the related implications for fuel consumption and battery capacity and lifetime. Addressing this problem can provide insights on how to prioritize these objectives based on consumers needs and preferences. The results of the proposed optimization approach can also be used to investigate the implications for HEV costs related to ownership and warranty.

25 ENERGY STORAGE↗
DOE OSTI2019

Economic Optimal Control for Minimizing Fuel Consumption of Heavy-Duty Trucks in a Highway Environment

This paper provides a comparative assessment of three economic optimal control strategies, aimed at minimizing the fuel consumption of heavy-duty trucks in a highway environment, under a representative lead vehicle model informed by traffic data. These strategies fuse a global, offline dynamic programming (DP) optimization with online model predictive control (MPC). We then show how two of the three strategies can be adapted to accommodate the presence of traffic and optimally navigate signalized intersections using infrastructure-to-vehicular (I2V) communication. The MPC optimization, which is local in nature, makes refinements to a coarsely (but globally, subject to grid resolution) optimized target velocity profile from the DP optimization. The three candidate economic MPC formulations that are evaluated include: a nonlinear time-based formulation that directly penalizes predicted fuel consumption, a nonlinear time-based formulation that penalizes braking effort as a surrogate for fuel consumption, and a linear distance-based convex formulation that maintains a tradeoff between energy expenditure and tracking of the coarsely optimized velocity profile obtained from DP. Using a medium-fidelity Simulink model, based on a Volvo truck's longitudinal and engine dynamics, we analyze the optimization's performance on four highway routes under various traffic scenarios. Results demonstrate 3.7-8.3% fuel economy improvement on highway routes without traffic and 6.5-10% on the same routes with traffic included. Furthermore, we present a detailed analysis of energy usage by "type" (aerodynamic losses, braking losses, and comparison of brake-specific fuel consumption), under each candidate control strategy.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Multilane Automated Driving With Optimal Control and Mixed-Integer Programming

Road vehicle lane changes often initiate traffic disturbances and can therefore impact road networks’ energy and time efficiency. Furthermore, unexpected changes in traffic conditions may also render lane changes counterproductive for the lane-changing vehicle. Vehicle-to-vehicle connectivity combined with anticipative control could address these challenges via improved lane change decisions by automated vehicles. In a move toward this objective, receding horizon control cast as a mixed-integer quadratic program is used to plan lane changing and acceleration in a coupled optimization. A long-term pacing module, based on Pontryagin’s minimum principle from optimal control theory, sets terminal and input references for receding horizon control to target a user’s expected travel time. To remove nonlinear vehicle dynamics from the receding horizon controller, lane change commands are passed to a pure pursuit steering module whose response is approximated by a second-order linear model. Here, comparison against a rule-based reactive algorithm in arterial and highway scenarios shows an 8.9%–13.7% reduction in energy consumption and a 5.2%–10.3% reduction in the travel time, along with navigational improvements.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Next-Cycle Optimal Dilute Combustion Control via Online Learning of Cycle-to-Cycle Variability Using Kernel Density Estimators

Dilute combustion using exhaust gas recirculation (EGR) presents a cost-effective method for increasing the efficiency of spark-ignition (SI) engines. However, the maximum amount of EGR that can be used at a given condition is limited by a rapid increment of cycle-to-cycle variability (CCV). This study describes a methodology to design a model-based stochastic optimal controller to adjust the cycle-to-cycle fuel injection quantity in order to reduce CCV and further extend the dilute limit. Given the complexity and chaotic nature of combustion events, the controller was enhanced with online learning in order to identify the statistical properties of combustion efficiency, which are needed to generate predictions for next-cycle events. This study showed that a kernel density estimator (KDE) can be used to learn the combustion properties in real time and can be incorporated into the feedback policy in order to calculate the optimal control command. Experimental results suggested that the dilute limit can be extended from 18.5% to 21% EGR fraction at an operating condition relevant for highway cruising. Additionally, the proposed controller can achieve a large CCV reduction with less fuel enrichment compared to previous methods, overall contributing to an increase in 0.2% indicated fuel conversion efficiency.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Real-Time Gear-Shift Optimization for an Autonomous Wheel Loader

Off-road vehicles, such as wheel loaders, consume a significant amount of fuel during the transportation of materials. The gear-shifting process is crucial in fuel savings for transportation, and therefore, optimization of gearshifts is important for vehicle control. For an autonomous off-road vehicle, there is potential for more fuel savings by proper coordination of gearshift optimization and optimization of other control inputs. This brief proposes a new method for integrating gear-shifting into transportation optimization for an autonomous wheel loader to minimize fuel consumption. Furthermore, tests conducted on short loading cycles show that this method can save around 10%–20% of fuel on average compared with a conventional gearshift-scheduling method.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Application of Site Controllers for Electrification of Commercial Fleet Vehicles

Electrification of transportation fleets presents a significant challenge for commercial customers. These challenges can be specific to region, weather, operating schedule, charging infrastructure, and other factors. This paper presents the value of integrating a site controller to monitor the health of assets and co-optimize the operation of commercial sites with multiple distributed energy resource technologies and electric vehicle fleets. The tests demonstrate smart-charging and vehicle-to-building uses compared to business-as-usual cases. The secondary objectives of power management for the customer vehicle are optimized by a planning tool (REopt) and integrated using the site controller. Any deviations from the planned dispatch are addressed by the real-time controllers offering tertiary controls, such as energy management, and limiting the reverse power flow (back into the grid). The results indicate that site controllers offer an efficient solution to manage the health of the connected assets and a scalable means to optimize the operations of a commercial customer with electrified transportation fleets.

ADVANCED PROPULSION SYSTEMS,POWER TRANSMISSION AND↗
DOE OSTI2019

Novel Rotary Transformer Topology With Improved Power Transfer Capability for High-Speed Applications

This article proposes a new rotary transformer topology enabling reduction of leakage inductances and significant improvement of power transfer capability compared to traditional topologies thanks to its interleaved windings and resonant compensation circuits. The proposed rotary transformer features a nonferrite rotating part made of lightweight strong nonconductive and nonmagnetic composite material and a large air gap, allowing safe operation at high rotational speeds. Hence, this novel rotary transformer can enable the adoption of wound rotor synchronous machines for electric or hybrid vehicle traction. Finally, the performance of the proposed new topology was evaluated and compared to the conventional one. Significant performance improvement was confirmed. A 10-kW proof of concept prototypes was built, and the test has validated the power transfer capability with 95.9% efficiency.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2019

Planning Optimization for Inductively Charged On-demand Automated Electric Shuttles Project at Greenville, South Carolina

Wireless charging technology presents an ideal fit for autonomous electric vehicles for realizing a fully automated system (vehicle and charger). This paper presents a planning optimization analysis for a pilot project of in-route wireless charging infrastructure serving fixed-route on-demand shared automated electric shuttles (SAESs) at Greenville, South Carolina, USA. A single-objective non-linear mixed integer system planning optimization problem is formulated. A comprehensive cost model representing the overall inductively charged SAESs system is developed, considering road construction, power electronics and materials, traction battery, and installation costs. The optimization problem is solved to determine the best combination of the system key design parameters (number and allocations of wireless chargers, charging power level, track length and on-board battery capacity) that show the most cost-effective solution and allow the SAESs to achieve charge-sustaining operation. The planning platform incorporates representative simulated traffic data (driving speed and routes) for four SAESs in the Greenville using the Simulation of Urban Mobility tool. These data are fed to a vehicle powertrain model and a wireless charger power model to predict the battery power, energy and state-of-charge profiles, which are provided to the search algorithm to assess the design objectives under specific constraints. Finally, the results indicate that implementing high-power (100 kW) wireless charger at a few designated stops for fixed-route SAESs with the proper track length allows the vehicles to realize charge-sustaining operation, infinite range, and zero recharge downtime, with a significant reduction in the on-board battery (36%) and road coverage (69%), at minimum cost.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Light-Duty Vehicle Trip Classification Using One-Class Novelty Detection and Exhaustive Feature Extraction

Travel mode classification within travel survey data sets, especially light-duty vehicle (LDV) trips, is foundational, though nontrivial, to emerging mobility systems, travel behavior analysis, and fuel consumption estimation. Current travel mode detection approaches require well-sampled and balanced data sets with ground truth travel mode labels. The detection approaches are rarely applied and validated on large-scale, real-world data sets, which may not satisfy the dataset requirements. This work proposes an LDV trip detection model as a supplement to current travel mode detection methods, for the case when the training set is highly (and/or completely) unbalanced, to the extent that classical machine-learning approaches become difficult or impossible to deploy. The proposed model uses a novelty detection technique - one-class support vector machines (OCSVMs) - and a novel exhaustive feature extraction (EFE) technique on continuous time series data (i.e., Global Positioning System [GPS] speed profiles) for single-mode trip trajectories. Training and validation of the model are conducted on a large-scale, real-world data set. The proposed method accurately identifies LDV trips from a broad set of multimodal trips by leveraging a wealth of preexisting in-vehicle GPS travel data. Additional sensitivity analysis sheds light on the optimal training size and feature selection, which will benefit applications limited by highly imbalanced data. The paper also discusses performance comparison with regular machine-learning approaches, the model's robustness, and the potential to extend the proposed model to multi-modal trip prediction.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Development of an Integrated Electro-Hydraulic Machine to Electrify Off-highway Vehicles

Electrification of off-highway vehicles is notoriously challenging due to extreme power density requirements. This paper proposes and develops an axial flux machine integrated with a hydraulic pump to realize a single modular, electro-hydraulic machine to electrify off-highway vehicle implements. This integrated machine eliminates redundant bearings, couplings, and shaft seals, re-uses surfaces, and enables direct cooling of the electric machine with the hydraulic fluid, to significantly increase power density. Three popular axial flux machine variants are first compared using an FEA-based design optimization approach. The single rotor, single stator variant is identified to be the most promising for integration with the hydraulic pump. Next, a multi-physics framework of the complete integrated hydraulic pump and axial flux machine is developed to characterize the design space. The results indicate promising potential for this concept to realize efficiency over 85% and power density over 5kW/kg for the complete machine (electric machine, hydraulic pump, and thermal management system), while utilizing conventional materials (thin gauge silicon steel, N45 magnets, and enamelled copper wire). Furthermore, a prototype axial flux machine has been experimentally characterized and integrated with a hydraulic pump to demonstrate the integrated electro-hydraulic machine concept.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Combined Dimensional and Topology Optimization of Synchronous Machine Rotors Using a Material Density Interpolation Method

This article presents a magneto-structural combined dimensional and topology optimization technique for interior permanent magnet synchronous machine (IPMSM) and wound field synchronous machine (WFSM) rotors. Dimensional changes to the permanent magnet (PM) and rotor winding location or size are accomplished by interpolating or projecting a smoothed Heaviside rectangular function representing the presence of PM material onto the IPMSM rotor design domain mesh, whereas the presence of copper material for WFSM rotor is interpolated in the same manner. A density based Solid Isotropic Material with Penalization (SIMP) topology optimization approach is then used to vary the presence of electrical steel in mesh elements to form flux barriers around the PM. The proposed method enforces a defined shape for the PM without requiring the mesh in the design domain to be deformed. Four examples are presented to demonstrate the technique: Two flat bar IPMSM, one V-shaped IPMSM, and a WFSM. Here, a comparative study is performed on one of the flat bar IPMSMs using Metamodel of Optimal Prognosis (MOP) based method.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Implementation of a Metamodel-Based Optimization for the Design of a High Power Density Wound Field Traction Motor

Traction motors onboard electric vehicles (EVs) are faced with ever-increasing power density requirements and cost reduction challenges. Although permanent magnet (PM) traction motors have been favored for their potential of higher power densities, the price and supply volatility of PM materials are the major drawbacks. Instead of a single load point, the performance of a traction motor needs to be optimized over various combinations of torque and speed to represent real-world vehicular driving scenarios, which tends to be a time-consuming task with direct optimizations. In this study, as a PM-free solution, a wound field synchronous motor (WFSM) is designed and optimized using a metamodel-based approach to maximize its efficiency over a custom set of load points, while utilizing a fully per-unitized geometry template. The workflow has proven to be timesaving and the optimal design is prototyped and tested.

33 ADVANCED PROPULSION SYSTEMS↗