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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
NASA NTRS1981

Vehicle test report: Electric Vehicle Associates electric conversion of an AMC Pacer

The change of pace, an electric vehicle was tested. These tests were performed to characterize certain parameters of the electric vehicle pacer and to provide baseline data that can be used for the comparison of improved batteries that may be incorporated into the vehicle at a later time. The vehicle tests were concentrated on the electrical drive subsystem, the batteries, controller and motor. Coastdowns to characterize the road load, and range evaluations for both cyclic and constant speed conditions were performed. The vehicle's performance was evaluated by comparing its constant speed range performance with described vehicles. It is found that the pacer performance is approximately equal to the majority of the vehicles tested in the 1977 assessment.

Price, T. W.↗
NASA NTRS1981

Vehicle test report: Electric Vehicle Associates electric conversion of an AMC Pacer

Tests were performed to characterize certain parameters of the EVA Pacer and to provide baseline data that can be used for the comparison of improved batteries that may be incorporated into the vehicle at a later time. The vehicle tests were concentrated on the electrical drive subsystem; i.e., the batteries, controller and motor. The tests included coastdowns to characterize the road load, and range evaluations for both cyclic and constant speed conditions. A qualitative evaluation of the vehicle's performance was made by comparing its constant speed range performance with other electric and hybrid vehicles. The Pacer performance was approximately equal to the majority of those vehicles assessed in 1977.

Price, T. W.↗
NASA NTRS1976

Baseline test data for the EVA electric vehicle

Two electric vehicles from Electric Vehicle Associates were evaluated for ERDA at the Transportation Research Center of Ohio. The vehicles, loaded to a gross vehicle weight of 3750 pounds, had a range of 56.3 miles at a steady speed of 25 mph and a 27.4 miles range during acceleration-deceleration tests to a top speed of 30 mph. Energy consumption varied from 0.48 kw-hr/mi. to 0.59 kw-hr/mi.

Harhay, W. C.↗
NASA NTRS1979

Study of advanced electric propulsion system concept using a flywheel for electric vehicles

Advanced electric propulsion system concepts with flywheels for electric vehicles are evaluated and it is predicted that advanced systems can provide considerable performance improvement over existing electric propulsion systems with little or no cost penalty. Using components specifically designed for an integrated electric propulsion system avoids the compromises that frequently lead to a loss of efficiency and to inefficient utilization of space and weight. A propulsion system using a flywheel power energy storage device can provide excellent acceleration under adverse conditions of battery degradation due either to very low temperatures or high degrees of discharge. Both electrical and mechanical means of transfer of energy to and from the flywheel appear attractive; however, development work is required to establish the safe limits of speed and energy storage for advanced flywheel designs and to achieve the optimum efficiency of energy transfer. Brushless traction motor designs using either electronic commutation schemes or dc-to-ac inverters appear to provide a practical approach to a mass producible motor, with excellent efficiency and light weight. No comparisons were made with advanced system concepts which do not incorporate a flywheel.

Younger, F. C.↗
NASA NTRS1982

Alternating-Current Motor Drive for Electric Vehicles

New electric drive controls speed of a polyphase as motor by varying frequency of inverter output. Closed-loop current-sensing circuit automatically adjusts frequency of voltage-controlled oscillator that controls inverter frequency, to limit starting and accelerating surges. Efficient inverter and ac motor would give electric vehicles extra miles per battery charge.

Krauthamer, S.↗
NASA NTRS1977

State-of-the-art assessment of electric vehicles and hybrid vehicles

The Electric and Hybrid Vehicle Research, Development, and Demonstration Act of 1976 (PL 94-413) requires that data be developed to characterize the state of the art of vehicles powered by an electric motor and those propelled by a combination of an electric motor and an internal combustion engine or other power sources. Data obtained from controlled tests of a representative number of sample vehicles, from information supplied by manufacturers or contained in the literature, and from surveys of fleet operators of individual owners of electric vehicles is discussed. The results of track and dynamometer tests conducted by NASA on 22 electric, 2 hybrid, and 5 conventional vehicles, as well as on 5 spark-ignition-engine-powered vehicles, the conventional counterparts of 5 of the vehicles, are presented.

Source record↗
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 NTRS1977

New batteries and their impact on electric vehicles

The paper is concerned with the development of electric vehicles and electric vehicle batteries. The present and predicted performance levels of some battery systems such as lead-acid, nickel-iron, nickel-zinc, and zinc-chlorine are considered, as are the characteristics that an electric vehicle must possess in order to appeal to customers. The implications of battery improvements for manufacturers of electric vehicles are discussed. Lack of knowledge of passenger range requirements for electric vehicles is noted.

Schwartz, H. J.↗
NASA NTRS1977

Evaluation of battery models for prediction of electric vehicle range

Three analytical models for predicting electric vehicle battery output and the corresponding electric vehicle range for various driving cycles were evaluated. The models were used to predict output and range, and then compared with experimentally determined values determined by laboratory tests on batteries using discharge cycles identical to those encountered by an actual electric vehicle while on SAE cycles. Results indicate that the modified Hoxie model gave the best predictions with an accuracy of about 97 to 98% in the best cases and 86% in the worst case. A computer program was written to perform the lengthy iterative calculations required. The program and hardware used to automatically discharge the battery are described.

Frank, H. A.↗
NASA NTRS2017

Prognostics and Health Monitoring: Application to Electric Vehicles

As more and more autonomous electric vehicles emerge in our daily operation progressively, a very critical challenge lies in accurate prediction of remaining useful life of the systemssubsystems, specifically the electrical powertrain. In case of electric aircrafts, computing remaining flying time is safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. In order to tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to predict the future state of the system, it is also required to possess knowledge of the current and future operations of the vehicle.Our research approach is to develop a system level health monitoring safety indicator either to the pilotautopilot for the electric vehicles which runs estimation and prediction algorithms to estimate remaining useful life of the vehicle e.g. determine state-of-charge in batteries. Given models of the current and future system behavior, a general approach of model-based prognostics can be employed as a solution to the prediction problem and further for decision making.

Prognostics↗
NASA NTRS1978

Preliminary power train design for a state-of-the-art electric vehicle

The state-of-the-art (SOTA) of electric vehicles built since 1965 was reviewed to establish a base for the preliminary design of a power train for a SOTA electric vehicle. The performance of existing electric vehicles were evaluated to establish preliminary specifications for a power train design using state-of-the-art technology and commercially available components. Power train components were evaluated and selected using a computer simulation of the SAE J227a Schedule D driving cycle. Predicted range was determined for a number of motor and controller combinations in conjunction with the mechanical elements of power trains and a battery pack of sixteen lead-acid batteries - 471.7 kg at 0.093 MJ/Kg (1040 lbs. at 11.7 Whr/lb). On the basis of maximum range and overall system efficiency using the Schedule D cycle, an induction motor and 3 phase inverter/controller was selected as the optimum combination when used with a two-speed transaxle and steel belted radial tires. The predicted Schedule D range is 90.4 km (56.2 mi). Four near term improvements to the SOTA were identified, evaluated, and predicted to increase range approximately 7%.

Ross, J. A.↗
NASA NTRS1979

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.↗
NASA NTRS2019

Prognostics and Health Monitoring: Application to Electric Vehicles

As more and more electric vehicles emerge in our daily operation progressively, a very critical challenge lies in the prediction of remaining driving-time distance (for cars) or flying time-distance (for aircraft). This information is important, particularly in the case of unmanned vehicles, because such vehicles can become self-aware, autonomously compute its own capabilities, and identify how to best plan and successfully complete vehicular missions safely. In case of electric aircrafts, computing remaining flying time is also safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. In order to tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to predict the future state of the system, it is also required to possess knowledge of the current and future operations of the vehicle.

Electric Vehicles↗
NASA NTRS2023

Energy Augmentation for Vehicle Electric Systems (EAVES)

This preliminary study evaluated eight concepts for augmenting the energy state of electric Vertical Take-Off and Landing vehicles. Advanced Air Mobility electric vehicles could need additional charge due to depleted batteries (e.g., strong winds along the way) while approaching their destination. There are five direct charging and three indirect charging concepts presented in this paper. The concepts are in the preliminary research stage and are being refined. Considering the concepts are for the year 2045 timeframe, there is sufficient time to evolve them, along with the designs of these electric air vehicles. This Technical Memorandum describes more detail and provides a discussion on the desirability, viability, feasibility, and wickedness of these energy augmentation concepts. A discussion of barriers and initial investigation approach for the concepts is presented as well. One intent for the presentation of this Technical Memorandum is to capture the work done from March through September 2022 within NASA’s Convergent Aeronautics Solutions (CAS) Project, in the Transformative Aeronautics Concepts Program. At the end of the effort, it was decided that only one concept would be further investigated. The rest of the concepts for energy augmentation would be described in this document and could be picked up later if NASA chose to further investigate them. This document is a collection of input from the authors regarding the concepts they worked on. It is not intended to be a conference or journal publication, but a record of research conducted on the concepts considered by the Energy Augmentation for Vehicle Electric Systems (EAVES) team consisting of the authors. Mr. Todd Stinchfield was the Principal Investigator.

Kapil Sheth↗
NASA NTRS2007

Electric Vehicles at Kennedy Space Center

The story of how the transportation office began by introducing low speed electric cars (LSEV) to the fleet managers and employees. This sparked and interest in purchasing some of these LSEV and the usage on KSC. Transportation was approached by a vender of High Speed Electric Vehicle (HSEV) we decided to test the HSEV to see if they would meet our fleet vehicle needs. Transportation wrote a Space Act Agreement (SAA) for the loan of three Lithium Powered Electric vehicles for a one year test. The vehicles have worked very well and we have extended the test for another year. The use of HSEV has pushed for an independent Electric Vehicle Study to be performed to consider ways to effectively optimize the use of electric vehicles in replacement of gasoline vehicles in the KSC vehicle fleet. This will help the center to move closer to meeting the Executive Order 13423.

Chesson, Bruce E.↗
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.↗
NASA NTRS2004

Nuclear Electric Vehicle Optimization Toolset (NEVOT)

The Nuclear Electric Vehicle Optimization Toolset (NEVOT) optimizes the design of all major nuclear electric propulsion (NEP) vehicle subsystems for a defined mission within constraints and optimization parameters chosen by a user. The tool uses a genetic algorithm (GA) search technique to combine subsystem designs and evaluate the fitness of the integrated design to fulfill a mission. The fitness of an individual is used within the GA to determine its probability of survival through successive generations in which the designs with low fitness are eliminated and replaced with combinations or mutations of designs with higher fitness. The program can find optimal solutions for different sets of fitness metrics without modification and can create and evaluate vehicle designs that might never be considered through traditional design techniques. It is anticipated that the flexible optimization methodology will expand present knowledge of the design trade-offs inherent in designing nuclear powered space vehicles and lead to improved NEP designs.

Tinker, Michael L.↗