Results for “battery charge controller” — Page 6

NASA NTRS1991

Space platform power system hardware testbed

The scope of the work on the NASA Space Platform includes the design of a multi-module, multi-phase boost regulator, and a voltage-fed, push-pull autotransformer converter for the battery discharger. A buck converter was designed for the charge regulator. Also included is the associated mode control electronics for the charger and discharger, as well as continued development of a comprehensive modeling and simulation tool for the system. The design of the multi-module boost converter is discussed for use as a battery discharger. An alternative battery discharger design is discussed using a voltage-fed, push-pull autotransformer converter. The design of the charge regulator is explained using a simple buck converter. The design of the mode controller and effects of locating the bus filter capacitor bank 20 feet away from the power ORU are discussed. A brief discussion of some alternative topologies for battery charging and discharging is included. The power system modeling is described.

Sable, D.↗

NASA NTRS2010

Smart Power Supply for Battery-Powered Systems

A power supply for battery-powered systems has been designed with an embedded controller that is capable of monitoring and maintaining batteries, charging hardware, while maintaining output power. The power supply is primarily designed for rovers and other remote science and engineering vehicles, but it can be used in any battery alone, or battery and charging source applications. The supply can function autonomously, or can be connected to a host processor through a serial communications link. It can be programmed a priori or on the fly to return current and voltage readings to a host. It has two output power busses: a constant 24-V direct current nominal bus, and a programmable bus for output from approximately 24 up to approximately 50 V. The programmable bus voltage level, and its output power limit, can be changed on the fly as well. The power supply also offers options to reduce the programmable bus to 24 V when the set power limit is reached, limiting output power in the case of a system fault detected in the system. The smart power supply is based on an embedded 8051-type single-chip microcontroller. This choice was made in that a credible progression to flight (radiation hard, high reliability) can be assumed as many 8051 processors or gate arrays capable of accepting 8051-type core presently exist and will continue to do so for some time. To solve the problem of centralized control, this innovation moves an embedded microcontroller to the power supply and assigns it the task of overseeing the operation and charging of the power supply assets. This embedded processor is connected to the application central processor via a serial data link such that the central processor can request updates of various parameters within the supply, such as battery current, bus voltage, remaining power in battery estimations, etc. This supply has a direct connection to the battery bus for common (quiescent) power application. Because components from multiple vendors may have differing power needs, this supply also has a secondary power bus, which can be programmed a priori or on-the-fly to boost the primary battery voltage level from 24 to 50 V to accommodate various loads as they are brought on line. Through voltage and current monitoring, the device can also shield the charging source from overloads, keep it within safe operating modes, and can meter available power to the application and maintain safe operations.

Krasowski, Michael J.↗

NASA NTRS2018

Health Monitoring and Prognostics for More Electric Aircrafts

As more and more electric vehicles emerge in our daily operation progressively, a very critical challenge lies in the prediction of remaining 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 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. Given models of the current and future system behavior, the general approach of model-based prognostics can be employed as a solution to the prior stated prediction problem.For electric aircraft, propulsion is based on power generated from batteries. Thus, it is critical to monitor battery state charge and to estimate the ability of the battery to support mission activities as it is being discharged during flight operation. The ability of the vehicle to complete its given mission very much depends on the charge left in the batteries based on its operational route, maneuvering, weather conditions along with aging health of the batteries. Hence, for the purpose this discussion, consider the scenario of an unmanned electric aircraft that has some planned sequence of waypoints to reach throughout its mission. In such a scenario, for this particular aircraft, and within the region it is flown, at most two minutes are required to safely land the aircraft. Thus, it is desired to predict at which point in time the aircraft must begin to head to the runway and land.

Goebel, Kai↗

NASA NTRS1971

The electrical power subsystem of the Pioneer F/G spacecraft.

The Pioneer F/G spacecraft is the first to use radioisotope thermoelectric generators (RTG's) for a long duration interplanetary mission. This paper describes the design of the spacecraft power subsystem which meets the unique requirements of such a mission. The primary power source consists of four SNAP 19 RTG's. The 4.2 volt dc outputs of the RTG's are inverted to higher voltage ac by four inverters which are connected in parallel at their outputs. A power control unit (PCU) regulates the ac output of the inverters such that the RTG's are operated at near maximum power voltage throughout the mission. The PCU also contains charge and discharge controls for a silver cadmium battery which supplements the RTG's if load requirements temporarily exceed RTG power output. Battery reliability is enhanced by overcharge and cell reversal protection electronics which are provided for each cell. Multiple, low voltage, regulated dc outputs which supply power to the spacecraft subsystems are provided by a central transformer-rectifier-filter unit (CTRF). The CTRF operates directly from the regulated ac bus.

Reppucci, G. M.↗

NASA NTRS2004

Integrated Solar-Energy-Harvesting and -Storage Device

A modular, integrated, completely solid-state system designed to harvest and store solar energy is under development. Called the power tile, the hybrid device consists of a photovoltaic cell, a battery, a thermoelectric device, and a charge-control circuit that are heterogeneously integrated to maximize specific energy capacity and efficiency. Power tiles could be used in a variety of space and terrestrial environments and would be designed to function with maximum efficiency in the presence of anticipated temperatures, temperature gradients, and cycles of sunlight and shadow. Because they are modular in nature, one could use a single power tile or could construct an array of as many tiles as needed. If multiple tiles are used in an array, the distributed and redundant nature of the charge control and distribution hardware provides an extremely fault-tolerant system. The figure presents a schematic view of the device.

whitacre, Jay↗

NASA NTRS1985

LEO life testing with different charge control

The effect of charge control on the performance of Nickel-Cadmium batteries is very important. The results of three tests performed in the Battery Test Centre of ESTEC are described. Two techniques were employed: (1) the tapering method well known for space applications, and (2) the temperature derivative technique (TDT) developed by ESTEC. In addition, a comparative study has been made between the behavior of a group of 3 batteries charged and discharged in parallel compared to an identical group discharged in parallel, but charged individually. An approach of evolution laws for the main electrical characteristics of cells is presented.

Baron, F.↗

NASA NTRS1977

Air Force nickel-hydrogen test program

After using all the instrumentation that is available on a nickel hydrogen battery it will be subjected to the same low earth orbit cycles experienced by the sixty amphere hour nickel cadmium batteries now being flown on some low earth orbit missions. The objective of the test is to demonstrate real differences between the two batteries. In order to simulate flight conditions, the thermal environment and criteria for charge control will vary. The depth of discharge will be increased and testing will continue until some failures or some degradation or spread between cell performances are revealed. Different charge control modes are being considered in order to demonstrate a reasonable life for the nickel hydrogen battery.

Gandel, M. G.↗

NASA NTRS2020

Prognostics for Systems Health Management - Model and Hybrid Based Approaches. Where are We Heading?

To facilitate and solve the prediction problem, awareness of the current state and health of the system is key, since it is necessary to perform condition-based system health predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditional. In case of next generation 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 health state 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 operational conditions and flight profiles for accurate estimation of end-of-discharge (EOD) for the batteries. Similar framework can be implemented to other complex systems and subsystems. Our research approach is to develop a system level health monitoring safety indicator which runs estimation and prediction algorithms to estimate remaining useful life predictions at system, subsystem swell as component levels. Given models of the current and future system behavior, a general approach of model-based prognostics is discussed as a solution to the prediction problem and further for decision making. Data driven prognostics approaches have been equally used with good results in the past, where respective approaches have their own challenges to tackle. This limits their applicability to complex real-world domains: (a) high complexity or incompleteness of physics-based models and (b) limited representativeness of the training dataset for data-driven models. With the advent of internet of things for data collection and increased use of ML algorithms, hybrid approaches are the next avenue to reduce the challenges and achieve better results. An hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, we use physics-based performance models to infer unobservable model parameters related to the system's components health solving a calibration problem.

Prognostics↗

NASA NTRS2020

Model Based Diagnostics and Prognostics Framework for Systems Health Management

In order to tackle and solve the system health 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 accurately predict the future state of any system, it is required to possess knowledge of its current and future operations. Given models of the current and future system behavior, the general approach of model-based prognostics can be employed as a solution to the prior stated prediction problem. 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. This presentation will cover a physics based-modeling approach implemented for case-studies in battery and composite structures for prognostics. 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.

Banerjee, Portia↗

NASA NTRSDate not supplied

Health Monitoring and Prognostics in Li-ion Batteries

Space applications need to overcome a very critical challenge of predicting remaining useful life of its critical systems/subsystems, with batteries being one of them. Batteries, power electronics conditioning system and motors and one of the most critical systems. Similarly 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. 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 accurately predict the future state of any system, it is required to possess knowledge of its current and future operations. Given models of the current and future system behavior, the general approach of model-based prognostics can be employed as a solution to the prior stated prediction problem. Two approaches are presented with battery prognostics application. The first approach presentation covers a physics based-modeling approach implemented for battery prognostics. 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. A second hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems i.e. batteries is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Batteries↗

NASA NTRSDate not supplied

Health Monitoring and Prognostics in Li-ion Batteries

Space applications need to overcome a very critical challenge of predicting remaining useful life of its critical systems/subsystems, with batteries being one of them. Batteries, power electronics conditioning system and motors and one of the most critical systems. Similarly 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. 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 accurately predict the future state of any system, it is required to possess knowledge of its current and future operations. Given models of the current and future system behavior, the general approach of model-based prognostics can be employed as a solution to the prior stated prediction problem. Two approaches are presented with battery prognostics application. The first approach presentation covers a physics based-modeling approach implemented for battery prognostics. 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. A second hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems i.e. batteries is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Battery Prognostics↗

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 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 NTRSDate not supplied

Systems Health Management and Decision Making

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 accurately predict the future state of any system, it is required to possess knowledge of its current and future operations. Given models of the current and future system behavior, the general approach of model-based prognostics can be employed as a solution to the prior stated prediction problem. 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. Discuss research approach to develop a system level health monitoring safety indicator which runs estimation and prediction algorithms to estimate remaining useful life predictions at system as well as subsystem levels. 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. In addition a digital twin concept is being implemented in the framework to demonstrate verification and validation of developed algorithms. Note - This presentation is for the short workshop conducted at the same conference and contains all previously approved and published information.

Systems Health Managent↗

NASA NTRSDate not supplied

Systems Health Management and Prognostics Approaches for Electric Aircrafts

As more and more electric vehicles emerge in our daily operation progressively, a very critical challenge lies in the prediction of remaining driving flying time/distance for the flying vehicles. This information is important, particularly in the case of auto 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 the 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. To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision-making. A systematic prediction framework is implemented to identify all possible sources of uncertainty, quantify each of them individually, and mathematically estimate their combined effect on the system-level quantity of interest, in this case, the remaining flying time/distance of the unmanned aircraft. Note - This presentation contains all previously published information.

Systems Health Managent↗

NASA NTRSDate not supplied

Health Monitoring and Prognostics for Electric Aircrafts

As more and more electric vehicles emerge in our daily operation progressively, a very critical challenge lies in the prediction of remaining driving flying time/distance for the flying vehicles. This information is important, particularly in the case of auto 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 the 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. To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision-making. A systematic prediction framework is implemented to identify all possible sources of uncertainty, quantify each of them individually, and mathematically estimate their combined effect on the system-level quantity of interest, in this case, the remaining flying time/distance of the unmanned aircraft. Note - This presentation contains all previously approved and published information.

Systems Health Managent↗

NASA NTRS1970

Nondissipative optimum charge regulator

Optimum charge regulator provides constant level charge/discharge control of storage batteries. Basic power transfer and control is performed by solar panel coupled to battery through power switching circuit. Optimum controller senses battery current and modifies duty cycle of switching circuit to maximize current available to battery.

Rosen, R.↗

NASA NTRS1999

MGS 2-Cell CPV NiH2 Batteries

The Nickel Hydrogen batteries used aboard the Mars Global Surveyor (MGS) is described. The mission itself is briefly described. This is followed by a description of the full electric power subsystem, and the design of the MGS 20 AH cell design. Graphic presentations show characteristics of the battery's performance. In summary, even though the charge control seems to be well, the method used is not recommended, due to an unexpected increase in pressure observed in flight. The operations have been modified to further minimize overcharge, and the pressure increase is not expected to impact the mission.

DiStefano, Sal↗