Engineering topics
Publications and source records attributed to Christophersen, Jon P..
Impedance testing devices, circuits, systems, and related methods are disclosed. A method may include exciting a device coupled to a load, and capturing a response of the device. The method may further include adjusting the response based on an estimated load response of the device, and estimating an impedance of the device based on the adjusted response.
Impedance testing devices, circuits, systems, and related methods are disclosed. A Device Under Test (DUT) is excited with a multispectral excitation signal for an excitation time period while the DUT is under a load condition from a load operably coupled to the DUT. A response of the DUT is sampled over a sample time period. The sample time period is configured such that it includes an in-band interval during the excitation time period and one or more out-of-band intervals outside of the in-band interval. A response of the DUT to the load condition during the in-band interval is estimated by analyzing samples of the response from the one or more out-of-band intervals. Adjusted samples are computed by subtracting the estimated load response during the in-band interval from the samples from the in-band interval. An impedance of the DUT is estimated by analyzing the adjusted samples.
Real-time battery impedance spectra are acquired by stimulating a battery or battery system with a signal generated as a sum of sine signals at related frequencies. An impedance measurement device can be used to interface between the battery system and a host computer for generating the signals. The impedance measurement device may be calibrated to adapt the response signal to more closely match other impedance measurement techniques. The impedance measurement device may be adapted to operate at mid-range voltages of about 50 volts and high-range voltages up to about 300 volts.
Battery packs for electric and stationary applications experience varied operating conditions, including abuse—e.g., fast charging, overcharging, thermal, vibration, shock, etc.—throughout their lifetimes. Innovative diagnostic tools and algorithms that go beyond single cells and deal with modules and packs are essential for early detection of off-normal issues. High-resolution tools with known detection limits are key to developing appropriate mitigation strategies. With the advent of rapid impedance spectroscopy that can generate a broadband impedance spectrum in ~10 sec, the case for impedance-based diagnostics that can be readily aligned with other methods, such as incremental capacity or dQ.dV -1 , has become promising. This study used the aforementioned diagnostic methods to identify realistic in-vehicle battery abnormalities (e.g., localized self-discharge and non-uniform aging), in series (up to 10S) and parallel (4P) strings, using 16 Ah graphite/NMC cells. The impedance-based diagnostic is found to be sensitive to the string size and state. Depending on the type of abnormality, detection frequency varied. The dQ.dV -1 method showed the potential to detect long-term aging-related heterogeneity in modules. In general, both the impedance and dQ.dV -1 methods were able to detect series strings’ abnormalities, but struggled to find those issues within parallel modules.
Real-time battery impedance spectra are acquired by stimulating a battery or battery system with a signal generated as a sum of sine signals at related frequencies. An impedance measurement device can be used to interface between the battery system and a host computer for generating the signals. The impedance measurement device may be calibrated to adapt the response signal to more closely match other impedance measurement techniques. The impedance measurement device may be adapted to operate at mid-range voltages of about 50 volts and high-range voltages up to about 300 volts.
Battery impedance testing devices, circuits, systems, and related methods are disclosed. An impedance measurement device includes a current driver configured to generate an excitation current signal to be applied to a test battery responsive to a control signal, and a processor operably coupled with the current driver. The processor is configured to generate the control signal during an auto-ranging mode and a measuring mode. The auto-ranging mode applies the excitation current signal to the test battery over a plurality of different amplitudes to measure a response to the excitation current signal at each amplitude. The measuring mode applies the excitation current signal to the test battery for an amplitude responsive to the results of the auto-ranging mode. Improved sensitivity and resolution may be achieved for low impedance batteries with a rapid measurement time.
Accelerated degradation tests can be used as the basis for predicting the performance or state of health of products and materials at use conditions over time. Measurements acquired at accelerated levels of stress are used to develop models that relate to the degradation of one or more performance measures. Frequently, products/materials of interest are subjected to variable stress levels during their lifetimes. However, testing is usually performed only at a few fixed stress levels. In such cases, cumulative degradation models are developed and assessed by using data acquired under those fixed stress conditions. The degradation rate at any stress condition within the range of the model can be estimated by the derivative of the cumulative model at that stress condition. It follows that, to predict cumulative degradation over variable use conditions, one might integrate the fluctuating degradation rate over time. Existing approaches for doing this consider degradation rates that depend only on the current stress level. Here, we propose to allow the degradation rate to also depend on the current state of health as indicated by the associated performance measure(s). The resulting modeling approach is capable of portraying a broader range of degradation behavior than existing approaches. The assertion of memoryless degradation by using this or any other approach should be assessed experimentally with data acquired under variable stress in order to increase confidence that the integrated rate model is accurate. In this article, we demonstrate the additional capability of the proposed approach by developing empirical memoryless rate-based degradation models to predict resistance increase and capacity decrease in lithium-ion cells that are being evaluated for use in electric vehicles. Here, we then assess the plausibility of these models.
Systems and computer-implemented methods are used for analyzing battery information. The battery information may be acquired from both passive data acquisition and active data acquisition. Active data may be used for feature extraction and parameter identification responsive to the input data relative to an electrical equivalent circuit model to develop geometric-based parameters and optimization-based parameters. These parameters can be combined with a decision fusion algorithm to develop internal battery parameters. Analysis processes including particle filter analysis, neural network analysis, and auto regressive moving average analysis can be used to analyze the internal battery parameters and develop battery health metrics. Additional decision fusion algorithms can be used to combine the internal battery parameters and the battery health metrics to develop state-of-health estimations, state-of-charge estimations, remaining-useful-life predictions, and end-of-life predictions for the battery.
Energy storage cell impedance testing devices, circuits, and related methods are disclosed. An energy storage cell impedance measuring device includes a sum of sinusoids (SOS) current excitation circuit including differential current sources configured to isolate a ground terminal of the differential current sources from a positive terminal and a negative terminal of an energy storage cell. A method includes applying an SOS signal comprising a sum of sinusoidal current signals to the energy storage cell with the SOS current excitation circuit, each of the sinusoidal current signals oscillating at a different one of a plurality of different frequencies. The method also includes measuring an electrical signal at a positive terminal and a negative terminal of the energy storage cell, and computing an impedance of the energy storage cell at each of the plurality of different frequencies using the measured electrical signal.
Two testing protocols, QC/T 743 and those used by the U.S. Advanced Battery Consortium (USABC), were compared using cells based on LiFePO4/graphite chemistry. Differences in the protocols directly affected the data and the performance decline mechanisms deduced from the data. In all cases, the rate of capacity fade was linear with time. Overall, the testing protocols produced very similar data when the testing conditions and metrics used to define performance were similar. The choice of depth of discharge and pulse width had a direct effect on the apparent rate of resistance increased and estimated cell life. At greater percent depth of discharge (%DOD) and pulse width, the estimated life was shorter that at lower %DOD and shorter pulse width. This indicates that cells which were at the end of life based on the USABC protocol were not at end of life based on the QC/T 743 protocol by a large margin. (C) 2016 ELSEVIER B.V. All rights reserved.
Real-time battery impedance spectrum is acquired using a one-time record. Fast Summation Transformation (FST) is a parallel method of acquiring a real-time battery impedance spectrum using a one-time record that enables battery diagnostics. An excitation current to a battery is a sum of equal amplitude sine waves of frequencies that are octave harmonics spread over a range of interest. A sample frequency is also octave and harmonically related to all frequencies in the sum. A time profile of this sampled signal has a duration that is a few periods of the lowest frequency. A voltage response of the battery, average deleted, is an impedance of the battery in a time domain. Since the excitation frequencies are known and octave and harmonically related, a simple algorithm, FST, processes the time profile by rectifying relative to sine and cosine of each frequency. Another algorithm yields real and imaginary components for each frequency.
This battery test procedure manual was prepared for the United States Department of Energy (DOE), Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office. It is based on technical targets for commercial viability established for energy storage development projects aimed at meeting system level DOE goals for Electric Vehicles (EV). The specific procedures defined in this manual support the performance and life characterization of advanced battery devices under development for EVs. However, it does share some methods described in the previously published battery test manual for plug-in hybrid electric vehicles. Due to the complexity of some of the procedures and supporting analysis, future revisions including some modifications and clarifications of these procedures are expected. As in previous battery and capacitor test manuals, this version of the manual defines testing methods for full-size battery systems, along with provisions for scaling these tests for modules, cells or other subscale level devices. The DOE-United States Advanced Battery Consortium (USABC), Technical Advisory Committee (TAC) supported the development of the manual. Technical Team points of contact responsible for its development and revision are Chul Bae of Ford Motor Company and Jon P. Christophersen of the Idaho National Laboratory. The development of this manual was funded by the Unites States Department of Energy, Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office. Technical direction from DOE was provided by David Howell, Energy Storage R&D Manager and Hybrid Electric Systems Team Leader. Comments and questions regarding the manual should be directed to Jon P. Christophersen at the Idaho National Laboratory (jon.christophersen@inl.gov).
The increasing demand of lithium ion batteries for vehicle electrification is changing the typical use conditions that batteries may see. Over time, batteries may develop defects that are difficult to detect with traditional measurements. It is also possible that batteries may be left in an unknown state after failure of the monitoring system, loss of communication, or a potentially damaging event (such as an auto accident). It is therefore useful to explore other monitoring and interrogation methods that can better determine the stability of a battery in an unknown state. This work explores the use of electrochemical impedance spectroscopy as a method to determine the stability of batteries by observing changes in the complex impedance measurement as the cell is exposed to abusive conditions. Very dramatic changes to the internal resistance were observed when single cells were exposed to abusive conditions, suggesting that even single frequency impedance measurements could be effective with single cells. However tests on three cell series and parallel strings yielded smaller changes, primarily to the charge transfer resistance, showing that complex impedance measurements are more appropriate as the system increases in complexity. A rapid impedance tool developed at Idaho National Laboratory was tested and compared to traditional potentiostat tools as well. This was shown to yield similar data to the traditional tools, providing a potential method for continuous monitoring of a battery system. It was observed, however, that shifts in the data are difficult to detect in very transient systems.
This battery test procedure manual was prepared for the United States Department of Energy (DOE), Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office. It is based on technical targets for commercial viability established for energy storage development projects aimed at meeting system level DOE goals for Plug-in Hybrid Electric Vehicles (PHEV). The specific procedures defined in this manual support the performance and life characterization of advanced battery devices under development for PHEV’s. However, it does share some methods described in the previously published battery test manual for power-assist hybrid electric vehicles. Due to the complexity of some of the procedures and supporting analysis, future revisions including some modifications and clarifications of these procedures are expected. As in previous battery and capacitor test manuals, this version of the manual defines testing methods for full-size battery systems, along with provisions for scaling these tests for modules, cells or other subscale level devices. The DOE-United States Advanced Battery Consortium (USABC), Technical Advisory Committee (TAC) supported the development of the manual. Technical Team points of contact responsible for its development and revision are Renata M. Arsenault of Ford Motor Company and Jon P. Christophersen of the Idaho National Laboratory. The development of this manual was funded by the Unites States Department of Energy, Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office. Technical direction from DOE was provided by David Howell, Energy Storage R&D Manager and Hybrid Electric Systems Team Leader. Comments and questions regarding the manual should be directed to Jon P. Christophersen at the Idaho National Laboratory (jon.christophersen@inl.gov).
Electrochemical impedance spectroscopy has been shown to be a valuable tool for diagnostics and prognostics of energy storage devices such as batteries and ultra-capacitors. Although measurements have been typically confined to laboratory environments, rapid impedance spectrum measurement techniques have been developed for on-line, embedded applications as well. The prototype hardware for the rapid technique has been validated using lithium-ion batteries, but issues with calibration had also been identified. A new, universal automatic calibration technique was developed to address the identified issues while also enabling a more simplified approach. A single, broad-frequency range is used to calibrate the system and then scaled to the actual range and conditions used when measuring a device under test. The range used for calibration must be broad relative to the expected measurement conditions for the scaling to be successful. Validation studies were performed by comparing the universal calibration approach with data acquired from targeted calibration ranges based on the expected range of performance for the device under test. First, a mid-level shunt range was used for calibration and used to measure devices with lower and higher impedance. Next, a high excitation current level was used for calibration, followed by measurements using lower currents. Finally, calibration was performed over a wide frequency range and used to measure test articles with a lower set of frequencies. In all cases, the universal calibration approach compared very well with results acquired following a targeted calibration. Additionally, the shunts used for the automated calibration technique were successfully characterized such that the rapid impedance measurements compare very well with laboratory-scale measurements. These data indicate that the universal approach can be successfully used for onboard rapid impedance spectra measurements for a broad set of test devices and range of measurement conditions.
Real-time battery impedance spectrum is acquired using a one-time record. Fast Summation Transformation (FST) is a parallel method of acquiring a real-time battery impedance spectrum using a one-time record that enables battery diagnostics. An excitation current to a battery is a sum of equal amplitude sine waves of frequencies that are octave harmonics spread over a range of interest. A sample frequency is also octave and harmonically related to all frequencies in the sum. The time profile of this signal has a duration that is a few periods of the lowest frequency. The voltage response of the battery, average deleted, is the impedance of the battery in the time domain. Since the excitation frequencies are known and octave and harmonically related, a simple algorithm, FST, processes the time record by rectifying relative to the sine and cosine of each frequency. Another algorithm yields real and imaginary components for each frequency.