Patent Application
20110270559
Embodiments of the present disclosure relate generally to determining energy-output device parameters and, more specifically, to determining impedance and output characteristics of energy-storage devices.
The demand for Energy Storage Devices (ESDs) is significantly increasing as more environmentally friendly energy sources are developed and implemented in the field. The United States automotive industry, for example, is seeking to develop plug-in hybrid electric vehicle technologies that can operate a battery in charge depleting mode (i.e., all electric) for up to a 40-mile commute after 15 years of operation. However, energy storage technologies can be very expensive, and the need for accurate state-of-health (SOH) assessment is increasing. Though many SOH assessment techniques have been offered, no industry standard has yet been developed due to the complexity of the problem. Simple passive monitoring of voltage, current, and temperature can yield valuable information about the remaining capacity and energy, but it yields no information about power capability. Power can be determined from resistance, which usually requires charge depleting pulse tests or time consuming electrochemical impedance spectroscopy (EIS) measurements. Neither of these options are suitable for on board, in-situ SOH assessment.
A battery converts stored chemical energy to electrical energy, which may be conveyed as a voltage source. As with any non-ideal voltage source, the battery will have an internal impedance including a combination of resistance and reactance. The internal impedance produces power loss in a system by consuming power as a voltage drop across the source impedance. Ideally, a perfect battery would have no source impedance and deliver any power to the extent of its stored energy, but this is not physically reasonable. Thus, within physical limits, a reduction in source impedance will increase deliverable power.
As a battery ages the internal impedance generally tends to become larger. A brand new battery will have a Beginning Of Life (BOL) impedance much smaller than the End Of Life (EOL) impedance. Similarly, storage capacity of the battery will decrease from BOL to EOL. Therefore, observations of battery parameters such as internal impedance and storage capacity may be used to determine the overall State Of Health (SOH) of a battery. When the internal impedance becomes too large and the battery capacity can no longer reliably deliver energy at the specified power the battery has effectively reached EOL. Furthermore, the rate of change of a battery's internal impedance may be closely related to the state of health of the battery. This is especially true when considering rechargeable or secondary cells. While different secondary battery chemistries undoubtedly perform differently throughout their lives, increases in internal impedance over life at certain frequencies show promise as a uniform method to classify SOH in most chemistries.
Battery impedance also may vary with the relative charge of the battery and temperature. In other words, a battery at half of its rated capacity will have different impedance than a battery at its full rated capacity. Similarly, a battery at different temperatures will exhibit different internal impedance characteristics.
Battery fuel gauges, battery capacity monitors, and battery status monitors attempt to predict battery capacities and give the user an idea of remaining capacity. Conventionally, battery capacity is estimated by current integration, voltage monitoring, or combinations thereof.
Current integration, or coulomb counting as it is commonly called, monitors the battery's available stored charge by measuring the amount of charge that enters and exits the battery through normal cycling. The basis for this approach is, that if all charge and discharge currents are known, the amount of coulometric capacity will be known.
Voltage monitoring methods are based on the recognized relationship between the battery terminal voltage and the remaining capacity. All that is required is voltage measurement of the battery terminals to acquire a rough idea of the State Of Charge (SOC) of the battery.
Both of these methods have limits when applied to actual conditions. Current integration requires a rigorous amount of external current tracking to remain accurate. SOC determination obtained from measurement and integration of external current suffers from errors caused by internal self-discharge currents. If the battery is not used for several days, this self-discharge current dissipates the charge within the battery and can affect the current integration approximation for battery charge.
Voltage monitoring may show errors when measurements are taken with load on the battery. When a load is applied, the voltage drop due to the internal impedance of the battery distorts battery voltage. For many batteries, such as lithium-ion batteries, even after the load is removed, slow time constants and relaxation processes may continue to change the battery voltage for hours. Also, some battery chemistries (e.g., nickel metal-hydride) exhibit a strong voltaic hysteresis, which hinders the possibility of using voltage to track capacity.
Usually, these two methods are combined to operate together under varying conditions. For example, current integration may monitor the SOC while under discharging and charging currents. Whereas, while the battery is at rest voltage monitoring may be employed to monitor self-discharge.
SOC algorithms and measurement techniques are well known, but methods to predict battery life, or state of health (SOH), are less common. As mentioned earlier, SOH is also very dependent on cell impedance. If the cell impedance dependencies on SOC and temperature are known, or closely approximated, it is possible to employ modeling techniques to determine when a discharged voltage threshold will be reached at the currently observed load and temperature. Cell impedance analysis for SOH may be enhanced even more if the battery impedance estimation process were fast enough to eliminate the impedance dependencies on comparatively slow changes like SOC variations and temperature variations. Therefore, a way to monitor battery impedance in-situ at near real time would greatly enhance SOC and SOH predictions due to aging cells.
Conventionally, Electrochemical Impedance Spectroscopy (EIS) is a popular method for analyzing battery impedance. EIS generates a sine excitation waveform at a specific frequency that is applied to the battery. The voltage and current responses are monitored and analyzed to arrive at battery impedance for that particular frequency. Then, the frequency of the sine excitation signal is modified over a range of frequencies to arrive at a frequency spectrum of the battery impedance. This process provides stable, accurate measurements of battery impedance, but is most practical for laboratory conditions, not during in situ operation. In other words, EIS may not work well when the battery is under changing loads as changes imposed upon the sine wave excitation may skew the results. Also, the methodology of the EIS system is inherently serial (i.e., a single frequency for each step), making its application time consuming (often several hours for lower frequency sweeps) and inappropriate for a near real time analysis.
Therefore, to enhance monitoring of life and in-situ charge of a battery or other energy-output device under normal conditions, there is a need for a method and apparatus for determining energy-output device impedance using near real time measurement and analysis that may be employed during in situ operation.
Embodiments of the present disclosure provide improvements in methods and apparatuses determining energy storage device impedance using near real time measurement and analysis that may be employed during in situ operation.
In accordance with one embodiment of the present disclosure, an impedance analysis system for characterizing an energy storage device includes a signal vector assembler configured to generate a signal vector from a composition of one or more waveforms over a stimulus duration and a signal generator configured for generating a stimulus signal responsive to the signal vector and for switchable coupling to an energy storage device. A response measurement device is operably coupled to the stimulus signal and is configured for measuring a response signal indicative of a response of the energy storage device substantially simultaneously with when the stimulus signal is applied to the energy storage device. A load variation monitor is operably coupled to the energy storage device and is configured for monitoring load variations on the energy storage device due to operational circuitry coupled thereto. An analyzer is operably coupled to the response signal and is configured for analyzing the response signal relative to the signal vector to determine an impedance of the energy storage device.
In accordance with another embodiment of the present disclosure, a method of analyzing an energy storage device includes sampling a direct current value of the energy storage device resulting from operational circuitry coupled thereto. One or more switches are closed after sampling the direct current value to operably couple an impedance analysis system to the energy storage device. A signal vector is formed for analysis of the energy storage device from a composition of one or more waveforms and the signal vector is biased proportional to the direct current value. An impedance analysis is performed by generating a stimulus signal correlated to the signal vector, applying the stimulus signal to a terminal of the energy storage device, sampling a response of the energy storage device to the stimulus signal over a sampling duration, and analyzing the response of the energy storage device relative to the signal vector over the sampling duration to determine an impedance of the energy storage device.
In accordance with a yet another embodiment of the present disclosure, a method of analyzing an energy storage device includes monitoring load variations on the energy storage device resulting from operational circuitry coupled thereto and detecting a condition of interest from the load variations. The method also includes forming a signal vector for analysis of the energy storage device from a composition of one or more waveforms and performing an impedance analysis responsive to detecting the condition of interest. The impedance analysis includes generating a stimulus signal correlated to the signal vector, applying the stimulus signal to a terminal of the energy storage device, sampling a response of the energy storage device to the stimulus signal over a sampling duration, and analyzing the response of the energy storage device relative to the stimulus signal over the sampling duration to determine an impedance of the energy storage device.
In the following description, reference is made to the accompanying drawings which form a part hereof, and in which is shown, by way of illustration, specific embodiments in which the disclosure may be practiced. The embodiments are intended to describe aspects of the disclosure in sufficient detail to enable those skilled in the art to practice the invention. Other embodiments may be utilized and changes may be made without departing from the scope of the disclosure. The following detailed description is not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims.
Furthermore, specific implementations shown and described are only examples and should not be construed as the only way to implement the present disclosure unless specified otherwise herein. It will be readily apparent to one of ordinary skill in the art that the various embodiments of the present disclosure may be practiced by numerous other partitioning solutions.
In the following description, elements, circuits, and functions may be shown in block diagram form in order not to obscure the present disclosure in unnecessary detail. Conversely, specific implementations shown and described are exemplary only and should not be construed as the only way to implement the present disclosure unless specified otherwise herein. Additionally, block definitions and partitioning of logic between various blocks is exemplary of a specific implementation. It will be readily apparent to one of ordinary skill in the art that the present disclosure may be practiced by numerous other partitioning solutions. For the most part, details concerning timing considerations and the like have been omitted where such details are not necessary to obtain a complete understanding of the present disclosure and are within the abilities of persons of ordinary skill in the relevant art.
Those of ordinary skill in the art would understand that information and signals may be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be referenced throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof. Some drawings may illustrate signals as a single signal for clarity of presentation and description. It will be understood by a person of ordinary skill in the art that the signal may represent a bus of signals, wherein the bus may have a variety of bit widths and the present disclosure may be implemented on any number of data signals including a single data signal.
The various illustrative logical blocks, modules, and circuits described in connection with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a special purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.
Also, it is noted that the embodiments may be described in terms of a process that is depicted as a flowchart, a flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe operational acts as a sequential process, many of these acts can be performed in another sequence, in parallel, or substantially concurrently. In addition, the order of the acts may be re-arranged. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. Furthermore, the methods disclosed herein may be implemented in hardware, software, or both. If implemented in software, the functions may be stored or transmitted as one or more instructions or code on computer-readable media. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another.
It should be understood that any reference to an element herein using a designation such as “first,” “second,” and so forth does not limit the quantity or order of those elements, unless such limitation is explicitly stated. Rather, these designations may be used herein as a convenient method of distinguishing between two or more elements or instances of an element. Thus, a reference to first and second elements does not mean that only two elements may be employed there or that the first element must precede the second element in some manner. In addition, unless stated otherwise a set of elements may comprise one or more elements.
Elements described herein may include multiple instances of the same element. These elements may be generically indicated by a numerical designator (e.g., 110) and specifically indicated by the numerical indicator followed by an alphabetic designator (e.g., 110A) or a numeric indicator preceded by a “dash” (e.g., 110-1). For ease of following the description, for the most part element number indicators begin with the number of the drawing on which the elements are introduced or most fully discussed. Thus, for example, element identifiers on a
Embodiments of the present disclosure provide improvements in methods and apparatuses determining energy storage device impedance using near real time measurement and analysis that may be employed during in situ operation.
For ease of description, the terms “battery” and “energy storage device” (ESD) are used interchangeably herein and refer to any type of electrochemical energy storage device suitable for impedance measurements thereof.
The present disclosure involves an impedance analysis system including battery interface hardware (also referred to herein as an Impedance Measurement Box (IMB)) and control software. The impedance analysis system may be configured to apply one or more stimulus signals to a battery. The stimulus signals are generally configured as low-level, charge neutral signals and the impedance analysis system may be configured to automatically calculate impedance spectra under both no-load conditions and load conditions on the battery.
The stimulus signals can be of any suitable waveform and frequency that can yield impedance information about the battery. As non-limiting examples, some possible stimulus signal generators are discussed below.
One such method is known as “Fast Summation Transformation (FST)” as described in U.S. patent application Ser. No. 12/217,013 to Morrison et al., the contents of which is hereby incorporated by reference in its entirety. The FST measurement uses a bandwidth limited octave harmonic sum-of-sines signal that is injected into the battery. For each frequency of interest, an output response is rectified relative to the sine and cosine, with the samples added and normalized to a number of periods. An impedance spectrum is then determined with a simple linear algorithm that solves for the real and complex response.
Other exemplary methods of generating test signals include; Impedance Noise Identification (INI) as described in U.S. Pat. No. 7,675,293, Compensated Synchronous Detection (CSD) as described in U.S. Pat. No. 7,675,293, Reduced Time FST (RTFST) as described in U.S. patent application Ser. No. 12/772,880. The contents of each of these references is hereby incorporated by reference in their entirety.
The energy storage device 160 may be coupled to operational circuitry 165 via connection 167. The operational circuitry 165 represents any loads configured to be driven by the battery 160, which may discharge the battery 160, as well as any charging circuitry for restoring charge to the battery 160. The impedance analysis system 100 may be configured for in-situ operation. As such, the stimulus signal 122 may be coupled to the energy storage device for performing impedance analysis during normal operation of the operational circuitry 165.
A bias generator 170 may be included to generate a bias signal 172, which approximates the present voltage on the energy storage device 160. A bias reducer 152 may be included to compare the voltage at the energy storage device 160 to the bias signal 172 to obtain a difference between the two signals, which represents a bias-reduced response 154 to the stimulus signal 122. A voltage measurement device 150 may be coupled to the energy storage device 160 (connection not shown), or the bias-reduced response 154 to determine the voltage of the energy storage device 160.
In some systems, according to the present disclosure, the bias generator 170, signal vector assembler 110, filter 112, a trigger monitor 185, and an analyzer 190 may be discrete elements targeted at their specific function. However, in other systems according to the present disclosure, these functions may be included in a computing system 105. Thus, the computing system 105 may include software for performing the functions of assembling signal vectors, digital filtering, averaging the sampled voltage of the energy storage device 160 to generate the bias signal 172, and analyzing various input signals relative to the signal vector to determine impedance of the energy storage device 160. In still other systems, some of these functions may be performed with dedicated hardware and others may be performed with software.
In addition, the computing system 105 may include a display 195 for presenting control selection operations and data in a format useful for interpreting impedance characteristics of the energy storage device, as is explained more fully below. The display may also be used for presenting more general battery characteristics of interest, such as, for example, SOH or SOC. The computing system 105 may also include storage 198 for storing sampled information from any of the processes described below as well as for containing computing instructions for execution by the analyzer 190 to carry out the processes described below.
The signal vector assembler 110 may be any suitable apparatus or software for generating the signal vector 115 with an average amplitude substantially near zero. The signal vector assembler 110 may be configured as digital logic or as computer instructions for execution on the computing system 105. The smoothing filter 112 may be a bandpass filter used for smoothing the signal vector 115 by removing high frequencies and low frequencies to present an analog signal more suitable for application to the energy storage device 160. The smoothing filter 112 may include a digital filter configured as digital logic or as computer instruction for execution on the computing system 105. The smoothing filter 112 also may include an analog filter configured as analog elements. Finally, the smoothing filter 112 may include a digital filter and an analog filter in combination.
For example, a digital smoothing filter may bandwidth limit a random noise signal as the signal vector 115 and smooth transitions between the random data points. The digitally filtered random noise may then be filtered with analog elements to limit the bandwidth to be less than the Nyquist frequency of the sample rate for the analyzer 190.
The stimulus signal 122 may be applied to the energy storage device 160 in-situ during normal operation or possibly during other testing operations. For the in-situ application, the stimulus signal 122 should keep the energy storage device 160 substantially charge neutral. In other words, the stimulus signal 122 should have an average current substantially near zero. Therefore, the signal generator 120 may be configured to be voltage controlled, while keeping the energy storage device 160 charge neutral relative to the absence of the stimulus signal 122, and be transparent to the rest of the energy storage device system when not in use.
The actual current at the energy storage device 160 as a result of the stimulus signal 122 may be determined by a current measurement device 140 coupled to the stimulus signal 122 and configured to generate a measured current response 145.
Depending on the system and energy storage device 160, in some embodiments, it may be better to sample the response as a voltage, while in other embodiments, it may be better to sample the response as a current. Therefore, the current measurement device 140 and the voltage measurement device 150 may be referred to herein generically as a signal measurement device.
As mentioned earlier, for measuring the response of the energy storage device 160 to the stimulus signal 122, the bias reducer 130 may be coupled to the stimulus signal 122 and configured to generate a bias-reduced response 154. Since the energy storage device 160 is generally holding a charge while impedance data are gathered, a large bias voltage may be constantly present within all the voltage measurements. Thus, measuring the large voltage of the energy storage device 160 may require a large dynamic range on the order of many volts, whereas measuring the voltage response to the stimulus signal 122 may require sampling small changes on the order of micro-volts. A measurement system having the dynamic range necessary to measure the DC offset of the energy storage device 160 may not have the precision to measure the small variations in the voltage response.
Thus, the bias reducer 130 effectively subtracts the DC voltage offset from the voltage at the energy storage device 160 leaving the bias-reduced response 154, which substantially represents only the response of the energy storage device 160 to the stimulus signal 122.
A parameter measurement module 146 may be included for measuring and reporting to the analyzer 190 additional parameters of interest in systems with an energy storage device 160, such as, for example, state of charge, temperature, and indications of a discharging or charging state. This information may be sent to the analyzer 190 as a parameter signal 148.
A measured voltage response 155, whether direct or bias-reduced, the measured current response 145, and the parameter signal 148 may be operably coupled to the analyzer 190. The analyzer 190 is configured for periodically sampling signals that may be analog input signals (e.g., 145, 155, and 148) and converting them to digital data to create records of a time-varying voltage response, a time-varying ESD voltage (i.e., the voltage of the ESD with no stimulus applied), or a combination thereof. The time-varying voltage response may be used by the analyzer 190 for determining impedance characteristics of the energy storage device, as is discussed more fully below. The time-varying ESD voltage may also be used by the bias generator 170 for creating the bias signal 172.
The input signals (145, 155, and 148) may include a relatively small range of values. Thus to condition the input signals (145, 155, and 148) for sampling by the analyzer 190, it may be desirable to amplify the input signals with an optional amplifier 156. Furthermore, it may be desirable to filter the input signals with an optional filter 158. Filtering may be useful to remove noise (e.g., unwanted instrumentation noise, as opposed to the desired stimulus signal 122). Filtering also may be useful for anti-aliasing to remove high frequencies above the Nyquist frequency relative to the sampling rate of the analyzer 190.
The connections of the amplifier 156 and filter 158 shown in
One or more switches 162 may be included to allow for selective coupling and decoupling of the energy storage device 160 from the battery interface hardware. The energy storage device 160 is normally connected to the operational circuitry 165 as illustrated by signal 167. In some embodiments, the stimulus signal 122 may be coupled directly to signal 167 or indirectly through switch 162.
Some embodiments may include a calibration module 130. The calibration module may include one or more switches 132 to selectively couple it to the stimulus signal 122 and a calibration response signal 135. While not shown, a person of ordinary skill in the art will recognize that the calibration response signal 135 may also be conditioned through one or more of the amplifier 156 and the filter 158. The calibration module 130 may be stimulated in a manner similar to the way the energy storage device 160 will be stimulated during an impedance analysis. In other words, the same signal vector may be applied to the calibration module 130 as is to be applied to the energy storage device 160. As a result, with a baseline operation defined with known values of impedance from the calibration module 130, the analyzer can compensate for any system induced changes, such as for example, noise due to circuits and connections in the system and temperature of the system. As one example, the calibration module may include a variable shunt, with selectable impedance values to perform the calibration at various impedance values.
The switches 162 and 132 may be manually controlled or may be electrically controlled by the analyzer 190 and may be any suitable switch such as, for example, Field Effect Transistors (FETs) and relays.
Some embodiments may include a current sensor 182 watching a load variation on signal 167 to generate a load variation signal 184. The trigger monitor 185 senses the load variation signal 184. The current sensor 182 and the trigger monitor 185 may be collectively referred to herein as a load variation monitor. The load variation monitor examines changes in the form of current between the operational circuitry 165 and the energy storage device 160. Thus, the impedance analysis system 100 may be automatically triggered to perform impedance analysis operations in response to conditions of interest occurring during operation of the operational circuitry 165. Such conditions may be, for example, specific levels of charging, specific levels of discharging, anomalous pulses, expected pulses, and combinations thereof, as explained more fully below.
Thus, the impedance analysis system 100 may be fully automated, as part of an overall diagnostic system (e.g., a battery management system), may include a user interface for manual control applications, and may include both automated control and manual control. In many circumstances, it may be advantageous to have an automated control system that also accepts manual user input when required (e.g., automatic onboard vehicle diagnostics with periodic user updates during regular automotive maintenance service).
Once triggered, the control software instructs the battery interface hardware to inject a current signal into the energy storage device 160 and sense the response of the energy storage device 160. From the collected data, the control software may then calculate impedance spectra, archive the impedance spectra in the storage 198 for diagnostic applications displays information about the impedance spectra on the display 195 for a user, and combinations thereof.
The control software 200 may be configured to provide a user friendly interface 250 in the form of functional controls and a Graphical User Interface (GUI) to enable control and operation of performing in-situ impedance measurements on the energy storage device 160 under load or no load conditions. As a non-limiting example, the software architecture may be based around a central core 210 responsible for the software execution and sequencing. The central core 210 may be written in a programming language (e.g., C) such that it can be distributed to multiple target platforms with minimal changes. When activated, the central core 210 may trigger a signal generator 220 to inject the stimulus signal 122 into the energy storage device 160. The response may then be measured through a data acquisition block 230. Both the input and response may then be used to determine system impedance with a signal processing block 240. The central core 210 may then display information related to the impedance to a user through the GUI 250 and store the results in a database 260 for future use (e.g., state-of-health prognostic assessment).
A calibration process 300 may begin with an operator establishing system connection (e.g., laptop USB hook-up to the IMB, IMB plug in and power on). At operation block 302, the operator starts the IMB system software and in the control dialog box 410 selects “Calibrate.”
The impedance analysis system 100 opens the calibrate dialog box 510 as shown in
As indicated by operation block 304, if the user clicks on “Calibrate” in the calibrate dialog box 510, a message will pop up and direct the user to hook up the low value shunt. The operator does that, clicks the “OK” button and then the message goes away.
Operation block 306 indicates the impedance analysis is performed on the calibration module 130 (
Decision block 308 tests to see if impedance analysis has been performed for all shunt values. If not, control passes back to operation block 304, where a new message to the user appears directing the user to hook up the middle value shunt, or the high value shunt. The operator does that, clicks the “OK” button and then the message goes away. The impedance analysis is then repeated.
If all shunt values have been analyzed, operation block 310 indicates that the impedance analysis system 100 determines the baseline operation parameters based on a combination of the impedance analyses at each of the shunt values. As a non-limiting example, this determination may include a least squares fit between the various shunt analyses and a least squares fit to signal vector preset phase shift detected for each frequency response for a given shunt value. One example of resulting magnitude and phase parameters determined from the least squares fit are shown at the bottom of the calibrate dialog box 510.
Decision block 312 indicates that the operator may decide to run the calibration operation again, if desired. If so, control passes back to operation block 302. If not, the calibration process 300 is complete.
When the calibration process 300 is complete, a new message to the operator appears telling the operator that the system is calibrated and the system displays updated calibration constants. When the user clicks the “OK” button, the system accepts the new calibration and the control dialog box 410 returns.
With the calibration process complete, the user would now click “run” in the control dialog box 410 to perform a test, change the test conditions, or “Exit” to close the program.
As one example of parameters used to generate a signal vector 115 (
After calibration, or to run the impedance analysis system 100 without calibration, the operator may perform the following operations.
1. The operator establishes system connection (e.g., laptop USB hook-up to the IMB, IMB plug in and power on).
2. The user starts the IMB system software and in the control dialog box 410, the user can change default settings and observe control parameters as per Table 1 below. These control parameters may be saved until the program is closed and then may revert back to default values when the program is restarted. When the user clicks “Run,” the system software performs a battery impedance measurement and writes the results to a data output file. If the user clicks “Run” again, the “as set” parameters will be used and the test will run again.
In one embodiment, the signal generation block 220 (
The control dialog box 410 that might implement the User Directed Method (UDM) is shown in
As indicated by operation block 602, within the control dialog box 410, a user would review and change the default settings, if so desired, of the test conditions illustrated in the control dialog box 410. Table 1 summarizes some possible data inputs and data displayed in control dialog box 410.
Operation block 604 indicates that the user has selected “Run” from the control dialog box 410 to begin the impedance analysis, which begins with information in the form of input parameters to perform the test. These input parameters consist of data such as the number of frequencies, start frequency, stop frequency, desired RMS current, etc. The impedance analysis is capable of performing multiple types of tests and is not dependent on a specific test structure.
Operation block 606 indicates that the signal vector is generated by the signal generation module 220. The parameters acquired from the program interface module contain the necessary parameters and are passed to the signal generation module. The signal generation module 220 then returns the appropriate signal to the program core 210.
Operation blocks 608 and 610 indicate that the software directs the DAQ to issue the digital signal that closes the relay to power up the IMB and close the switches 162 to connect the impedance analysis system 100 to the energy storage device 160.
Operation block 612 indicates that a measurement of the Direct Current (DC) voltage of the energy storage device 160 may be sampled prior to a test. This measurement may be averaged and is output to the bias reducer 152 as the bias signal 172 prior to initiating the impedance analysis.
Operation block 614 indicates that the bias signal 172 and the signal vector 115 are output to the bias reducer 152 and the signal generator 120, respectively to perform the impedance analysis. Operation block 616 indicates that the analyzer 190 samples the measured voltage response 155 to create a time-varying voltage response for analysis.
Operation block 618 indicates that the switches 162 may now be opened to disconnect the impedance analysis system 100 from the energy storage device 160.
Operation block 620 indicates that the signal processing module 240 calculates impedance responsive to the signal vector 115 and the time-varying voltage response. Information about the energy storage device 160, such as, for example, a battery internal impedance spectrum may be derived.
Decision block 624 tests to see if the user desires to have the present analysis stored to a file. If so, operation block 626 indicates that the data is stored to a file.
Decision block 628 tests to see if the user desires to have the present analysis presented on the display 195. If so, operation block 630 indicates that the data is presented to the user.
Following this sequence, the program core 210 then may perform any necessary test cleanup, such as freeing temporary variables. After this step, the program core is ready to receive a “Start Test” call and perform another test. If no more tests are desired, the user exits the program and the interface module 250 notifies the program core 210. The program core 210 then performs all cleanup operations such as freeing persistent data, writing any desired performance variables to the system file, closing all libraries and then returning, thus, ending the session.
Differential amplifier 182 is configured to amplify a voltage drop across shunt resistor R1 and, therefore, gives a voltage signal (i.e., the load variation signal 184) indicative of the current being drawn from, or injected in, the energy storage device 160.
Another differential amplifier 150 acts as the voltage measurement device 150 (as shown in
Group 910 indicates operations and decisions to be performed by the user to set up the automated process. Group 930 indicates operations and decisions to be performed during the automated process.
Operation blocks 912 and 914 indicates that the user designs a test sequence by entering trigger parameters and test parameters, such as, for example, using the GUI illustrated in
Operation block 916 indicates that the designed test sequence is added to a list of test loops. Decision block 918 determines if the user wants to design more test sequences. If so, control transfers back to operation block 912. If not, control transfers to the automated process, indicated by Operation block 932 to start the automated run. While not shown, the user may set other parameters for the test sequence, such as, for example, how many times the test loop executes and the location of the results file.
Operation block 934 and decision block 936 indicate that the process executes a loop until a desired trigger condition is met. When a trigger condition is met, operation block 938 indicates that the impedance analysis associated with that trigger condition is performed.
Decision block 940 tests to see if there are more trigger conditions to monitor for within the current test sequence loop. If so, operation block 942 indicates that the next trigger condition is set and the process loops to begin monitoring for the next trigger condition.
Decision block 946 test to see if there are more test sequence loops to perform, of so, operation block 940 indicates that the first trigger for the next test sequence loop is set and the process loops to begin monitoring for the next trigger condition.
The IMB portable hardware components may be chosen such that they meet the particular requirements of the embedded electrochemical energy storage system to be measured. Additionally, the circuitry can be constructed for use with multiple DAQ system connections to allow for more flexibility in the measurement process (e.g., portable systems, DAQs with greater frequency ranges, etc.).
The impedance analysis system 100 may be configured for compact in-situ applications. In some embodiments, a protection circuit may be included to isolate the ESD 160 from the input signal when not in use. When triggered, the protection circuit initiates a pre-amp that activates an injection signal into the ESD and monitors the response through a data acquisition (DAQ) card. The data acquisition sends the necessary data back into the control software residing on a desktop computer, portable computer, or an embedded processor unit to calculate the impedance spectra. In this embodiment the combination of control using the DAQ performs functions similar to those discussed above for the switches 162 in
Operation block 1006 indicates that the DAQ performs a measurement of the ESD DC voltage just prior to the sending the excitation signal to the test ESD. Operation block 1008 indicates that the DAQ outputs a constant DC bias voltage to the IMB that is equal to an average of what the DAQ measured above. This bias voltage may be analog subtracted from the ESD voltage to enable a high-resolution detection of the ESD response to the excitation signal. Operation block 1010 indicates that the bias voltage is sent out by the DAQ.
Operation block 1012 indicates that the signal vector is sent to the IMB and, in turn, the IMB signal current to the ESD. This process initiates the stimulus signal to the test ESD. Operation block 1012 indicates that the analog voltage response from the ESD with the DAQ provided bias voltage subtracted off is sampled and digitized. Operation block 1014 indicates that the voltage response is recorded by the DAQ over the duration of the excitation signal.
Operation blocks 1016 and 1018 indicate the end of the impedance test and the DAQ ends the transmission of the stimulus signal and bias signal to the IMB and the excitation signal sent to the ESD ends. Operation block 1020 indicates that the DAQ directs the safety switches that connect the IMB to the ESD to open. Finally, operation block 1022 indicates that the DAQ disconnects the IMB power supply from its power source and, thus, shuts it down.
Table 2 lists the user interface features for an illustrative example of the present disclosure. An arrow pointing to the right indicates input by the user to the system and an arrow pointing to the left indicates input to the user by the system. All inputs have, as shown, default values that will change to what the user inputs. Those user inputs will be preserved until the program is closed and upon reopening will reset to the default values (except for calibration constants). When first started, a control dialog box opens (e.g.,
While the invention is susceptible to various modifications and implementation in alternative forms, specific embodiments have been shown by way of non-limiting examples in the drawings and have been described in detail herein. However, it should be understood that the invention is not intended to be limited to the particular forms disclosed. Rather, the invention includes all modifications, equivalents, and alternatives falling within the scope of the following appended claims and their legal equivalents.
This application claims the benefit of U.S. Provisional Patent Application Ser. No. 61/330,766, filed May 3, 2010, the disclosure of which is hereby incorporated herein in its entirety by this reference.
This invention was made with government support under Contract No. DE-AC07-05ID14517 awarded by the United States Department of Energy. The government has certain rights in the invention.
| Number | Date | Country | |
|---|---|---|---|
| 61330766 | May 2010 | US |
