Batteries and other electrical energy storage devices have become widely used in not only military, space, and commercial applications but also in domestic applications. Therefore, it has become even more important to be able to efficiently and effectively obtain an accurate estimation of the battery's state-of-health. While voltage, current, and temperature may be used to gauge the remaining capacity of a battery, in critical applications it is also necessary to know impedance and power capability to get an accurate picture of battery health. Ideally, any measurement of battery health is done in-situ and has minimal impact on the battery. A great deal of work has been conducted to test battery impedance without effecting battery status. This work is documented in, for example, U.S. Pat. Nos. 7,688,036; 7,395,163 B1; 7,675,293 B2; 8,150,643 B1; 8,352,204 B2; 8,762,109 B2; 8,868,363 B2; and 9,244,130 B2, and U.S. Published Patent Application Nos. 2011/0270559 A1; 2014/0358462 A1; and 2017/0003354 A1. Each variation of the methods described in these documents improve the process of assessing battery health by, for example, increasing resolution. Recently, a method for testing battery impedance has been described that increases the resolution of a known system by a factor of ten. Key features of this high resolution method involve a new algorithm, auto-ranging to obtain the optimum level of excitation current, and increased preamplifier gain. The method also required an additional measurement channel that captures time records of the Sum-Of-Sines (SOS) current in addition to the SOS voltage from the test battery.
Although the above-methods have refined this important process, an improved method for calibration that will greatly simplify the calibration process and eliminate the extra measurement channel needed for some methods is still needed.
All patents, patent applications, provisional patent applications and publications referred to or cited herein, are incorporated by reference in their entirety to the extent they are not inconsistent with the teachings of the specification.
The invention involves an improved method of calibrating impedance measurements of a battery. The method needs only a single measurement with a known Sum of Sines (SOS) current, at the desired frequency spread and known root mean squared (RMS) current.
The method of the subject invention is involves single shunt calibration (SSC) that applies to all generations of (Impedance Measurement Box) IMB. The subject method simplifies use of the IMB to assess battery health. The Idaho National Laboratory (INL) has described the design and construction of the IMB in numerous patent documents (see, for example, U.S. Pat. Nos. 7,688,036; 7,395,163 B1; 7,675,293 B2; 8,150,643 B1; 8,352,204 B2; 8,762,109 B2; 8,868,363 B2; and 9,244,130 B2, and U.S. Published Patent Application Nos. 2011/0270559 A1; 2014/0358462 A1; and 2017/0003354 A1). Spectrum algorithms used in the implementation are also described in the above patent documents and include, but are not limited to, harmonic compensated synchronous detection (HCSD), fast summation transformation (FST), generalized fast summation transformation (GFST), frequency cross talk compensation (FCTC), time cross talk compensation (TCTC), harmonic orthogonal synchronous transformation (HOST). Each of these spectrum algorithms are special cases of a rapid Fourier Transform that bring the measurement time record captured by the IMB measurement into the frequency domain at only the frequencies that were part of the IMB excitation signal to the test battery. The calibration in the present generation 50V IMB (U.S. Patent Application Publication No. 2014/0358462) is accomplished by a complicated measurement scheme which uses 3 different shunts to generate calibration constants that yield a very accurate measurement of the impedance spectra from a test battery (Morrison, William. H., thesis, 2012). In contrast, the claimed method requires only a single measurement with a known Sum Of Sines (SOS) current, at the desired frequency spread and known RMS current.
As an example consider application to the 50V IMB (U.S. Patent Application Publication No. 2014/0358462). With the present 50V IMB HCSD algorithm system (U.S. Patent Application Publication No. 2014/0358462), the calibration for a given SOS frequency spread (octave harmonic short 0.1 Hz to 1638.4 Hz or long 0.0125 Hz to 1638.4 Hz) and a given SOS RMS current, the measurement time record that is processed into the frequency domain is typically one period of the lowest frequency. As part of the calibration the SOS current output is pre-emphasized to mitigate the IMB system frequency response. Additionally, the 3 shunt calibration scheme computes gain and offset constants for both magnitude and phase at each frequency. Equation 1 represents the time record captured by the IMB from a measurement on a test battery.
VB(t)=ISOS(t)*AS(t)*ZB(t) (1)
Where: ISOS(t) is the SOS current time record
The * in Equation 1 is a convolution operation. Because of the calibration pre-emphasis, ISOS(t) is given by:
Where: RMS is the RMS of the SOS current
Equations 1 and 2 brought into the frequency domain via the 50V IMB HCSD algorithm (Morrison, William H., thesis, 2012) becomes:
Where: ASi□ ϕSi is the measurement system frequency response at the ith frequency
Clearly the calibration applied to Equation 3 results in the desired battery impedance and the 50V IMB has demonstrated this with great success via the 3 shunt magnitude calibration and the stepped phase shift calibration both yielding gain offset calibration constants that represent Equation 4 (Morrison, William H. thesis, 2012). Observe that Equation 4 is a calibration constant that is a combination of SOS current pre-emphasis and magnitude phase calibration at each frequency. The subject method does everything in a single measurement with a single shunt, single shunt calibration (SSC).
For the 50V IMB system the concept is very simple. The system will perform a spectrum measurement on a known non-inductive shunt for example a 50 mOhm non-inductive calibration shunt (as shown in
For the single shunt calibration (SSC), we assume that single shunt used is constant and independent of frequency over the frequency range of the IMB. Additionally, all measurements are made without any pre-emphasis. Thus as a function of time the IMB measurement of that shunt VSHUNT(RMS,iΔt) is given by Equation 5.
VSHUNT(RMS,iΔt)=VSOS(RMS,iΔt)*HOUT(t)*RSHUNT*HIN(iΔt) (5)
Where *: indicates the convolution operation
Where: ZBAT(t) is the impedance impulse response of the battery as a function of time.
For the SSC the time record of the shunt (Equation 5) is processed by the HCSD algorithm of the IMB, normalized by RSHUNT and stored as calibration. Equation 7 illustrated the shunt time record brought into the frequency domain at one of the SOS frequencies ωi.
VSHUNT(ωi)=VSOS(RMS,ωi)HOUT(ωi)RSHUNTHIN(ωi) (7)
Where: ωi is radians/sec
Note that the convolution operation in Equation 5 goes to multiplication in Equation 7. The time record of the battery given by Equation 6 when brought into the frequency domain at one of the SOS frequencies ωi is given by Equation 8.
VMeas(ωi)=VSOS(RMS,ωi)HOUT(ωi)ZBAT(ωi)HIN(ωi) (8)
Performing division in the frequency domain the essence of calibration is given by Equation 9.
Thus the SSC is a collection of measurements of RSHUNT at standardized RMS currents and SOS frequency spreads brought into the frequency domain by the HCSD algorithm. For the IMB there are 2 standardized frequency ranges and 4 standardized RMS currents. To calibrate for this, results in 8 measurements with the single shunt for SSC which are performed fully automated with a single shunt hook-up. A vast improvement over the original manual 3 shunt calibration process.
Observe Equation 7, if in addition to being normalized to the shunt if it were normalized also to the calibration RMS current it can be used as a calibration for any battery measurement RMS current by scaling it to that measurement RMS current.
The 50V IMB at Montana Tech of the University of Montana (Butte, Mont.) was used for initial testing. A long run frequency (0.0125 Hz to 1638.4 Hz) domain calibration file was generated (via HCSD) with-out any calibration or pre-emphasis at an SOS current of 500 mA and a 50 mOhm shunt (as shown in
These results show that 500 mA shunt data can reach all the way down the 62.5 mA to capture the spectra of TC #3 and the results match closely with INL EIS (Solartron Analytical, 2012) data for TC #3 (as shown in
As stated previously, with the single shunt calibration, with standard RMS currents and standard frequency ranges, a calibration is fully automated with as few as 8 measurements. Never the less, that can be reduced to a single calibration measurement with frequency scaling and RMS current scaling. In examining Equation 5 for calibration it would be normalized to the calibration RMS current and the shunt value. Then for a calibration it would be scaled by the measurement RMS. Consider the RMS of an SOS:
Where: M is the number of frequencies
Small lead acid battery measured by IMB with 62.5 mA and 15 frequency SOS. IMB spectrum obtained with normal IMB calibration. Uncalibrated time record post processed to the frequency domain and calibrated by an 18 frequency 500 mA shunt time record brought to the frequency domain and scaled to 15 frequency and 62.5 mA RMS. Both spectra are given in
The fundamental assumption of all IMB data processing algorithms is that the system being measured is in steady state relative to all excitation frequencies. Clearly this is in contradiction to the requirement of performing a rapid measurement. The IMB measurement technique is to excite the test article with a sum of sinusoids with an excitation time record of no more than one period of the lowest frequency. Some researchers using the IMB measurement concept (Waligo, A., 2016) have resorted to using multiple periods of the lowest frequency in order to re-inforce this assumption. A better solution is “Negative Time” (NT), whereby the sum of sinusoids starting at time zero would all be zero but if one goes backwards in time for a fraction of the period of the lowest frequency, then start the excitation there, this has been shown to work very well to establish the steady state approximation (10% is typical) (as shown in
When a calibration is scaled the objective is to make VP of a measurement and calibration the same thus the frequency range could be kept standardized as subsets of the calibration frequency range. Never the less, for non-standard subsets, even non-octave harmonic subsets processed via time or frequency CTC (U.S. Pat. No. 8,762,109) the technique of “cubic spline” (U.S. Pat. No. 8,868,363) will select out the calibration constants and they will scale exactly as the above relationship (as shown in
A critical feature of the concept for a High Resolution Impedance Measurement Box (HRIMB) is its ability to digitize signals where the voltage level of the signal is near and occasionally beyond the saturation level of the digitizer within the Data Acquisition system (DAQ). This capability of the HRIMB is realized by replacing the data processing algorithm (HCSD Morrison, W. H., thesis, 2012)) with a variation of time or frequency CTC (U.S. Pat. No. 8,762,109) (TCTC, FCTC). This feature for these 2 algorithms is achieved by examining the captured voltage time record for saturation points (as shown in
Demonstration of saturation tolerance Time CTC algorithm with a 12V lead acid car battery, 500 mA RMS SOS current, 15 frequencies (0.1 Hz to 1638.4 Hz) plotted with the IMB HCSD measurement response is shown in
It is understood that the foregoing examples are merely illustrative of the present invention. Certain modifications of the articles and/or methods may be made and still achieve the objectives of the invention. Such modifications are contemplated as within the scope of the claimed invention.
This United States patent application is a continuation of U.S. patent application Ser. No. 16/852,231, filed Apr. 17, 2020, now U.S. Pat. No. 10,942,240, issued Mar. 9, 2021, which is a continuation of U.S. patent application Ser. No. 16/432,822, filed Jun. 5, 2019, now U.S. Pat. No. 10,656,233, issued May 19, 2020, which is a continuation of U.S. patent application Ser. No. 15/497,142, filed Apr. 25, 2017, now U.S. Pat. No. 10,436,873, issued Oct. 8, 2019, which claims the benefit of U.S. Provisional Patent Application No. 62/331,730, filed May 4, 2016, and U.S. Provisional Patent Application No. 62/326,923, filed Apr. 25, 201, the disclosures of which are hereby incorporated by reference in their entirety including all figures, tables and drawings.
Number | Name | Date | Kind |
---|---|---|---|
4498044 | Horn | Feb 1985 | A |
4697134 | Burkum et al. | Sep 1987 | A |
5061890 | Longini | Oct 1991 | A |
5261007 | Hirsch | Nov 1993 | A |
5281920 | Wurst | Jan 1994 | A |
5349535 | Gupta | Sep 1994 | A |
5406496 | Quinn | Apr 1995 | A |
5457377 | Jonsson | Oct 1995 | A |
5512832 | Russel et al. | Apr 1996 | A |
5747456 | Chorev et al. | May 1998 | A |
5773978 | Becker | Jun 1998 | A |
5821757 | Alvarez et al. | Oct 1998 | A |
5946482 | Barford et al. | Aug 1999 | A |
5969625 | Russo | Oct 1999 | A |
6002238 | Champlin | Dec 1999 | A |
6072299 | Kurle et al. | Jun 2000 | A |
6160382 | Yoon et al. | Dec 2000 | A |
6208147 | Yoon et al. | Mar 2001 | B1 |
6222369 | Champlin | Apr 2001 | B1 |
6249186 | Ebihara et al. | Jun 2001 | B1 |
6262563 | Champlin | Jul 2001 | B1 |
6307378 | Kozlowski | Oct 2001 | B1 |
6313607 | Champlin | Nov 2001 | B1 |
6330933 | Boeckman et al. | Dec 2001 | B1 |
6340889 | Sakurai | Jan 2002 | B1 |
6359419 | Verbrugge et al. | Mar 2002 | B1 |
6417669 | Champlin | Jul 2002 | B1 |
6472847 | Lundberg | Oct 2002 | B2 |
6481289 | Dixon et al. | Nov 2002 | B2 |
6519539 | Freeman et al. | Feb 2003 | B1 |
6532425 | Boost et al. | Mar 2003 | B1 |
6542077 | Joao | Apr 2003 | B2 |
6556001 | Wiegand et al. | Apr 2003 | B1 |
6639385 | Verbrugge et al. | Oct 2003 | B2 |
6646419 | Uing | Nov 2003 | B1 |
6653817 | Tate, Jr. et al. | Nov 2003 | B2 |
6691095 | Singh et al. | Feb 2004 | B2 |
6693439 | Liu et al. | Feb 2004 | B1 |
6778913 | Tinnemeyer | Aug 2004 | B2 |
6816797 | Freeman et al. | Nov 2004 | B2 |
6832171 | Barsoukov et al. | Dec 2004 | B2 |
6839597 | Hattori et al. | Jan 2005 | B2 |
6876174 | Samittier Marti et al. | Apr 2005 | B1 |
6922058 | Potempa | Jun 2005 | B2 |
7019542 | Tinnemeyer | Mar 2006 | B2 |
7051008 | Singh et al. | May 2006 | B2 |
7065474 | Petchenev et al. | Jun 2006 | B2 |
7072871 | Tinnemeyer | Jul 2006 | B1 |
7113853 | Hecklinger | Sep 2006 | B2 |
7259572 | Houldsworth et al. | Aug 2007 | B2 |
7349816 | Quint et al. | Mar 2008 | B2 |
7395163 | Morrison | Jul 2008 | B1 |
7567057 | Elder et al. | Jul 2009 | B2 |
7598700 | Elder et al. | Oct 2009 | B2 |
7616003 | Satoh et al. | Nov 2009 | B2 |
7675293 | Christophersen et al. | Mar 2010 | B2 |
7688036 | Yarger et al. | Mar 2010 | B2 |
7688074 | Cox et al. | Mar 2010 | B2 |
7698078 | Kelty et al. | Apr 2010 | B2 |
7898263 | Ishida et al. | Mar 2011 | B2 |
7928735 | Huang et al. | Apr 2011 | B2 |
8035396 | Kim | Oct 2011 | B2 |
8150643 | Morrison | Apr 2012 | B1 |
8193771 | Coccio | Jun 2012 | B2 |
8332342 | Saha et al. | Dec 2012 | B1 |
8352204 | Morrison et al. | Jan 2013 | B2 |
8368357 | Ghantous et al. | Feb 2013 | B2 |
8410783 | Staton | Apr 2013 | B2 |
8415926 | Bhardwaj et al. | Apr 2013 | B2 |
8427112 | Ghantous et al. | Apr 2013 | B2 |
8447544 | Hsu et al. | May 2013 | B2 |
8467984 | Gering | Jun 2013 | B2 |
8487628 | Sciarretta et al. | Jul 2013 | B2 |
8513921 | Berkowitz et al. | Aug 2013 | B2 |
8521497 | Gering | Aug 2013 | B2 |
8532945 | Sciarretta et al. | Sep 2013 | B2 |
8548762 | Prada et al. | Oct 2013 | B2 |
8582675 | Harris | Nov 2013 | B1 |
8598849 | Bhardwaj et al. | Dec 2013 | B2 |
8638070 | Maluf et al. | Jan 2014 | B2 |
8648602 | van Lammeren | Feb 2014 | B2 |
8680868 | van Lammeren et al. | Mar 2014 | B2 |
8710847 | Marvin et al. | Apr 2014 | B2 |
8725456 | Saha et al. | May 2014 | B1 |
8738310 | Swanton | May 2014 | B2 |
8738311 | Wu | May 2014 | B2 |
8762109 | Christophersen et al. | Jun 2014 | B2 |
8773145 | Phlippoteau et al. | Jul 2014 | B2 |
8791669 | Ghantous et al. | Jul 2014 | B2 |
8831897 | McHardy | Sep 2014 | B2 |
8838401 | Kelly | Sep 2014 | B2 |
8849598 | Mingant et al. | Sep 2014 | B2 |
8868363 | Morrison et al. | Oct 2014 | B2 |
8878549 | Nakanishi et al. | Nov 2014 | B2 |
8889309 | Manabe et al. | Nov 2014 | B2 |
8901886 | Berkowitz et al. | Dec 2014 | B2 |
8907631 | Gurries et al. | Dec 2014 | B1 |
8907675 | Phlippoteau et al. | Dec 2014 | B2 |
8952823 | Xie et al. | Feb 2015 | B2 |
8970178 | Berkowitz et al. | Mar 2015 | B2 |
8975874 | Berkowitz et al. | Mar 2015 | B2 |
9030173 | McHardy et al. | May 2015 | B2 |
9035621 | Berkowitz et al. | May 2015 | B2 |
9035623 | Berkowitz et al. | May 2015 | B1 |
9063018 | Ghantous et al. | Jun 2015 | B1 |
9121910 | Maluf et al. | Sep 2015 | B2 |
9142994 | Berkowitz et al. | Sep 2015 | B2 |
9207285 | Swanton et al. | Dec 2015 | B1 |
9244130 | Morrison et al. | Jan 2016 | B2 |
9252465 | Hariharan | Feb 2016 | B2 |
9312577 | Jamison | Apr 2016 | B2 |
9373972 | Ghantous et al. | Jun 2016 | B2 |
9385555 | Ghantous et al. | Jul 2016 | B2 |
9461492 | Berkowitz et al. | Oct 2016 | B1 |
9465077 | Love et al. | Oct 2016 | B2 |
9519031 | Jamison | Dec 2016 | B2 |
9669723 | Sugeno et al. | Jun 2017 | B2 |
9851414 | Morrison et al. | Dec 2017 | B2 |
10189354 | Brochhaus | Jan 2019 | B2 |
10345384 | Christophersen et al. | Jul 2019 | B2 |
10379168 | Christophersen et al. | Aug 2019 | B2 |
10436873 | Morrison et al. | Oct 2019 | B1 |
10656233 | Morrison et al. | May 2020 | B2 |
10942240 | Morrison et al. | Mar 2021 | B2 |
20010035756 | Kozlowski | Nov 2001 | A1 |
20020065621 | Jungerman | May 2002 | A1 |
20030184307 | Kozlowski et al. | Oct 2003 | A1 |
20030206021 | Laletin et al. | Nov 2003 | A1 |
20040095249 | Zaccaria | May 2004 | A1 |
20040162683 | Verbrugge et al. | Aug 2004 | A1 |
20050086070 | Engelman | Apr 2005 | A1 |
20050127908 | Schlicker et al. | Jun 2005 | A1 |
20050182584 | Plusquellic | Aug 2005 | A1 |
20060111854 | Plett | May 2006 | A1 |
20060111870 | Plett | May 2006 | A1 |
20060170397 | Srinivasan et al. | Aug 2006 | A1 |
20060186890 | Iwane et al. | Aug 2006 | A1 |
20060284617 | Kozlowski et al. | Dec 2006 | A1 |
20060284618 | Cho et al. | Dec 2006 | A1 |
20070172708 | Takebe et al. | Jul 2007 | A1 |
20070182371 | Boebel | Aug 2007 | A1 |
20070182576 | Proska et al. | Aug 2007 | A1 |
20070257681 | Christophersen et al. | Nov 2007 | A1 |
20080303528 | Kim | Dec 2008 | A1 |
20090076752 | Wang et al. | Mar 2009 | A1 |
20090278037 | Grothe, Jr. | Nov 2009 | A1 |
20100010762 | Seki | Jan 2010 | A1 |
20100121588 | Elder et al. | May 2010 | A1 |
20100201320 | Coe et al. | Aug 2010 | A1 |
20100207772 | Yamamoto | Aug 2010 | A1 |
20100274510 | Morrison | Oct 2010 | A1 |
20100332165 | Morrison et al. | Dec 2010 | A1 |
20110018543 | Bos et al. | Jan 2011 | A1 |
20110077879 | Paryani | Mar 2011 | A1 |
20110077880 | Gering | Mar 2011 | A1 |
20110082621 | Berkobin et al. | Apr 2011 | A1 |
20110169452 | Cooper et al. | Jul 2011 | A1 |
20110270559 | Christophersen et al. | Nov 2011 | A1 |
20110301931 | Gering | Dec 2011 | A1 |
20120019253 | Ziegler et al. | Jan 2012 | A1 |
20120032688 | Christophersen et al. | Feb 2012 | A1 |
20120038452 | Phlippoteau et al. | Feb 2012 | A1 |
20120078552 | Mingant et al. | Mar 2012 | A1 |
20120105070 | van Lammeren et al. | May 2012 | A1 |
20120188086 | Xie et al. | Jul 2012 | A1 |
20120217985 | Amanuma | Aug 2012 | A1 |
20120262186 | Morrison et al. | Oct 2012 | A1 |
20120316815 | Morigaki | Dec 2012 | A1 |
20130002267 | Kothandaraman et al. | Jan 2013 | A1 |
20130069660 | Bernard et al. | Mar 2013 | A1 |
20130135110 | Xie et al. | May 2013 | A1 |
20130141109 | Love et al. | Jun 2013 | A1 |
20130229156 | Brandon et al. | Sep 2013 | A1 |
20130245973 | Ross, Jr. et al. | Sep 2013 | A1 |
20130267943 | Hancock | Oct 2013 | A1 |
20140125284 | Qahouq | May 2014 | A1 |
20140188414 | Jeong et al. | Jul 2014 | A1 |
20140358462 | Christophersen | Dec 2014 | A1 |
20140372054 | Wang et al. | Dec 2014 | A1 |
20140372055 | Wang et al. | Dec 2014 | A1 |
20150002105 | Kelly | Jan 2015 | A1 |
20150165921 | Paryani | Jun 2015 | A1 |
20150168500 | Jamison | Jun 2015 | A1 |
20150197159 | Lee | Jul 2015 | A1 |
20150280290 | Saha et al. | Oct 2015 | A1 |
20160157014 | Van Schyndel et al. | Jun 2016 | A1 |
20160157015 | Van Schyndel | Jun 2016 | A1 |
20160274060 | Denenberg et al. | Sep 2016 | A1 |
20170003354 | Morrison et al. | Jan 2017 | A1 |
20170254859 | Christophersen et al. | Sep 2017 | A1 |
20180143257 | Garcia et al. | May 2018 | A1 |
20190214937 | Schmidt | Jul 2019 | A1 |
Number | Date | Country |
---|---|---|
2447728 | Jun 2013 | EP |
2000-009817 | Jan 2000 | JP |
2003-090869 | Mar 2003 | JP |
2003-223918 | Aug 2003 | JP |
2007-085772 | Apr 2007 | JP |
2011-174925 | Sep 2011 | JP |
2012-078287 | Apr 2012 | JP |
2013-517755 | May 2013 | JP |
014-106119 | Jun 2014 | JP |
2015-078992 | Apr 2015 | JP |
2004106946 | Dec 2004 | WO |
2010144834 | Dec 2010 | WO |
2010144857 | Dec 2010 | WO |
2011041094 | Apr 2011 | WO |
2011140123 | Nov 2011 | WO |
2011140131 | Nov 2011 | WO |
WO 2012025706 | Mar 2012 | WO |
2013085996 | Jun 2013 | WO |
2014070831 | May 2014 | WO |
2015029647 | Mar 2015 | WO |
2016012922 | Jan 2016 | WO |
WO 2017003917 | Jan 2017 | WO |
2020223630 | Nov 2020 | WO |
2020223651 | Nov 2020 | WO |
Entry |
---|
Adany et al. Switching algorithms for extending battery life in Electric Vehicles. Journal of Power Sources, Jun. 2013, 231:50-59. |
Ahmed et al. Enabling fast charging—A battery technology gap assessment. Journal of Power Sources, Nov. 2017, 367:250-262. |
Baert et al. Determination of the State-of-Health of VRLA Batteries by Means of Noise Measurements. Intelec 2001, Nov. 2001, Conference Publication No. 484, pp. 301-306. |
Bald et al. Hardware Architecture for Rapid Impedance Measurements of 50V Battery Modules. San Diego: The International Society of Automation, 58th International Instrumentation Symposium, INL/CON-12-24516, Jun. 2012, 18 pages. |
Banaei et al. Online Detection of terminal voltage in Li-ion Batteries via Battery Impulse Response. IEEE, Oct. 2009, pp. 194-198. |
Barsukov et al. Challenges and Solutions in Battery Fuel Gauging. Power Management Workbook, 2004, 10 pages, Texas Instruments Inc. |
Beelen et al. A comparison and accuracy analysis of impedance-based temperature estimation methods for Li-ion batteries. Applied Energy, Aug. 2016, 175:128-140. |
Berecibar et al. Critical review of state of health estimation methods of Li-ion batteries for real applications. Renewable and Sustainable Energy Reviews, Apr. 2016, 56:572-587. |
Blanke, et al. Impedance measurements on lead-acid batteries for state-of-charge, state-of-health and cranking capability prognosis in electric and hybrid electric vehicles. Journal of Power Sources, Jun. 2005, 144:418-425. |
Blidberg. Correlation between different impedance measurement methods for battery cells. KTH Chemical Science and Engineering, 2012, 42 pages, Stockholm, Sweden. |
Bohlen et al. Impedance Based Battery Diagnosis for Automotive Applications. 35th Annual IEEE Power Electronics Specialists Conference, Apr. 2004, 4:2192-2797. |
Bose et al. Battery state of health estimation through coup de fouet: field experience. INTELEC, Twenty-Second International Telecommunications Energy Conference (Cat. No.00CH37131), 2000, pp. 597-601. |
Bose et al. Lessons Learned in Using OHMIC Techniques for Battery Monitoring. IEEE, 2001, pp. 99-104. |
Brauer et al. Residential Energy Storage from Repurposed Electric Vehicle Batteries: Market Overview and Development of a Service-Centered Business Model. IEEE 18th Conference on Business Informatics, Aug. 2016, pp. 143-152. |
Breugelmans et al. Odd random phase multisine electrochemical impedance spectroscopy to quantify a non-stationary behaviour: Theory and validation by calculating an instantaneous impedance value. Electrochimica Acta, Aug. 2012, 76:375-382. |
Burnham et al. Enabling fast charging—Infrastructure and economic considerations. Journal of Power Sources, Nov. 2017, 367:237-249. |
Cabrera-Castillo et al. Calculation of the state of safety (SOS) for lithium ion batteries. Journal of Power Sources, Aug. 2016, 324:509-520. |
Carkhuff et al. Impedance-Based Battery Management System for Safety Monitoring of Lithium-Ion Batteries. IEEE Transactions on Industrial Electronics, Aug. 2018, 65(8):6497-6504. |
Chan. Swept Sine Chirps for Measuring Impulse Response. Stanford Research Systems Inc., https://thinksrs.com/downloads/pdfs/applicationnotes/SR1_SweptSine.pdf, 2010, 6 pages. |
Cheng et al. Battery-Management System (BMS) and SOC Development for Electrical Vehicles. IEEE Transactions on Vehicular Technology, Jan. 2011, 60(1):76-88. |
Christensen et al. Using on-board Electrochemical Impedance Spectroscopy in Battery Management Systems. World Electric Vehicle Journal, Nov. 2013, 6:0793-0799. |
Cordioli et al. Development of a Methodology Based on Odd Random Phase Electrochemical Impedance Spectroscopy to Evaluate Corrosion Protection of Coatings. Proceedings of the 4th International Conference on Self-Healing Materials, Jun. 2013, pp. 152-155. |
Cox et al. Battery State of Health Monitoring, Combining Conductance Technology with other Measurement Parameters for Real-Time Battery Performance Analysis. INTELEC, International Telecommunications Energy Conference (Proceedings), Feb. 2000, 19-2, pp. 342-347. |
Crow et al. Integrated Prognostic Health Monitoring of Battery Health in Ground Robots. Penn State Applied Research Laboratory, 32nd Association for Unmanned Vehicle Systems International Meeting, Jun. 2005, 16 pages. |
Damlund. Analysis and Interpretation of AC-measurements on Batteries used to assess State-of-Health and Capacity-condition. IEEE, 1995, pp. 828-833. |
Diard et al. Constant load vs constant current EIS study of electrochemical battery discharge. Electrochimica Acta, 1997, 42(23-24):3417-3420. |
Diard et al. EIS study of electrochemical battery discharge on constant load. Journal of Power Sources, Jan. 1998, 70(1):78-84. |
Diard et al. Impedance measurements of polymer electrolyte membrane fuel cells running on constant load. Journal of Power Sources, Aug. 1998, 74(2):244-245. |
Dung et al. ILP-Based Algorithm for Lithium-Ion Battery Charging Profile. IEEE, 2010, pp. 2286-2291. |
Farmann et al. Critical review of on-board capacity estimation techniques for lithium-ion batteries in electric and hybrid electric vehicles. Journal of Power Sources, May 2015, 281:114-130. |
Goebel et al. Prognostics in Battery Health Management. IEEE Instrumentation & Measurement Magazine, Sep. 2008, 11(4):33-40. |
Gopalakrishnan et al. Electrochemical impedance spectroscopy characterization and parameterization of lithium nickel manganese cobalt oxide pouch cells: dependency analysis of temperature and state of charge. Ionics 25(1), Jan. 2019 (published online Jun. 2018, Springer, Germany), 14 pages. |
Gould et al. New Battery Model and State-of-Health Determination Through Subspace Parameter Estimation and State-Observer Techniques. IEEE Transactions on Vehicular Technology. Oct. 2009, 58(8):3905-3916. |
Guha et al. Remaining Useful Life Estimation of Lithium-Ion Batteries based on the Internal Resistance Growth Model. Indian Control Conference (ICC), Jan. 2017, pp. 33-38. |
Hariprakash et al. Monitoring sealed automotive lead-acid batteries by sparse-impedance spectroscopy. Proc. Indian Acad. Sci. (Chem. Sci.), Oct. 2003, 115(5):465-472. |
Hariprakash et al. On-line monitoring of lead-acid batteries by galvanostatic non-destructive technique. Journal of Power Sources, Oct. 2004, 137(1):128-133. |
Harting et al. State-of-Health Diagnosis of Lithium-Ion Batteries Using Nonlinear Frequency Response Analysis. Journal of The Electrochemical Society, Jan. 2019, 166(2):A277-A285. |
Hill et al. Steady State Frequency Response Utilizing an Enhanced Chirp Test Signal. 2019 IEEE Aerospace Conference, Mar. 2019, pp. 1-8. |
Hlavac et al. VRLA Battery Monitoring Using Conductance Technology. IEEE, 12-3 (1995) pp. 284-291. |
Howey et al. On-line measurement of battery impedance using motor controller excitation. IEEE Transactions on Vehicular Technology, Jul. 2014, 63(6):2557-2566. |
Huang et al. An Online Battery Impedance Measurement Method Using DC-DC Power Converter Control. IEEE Transactions on Industrial Electronics, Nov. 2014, 61(11):5987-5995. |
Karden et al. A method for measurement of interpretation of impedance spectra for industrial batteries. Journal of Power Sources, Jan. 2000, 85(1):72-78. |
Kolmel et al. Quality-oriented production planning of battery assembly systems for electric mobility. Procedia CIRP 23, Dec. 2014, pp. 149-154. |
Kozlowski A Novel Online Measurement Technique for AC Impedance of Batteries and Other Electrochemical Systems. The Sixteenth Annual Battery Conference on Applications and Advances (Proceedings), Jan. 2001, pp. 257-262. |
Kozlowski. Electrochemical Cell Prognostics using Online Impedance Measurements and Model-Based Data Fusion Techniques. Aerospace Conference, 2003 Proceedings, vol. 7-3257, Mar. 2003, 14 pages. |
Lamb et al. Determination of Battery Stability With Advanced Diagnostics. SAND2017-6959, Unlimited Release, Jul. 2017, 56 pages. |
Lamb et al. Determination of Battery Stability With Advanced Diagnostics. (Report No. DOT HS 812 249), Washington, DC: National Highway Traffic Safety Administration, Mar. 2016, 42 pages. |
Le et al. Lithium-ion Battery State of Health Estimation Using Ah-V Characterization. Annual Conference of the Prognostics and Health Management Society, 2011, 3(1), 7 pages. |
Li et al. Understanding the molecular mechanism of pulse current charging for stable lithium-metal batteries. Science Advances, Jul. 2017, 3(7), 10 pages. |
Love et al. State-of-Health Monitoring of 18650 4S Packs With a Single-Point Impedance Diagnostic. Journal of Power Sources, Oct. 2014, 266:512-519. |
Lu et al. A review on the key issues for lithium-ion battery management in electric vehicles. Journal of Power Sources, Mar. 2013, 226:272-288. |
Mingant et al. Towards onboard Li-ion battery state-of-health diagnosis by a virtual sensor. World Electric Vehicle Journal, May 2012, 5(2):405-411. |
Novak. Developing an advanced, predictive battery health monitoring solution with a low-cost microcontroller solution. Texas Instruments, White Paper, Sep. 2012, 6 pages. |
Noworolski et al. Reducing and Utilizing Electrical Noises for Battery Monitoring Purposes. IEEE 32-4 (Sep. 2004), pp. 511-614. |
Okoshi et al. Battery condition monitoring (BCM) technologies about lead-acid batteries. Journal of Power Sources, Aug. 2006, 158(2):874-878. |
Pastor-Fernandez et al. A Comparison between EIS and IC-DV as Li-ion Diagnostic Techniques to Identify and Quantify the Effects of Degradation Modes within BMS. Journal of Power Sources, Aug. 2017, 360:301-318. |
Pastor-Fernandez et al. A Study of Cell-to-Cell Interactions and Degradation in Parallel Strings: Implications for the Battery Management System. Journal of Power Sources, Oct. 2016, 329:574-585. |
PEREZ e t al. Guidelines for the characterization of the internal impedance of lithium-ion batteries in PHM algorithms. International Journal of Prognostics and Health Management, Apr. 2018, ISSN 2153-2648, 11 pages. |
Piret et al. Tracking of electrochemical impedance of batteries. Journal of Power Sources, Apr. 2016, 312:60-69. |
Pop et al. State-of-the-art of battery state-of-charge determination. Measurement Science and Technology, Dec. 2005, 16(4) R93-R110. |
Qnovo. Fundamentals of Qnovo Adaptive Charging in Lithium Ion Batteries. http://qnovo.com/wp-content/uploads/2015/12/Qvovo_TechWhitePaper_v2.4.pdf, Dec. 2015, 13 pages. |
Rahmoun et al. Determination of the Impedance of Lithium-Ion Batteries using Methods of Digital Signal Processing. Energy Procedia, Dec. 2014, 46:204-213. |
Raijmakers et al. Crosstalk Interferences on Impedance Measurements in Battery Packs. IFAC-PapersOnline, Jun. 2016, 49(11):042-047. |
Saha et al. Comparison of Prognostic Algorithms for Estimating Remaining Useful Life of Batteries. Transactions of the Institute of Measurement and Control, Jun. 2009, 31(3), 10 pages. |
Saha et al. Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework. IEEE Transactions on Instrumentation and Measurement, Feb. 2009, 58(2):291-296. |
Salehen et al. Development of battery management systems (BMS) for electric vehicles (EVs) in Malaysia. MATEC Web of Conferences, Jan. 2017, 90(11):01001, 8 pages. |
Sazhin et al. Enhancing Li-Ion Battery Safety by Early Detection of Nascent Internal Shorts. Journal of The Electrochemical Society, Jan. 2017, 164(1):A6281-A6287. |
Schweiger et al. Comparison of Several Methods for Determining the Internal Resistance of Lithium Ion Cells. Sensors, Jun. 2010, 10(6):5604-5625. |
Singh et al. Fuzzy logic modeling of EIS measurements on lithium-ion batteries. Electrochimica Acta, Jan. 2006, 51(8):1673-1679. |
Socher et al. Improving the functional safety of automotive batteries using in-situ impedance spectroscopy. Transportation Research Procedia, Dec. 2016, 14:3661-3666. |
Srivastav et al. State-of-charge indication in Li-ion batteries by simulated impedance spectroscopy. J Appl Electrochem, Feb. 2017, 47(2):229-236. |
Sternad et al. Condition monitoring of Lithium-Ion Batteries for electric and hybrid electric vehicles. Elektrotechnik & Informationstechnik, May 2009, 126(5):186-193. |
Stroe et al. Diagnosis of Lithium-Ion Batteries State-of-Health based on Electrochemical Impedance Spectroscopy Technique. Proceedings of the 2014 Energy Conversion Congress and Exposition (ECCE) IEEE Press, Sep. 2014, pp. 1576-4582. |
Jespersen et al. Capacity Measurements of Li-Ion Batteries using AC Impedance Spectroscopy. EVS24 International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium, May 2009, in: 2009 World Electric Vehicle Journal, 3(1):0127-0133. |
Jiang et al. Electrochemical impedance spectra for lithium-ion battery ageing considering the rate of discharge ability. Energy Procedia, May 2017, 105:844-849. |
Kiel et al. Extensive Validation of A Nonintrusive Continuous Battery Monitoring Device. Battcon 2008, in: Proc. BattCon Conference May 2008, pp. 18-1-18-10. |
Mingant et al. Novel state-of-health diagnostic method for Li-ion battery in service. Applied Energy, Elsevier, Dec. 2016, 183:390-398. |
Perez et al. Guidelines for the Characterization of the Internal Impedance of Lithium-Ion Batteries in PHM Algorithms. International Journal of Prognostics and Health Management, Apr. 2018, 9, 11 pages. |
Remy et al. Qualification and Life Testing of Li-ion VES16 Batteries. E3S Web of Conferences 16, Jan. 2017, 8 pages. |
Seo et al. Detection of internal short circuit in Li-ion battery by estimating its resistance. Proceedings of the 4th IIAE International Conference on Intelligent Systems and Image Processing, Jan. 2016, 6 pages. |
Taberna et al. Electrochemical Characteristics and Impedance Spectroscopy Studies of Carbon-Carbon Supercapacitors. Journal of the Electrochemical Society, Jan. 2003, 150(3):A292-A300. |
Tachibana et al. Development of in situ a.c. impedance measurement system under constant-current conditions and its application to galvanostatic discharge of electrolytic manganese dioxide in alkaline solution. Journal of Power Sources, Jul. 1998, 74(1):29-33. |
Tang et al. Temperature Dependent Performance and in Situ AC Impedance of High-Temperature PEM Fuel Cells Using the Nafion-112 Membrane. Journal of The Electrochemical Society, Sep. 2006, 153(11):A2036-A2043. |
Varnosfaderani et al. A Comparison of Online Electrochemical Spectroscopy Impedance Estimation of Batteries. IEEE Access, Feb. 2018, vol. 6, pp. 23668-23677. |
Verizon. Verizon NEBSTM Compliance: Qualification Requirements for Lithium Ion (Li-ion) Cells Batteries and Battery Strings. Verizon Technical Purchasing Requirements VZ.TPR.9810, Sep. 2015, Issue 1, 29 pages. |
Waligo et al. A Comparison of the Different Broadband Impedance Measurement Techniques for Lithium-Ion Batteries. Energy Conversion congress and Exposition (ECCE), IEEE (Sep. 2016), 7 pages. |
Waters. Monitoring the state of health of VRLA batteries through ohmic measurements. Proceedings of Power and Energy Systems in Converging Markets, Oct. 1997, 6 pages. |
Weng et al. On-board state of health monitoring of lithium-ion batteries using incremental capacity analysis with support vector regression. Journal of Power Sources, Aug. 2013, 235:36-44. |
Wu et al. A Review on Fault Mechanisms and Diagnosis Approach for Li-Ion Batteries. Journal of Nanomaterials, Oct. 2015, vol. 2015, Article ID 631263, 10 pages. |
Xing et al. Battery Management Systems in Electric and Hybrid Vehicles. Energies, Oct. 2011, 4(11):1840-1857. |
Xing et al. Prognostics and Health Monitoring for Lithium-ion Battery. Proceedings of the IEEE International Conference on Intelligence and Security Informatics, Jul. 2011, pp. 242-247. |
Yamada et al. The intelligent automotive battery, ‘CYBOX’. Journal of Power Sources, Dec. 2008, 185(2):1478-1483. |
Yoo et al. An Electrochemical Impedance Measurement Technique Employing Fourier Transform. Analytical Chemistry, May 2000, 72(9):2035-2041. |
Zechang et al. Battery Management Systems in the China-made ‘Start’ series FCHVs. IEEE Vehicle Power and Propulsion Conference (VPPC) (Sep. 2008) 6 pages. |
Zenati et al. Estimation of the SOC and the SOH of li-ion batteries, by combining impedance measurements with the fuzzy logic inference. Proceedings of the 36th Annual Conference of IEEE Industrial Electronics, Nov. 2010, pp. 1767-1772. |
Zhai et al. The Application of the EIS in Li-ion Batteries Measurement. Journal of Physics: Conference Series, Oct. 2006, 48(1):1157-1161. |
Zhang et al. Cycling degradation of an automotive LiFePO4 lithium-ion battery. Journal of Power Sources, Feb. 2011, 196(3):1513-1520. |
Zhang et al. Prediction of Lithium-Ion Battery's Remaining Useful Life Based on Relevance Vector Machine. SAE Int. J. All. Power, May 2016, 5(1):30-40. |
Zhang et al. Remote Vehicle State of Health Monitoring and Its Application to Vehicle No-Start Prediction. 2009 IEEE Autotestcon, Oct. 2009, pp. 88-93. |
Zhu et al. PSpice Simulation via AC Impedance for PEFC at Operational Loads. http://folk.ntnu.no/skoge/prosl/proceedings/aiche-2005/topical/pdffiles/T1/papers/215c.pdf, 2005, 3 pages. |
Zhu et al. In-Situ Electrical Characterization of PEM Fuel Cells at Load. American Institute of Chemical Engineers, 2007, 5 pages. |
Zhu et al. In-Stu Assessment of PEM Fuel Cells via AC Impedance at Operational Loads. http://folk.ntnu.no/skoge/prost/proceedings/aiche-2004/pdffiles/papers/014g.pdf, 2004, 5 pages. |
Zou et al. Combined State of Charge and State of Health estimation over lithium-ion battery cell cycle lifespan for electric vehicles. Journal of Power Sources, Jan. 2015, 273:793-803. |
Morrison, W.H., Development and Implementation of a Calibration Procedure for Complex Impedance Spectrum Measurements with Applications to Embedded Battery Health Monitoring and Management Systems, University of Connecticut Master's Theses 353, 2012, digitalcommons.uconn.edu/gs_theses/353, Hartford, Connecticut. |
Naligo, A., Barendse, P., A comparison of the Different Broadband Impedance Measurement Techniques for Lithium-ion Batteries, IEEE Energy Conversion Congress and Exposition (ECCE), Sep. 2016. |
Solartron Analytical, 1260A Impedance / Gain-Phase Analyzer (2017); Website, https://www.ameteksi.com, 2 pages, originally downloaded Apr. 19, 2019. |
U.S. Appl. No. 15/497,142, filed Apr. 25, 2017. |
U.S. Appl. No. 62/331,730, filed May 4, 2016. |
U.S. Appl. No. 62/326,923, filed Apr. 25, 2016. |
U.S. Department of Energy. Battery Calendar Life Estimator Manual: Modeling and Simulation. U.S. Department of Energy Vehicle Technologies Program, Revision 1, Oct. 2012, INL-EXT-08-15136, 84 pages. |
Aglzim et al. Characterization of the Resistance of a Fuel Cell on Load by Electrochemical Impedance Spectroscopy. Proceedings from the EUROCON Conference, IEEE 2007, pp. 1489-1492. |
Albrecht. Battery Complex Impedance Identification with Random Signal Techniques. MS Thesis, Montana Tech of the University of Montana, May 2005, 99 pages. |
Ashtiani. Battery Hazard Modes and Risk Mitigation Analysis. USABC Version 0.0, Aug. 2007, 10 pages. |
Bald. Rapid Impedance Measurements for 50-V Battery Modules. Montana Tech MS Thesis, 2012, 115 pages. |
U.S. Department of Energy. Battery Test Manual For 12 Volt Start/Stop Vehicles, U.S. Department of Energy Vehicle Technologies Program, INL/EXT-12-26503, Revision 1, May 2015, 67 pages. |
U.S. Department of Energy. Battery Test Manual For 48 Volt Mild Hybrid Electric Vehicles, U.S. Department of Energy Vehicle Technologies Program, INL/EXT-15-36567, Revision 0, Mar. 2017, 70 pages. |
U.S. Department of Energy. Battery Test Manual for Plug-In Hybrid Electric Vehicles, U.S. Department of Energy Vehicle Technologies Program, INL/EXT-14-32849, Revision 3, Sep. 2014, 83 pages. |
Belt et al. Calendar and PHEV cycle life aging of high-energy, lithium-ion cells containing blended spinel and layered-oxide cathodes. Journal of Power Sources, Dec. 2011, 196(23):10213-10221. |
Bloom et al. An Investigation of the Impedance Rise and Power Fade in High-Power Li-Ion Cells. 19th International Electric Vehicle Symposium (EVS-19), Oct. 2002, 14 pages. |
Chan. Swept Sine Chirps for Measuring Impulse Response. Application Note, Stanford Research Systems Inc., 2010, https://thinksrs.com/downloads/pdfs/applicationnotes/SR1_SweptSine.pdf. |
Chen et al. Sinusoidal-Ripple-Current Charging Strategy and Optimal Charging Frequency Study for Li-Ion Batteries. IEEE Transactions on Industrial Electronics, Jan. 2013, 60(1):88-97. |
Cho et al. Battery Impedance Analysis Considering DC Component in Sinusoidal Ripple-Current Charging. IEEE Transactions on Industrial Electronics, Mar. 2016, 63(3):1561-1573. |
Christophersen et al. Battery Technology Life Verification Testing and Analysis. Idaho National Laboratory INL/CON-07-12282, Dec. 2007, 12 pages. |
Christophersen et al. Performance Evaluation of Gen3 Advanced Technology Development Cells. 214th ECS Meeting, Abstract #549, The Electrochemical Society, 2008, 1 page. |
Christophersen et al. Pulse resistance effects due to charging or discharging of high-power lithium-ion cells: A path dependence study. Journal of Power Sources, Nov. 2007,173(2):998-1005. |
Christophersen et al. Advanced Technology Development Program for Lithium-Ion Batteries: Gen 2 Performance Evaluation Final Report. INL/EXT-05-00913, Jul. 2006, 140 pages. |
Christophersen et al. Crosstalk Compensation for a Rapid, Higher-Resolution Impedance Spectrum Measurement. Aerospace Conference, 2012 IEEE, Mar. 2012, 16 pages. |
Christophersen et al. Effects of Reference Performance Testing during Aging Using Commercial Lithium-ion Cells. J. Electrochem Soc., May 2006, 153(7):A1406-A1416. |
Christophersen et al. Electrochemical Impedance Spectroscopy Testing on the Advanced Technology Development Program Lithium-ion Cells. Sep. 2002, IEEE Trans. Veh. Technol., 56(3):1851-1855. |
Christophersen et al. Long-Term Validation of Rapid Impedance Spectrum Measurements as a Battery State-of-Health Assessment Technique. SAE Int. J. Alt. Power, May 2013, 6(1):146-155. |
Christophersen et al. Lumped Parameter Modeling as a Predictive Tool for a Battery Status Monitor. Oct. 2003, Proceedings from IEEE Vehicular Technology Conference, 6 pages. |
Christophersen et al. Rapid Impedance Spectrum Measurements for State-of-Health Assessment of Energy Storage Devices. SAE Int. J. Passeng. Cars—Electron. Electr. Syst., Apr. 2012, 5(1), 11 pages. |
Christophersen et al. Impedance Noise Identification for State-of-Health Prognostics. 43rd Power Sources Conference, Jul. 2008, 4 pages. |
Christopherson. Battery Test Manual For Electric Vehicles. Idaho National Laboratory, U.S. Department of Energy Vehicle Technologies Program, INL/ EXT-15-34184, Revision 3, Jun. 2015, 67 pages. |
Delaille et al. Study of the ‘coup de foue7’ of lead-acid cells as a function of their state-of-charge and state-of-health. Journal of Power Sources, Aug. 2006,158(2):1019-1028. |
Din et al. A Scalable Active Battery Management System With Embedded Real-Time Electrochemical Impedance Spectroscopy. IEEE Transactions on Power Electronics, Jul. 2017, 32(7):5688-5698. |
Din et al. Online Spectroscopic Diagnostics Implemented in an Efficient Battery Management System. 16th Workshop on Control and Modeling for Power Electronics, 2015, 7 pages. |
Doan et al. Intelligent Charger with Online Battery Diagnosis Function. 9th International Conference on Power Electronics-ECCE Asia, Jun. 2015, pp. 1644-1649. |
Doughty et al. FreedomCAR Electrical Energy Storage System Abuse Test Manual for Electric and Hybrid Electric Vehicle Applications. SAND2005-3123, Aug. 2006, 46 pages. |
Egloff et al. A Critical Analysis of an Instrumentation Current Sources. 59th International Instrumentation Symposium, May 2013, 12 pages. |
Fasmin et al. Review—Nonlinear Electrochemical Impedance Spectroscopy. Journal of The Electrochemical Society, May 2017, 164(7):H443-H455. |
Fenton et al. BSM Development Documentation Senior Project Final Report for the Idaho National Laboratory. May 2005, Montana Tech of the University of Montana, 21 pages. |
Ford, Jr. Validation of Push Pull Current. Proceedings of the Annual Montana Tech Electrical and General Engineering Symposium, Jan. 2016, 25 pages. |
Garcia et al. On-line State-of-Health and Remaining-Useful-Life Assessment of Batteries using Rapid Impedance Spectrum Measurements. 45th Power Sources Conference Proceedings, Jun. 2012, 7.3, pp. 115-118. |
Haskins et al. Battery Technology Life Verification Test Manual. Idaho National Laboratory, Feb. 2005, INEEL/EXT-04-01986, 133 pages. |
Hirschorn et al. On Selection of the Perturbation Amplitude Required to Avoid Nonlinear Effects in Impedance Measurements. Israel Journal of Chemistry, 2008, vol. 48, pp. 133-142. |
Hoffmann et al. Development and Test of a Real Time Battery Impedance Estimation System. IEEE Aerospace 2006 Conference, Mar. 2006, IEEE 0-7803-9546-8/06, 8 pages. |
Huet. A review of impedance measurements for determination of the state-of-charge or state-of-health of secondary batteries. Journal of Power Sources, Jan. 1998, 70(1):59-69. |
Morrison et al. An Advanced Calibration Procedure for Complex Impedance Spectrum Measurements of Advanced Energy Storage. 58th International Instrumentation Symposium, Jun. 2012, INL/CON-12-24519, 17 pages. |
Morrison et al. Fast Summation Transformation for Battery Impedance Identification. IEEE Aerospace Conference, Mar. 2009, 9 pages. |
Morrison et al. Real Time Estimation of Battery Impedance. IEEE Aerospace Conference, Mar. 2006, 13 pages. |
Morrison. DC Buffering and Floating Current for a High Voltage IMB Application. INL/EXT-14-32858, Aug. 2014, 8 pages. |
Morrison. Development and Implementation of a Calibration Procedure for Complex Impedance Spectrum Measurements with Applications to Embedded Battery Health Monitoring and Management Systems. University of Connecticut Master's Thesis, 2012, 119 pages. |
Morrison. Signals and Systems: State Variable Description of Linear Time Invariant Systems. Montana Tech Digital Commons, Sep. 2013, Chapter 17, pp. 198-216. |
Morrison. Signals and Systems: Synchronous Detection. Montana Tech Digital Commons, Sep. 2013, Chapter 20, pp. 243-246. |
Motloch et al. High-Power Battery Testing Procedures and Analytical Methodologies for HEV's. 7, SAE Int. Passenger Cars Electron. Electr. Syst., vol. 111 (2002), pp. 797-802. |
Nikolopoulos et al. Accurate Method of Representation of High-Voltage Measuring Systems and its Application in High-Impulse-Voltage Measurements. IEEE, Mar. 1989, 136(2):66-72. |
Piller et al. Methods for state-of-charge determination and their applications. Journal of Power Sources, Jun. 2001, 96(1):113-120. |
Qahouq et al. Single-Perturbation-Cycle Online Battery Impedance Spectrum Measurement Method With Closed-Loop Control of Power Converter. IEEE Transactions on Industrial Electronics, Sep. 2017, 64(9):7019-7029. |
Qahouq. Online Battery Impedance Spectrum Measurement Method. IEEE Applied Power Electronics Conference and Exposition, Mar. 2016, pp. 3611-3615. |
Ramos et al. Comparison of impedance measurements in a DSP using ellipse-fit and seven-parameter sine-fit algorithms. Measurement, May 2009, 42(9):1370-1379. |
Ran et al. Prediction of State of Charge of Lithium-ion Rechargeable Battery with Electrochemical Impedance Spectroscopy Theory. 5th IEEE Conference on Industrial Electronics and Applications, Jul. 2010, pp. 684-688. |
Ranade et al. An overview of harmonics modeling and simulation, Tutorial on Harmonics Modeling and Simulation. IEEE Power Engineering Society, 1998, Chapter 1, 7 pages. |
Ranieri et al. Electronic Module for the Thermal Monitoring of a Li-ion Battery Cell through the Electrochemical Impedance Estimation. 22nd International Workshop on Thermal Investigations of ICs and Systems, Sep. 2016, pp. 294-297. |
Smith et al. Model Validation Approaches for Nonlinear Feedback Systems Using Frequency Response Measurements. IEEE Proceedings of the 38th IEEE Conference on Decision and Control, Dec. 1999, vol. 2, pp. 1500-1504. |
Smyth. Development of a Real Time Battery Impedance Measuring System. M.S. Thesis, Montana Tech of the University of Montana, 2008, 128 pages. |
Thomas et al. Statistical methodology for predicting the life of lithium-ion cells via accelerated degradation testing. Journal of Power Sources, Sep. 2008, 184(1):312-317. |
Unkflhaeuser et al. Electrochemical Storage System Abuse Test Procedure Manual. United States Advanced Battery Consortium, SAND99-0497, Jul. 1999, 33 pages. |
Varnosfaderani et al. Online Impedance Spectroscopy Estimation of a dc-dc converter connected Battery using an Earth Leakage Monitoring Circuit. 19th European Conference on Power Electronics and Applications, Sep. 2017, pp. P.1-P.10. |
Verbrugge et al. Adaptive state of charge algorithm for nickel metal hydride batteries including hysteresis phenomena. Journal of Power Sources, Feb. 2004, 126(1-2):236-249. |
Verbrugge. Adaptive, multi-parameter battery state estimator with optimized time-weighting factors. J Appl Electrochem, May 2007, 37(5):605-616. |
Wang et al. State Estimation of Lithium ion Battery Based on Electrochemical Impedance Spectroscopy with On-board Impedance Measurement System. IEEE Vehicle Power and Propulsion Conference, Oct. 2015, 5 pages. |
Zhu et al. PSpice Simulation via AC Impedance for PEFC at Operational Loads. http://folk.ntnu.no/skoge/prost/proceedings/aiche-2005/topical/pdffiles/T1/papers/215c.pdf, 2005, 3 pages. |
Ziemer et al. Signals and Linear System Analysis, Chapter 2, pp. 16-100, in: Principles of Communications, 5th edition, John Wiley & Sons. |
Solartron Analytical. 1260 Impedance/Gain-Phase Analyzer. Operating Manual, Jan. 1996, 215 pages. |
Solartron Analytical. 1287 Electrochemical Interface, User Guide, Aug. 2002, 134 pages. |
Ineel. FreedomCAR Battery Test Manual for Power-Assist Hybrid Electric Vehicles. Oct. 2003, DOE/ID-11069, 130 pages. |
Ineel. FreedomCAR Ultracapacitor Test Manual. DOE/ID-11173, Revision 0, Sep. 2004, 116 pages. |
Idaho National Laboratory. Battery Test Manual for Plug-In Hybrid Electric Vehicles, INL/EXT-07-12536, Revision 0, Mar. 2008, 68 pages. |
Idaho National Laboratory. Battery Test Manual for Plug-In Hybrid Electric Vehicles, INL/EXT-07-12536, Revision 2, Dec. 2010, 71 pages. |
Katayama et al. Real-Time Electrochemical Impedance Diagnosis for Fuel Cells Using a DC-DC Converter. IEEE Transactions on Energy Conversion, Jun. 2015, 30(2):707-713. |
Koch et al. Electrochemical Impedance Spectroscopy for Online Battery Monitoring—Power Electronics Control. 16th European Conference on Power Electronics and Applications, 2014, 10 pages. |
Koch et al. Impedance Spectroscopy for Battery Monitoring with Switched Mode Amplifiers. 16th International Power Electronics and Motion Control Conference and Exposition, Sep. 2014, pp. 496-501. |
Koch et al. On-line Electrochemical Impedance Spectroscopy Implementation for Telecommunication Power Supplies. IEEE International Telecommunications Energy Conference, 2015, 6 pages. |
Number | Date | Country | |
---|---|---|---|
20210181290 A1 | Jun 2021 | US |
Number | Date | Country | |
---|---|---|---|
62331730 | May 2016 | US | |
62326923 | Apr 2016 | US |
Number | Date | Country | |
---|---|---|---|
Parent | 16852231 | Apr 2020 | US |
Child | 17188741 | US | |
Parent | 16432822 | Jun 2019 | US |
Child | 16852231 | US | |
Parent | 15497142 | Apr 2017 | US |
Child | 16432822 | US |