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ABSTRACTLithium-ion (LI) batteries are rapidly becoming a viable choice for military and civil electric vehicles (EV), hybrid electric vehicles (HEVs), unmanned systems, and other applications, mainly because they contain higher energy density, provide higher cycle life, offer better resistance to memory effects, and weigh less than other potential technologies. These same benefits have also led to widespread integration of lithium-ion products into the portable electronics markets. However, lithium-ion batteries carry their own disadvantages, including degradation at deep discharge, capacity loss at high temperatures, and susceptibility to catastrophic failure from venting (especially during charging), shorting, etc. that can have dire consequences on the platform. Another concern with EV/HEV applications is that many cells (packaged as battery packs/modules) are needed to provide sufficient power. This situation leads to thermal and electrical cell imbalances,which significantly reduce the performance of the system. If LI batteries are to be effectively fielded for high power applications (i.e. 10+ kWh), technologies are needed that can be used to actively mitigate the risk of catastrophic failure and ensure proper balancing across the pack.In order to address this situation, the authors are developing an advanced battery management system for LI battery packs that ensures adequate, safe, and reliable operation. This paper presents the results of experimental and analytical work that is being performed by the authors for both military and commercial applications. Experimental results include discharge/charge cycle tests that were conducted while collecting common measurement signals (i.e. voltage,current) and battery impedance. Advanced electrochemical models have also been developed that aim to capture physical phenomena that cause capacity degradation in cells. The work includes novel methods for impedance measurement,system modeling, SOC/SOH assessment, and advanced prognostic algorithms for remaining life and charge assessment.INTRODUCTIONLithium-ion batteries are being widely investigated for use in high power applications due to their relatively high energy density and other advantages such as higher cycle life,resistance to memory effects, and lower weight compared to other potential technologies. However, there are risks involved in their implementation that are rooted in their sensitivity to temperature, voltage, and abuse. A Battery Management System (BMS) is therefore needed to mitigate these risks and allow safe deployment of LI batteries in these applications. Development of an effective BMS involves two fundamental steps: 1) the development of a reliable and accurate means to assess the health/state of the battery and 2)the development of a system that uses that information to balance and control the pack. This paper is focused on the former.In order to address the needs described above, the authors are developing an approach to produce a reliable means of predicting the state of charge (SOC), state of health (SOH),and state of life (SOL) of LI batteries. The battery state prediction portion of the system (shown in Figure 1) is largely software-focused and uses traditional measurements (voltage, current, temperature). However, it also features an innovative broadband impedance measurement system that has been proven to be an effective solution for battery state prediction [1]. This proven technology is complemented by new feature extraction methods, along with the implementation of fault classification, health prediction, and prognostic techniques developed by the authors and proven on a wide variety of applications. For example, a 3-tier failure mode classifier is used to produce an assessment of the mostprobable failure mode in the system and the severity of eachFault Mitigation and Cell Balancing of High Power Lithium Ion Battery Packs2010-01-1766Published 11/02/2010Matthew Watson, Carl Byington, Genna Mott and Sudarshan BharadwajImpact Technologies, LLCCopyright © 2010 SAE Internationalpotential failure mode. This information is then used with feature information, extracted in near real-time, and historical battery operation information to predict the current charge capacity and SOC. This is performed with a hybrid approach that fuses implicit and explicit models with a usage model for more accurate prediction. Advanced forecasting and evolutionary prognostics approaches are then used to project these values to known failure regions, using expected or use-case operational profiles, to determine the remaining discharge time and useful life remaining (SOL). Various fusion architectures and algorithms are also implemented to fuse the different pieces of diagnostic and prognostic information, resulting in a more confident assessment.Implementation of this technology gives clear insight into the battery state, which will allow proper electrical and thermal control, as well as accurate battery health prediction to allow maintenance to be efficiently planned and correctlyperformed.Figure 1. Technical Approach for LI Battery StatePrediction Within large battery banks, accurate estimation of the SOC and SOH of individual cells will also allow cell balancing to be performed. Cell balancing consists of identifying cells that have vastly different SOC or charge capacity than the other cells in the pack and either selectively charging these cells,selectively removing the load (or charge) from these cells or,if the risk of a catastrophic failure is high, actively taking the cells offline so that propagation of faults between cells is avoided. To this end, the SOH of individual cells can be used to predict the risk of catastrophic cell failure. Catastrophic failure of a single cell within a LI battery bank could cause cascading failures, which propagate to battery modules, full battery systems, and eventually to the entire platform on which the battery is fielded. However, the health assessments that a BMS produces could be used to identify high risk cells that contain a low number of remaining cycles. Hardware could then be configured to employ intelligent charging and loading schemes that would accommodate the various health states, thereby prolonging the life of the battery pack as a whole and preventing damage to the platform. As mentioned,the first step in the implementation of cell balancing and failure mitigation capabilities is the accurate estimation of SOC, SOH, and SOL, and the BMS under development would thus cater to the twin needs of failure mitigation and cell balancing within LI battery systems.BROADBAND IMPEDANCE MEASUREMENTInternal wide-band impedance provides a direct link to the physical parameters of the battery and is widely recognized as a good indication of a battery's health [2]. Internal impedance is therefore one of the primary measurements used to extract health information. Historically, equipment used to measure internal battery impedance or perform electrochemical impedance spectroscopy (EIS) has been expensive, laboratory-grade rack-mount systems, which are not practical for online implementation in a PHM application.However, a low-power/low-cost measurement approach to measure the impedance of electrochemical cells is possible [3, 4]. Important to this approach is the signal used to interrogate the cell. An interrogation signal that contains a composite of signals at different frequencies provides a broad range of excitation to the battery and is thus preferred. The change in magnitude and phase at each frequency can then be plotted and used to calculate the impedance of the battery at each frequency. The shape of this plot is directly related to the electrochemical state of the battery [5]. An example of an impedance curve previously collected for various battery types can be seen in the graph on the left of Figure 2. Other researchers have also validated the method of using the broadband impedance value as an indicator of LI battery health, as seen on the right of Figure 2.Kozlowski, Byington, Watson, et al. have described in detail the advantages of using a broadband EIS measurement [1,3,4,5,13,18]. A brief summary is provided here. A common approach when using EIS is to track changes in impedances at specific interrogation frequencies. However,the measurement produced by a single tone doesn't provide sufficient information to characterize a battery's health. This is exemplified in Figure 3, which represents an EIS measurement taken with a single tone (100 Hz in this example) from three different cases: 1) Battery ‘A’ at t=0min, Battery ‘A’ at a later time (t=100 min), and 3) a different Battery ‘B’ at t = 0 min. In this example, the 3 cases represent different health states, states of charge, and/or current capacity. As seen, a measurement taken with a single tone could produce the exact same impedance result for all 3cases. However, the broadband response, which is characterized by the whole curve in Figure 3, shows that the condition of the three batteries is distinctively different.Therefore, rather than limiting observations to changes of impedance at specific frequencies (i.e. isolated points), the entire shape of the impedance curve should be considered for estimation of the battery SOC, capacity, or health.More importantly, the shape of the broadband response, when represented as a Nyquist curve, has direct ties to the internal physics of the battery. Figure 4 shows the various parts of the Nyquist curve and their relationship to different electrochemical activities. The battery health is revealed by changes in these physical mechanisms. For example,increases in the ohmic resistance (which is correlated to the point where the left side of the semicircle crosses the x-axis)over the life of the battery would indicate that the electrolyte was starting to weaken, whereas a decreasing double layer capacitance (which is correlated to the height of the semi-circle) would indicate a decrease in the available active ions.Thus, physical parameters derived from the impedance curve are more representative of the state of the battery than external parameters, such as the terminal voltage (which capture a combination of ohmic resistance, charge transfer resistance, and diffusion layer impedance). Likewise,coulomb counters or amp-hour gages do not capture the physics of what is driving the battery state and are ofteninfluenced by changes in temperature, usage, self discharge,etc.In practice, this information can be captured using electrochemical models. Broadband impedance data from an electrochemical source can be modeled using an electrical circuit (Figure 5) that can reproduce the measured impedance curve. Therefore, once a broadband EIS measurement is acquired, parameter identification can be used to determine the physical battery parameters that are needed to minimize the error between the model predicted response and the measured EIS data. This provides parametric information for classification algorithms, which can be used to infer the battery state and the physical processes driving the battery's health. Since the nature of the objective function in the parameter space is unknown, global search methods such as genetic algorithms, simulated annealing, and Probabilistic Global Search Lausanne (PGSL) are preferred [8, 9]. Thisapproach takes advantage of physical changes that are evidentFigure 2. Impedance of Various Battery Types (left) and Impedance Change of an 18650-size Li-Ion Battery from Literature [6](right)Figure 3. Single Tone vs. Broadband Characterization of Three Different Battery Statesbefore capacity loss is noticeable, allowing earlier state detection and, therefore, earlier calendar life prediction [10].Figure 4. Relationship of Electrochemical Impedance to Physical Activity in the Battery [19]Figure 5. Modified Randle's Circuit Model [19] AUTOMATED REASONING, CLASSIFICAITON, AND PROGNOSTICSDiagnostic technologies utilize measured features and occasionally models to detect anomalous system behavior and relate them to the state of the system. Automated Reasoning (AR) and classification techniques can be used to autonomously map feature “vectors” into indicators of distinct system conditions (i.e. SOC) or health states (i.e. failure modes). The desired result after modeling and feature extraction is clustering or membership in classes, where patterns can be more easily associated with a degraded condition or damage. This association may also represent the various types of faults or the degree of a particular fault (damage level). The authors have leveraged their experience in multi-sensor data fusion, classification, and data mining [11, 12, 13, 14, 15] to build a classifier that best meets the application requirements for a real-time, in-situ, stand-alone system. In the developed approach, automated reasoning utilizes raw sensor measurements, features extracted from these measurements, and model-identified parameters for SOC and charge capacity predictions.For example, the model-based approach developed by the authors includes a classification system for translating the model parameters (known evidence) to a current level of damage for each failure mode. Once faults are detected and the current damage level is assessed, prognostics are implemented to predict the progression of the fault towards failure. Failure prediction is the most uncertain step in the health management process, as there is tremendous uncertainty in predicting future occurrences. However, by applying advanced methods and assessing prognostic confidence, the model-based approach provides the system maintainer with substantially more end-of-life health state information than statistics-based, reliability methods. The developed model-based reasoner for the BMS employs a probabilistic fault classification methodology that is coupled with a statistical trending routine to predict fault-to-failure propagation and remaining useful life (RUL). Additionally, a fusion scheme is employed that combines failure mode probability with prognostic confidence in order to produce a more robust prediction of RUL. The trend-based or evolutionary prognostics approach has proven to be very effective at predicting slow degradation mechanisms within gas turbine engines, and for many of the same reasons, is attractive for battery prognostics. This approach relies on gauging the proximity and rate of change of the current component condition to known fault conditions within N-dimensional parameter space. This approach requires that sufficient information is available to assess the current condition of the system or subsystem and the relative level of uncertainty in the measurement. Furthermore, the parametric conditions that signify known condition-related faults must be identifiable. The evolutionary prognostics routine works well within the model-based PHM architecture. Figure 6 illustrates this approach in two-dimensional parameter space. Starting at the origin, (representing initial, normal operation) measured parameter distributions begin to shift as some type of degradation begins to occur. In the figure, the points labeled “2% Fault” and “4% Fault” represent the parameter space at known fault conditions. Over time, the measured parameter joint distribution moves to other points in the space (Time1 and Time2) and the path of this movement can be projected to determine the future health state of the system.Figure 6. Evolutionary PHM Approach MODELING USAGE AND OPERATION EFFECTSThe developed approach also ims to capture the effects that operation and usage have on a battery's maximum available charge capacity. The total capacity that is available when the battery is new is specific to each individual battery. As a battery is operated, the available capacity will decrease due to aging of the cell(s), whether it is in standby or cycling mode. This is caused by changes in the electrochemical make-up of the battery (i.e. passivation of the plates, corrosion, etc.). The rate of this decrease is dependent on the failure mechanism that is affecting the battery and is non-recoverable. This results in a decreased level of maximum available capacity. In the case of LI batteries, standby operation primarily results in oxidation, which causes formation of a passivated layer (on either the anode or cathode), plate corrosion, or gassing (CO2 generation) [20]. The actual amount of capacity that can be delivered by the battery is, however, also affected by how the battery is operated (charge/discharge rate, temperature, etc). Aggressive operation will further reduce the available capacity and result in an increased non-recoverable loss. Changes in this parameter over time can be tracked to determine remaining life. As such, models have been developed to capture the effects that usage and operation have on available capacity using a usage model (to estimate non-recoverable losses) and an operational model (to capture the effects of current operation) to predict available capacity. Cycle counting techniques are commonly used to determine the level of damage present in a material or structure. This approach accumulates load cycles (temperature, stress, or strain) and relates them to material damage. A nonlinear approach can be used to not only account for nonlinear effects, but to also capture the effects of the current damage level on the next loading cycle since the effects that identical loads have on relatively new and highly damaged samples will be different [21]. This approach can also be applied to battery health prediction. The major factors affecting the health of a battery are the charge/discharge characteristics,which can be thought of as strain cycles, and thermal cycling of the battery. An approach to count these cycles and relate them to decreased capacity is therefore being developed. Cycle counting is performed using a modified rainflow counting procedure. An important characteristic of this form of modeling is that damage is accumulated in a time-dependent fashion. In simpler terms, two identical components that are run under different operating profiles but contain the same strain cycles may produce different damage levels. The rate of decay of a battery's capacity can be thought of as a nonlinearity coefficient. This value is largely driven by the failure mode that is affecting the health of the battery. As such, coefficients can be developed for each major failure mode. Fault classification routines are then used to determine the most probable failure modes and a weighted fusion approach used to determine which nonlinearity coefficient to use in the calculation.RESULTSThe approach described above has been successfully applied to both lead acid and lithium ion batteries. This section presents some sample results from prior work on valve regulated lead acid (VRLA) batteries and some recent efforts to transition these capabilities to lithium ion batteries. For the lead acid example, a total of 23 batteries of different health state and available capacity were tested (Table 1).Table 1. Lead Acid Batteries TestedEIS, voltage, and temperature measurements were taken and used to identify the type of battery being measured, as well as quantify its health state (healthy or faulted), the SOC, and battery capacity. The effort featured a classification system that was based on fusing the results from an implicit reasoner and a statistical classifier. The fault classification results for the two different types of lead acid batteries are presented in the confusion matrix shown in Table 2. As seen, the fused output accurately classified the health state of 98.7% of the batteries for both types (misclassifying only 4 out of 318 cases), even though SOC was varied significantly (from 100-0%) throughout the testing (hence the 318 test cases, which were collected across the 23 batteries).It was also desired to distinguish between reversible loss of charge and irreversible capacity loss. Figure 7 and Figure 8 are examples of the fused SOC and charge capacity prediction results that were calculated. The x-axes of the plots show the battery number of the individual batteries being tested (see Table 1). Since the batteries were aged to differentlevels prior to the testing, each battery had a different capacity value. Also, each battery was tested at specific intervals during a discharge test to zero charge. Thus, several datasets were available for each battery at multiple SOCs. As Figure 7 shows, the fused classifier was able to accurately predict the SOC of the battery to within the desired tolerance band of ±5%, independent of the battery capacity.The charge capacity predictions were also within the required tolerance band (Figure 8). As seen, despite the varying SOC for each battery during the discharge test, the fused outputs accurately predict the battery capacity. This amply demonstrates the ability of the developed BMS approach to distinguish between loss of battery capacity and reduced SOC. The authors are pursuing the same accuracy in results for LI batteries.Initial work is underway to also apply the approach to LI batteries. Specifically, the authors are evaluating datacollected from discharge tests on a 20 Ah Lithium Iron Phosphate (LiFePO4) battery. In this case, EIS measurements were taken from the battery during discharge after every ∼5% decrease in SOC. This was achieved by discharging at 20 A (1 C) for a period of 3 minutes (for a total discharge of 1 Ah). In addition, 3 measurements were taken continuously from the battery - the current, voltage, and temperature. The parameters calculated from the available LiFePO4 data were then combined to achieve the SOC estimates shown in Figure 9. It is clear that the SOC estimates follow a steady linear trend and produces a metric that changes more predictably according to battery state, which would enable more reliable and accurate assessments to be made throughout the battery's life cycle. In this case, the average SOC estimation error was approximately 7.8%. Some of this error is believed to be caused by temperature variation that occurred during data collection, which is known to affect impedance readings.However, work is underway to account for thermal effects and improve the accuracy of the health assessments.Table 2. State of Health Predictions from Developed BMSFigure 7. Fused SOC Prediction Results for Lead Acid BatteriesSUMMARY/CONCLUSIONSLithium batteries are rapidly becoming a preferred choice for designers of high power applications for several reasons (energy density, low weight, etc.). However, the disadvantages of the chemistry bring inherit risk to the platform. As such, advanced Battery Monitoring Systems (BMS) are needed that can make accurate health assessments (including SOC, SOH, and SOL predictions) and provide cell balancing and thermal control. For example, monitoring the SOC and SOH of individual cells would enable cell balancing by allowing weaker cells to be used differently than healthycells. The goal of failure mitigation is also served by identifying the risk of catastrophic failure of individual cells,and performing intelligent charging and loading cycles to prevent failure in the high risk cells. The capabilities of the BMS will thus ensure that the best performance is extracted from the battery and catastrophic events are reduced or eliminated.The authors are developing a health estimation approach that will provide the foundation of such a system. The approach involves traditional measurements coupled with a novel impedance measurement thatcan be used to provide insightFigure 8. Fused Capacity Prediction Results for Lead Acid BatteriesFigure 9. SOC estimation resultsinto the physical processes occurring within the battery. A combination of analytic methods is also employed to enhance the robustness of the health assessment. Establishing sound technical achievements in this area and building confidence in the technology is critical to the development of a BMS that will enable safe and effective implementation of LI batteries for high power applications.Preliminary results, included in this paper, show promising indications of accurate battery state predictions. Specifically, accurate (within ±3.5%) estimation of SOC and charge capacity have been achieved for lead acid batteries and classifiers have been demonstrated to accurately classified the health state of 98.7% of the batteries tested, including tests that spanned 3 different health states, 2 different battery sizes, varying SOC and charge capacity, and a total of 318 different test cases. Work is also underway to adapt the approach for LI battery and initial SOC prediction results are promising. This capability will enable effective cell balancing and detection of imminent failure with sufficient detection horizon to avoid catastrophic failures. Implementation of this technology will allow wider deployment of LI batteries in high power applications in a safe and reliable manner and will increase mission assurance and lower O&S costs in both military and commercial systems. REFERENCES1. Kozlowski, J. D., 2004, “Diagnostic and Prognostic Methods for the Health and Condition of Primary and Secondary Batteries,” Proc. 41st Power Sources Conference, June 14-17, 2004.2. Nagasubramanian, G., 2000, “Two and Three-Electrode Impedance Studies on 18650 Li-Ion Cells,” J. of Power Sources, 87:1-2, pp. 226-229.3. Kozlowski, J. D., U.S. Patent No. 6,307,378 B1, “Method and Apparatus for Measurement of Electrochemical Cell and Battery Impedances,” granted October 23, 2001.4. Kozlowski, J. D., 2003, “Electrochemical Cell Prognostics using Online Impedance Measurements and Model-Based Data Fusion Techniques,” Proc. 2003 IEEE Aerospace Conference, 7, pp. 3257-3270.5. Kozlowski,J.D., Penn State University Invention Disclosure 2001-0729 entitled “Model-Based Predictive Diagnostic Tool for Primary and Secondary Batteries”.6. Jungst, Nagasubramanian, et. al. “Accelerated calendar & pulse life analysis of Li-ion cells”. Journal of Power Sources, 119-21.7. MacDonald, J. R., and Potter, L. D., 1987, “A Flexible Procedure for Analyzing Impedance Spectroscopy Results: Description and Illustrations,” Solid State Ionics, 23, pp.61-79.8. Kozlowski, Byington, Garga, Watson, and Hay,, “Model-based Predictive Diagnostics for Electrochemical Energy Sources,” Long Beach Battery Conference, January 2001.9. Kozlowski, Watson, Byington, Garga, and Hay,,“Electrochemical Cell Diagnostics Using Online Impedance Measurement, State Estimation, and Data Fusion Techniques,” 36th Intersociety Energy Conversion Engineering Conference, Savannah, GA, July 29-August 2, 2001.10. Sarre, G., et al. 2003, “Aging of Lithium-Ion Batteries,”J. of Power Sources. 127:1-2, pp. 65-71.11. Byington, C.S., and Garga, A.K., “Data Fusion for Developing Predictive Diagnostics for Electromechanical Systems,” in Handbook of Multisensor Data Fusion, edited by Hall, D. L. and Linas, J., CRC Press, 2001.12. Byington, C.S., and Kacprzynski, G., 2006, “Reasoning Methods,” PHM/CBM Design Course, Impact Technologies, LLC, Orlando, Florida, November 3, 2004.13. Kozlowski, J.D., Byington, C.S., Garga, A.K, Watson, M.J., and Hay, T.A., 2001, “Model-based Predictive Diagnostics for Primary and Secondary Batteries,” Proc. of the Annual Battery Conference on Applications and Advances, pp. 251-256.14. Smith, M., Byington, C, et al, 2006, “Layered Classification for Improved Diagnostic Isolation in Drivetrain Components,” IEEE Aerospace Conference, p. 8.15. Kozlowski, Watson, Byington, Garga, and Hay,,“Prediction Methods and Data Fusion for Prognostics of Primary and Secondary Batteries,” 55th Meeting of the Society for MFPT, April 2-5, 2001.16. Byington, C., Watson, M., Edwards, D., Dunkin, D., “In-Line Health Monitoring System for Hydraulic Pumps and Motors,” STLE Annual Meeting, New York, New York, April 28 - May 1, 2003.17. Byington, C., Brewer, R., et al, 2007, “Identifying and Classifying Contaminants with an On-line Oil Quality Monitor for Diesel Engines”, Proc. 61st Meeting of the MFPT Society, Virginia Beach, VA.18. Kozlowski, Cawley, and Byington,, “Model-Based Predictive Diagnostics for Primary and Secondary Batteries: Phase I Report,” Technical Memorandum, File No. 99-076, Penn State Applied Research Laboratory, June 17, 1999. 19. Kozlowski, Byington, Garga, Watson, and Hay,, “Model-based Predictive Diagnostics for Electrochemical Energy Sources,” IEEE Aerospace Conference, March 10-17, 2001.20. Choi, S., and Lim, H., 2002, “Factors that Affect Cycle-life and Possible Degradation Mechanisms of a Li-ion Cell Based on LiCoO2,” J. of Power Sources, 111:1, pp. 130-136.21. Bolotin, V.V., Prediction of Service Life for Machines and Structures, ASME Press, New York, 1989.。