CALCE Dec. Battery Newsletter 01 04 2021...lithium-ion batteries, it is essential to monitor and manage batteries safely and efficiently. The evolution of battery management systems

Most of today’s consumer electronic products use pouch lithium-ion cells because thisformat makes the most efficient use of space compared to the cylindrical and prismaticformats. But eliminating the metal enclosure comes at a cost if the pouch cell aredefective, swells and destroys the product that it was embedded in.

Various Apple products have been reported with swollen battery issues. To date,however, Apple has not provided technical details on the swollen battery issues. CALCEis studying pouch cell swelling and is interested in obtaining more samples. If you havean Apple Product: MacBook, iPhone, iWatch, etc. that has been damaged due to batteryswelling, please send pictures and contact us at CALCE ([email protected]) for furtherinvestigation.

BATTERY RESEARCHIMPROVING PERFORMANCE, RELIABILITY, AND SAFETY OF BATTERY-POWERED SYSTEMS

www.calce.umd.edu/batteries

INSIDE THIS ISSUE:

APPLE HAS AGREED TO PAY $113 MILLION TO SETTLE ITS SLOW DOWN PROBLEM, CALCE ANTICIPATED THE PROBLEM

1

ARE YOUR APPLE PRODUCTS SWELLING? 1

ROLE OF THE REST PERIOD IN CAPACITY FADE OF GRAPHITE/LICOO2BATTERIES

2

CHARGING INDUCED ELECTRODE LAYER FRACTURING OF 18650 LITHIUM-ION BATTERIES

3

WATER-FREE LOCALIZATION OF ANION AT ANODE FOR SMALL-CONCENTRATION WATER-IN-SALT ELECTROLYTES CONFINED IN BORON-NITRIDE NANOTUBE

4

ADVANCED BATTERY MANAGEMENT STRATEGIES FOR A SUSTAINABLE ENERGY FUTURE:MULTILAYER DESIGN CONCEPTS AND RESEARCH TRENDS

5

HYBRID ELECTROCHEMICAL ENERGY STORAGE SYSTEMS: AN OVERVIEW FOR SMART GRID AND ELECTRIFIED VEHICLE APPLICATIONS

6

CALCE BATTERY DATABASE 7

CALCE BATTERY PUBLICATIONS WITH 50+ CITATIONS

7, 8

JANUARY 2021

In November 2020, Apple agreed to pay $113 million to settle an investigation by statesincluding California and Arizona over how Apple wasn't transparent about its iPhonebattery problems that led to unexpected device shutdowns. Users weren’t informed thatonce the iOS was updated, it was impossible to reverse the process to go back to theprevious version, causing some users to purchase new devices. This is the second loss toApple since in February 2020, a French watchdog for competition and fraud levied a$27.4 million fine against Apple after the company failed to warn iPhone users thatupdating their iOS could slow down their device.

CALCE anticipated this problem with the iPhones and published an article on May 22,2019 (Li-ion Battery Reliability – A Case Study of the Apple iPhone). It was ourassessment that the slowdown and “sudden shutdowns” were implemented as a result ofthe high C-rate (electrical current drain rate) of the battery when certain new applicationswere used. In fact, CALCE understood that C-rates in Apple iPhone apps could reach ashigh as 6C. The high C-rate results in such a rapid loss of capacity and low outputvoltage of the battery. While reducing high electrical current (C-rates) would result inlonger battery use for a given charge and avoiding shutdown, the unintendedconsequence is slow phone performance. CALCE has continued interest in understandingand preventing battery-related issues.

Apple Has Agreed to Pay $113 Million to Settle its Slow Down Problem, CALCE Anticipated the Problem

Are Your Apple Products Swelling?

Role of the Rest Period in Capacity Fade of Graphite/LiCoO2 Batteries


Degradation of lithium-ion batteries is affected by various operational and environmental conditions, includingtemperature, discharge and charge C-rates, and depth of discharge. Another factor is the open rest period, whichoccurs in all laptops, smartphones, and electric vehicles that are kept connected to a charger, even after the batteryis fully charged.

CALCE investigated the effects of the rest period after a full charge on the capacity degradation behavior ofgraphite/LiCoO2 pouch batteries under four different ambient temperatures. The interplay between rest time,battery state of charge, and the number of cycles is investigated to explain the capacity fade trends. A capacity fadetrend model is then developed and applied to the experimental data, and the applicability of rest time as anaccelerating stress factor for lithium-ion battery testing is presented.

Research indicates that capacity fade per cycle in graphite/LiCoO2 pouch batteries increases monotonically with theincrease in the rest time after a full charge during the charge-discharge cycling. An increase in the rest time from0.17 h to 24 h reduced the number of cycles to reach 80% capacity by a factor of about 6 at all the testedtemperatures above 25 ◦C. Different from previous studies on open rest time from literature, this study shows thatopen rest time after full charge plays a role in battery degradation and affects capacity fade trends for the batteries

This research is published in the Journal of Power Sources. .

CALCE BATTERY NEWSLETTER

Variation of normalized discharge capacity with cycles for three different temperatures at a fixed rest time of (a) 0.17 h, (b) 12 h, and (c) 24 h

Charging Induced Electrode Layer Fracturing of 18650 Lithium-ion Batteries


Lithium-ion batteries are considered fully charged when they are charged to the battery manufacturer’s specifiedcut-off voltage and when the charging current drops to the cut-off C-rate. To assess the consequences of goingbeyond this charged state, four types of 18650 lithium-ion battery cells were charged from 100% state of chargeuntil the cells’ internal safety mechanisms were triggered, or thermal runaway occurred. The electrical, thermal, andmechanical behavior of the cells was monitored and compared. In some samples, a unique degradationmechanism — electrode layer fracturing is identified, which is shown and discussed.

The figure above shows the electrode layer fracturing in the 1st charged cell in test #4 from the top view. Thefracturing went through from the outermost to the middle electrode layer. The 2nd cell in test #1 showed similarfracturing in the electrode layers. The electrode layer fracturing, especially when it occurs near the positive tab ornegative tab, will cut off the electrical path from a significant amount of active electrode material to the cell terminals.As a result, lithium de-intercalation and intercalation are not possible in the active electrode material; thus, the cellloses capacity.

The experimental results and CT scan/disassembly analysis shows that the charge cut-off SOC level, rather than thecharge C-rate and cell temperature, determines the occurrence of the electrode layer fracturing in type 4 cells. Thegraphite particle expansion with an increase of lithium concentration caused uneven tension stress on the two sides ofthe anode current collector in the microscale (µm) and resulted in the wrinkling of the anode current collector. Theuneven stress on the two sides of some local areas broke the anode current collector of type 4 cells, which then causedthe electrode layer fracturing in the macroscale (cm). These findings suggest a design change to avoid electrode layerfracturing.

The study is published in the Journal of Power Sources

Cell type Conditions

1 Test #1: 5 A to thermal runaway or safety components were triggered

2 Test #2: 5 A to safety mechanisms triggered or thermal runaway occurred.

3 Test #3: 5 A to safety mechanisms triggered or thermal runaway occurred.

4

Test #4: 5 A to thermal runaway or safety components were triggered

Test #5: 1.25 A to 155% SOC

Test #6: 5 A to 153% SOC

Test #7: 5 A to 140% SOC

JANUARY 2021

Water-free Localization of Anion at Anode for Small-Concentration Water-in-Salt Electrolytes Confined in Boron-Nitride Nanotube


The adoption of aqueous electrolytes in lithium-ion batteries is critical due to the safety and environmentalconcerns associated with using nonaqueous electrolytes. However, aqueous electrolytes limit energy density.Molecular dynamics simulations have established the possibility of water-free bistriflimide - localization near thenegative electrode by confining a lithium bistriflimide water-in-salt electrolytes in a 1-nm-diameter boron nitridenanotube. This work presents the promise of safer lithium-ion batteries without reducing energy density.

Highly concentrated water-in-salt electrolytes (e.g., lithium bistriflimide (LiTFSI) electrolyte solution) demonstratesa wide electrochemical stability window due to the formation of solid electrolyte interphase (SEI) at the negativeelectrode, which prevents the reduction of water. A large population of anions localizes near the negative electrodedue to stronger cation-anion interactions compared to ion-water interactions, which results in a lack of free waternear the negative electrode.

The localized anions are reduced near the negative electrode to form a SEI, preventing a further reduction of anionsand water. But the use of large amounts of salt increases the production costs, which is a bottleneck in thecommercialization of such aqueous Li-ion batteries. Molecular dynamics (MD) simulations show that water-freelocalization of anion near the negative electrode is possible for an aqueous LiTFSI electrolyte of relatively lowerconcentration (5 molals) when confined in a 1-nm-diameter boron nitride nanotube (BNNT).

In presence of such strong nanoconfinement, the TFSI ions and water molecules tend to form a strongly orderedstructure characterized by axially non-overlapping blocks of TFSI ion and water molecules. Further, the TFSI ionshave a higher affinity to enter an empty nanometer-wide BNNT before other species of the electrolyte solution.These two mechanisms cause a synergistic effect resulting in the water-free localization of TFSI ion at the negativeelectrode, as shown in the figure. In such circumstances, a pre-lithiated anode could ensure SEI formation,preventing water reduction at the negative electrode.


Highly concentrated water-in-salt electrolytes(WISE) are being extensively researched forapplication in aqueous Li-ion batteries.

Here, Chava et al. use MD simulations to establishthe possibility of water-free TFSI- localization nearthe negative electrode by confining a LiTFSIWISE in a 1-nm-diameter boron nitride nanotube.

The study is published in the CELL PRESSjournal Cell Reports Physical Sciences,

Advanced Battery Management Strategies for a Sustainable Energy Future:Multilayer Design Concepts and Research Trends


Lithium-ion batteries are promising energy storage devices for electric vehicles and renewable energy systems.However, due to the complex electrochemical processes, potential safety issues, and inherent poor durability oflithium-ion batteries, it is essential to monitor and manage batteries safely and efficiently.

The evolution of battery management systems from past implementations in the industry has introduced amultilayer design architecture for advanced battery management, which consists of three progressive layers. Thefoundation layer focuses on the system's physical basis and theoretical principle, the algorithm layer aims atproviding a comprehensive understanding of battery, and the application layer ensures a safe and efficient batterysystem through sufficient management. Using data intelligence, next-generation battery management will steptowards better safety, performance, and interconnectivity.

The paper is published in the Renewable and Sustainable Energy ReviewsThe evolution of battery management technology.

JANUARY 2021


Electrochemical energy storage systems are fundamental to renewable energy integration and electrified vehiclepenetration. Hybrid electrochemical energy storage systems (HEESSs) are an attractive option because they oftenexhibit superior performance over the independent use of each constituent energy storage.

This article provides an overview of the HEESSs that focuses on battery-supercapacitor hybrids and coversdifferent aspects in smart grid and electrified vehicle applications. The primary goal is to summarize recentresearch progress and stimulate innovative thoughts for HEESS development. To this end, system configuration,DC/DC converter design and energy management strategy development are covered in great details. The state-of-the-art methods to approach these issues are surveyed; the relationship and technological details in between arealso expounded. A case study is presented to demonstrate a framework of integrated sizing formulation andenergy management strategy synthesis.

With appropriate sizing and energy management, we prove that hybrid electrochemical energy storage systemscan reduce the battery degradation rate by about 40%. The extra cost of the system is only 1/8 of the battery-onlyenergy storage.

The paper is published on Renewable and Sustainable Energy Reviews

Hybrid Electrochemical Energy Storage Systems: An Overview for Smart Grid and Electrified Vehicle Applications

Typical smart grid configuration


Open Access to CALCE Battery Data


The CALCE Battery Database contains data from our research and experiments. The data from these tests can beused for battery state of charge and health estimation, remaining useful life prediction, accelerated batterydegradation modeling, and reliability analysis. CALCE has published many articles using this data, and weencourage others to do the same. The cycling data has been generated using Arbin, Cadex, and Neware batterytesters. Impedance data has been collected using the Idaho National Laboratory’s Impedance Measurement Box(IMB). For questions on the CALCE Battery Database, contact Michael Pecht ([email protected]).

CALCE Battery Publications with 50+ Citations

ArticlesGoogle scholar citations

W. He, N. Williard, M. Osterman, and M. Pecht, “Prognostics of lithium-ion batteries based on Dempster-Shafertheory and the Bayesian Monte Carlo method,” J. Power Sources, vol. 196, no. 23, pp. 10314–10321, Dec. 2011. 453

Y. Xing, W. He, M. Pecht, and K. L. Tsui, “State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures,” Appl. Energy, vol. 113, pp. 106–115, Jan. 2014. 386

Y. Xing, E. W. M. Ma, K. L. Tsui, and M. Pecht, “Battery Management Systems in Electric and HybridVehicles,” Energies, vol. 4, no. 11, pp. 1840–1857, Oct. 2011. 316

Q. Miao, L. Xie, H. Cui, W. Liang, and M. Pecht, “Remaining useful life prediction of lithium-ion battery withunscented particle filter technique,” Microelectron. Reliab., vol. 53, no. 6, pp. 805–810, Jun. 2013. 311

W. He, N. Williard, C. Chen, and M. Pecht, “State of charge estimation for electric vehicle batteries usingunscented kalman filtering,” Microelectron. Reliab., vol. 53, no. 6, pp. 840–847, Jun. 2013. 265

D. Liu, J. Pang, J. Zhou, Y. Peng, and M. Pecht, “Prognostics for state of health estimation of lithium-ionbatteries based on combination Gaussian process functional regression,” Microelectron. Reliab., vol. 53, no. 6,pp. 832–839, Jun. 2013.

232

Y. Xing, E. W. M. Ma, K. L. Tsui, and M. Pecht, “An ensemble model for predicting the remaining usefulperformance of lithium-ion batteries,” Microelectron. Reliab., vol. 53, no. 6, pp. 811–820, Jun. 2013. 229

W. He, N. Williard, C. Chen, and M. Pecht, “State of charge estimation for Li-ion batteries using neural networkmodeling and unscented Kalman filter-based error cancellation,” Int. J. Electr. Power Energy Syst., vol. 62, pp.783–791, Nov. 2014.

211

D. Wang, Q. Miao, and M. Pecht, “Prognostics of lithium-ion batteries based on relevance vectors and aconditional three-parameter capacity degradation model,” J. Power Sources, vol. 239, pp. 253–264, Oct. 2013. 210

Ph.D. Candidate Weiping Diao Receives University of Maryland's Outstanding Research Assistant AwardWeiping Diao has been selected for the University of Maryland's Outstanding Research Assistant Award. Over4,000 UMD graduate students serve the campus as administrative, research, or teaching assistants. UMDGraduate School established this award to recognize and honor the outstanding contributions graduate assistantsprovide to students, faculty, departments, and the University as a whole. The award recognizes the honorees asthe top performers.

JANUARY 2021



ArticlesGoogle scholar citations

F. Leng, C. M. Tan, and M. Pecht, “Effect of Temperature on the Aging rate of Li Ion Battery Operating aboveRoom Temperature,” Sci. Rep., vol. 5, no. 1, p. 12967, Aug. 2015. 210

N. Williard, W. He, C. Hendricks, and M. Pecht, “Lessons Learned from the 787 Dreamliner Issue on Lithium-IonBattery Reliability,” Energies, vol. 6, no. 9, pp. 4682–4695, Sep. 2013. 203

Y. Zhang, R. Xiong, H. He, and M. G. Pecht, “Long short-term memory recurrent neural network for remaininguseful life prediction of lithium-ion batteries,” IEEE Trans. Veh. Technol., vol. 67, no. 7, pp. 5695–5705, Jul.2018.

178

C. Hendricks, N. Williard, S. Mathew, and M. Pecht, “A failure modes, mechanisms, and effects analysis(FMMEA) of lithium-ion batteries,” Journal of Power Sources, vol. 297. Elsevier B.V., pp. 113–120, 11-Aug-2015.

138

C. Zou, L. Zhang, X. Hu, Z. Wang, T. Wik, and M. Pecht, “A review of fractional-order techniques applied tolithium-ion batteries, lead-acid batteries, and supercapacitors,” Journal of Power Sources, vol. 390. Elsevier B.V.,pp. 286–296, 30-Jun-2018.

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R. Xiong, Y. Zhang, H. He, X. Zhou, and M. G. Pecht, “A double-scale, particle-filtering, energy state predictionalgorithm for lithium-ion batteries,” IEEE Trans. Ind. Electron., vol. 65, no. 2, pp. 1526–1538, Jul. 2017. 120

J. Guo, Z. Li, and M. Pecht, “A Bayesian approach for Li-Ion battery capacity fade modeling and cycles to failureprognostics,” J. Power Sources, vol. 281, pp. 173–184, May 2015. 88

G. Bai, P. Wang, C. Hu, and M. Pecht, “A generic model-free approach for lithium-ion battery healthmanagement,” Appl. Energy, vol. 135, pp. 247–260, Dec. 2014. 73

R. Xiong, Y. Zhang, J. Wang, H. He, S. Peng, and M. Pecht, “Lithium-Ion Battery Health Prognosis Based on aReal Battery Management System Used in Electric Vehicles,” IEEE Trans. Veh. Technol., vol. 68, no. 5, pp.4110–4121, May 2019.

63

S. Saxena, C. Hendricks, and M. Pecht, “Cycle life testing and modeling of graphite/LiCoO2 cells under differentstate of charge ranges,” J. Power Sources, vol. 327, pp. 394–400, Sep. 2016. 59

Z. Liu, G. Sun, S. Bu, J. Han, X. Tang, and M. Pecht, “Particle Learning Framework for Estimating the RemainingUseful Life of Lithium-Ion Batteries,” IEEE Trans. Instrum. Meas., vol. 66, no. 2, pp. 280–293, Feb. 2017. 58

Y. Zhang, R. Xiong, H. He, and M. G. Pecht, “Lithium-Ion Battery Remaining Useful Life Prediction with Box-Cox Transformation and Monte Carlo Simulation,” IEEE Trans. Ind. Electron., vol. 66, no. 2, pp. 1585–1597, Feb.2019.

58

C. Chen and M. Pecht, “Prognostics of lithium-ion batteries using model-based and data-driven methods,” inProceedings of IEEE 2012 Prognostics and System Health Management Conference, PHM-2012, 2012. 56

S.-C. Huang, K.-H. Tseng, J.-W. Liang, C.-L. Chang, and M. Pecht, “An Online SOC and SOH Estimation Modelfor Lithium-Ion Batteries,” Energies, vol. 10, no. 4, p. 512, Apr. 2017. 52

N. Williard, W. He, M. Osterman, and M. Pecht, “Comparative analysis of features for determining state of healthin lithium-ion batteries,” Int. J. Progn. Heal. Manag., vol. 4, no. 1, 2013. 51

L. Kong, C. Li, J. Jiang, and M. Pecht, “Li-Ion Battery Fire Hazards and Safety Strategies,” Energies, vol. 11, no.9, p. 2191, Aug. 2018. 50

CALCE Battery Publications with 50+ Citations

CALCE Dec. Battery Newsletter 01 04 2021...lithium-ion batteries, it is essential to monitor and manage batteries safely and efficiently. The evolution of battery management systems

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