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Jaime-Barquero, E.; Bekaert, E.; Olarte, J.; Zulueta, E.; Lopez-Guede, J.M. Degradation Modes for Safety Operation in Lithium Batteries. Encyclopedia. Available online: https://encyclopedia.pub/entry/47543 (accessed on 18 November 2024).
Jaime-Barquero E, Bekaert E, Olarte J, Zulueta E, Lopez-Guede JM. Degradation Modes for Safety Operation in Lithium Batteries. Encyclopedia. Available at: https://encyclopedia.pub/entry/47543. Accessed November 18, 2024.
Jaime-Barquero, Edurne, Emilie Bekaert, Javier Olarte, Ekaitz Zulueta, Jose Manuel Lopez-Guede. "Degradation Modes for Safety Operation in Lithium Batteries" Encyclopedia, https://encyclopedia.pub/entry/47543 (accessed November 18, 2024).
Jaime-Barquero, E., Bekaert, E., Olarte, J., Zulueta, E., & Lopez-Guede, J.M. (2023, August 02). Degradation Modes for Safety Operation in Lithium Batteries. In Encyclopedia. https://encyclopedia.pub/entry/47543
Jaime-Barquero, Edurne, et al. "Degradation Modes for Safety Operation in Lithium Batteries." Encyclopedia. Web. 02 August, 2023.
Degradation Modes for Safety Operation in Lithium Batteries
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This entry is adapted from the peer-reviewed paper 10.3390/batteries9070388

The degradation and safety study of lithium-ion batteries is becoming increasingly important given that these batteries are widely used not only in electronic devices but also in automotive vehicles. Consequently, the detection of degradation modes that could lead to safety alerts is essential. Existing methodologies are diverse, experimental based, model based, and the new trends of artificial intelligence.

Li-ion battery safety degradation mechanism

1. Introduction

The performance of lithium-ion batteries (LIBs) decreases with use due to parasitic reactions occurring at the positive and negative electrodes and even in the electrolyte [1][2][3]. This degradation is caused by the interaction of chemical and physical mechanisms within the cell, resulting in power and capacity loss.
As summarized in Figure 1, LIBs are degraded by various aging factors or external conditions, ranging from elevated temperature to mechanical stress, among others, leading to performance loss or failure to operate in safe conditions [3][4].

1.1. Degradation Mechanisms for LIBs

The interrelated cause–effect connections or degradation process between the aging factors and the degradation mechanisms are diverse, and many occur simultaneously. The best-known degradation processes are SEI growth, electrode particle cracking, electrolyte decomposition, and delamination [5][6][7][8][9]. Although diverse in origin and nature, they have a limited electrochemical response [10]. It is also common to classify degradation processes into the categories of degradation mechanisms (DMs) listed in Figure 1 [10][11][12].
The degradation mechanisms are very diverse. Considering thermodynamics as the main degradation axis of a lithium-ion cell, loss of lithium inventory (LLI), loss of active material of the negative electrode (or anode) (LAMNE), loss of the active material of the positive electrode (or cathode) (LAMPE), and kinetic alterations resulting in capacity fade or power fade are considered to be the predominant ones, as mentioned in the previous section [2][8][11].
  • LLI: Parasitic reactions such as surface film formation (SEI), decomposition reactions, or lithium plating are the cause of lithium consumption in batteries. This leads to a lack of cycling between the positive and negative electrodes, resulting in a drop in the cell’s capacity. In addition, SEI can cause a loss of power [11].
  • LAMNE: Due to cracking and the loss of electrical contact or the blocking of active sites by resistive surface layers, the active mass of the NE is no longer available, and hence, lithium insertion ceases. This leads to a reduction in the capacity of the battery power [11].
  • LAMPE: Due to structural disorders, particle cracking, or loss of electrical contact, the active mass of the PE is no longer available and insertion ceases, causing the capacity and the power of the battery to decrease [11].
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Figure 1. Relation of aging factors and external abuses with their respective degradation processes and mechanisms.

1.2. LIB Safety Management

Some of the DMs can generate operating conditions in lithium batteries that generate risky or unsafe situations.
The study of lithium-ion battery degradation and safety is gaining importance due to the widespread use of LIBs in electronic devices and vehicles. Therefore, it is crucial to detect degradation modes that may pose safety risks. Developing new models that help understand the relationships between aging factors, degradation processes, degradation mechanisms, and safety condition estimators is a challenge for manufacturers and operators alike.
For electrochemical and thermal stabilities, lithium-ion batteries need to operate within specific temperature and voltage ranges to ensure safety [13][14][15]. These ranges can be compromised due to improper usage, such as overcharging, over-discharging, high temperatures, or vibrations [13][16][17]. Once the temperature of the battery exceeds a certain level, self-sustaining exothermic cascade reactions can be triggered and the heat generated can no longer be dissipated efficiently, which could end up causing a thermal runaway (TR) [16][18]. To prevent a hazard, in electric vehicle (EV) applications batteries are positioned centrally, and cooling systems and battery management systems (BMS) are employed [13][14][15][17]. When considering safety, the analyzed system has its importance, as the study of an individual cell is different than that of a battery pack. In addition, three different safety-level categories exist: cell design, abuse tolerance, and parts per billion (PPB). Cell design safety level corresponds to materials and process conditions. Cell manufacturers try to automatize the process, making it more efficient together with quality controls based on optical and X-ray inspection techniques. Moreover, these quality control tests search for electrode misalignments, the presence of metal contaminants, and so on [19]. Abuse tolerance also corresponds to cell safety level, and the cell is tested in conditions outside of those estimated by the supplier. Cells that tolerate these abuse conditions without venting or self-destructions are essential. Nevertheless, global safety is not ensured. Finally, PPB-level safety consists of Li-ion cells operating under recommended conditions that failed. In this category the main failure mode is TR [19].
Various methods are being explored to prevent this phenomenon. Some studies focused on adding additives to shield the battery from overcharging [16][20][21]. Others aimed to protect the battery from overheating through strategies like enhancing cathode and anode materials, using thermoprotective separators, or incorporating flame retardants [22][23][24][25][26]. Additionally, modern battery management systems and battery thermal management systems are gaining popularity [27][28][29].

1.3. Methodologies for Hazard Detection in LIBs

Furthermore, safety tests for batteries can be categorized into three groups based on the type of abuse condition: thermal, electrical, or mechanical [30][31]. Thermal abuse tests subject batteries to high temperatures, either directly or indirectly, to assess their thermal stability and predict the occurrence of thermal runaway (TR) [32][33]. Thermal abuse tests subject batteries to high temperatures, either directly or indirectly, to assess their thermal stability and predict the occurrence of thermal runaway TR [34][35][36][37]. Mechanical abuse entails deforming the battery to create a short circuit and initiate TR [38]. It is worth noting that modeling the failure process of one or multiple batteries is complex, and although thermal, electric, and electrochemical models exist, the current trend is to use multi-physics models that combine multiple models, such as electric and thermal, to seek comprehensive answers [39][40][41][42].
The internal resistance and electrochemical reactions in lithium-ion batteries generate heat [16][27]. Overcharging the battery during charge and discharge operations can produce additional thermal energy, which must be effectively managed to avoid thermal runaway (TR) or explosions. Battery management systems (BMS) located inside the batteries are responsible for preventing overcharging and over-discharging, thereby extending the battery’s lifespan. A BMS also monitors various factors related to degradation mechanisms and safety (DM&S), including the state of charge (SOC), state of health (SOH), operating status, and safety status. Additionally, the BMS ensures energy balance within battery packs, monitors temperature, and provides real-time information to external devices such as electric motors, chargers, and data loggers [27][28][43].
In contrast, a BTMS (battery thermal management system) regulates the temperature inside the battery pack for both high and low temperatures to avoid overheating and achieve an improvement in electrochemical performance. In addition to ensuring cooling, it guarantees temperature homogeneity and optimum operating temperature [29][44][45]. This system can decrease the internal resistance but cannot cope with the heat generated by the internal electrochemical reactions. Nowadays, BTMS has been applied as a key and integral part to maintain the temperature in an optimum range [45].
Reliability and safety concerns arise from DMs [1][5]. Estimating the battery’s state of health (SOH) and state of charge (SOC) enables the study, diagnosis, and prediction of DMs [46][47]. Various methodologies exist, including experimental testing, physical modeling, data-driven approaches, and hybrid methods, each of which have strengths and challenges [48][49][50]. Experimental methods require time and scientific knowledge for data interpretation, whereas physical models rely on extensive experimental databases [51]. Data-driven methods have gained importance and consistency, with initial models being constructed and refined using abundant data to align with the collected information [52][53].
Feinauer et al. proposed combining data from various sensors (temperature, voltage, resistance, audio, ultrasound transmission, and reflection) to estimate the safety level of the cell [54]. Data-driven approaches are valuable for studying battery health due to the correlation between the state of health (SOH) and electrical, thermal, and mechanical behaviors. Lifetime estimation models have gained popularity due to their ability to fit extensive data collected under controlled experimental conditions. These models exhibit high computational efficiency and acceptable accuracy under similar operating conditions [52][53].
Data-driven methods are gaining prominence in real-world applications for battery health estimation and prediction. These methods provide advantages over complex physical models and are increasingly preferred.
According to data-driven methodologies, one of the approaches that is gaining popularity is the use of artificial intelligence (AI) and machine learning (ML) together with neural networks (NNs) and deep learning (DL) [55][56][57][58]. Machine learning methods, known for their flexibility and nonlinear matching capabilities, are highly favored for health estimation and prediction. Specialized aging tests considering multiple factors that impact battery health are conducted to generate a suitable training dataset. Intelligent techniques are then used to map these factors to the battery’s health state, synthesizing an underlying relation. The advances in capacity processors, communications, and AI are increasingly being used to predict and diagnose the SOH and the SOC along with battery DMs. DL algorithms are the most widely used. The database that feeds these algorithms at their core includes images, text, or time series, which, translated to the battery field, would correspond to current, voltage, temperature, temperature maps, time series, charge/discharge cycles, or calendar aging [59][60][61]. On top of the growing sophistication of the algorithms required, the quantity of data needed for training and validation is also critical, as battery data generation is challenging and time-consuming [62][63][64]. The existing datasets, despite providing invaluable information, are sparse and only provide data from a few cells under limited test conditions [65][66]. Hence, this is a major obstacle to the application of DL algorithms, as large amounts of data are needed for the training process. Nonetheless, initiatives such as battery archives and battery data genomes are facilitating future work [66].

2. Conventional DM&S Estimation Methods

2.1. Experiment-Based Methods

Experimental methods are of considerable importance in the assessment of the SOH and the DM&S of batteries. They are usually laboratory based due to the need for specific equipment and often are time-consuming, involving many procedures [7][67]. During the process there may be systematic errors and external factors that affect the results obtained. The battery behavior is obtained by the voltage, current, and temperature applied directly, like capacity measurements or impedance measurements. Other indirect measurements [12] are the optimization and processing of data to locate parameters, the load curve method, ICA (Incremental Capacity Analysis) and DVA(Differential Voltage Analysis), and ultrasonic inspection [12][50].

2.1.1. Internal Resistance

One of the methodologies that plays a significant role is internal resistance measurement, which provides substantial information about the end of life of a battery [12][49][68]. It consists of the resistance of a substance when an electric current passes through. There are several factors affecting the internal resistance of a battery, such as the constituent materials and their structure, state of charge (SOC), electrolyte internal temperature, load current, battery capacity, and rate of discharge of the battery [69][70]. Furthermore, polarization resistance (PR) and ohmic resistance (OR) are the two main contributions of internal resistance. In addition, OR contributes to the contact resistance of the separator, electrolyte, and electrode material cell components [49][71]. However, PR consists of the conversion state between the electrodes and the electrolyte during the correspondent electrochemical reactions. Therefore, it could be said that the increase in internal capacity is directly related to battery capacity and discharge time. It is important to remark that even if it is a time-consuming and non-suitable technique for online assessment, it is reliable, noninvasive, direct, and widely used as an indicator for charge evaluation [49][67][72][73]. One of the most common methods is the current pulse method, based on Ohm’s law. It consists of measuring the voltage drop of a battery for a given current, then calculating the internal resistance with the following equation [67]:
R b S O C , T = O C V S O C , T − V b a t ( S O C , T ) I p u l s e
Rb represents the internal resistance of the battery, OCV is its open circuit voltage, Vbat is its voltage, and Ipulse is the applied current [67]. This method is widely used in laboratories to define the behavior of the internal resistance of a battery under different operating conditions with very good accuracy. However, it is more suitable for stationary and laboratory applications due to its slow process.

2.1.2. Electrochemical Impedance Spectroscopy

Another non-destructive or non-invasive technique is electrochemical impedance spectroscopy (EIS) [12][49]. Impedance is the total resistance of a device or circuit to the flow of an alternating current at a certain given frequency. It is commonly used to predict the aging state of a battery by providing information about the tested coin cell, reaction kinetics, local corrosion rate, electrochemical mechanisms, and remaining useful life (RUL) of a lithium-ion battery [50][74][75]. This methodology offers the opportunity to study both solid and liquid materials, i.e., insulators, semiconductors, and mixed and ionic materials. EIS enables the study of charge transfer in heterogeneous systems with the possibility of chemical sensors, fuel cells, and corrosion processes [12][49][51][76][77]. Furthermore, it can be applied in diverse ways depending on the purpose of the experiment. This choice would depend on the specific conditions and the range of measurement requirements, the accuracy of the measurement, and the ease of experimentation [50][78].
Depending on the test, it is applied in various ways, although always dependent on the specific conditions, the range requirements, the accuracy of the measurement, and the experimental facility. Finally, as it is a non-destructive methodology, battery cell disassembly is not needed. In this way, cells are protected from moisture and oxygen [12][79][80]. In addition, it is a technique that could be applied in operando conditions. It is time-consuming and only applicable for stable environments. Nevertheless, it provides an accurate estimation of the RUL and predicts battery degradation [49].

2.1.3. Battery Capacity Measurement

Battery capacity measurement corresponds to the amount of energy contained in a battery, which deteriorates over time [49][81][82]. It reflects the amount of energy that is storable in that battery. Energetic capacity corresponds to the energy that can be removed or retained in a LIB [7][83]. Battery capacity measurement is the fastest method. Nonetheless, it is not suitable for online assessment, but in this case, it is necessary for the battery to be fully charged [84].

2.1.4. Incremental Capacity Analysis and Differential Voltage Analysis

Incremental capacity analysis (ICA) and differential voltage analysis (DVA) are parameters that change during the operation of batteries, providing the possibility to track their aging by experimental testing [74][85][86]. They are time-consuming techniques, as the curves are obtained at low currents, such as C/20. According to the ICA method, it is considered a valuable method, as accurate battery characteristics are obtained by integrating changes in capacitance and battery voltage. Nevertheless, high-quality research data are required, as are long input preprocessing hours. This methodology is only suitable for LIBs, and it is efficient for analyzing the capacity loss of batteries and is robust and reliable, providing high accuracy [49][67][87]. Figure 2 shows the process of combining both methodologies. It consists of four steps. First, the load tests are performed, and then the voltage-based combination is generated, after which the data are transformed and passed to the SOC-based ones. Finally, by means of fusion algorithms such as Kalman filters or artificial neural network combinations, the desired features are obtained, as described by Zheng et al. [87].
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Figure 2. ICA/DVA combination methodology process.

References

  1. Costa, N.; Sánchez, L.; Anseán, D.; Dubarry, M. Li-Ion Battery Degradation Modes Diagnosis via Convolutional Neural Networks. J. Energy Storage 2022, 55, 105558.
  2. Pastor-Fernández, C.; Yu, T.F.; Widanage, W.D.; Marco, J. Critical Review of Non-Invasive Diagnosis Techniques for Quantification of Degradation Modes in Lithium-Ion Batteries. Renew. Sustain. Energy Rev. 2019, 109, 138–159.
  3. Deng, D. Li-Ion Batteries: Basics, Progress, and Challenges. Energy Sci. Eng. 2015, 3, 385–418.
  4. Etacheri, V.; Marom, R.; Elazari, R.; Salitra, G.; Aurbach, D. Challenges in the Development of Advanced Li-Ion Batteries: A Review. In Energy & Environmental Science; RSC Publishing: Cambridge, UK, 2011; Available online: https://pubs.rsc.org/en/content/articlelanding/2011/EE/c1ee01598b (accessed on 10 July 2023).
  5. Han, X.; Lu, L.; Zheng, Y.; Feng, X.; Li, Z.; Li, J.; Ouyang, M. A Review on the Key Issues of the Lithium Ion Battery Degradation among the Whole Life Cycle. eTransportation 2019, 1, 100005.
  6. Dubarry, M.; Baure, G.; Anseán, D. Perspective on State-of-Health Determination in Lithium-Ion Batteries. J. Electrochem. Energy Convers. Storage 2020, 17, 044701.
  7. Xiong, R.; Li, L.; Tian, J. Towards a Smarter Battery Management System: A Critical Review on Battery State of Health Monitoring Methods. J. Power Sources 2018, 405, 18–29.
  8. Diao, W.; Kim, J.; Azarian, M.H.; Pecht, M. Degradation Modes and Mechanisms Analysis of Lithium-Ion Batteries with Knee Points. Electrochim. Acta 2022, 431, 141143.
  9. Dubarry, M.; Devie, A.; Stein, K.; Tun, M.; Matsuura, M.; Rocheleau, R. Battery Energy Storage System Battery Durability and Reliability under Electric Utility Grid Operations: Analysis of 3 Years of Real Usage. J. Power Sources 2017, 338, 65–73.
  10. Vetter, J.; Novák, P.; Wagner, M.R.; Veit, C.; Möller, K.-C.; Besenhard, J.O.; Winter, M.; Wohlfahrt-Mehrens, M.; Vogler, C.; Hammouche, A. Ageing Mechanisms in Lithium-Ion Batteries. J. Power Sources 2005, 147, 269–281.
  11. Birkl, C.R.; Roberts, M.R.; McTurk, E.; Bruce, P.G.; Howey, D.A. Degradation Diagnostics for Lithium Ion Cells. J. Power Sources 2017, 341, 373–386.
  12. Berecibar, M.; Gandiaga, I.; Villarreal, I.; Omar, N.; Van Mierlo, J.; Van den Bossche, P. Critical Review of State of Health Estimation Methods of Li-Ion Batteries for Real Applications. Renew. Sustain. Energy Rev. 2016, 56, 572–587.
  13. Deng, J.; Bae, C.; Marcicki, J.; Masias, A.; Miller, T. Safety Modelling and Testing of Lithium-Ion Batteries in Electrified Vehicles. Nat. Energy 2018, 3, 261–266.
  14. Bandhauer, T.M.; Garimella, S.; Fuller, T.F. A Critical Review of Thermal Issues in Lithium-Ion Batteries. J. Electrochem. Soc. 2011, 158, R1.
  15. Wang, Q.; Jiang, B.; Li, B.; Yan, Y. A Critical Review of Thermal Management Models and Solutions of Lithium-Ion Batteries for the Development of Pure Electric Vehicles. Renew. Sustain. Energy Rev. 2016, 64, 106–128.
  16. Chombo, P.V.; Laoonual, Y. A Review of Safety Strategies of a Li-Ion Battery. J. Power Sources 2020, 478, 228649.
  17. Xia, B.; Mi, C.; Chen, Z.; Robert, B. Multiple Cell Lithium-Ion Battery System Electric Fault Online Diagnostics. In Proceedings of the 2015 IEEE Transportation Electrification Conference and Expo (ITEC), Dearborn, MI, USA, 14–17 June 2015; pp. 1–7.
  18. Doughty, D.H.; Roth, E.P. A General Discussion of Li Ion Battery Safety. Electrochem. Soc. Interface 2012, 21, 37.
  19. Zhang, Z.J.; Ramadass, P.; Fang, W. 18—Safety of Lithium-Ion Batteries. In Lithium-Ion Batteries; Pistoia, G., Ed.; Elsevier: Amsterdam, The Netherlands, 2014; pp. 409–435. ISBN 978-0-444-59513-3.
  20. Kong, L.; Li, C.; Jiang, J.; Pecht, M.G. Li-Ion Battery Fire Hazards and Safety Strategies. Energies 2018, 11, 2191.
  21. Wen, J.; Yu, Y.; Chen, C. A Review on Lithium-Ion Batteries Safety Issues: Existing Problems and Possible Solutions. Mat Express 2012, 2, 197–212.
  22. Feng, X.; Ouyang, M.; Liu, X.; Lu, L.; Xia, Y.; He, X. Thermal Runaway Mechanism of Lithium Ion Battery for Electric Vehicles: A Review. Energy Storage Mater. 2018, 10, 246–267.
  23. Wang, Q.; Ping, P.; Zhao, X.; Chu, G.; Sun, J.; Chen, C. Thermal Runaway Caused Fire and Explosion of Lithium Ion Battery. J. Power Sources 2012, 208, 210–224.
  24. Liu, K.; Liu, Y.; Lin, D.; Pei, A.; Cui, Y. Materials for Lithium-Ion Battery Safety. Sci. Adv. 2018, 4, eaas9820.
  25. Naguib, M.; Allu, S.; Simunovic, S.; Li, J.; Wang, H.; Dudney, N.J. Limiting Internal Short-Circuit Damage by Electrode Partition for Impact-Tolerant Li-Ion Batteries. Joule 2018, 2, 155–167.
  26. Peled, E.; Menkin, S. Review—SEI: Past, Present and Future. J. Electrochem. Soc. 2017, 164, A1703.
  27. Piao, C.; Wang, Z.; Cao, J.; Zhang, W.; Lu, S. Lithium-Ion Battery Cell-Balancing Algorithm for Battery Management System Based on Real-Time Outlier Detection. Math. Probl. Eng. 2015, 2015, e168529.
  28. Gallardo-Lozano, J.; Romero-Cadaval, E.; Milanes-Montero, M.I.; Guerrero-Martinez, M.A. A Novel Active Battery Equalization Control with On-Line Unhealthy Cell Detection and Cell Change Decision. J. Power Sources 2015, 299, 356–370.
  29. An, Z.; Jia, L.; Ding, Y.; Dang, C.; Li, X. A Review on Lithium-Ion Power Battery Thermal Management Technologies and Thermal Safety. J. Therm. Sci. 2017, 26, 391–412.
  30. Roth, E.P.; Doughty, D.H. Thermal Abuse Performance of High-Power 18650 Li-Ion Cells. J. Power Sources 2004, 128, 308–318.
  31. Hatchard, T.D.; MacNeil, D.D.; Basu, A.; Dahn, J.R. Thermal Model of Cylindrical and Prismatic Lithium-Ion Cells. J. Electrochem. Soc. 2001, 148, A755.
  32. Finegan, D.P.; Scheel, M.; Robinson, J.B.; Tjaden, B.; Hunt, I.; Mason, T.J.; Millichamp, J.; Di Michiel, M.; Offer, G.J.; Hinds, G.; et al. In-Operando High-Speed Tomography of Lithium-Ion Batteries during Thermal Runaway. Nat. Commun. 2015, 6, 6924.
  33. Smith, K.; Kim, G.-H.; Darcy, E.; Pesaran, A. Thermal/Electrical Modeling for Abuse-Tolerant Design of Lithium Ion Modules. Int. J. Energy Res. 2010, 34, 204–215.
  34. Ramadass, P.; Fang, W.; Zhang, Z. Study of Internal Short in a Li-Ion Cell I. Test Method Development Using Infra-Red Imaging Technique. J. Power Sources 2014, 248, 769–776.
  35. Orendorff, C.J.; Roth, E.P.; Nagasubramanian, G. Experimental Triggers for Internal Short Circuits in Lithium-Ion Cells. J. Power Sources 2011, 196, 6554–6558.
  36. Cai, W.; Wang, H.; Maleki, H.; Howard, J.; Lara-Curzio, E. Experimental Simulation of Internal Short Circuit in Li-Ion and Li-Ion-Polymer Cells. J. Power Sources 2011, 196, 7779–7783.
  37. Maleki, H.; Howard, J.N. Internal Short Circuit in Li-Ion Cells. J. Power Sources 2009, 191, 568–574.
  38. Feng, X.; Sun, J.; Ouyang, M.; Wang, F.; He, X.; Lu, L.; Peng, H. Characterization of Penetration Induced Thermal Runaway Propagation Process within a Large Format Lithium Ion Battery Module. J. Power Sources 2015, 275, 261–273.
  39. Cai, L.; White, R.E. Mathematical Modeling of a Lithium Ion Battery with Thermal Effects in COMSOL Inc. Multiphysics (MP) Software. J. Power Sources 2011, 196, 5985–5989.
  40. Kim, G.-H.; Smith, K.; Lee, K.-J.; Santhanagopalan, S.; Pesaran, A. Multi-Domain Modeling of Lithium-Ion Batteries Encompassing Multi-Physics in Varied Length Scales. J. Electrochem. Soc. 2011, 158, A955.
  41. Gerver, R.E.; Meyers, J.P. Three-Dimensional Modeling of Electrochemical Performance and Heat Generation of Liium-Ion Batteries in Tabbed Planar Configurations. J. Electrochem. Soc. 2011, 158, A835.
  42. Guo, M.; White, R.E. A Distributed Thermal Model for a Li-Ion Electrode Plate Pair. J. Power Sources 2013, 221, 334–344.
  43. Huang, S.-C.; Tseng, K.-H.; Liang, J.-W.; Chang, C.-L.; Pecht, M.G. An Online SOC and SOH Estimation Model for Lithium-Ion Batteries. Energies 2017, 10, 512.
  44. Mohammadian, S.K.; He, Y.-L.; Zhang, Y. Internal Cooling of a Lithium-Ion Battery Using Electrolyte as Coolant through Microchannels Embedded inside the Electrodes. J. Power Sources 2015, 293, 458–466.
  45. Smith, J.; Hinterberger, M.; Hable, P.; Koehler, J. Simulative Method for Determining the Optimal Operating Conditions for a Cooling Plate for Lithium-Ion Battery Cell Modules. J. Power Sources 2014, 267, 784–792.
  46. Waldmann, T.; Hogg, B.-I.; Wohlfahrt-Mehrens, M. Li Plating as Unwanted Side Reaction in Commercial Li-Ion Cells—A Review. J. Power Sources 2018, 384, 107–124.
  47. Lin, X.; Khosravinia, K.; Hu, X.; Li, J.; Lu, W. Lithium Plating Mechanism, Detection, and Mitigation in Lithium-Ion Batteries. Prog. Energy Combust. Sci. 2021, 87, 100953.
  48. Yang, S.; Zhang, C.; Jiang, J.; Zhang, W.; Zhang, L.; Wang, Y. Review on State-of-Health of Lithium-Ion Batteries: Characterizations, Estimations and Applications. J. Clean. Prod. 2021, 314, 128015.
  49. Nuroldayeva, G.; Serik, Y.; Adair, D.; Uzakbaiuly, B.; Bakenov, Z. State of Health Estimation Methods for Lithium-Ion Batteries. Int. J. Energy Res. 2023, 2023, e4297545.
  50. Choi, W.; Shin, H.-C.; Kim, J.M.; Choi, J.-Y.; Yoon, W.-S. Modeling and Applications of Electrochemical Impedance Spectroscopy (EIS) for Lithium-Ion Batteries. J. Electrochem. Sci. Technol. 2020, 11, 1–13.
  51. Barai, A.; Uddin, K.; Dubarry, M.; Somerville, L.; McGordon, A.; Jennings, P.; Bloom, I. A Comparison of Methodologiefor the Non-Invasive Characterisation of Commercial Li-Ion Cells. Prog. Energy Combust. Sci. 2019, 72, 1–31.
  52. Li, Y.; Liu, K.; Foley, A.M.; Zülke, A.; Berecibar, M.; Nanini-Maury, E.; Van Mierlo, J.; Hoster, H.E. Data-Driven Health Estimation and Lifetime Prediction of Lithium-Ion Batteries: A Review. Renew. Sustain. Energy Rev. 2019, 113, 109254.
  53. Oji, T.; Zhou, Y.; Ci, S.; Kang, F.; Chen, X.; Liu, X. Data-Driven Methods for Battery SOH Estimation: Survey and a Critical Analysis. IEEE Access 2021, 9, 126903–126916.
  54. Feinauer, M.; Abd-El-Latif, A.A.; Sichler, P.; Aracil Regalado, A.; Wohlfahrt-Mehrens, M.; Waldmann, T. Change of Safety by Main Aging Mechanism—A Multi-Sensor Accelerating Rate Calorimetry Study with Commercial Li-Ion Pouch Cells. J. Power Sources 2023, 570, 233046.
  55. Ward, L.; Babinec, S.; Dufek, E.J.; Howey, D.A.; Viswanathan, V.; Aykol, M.; Beck, D.A.C.; Blaiszik, B.; Chen, B.-R.; Crabtree, G.; et al. Principles of the Battery Data Genome. Joule 2021, 6, 2253–2271.
  56. Zhang, Y.; Xiong, R.; He, H.; Pecht, M.G. Long Short-Term Memory Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-Ion Batteries. IEEE Trans. Veh. Technol. 2018, 67, 5695–5705.
  57. Song, K.; Hu, D.; Tong, Y.; Yue, X. Remaining Life Prediction of Lithium-Ion Batteries Based on Health Management: A Review. J. Energy Storage 2023, 57, 106193.
  58. Li, X.; Zhang, L.; Wang, Z.; Dong, P. Remaining Useful Life Prediction for Lithium-Ion Batteries Based on a Hybrid Model Combining the Long Short-Term Memory and Elman Neural Networks. J. Energy Storage 2019, 21, 510–518.
  59. Hu, X.; Zhang, K.; Liu, K.; Lin, X.; Dey, S.; Onori, S. Advanced Fault Diagnosis for Lithium-Ion Battery Systems: A Review of Fault Mechanisms, Fault Features, and Diagnosis Procedures. IEEE Ind. Electron. Mag. 2020, 14, 65–91.
  60. Zhang, W.; Li, X.; Li, X. Deep Learning-Based Prognostic Approach for Lithium-Ion Batteries with Adaptive Time-Series Prediction and on-Line Validation. Measurement 2020, 164, 108052.
  61. Fan, Y.; Xiao, F.; Li, C.; Yang, G.; Tang, X. A Novel Deep Learning Framework for State of Health Estimation of Lithium-Ion Battery. J. Energy Storage 2020, 32, 101741.
  62. Cui, S.; Joe, I. A Dynamic Spatial-Temporal Attention-Based GRU Model With Healthy Features for State-of-Health Estimation of Lithium-Ion Batteries. IEEE Access 2021, 9, 27374–27388.
  63. Attia, P.M.; Bills, A.; Planella, F.B.; Dechent, P.; dos Reis, G.; Dubarry, M.; Gasper, P.; Gilchrist, R.; Greenbank, S.; Howey, D.; et al. Review—“Knees” in Lithium-Ion Battery Aging Trajectories. J. Electrochem. Soc. 2022, 169, 060517.
  64. Anseán, D.; Dubarry, M.; Devie, A.; Liaw, B.Y.; García, V.M.; Viera, J.C.; González, M. Operando Lithium Plating Quantification and Early Detection of a Commercial LiFePO4 Cell Cycled under Dynamic Driving Schedule. J. Power Sources 2017, 356, 36–46.
  65. Baure, G.; Dubarry, M. Synthetic vs. Real Driving Cycles: A Comparison of Electric Vehicle Battery Degradation. Batteries 2019, 5, 42.
  66. dos Reis, G.; Strange, C.; Yadav, M.; Li, S. Lithium-Ion Battery Data and Where to Find It. Energy AI 2021, 5, 100081.
  67. Noura, N.; Boulon, L.; Jemeï, S. A Review of Battery State of Health Estimation Methods: Hybrid Electric Vehicle Challenges. World Electr. Veh. J. 2020, 11, 66.
  68. Haifeng, D.; Xuezhe, W.; Zechang, S. A New SOH Prediction Concept for the Power Lithium-Ion Battery Used on HEVs. In Proceedings of the 2009 IEEE Vehicle Power and Propulsion Conference, Dearborn, MI, USA, 7–11 September 2009; pp. 1649–1653.
  69. Wei, X.; Zhu, B.; Xu, W. Internal Resistance Identification in Vehicle Power Lithium-Ion Battery and Application in Lifetime Evaluation. In Proceedings of the 2009 International Conference on Measuring Technology and Mechatronics Automation, Zhangjiajie, China, 11–12 April 2009; Volume 3, pp. 388–392.
  70. Zhang, C.P.; Liu, J.Z.; Sharkh, S.M. Identification of Dynamic Model Parameters for Lithium-Ion Batteries Used in Hybrid Electric Vehicles. High Technol. Lett. 2009, 16, 6–12.
  71. Wang, K.; Gao, F.; Zhu, Y.; Liu, H.; Qi, C.; Yang, K.; Jiao, Q. Internal Resistance and Heat Generation of Soft Package Li4Ti5O12 Battery during Charge and Discharge. Energy 2018, 149, 364–374.
  72. Chaoui, H.; Gualous, H. Online Parameter and State Estimation of Lithium-Ion Batteries under Temperature Effects. Electr. Power Syst. Res. 2017, 145, 73–82.
  73. Schweiger, H.-G.; Obeidi, O.; Komesker, O.; Raschke, A.; Schiemann, M.; Zehner, C.; Gehnen, M.; Keller, M.; Birke, P. Comparison of Several Methods for Determining the Internal Resistance of Lithium Ion Cells. Sensors 2010, 10, 5604–5625.
  74. Zhang, S.; Hosen, M.S.; Kalogiannis, T.; Mierlo, J.V.; Berecibar, M. State of Health Estimation of Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy and Backpropagation Neural Network. World Electr. Veh. J. 2021, 12, 156.
  75. Barai, A.; Uddin, K.; Widanage, W.D.; McGordon, A.; Jennings, P. A Study of the Influence of Measurement Timescale on Internal Resistance Characterisation Methodologies for Lithium-Ion Cells. Sci. Rep. 2018, 8, 21.
  76. Hatzell, K.B.; Sharma, A.; Fathy, H.K. A Survey of Long-Term Health Modeling, Estimation, and Control of Lithium-Ion Batteries: Challenges and Opportunities. In Proceedings of the 2012 American Control Conference (ACC), Montreal, QC, Canada, 27–29 June 2012; pp. 584–591.
  77. Büschel, P.; Tröltzsch, U.; Kanoun, O. Use of Stochastic Methods for Robust Parameter Extraction from Impedance Spectra. Electrochim. Acta 2011, 56, 8069–8077.
  78. Wang, X.; Wei, X.; Chen, Q.; Dai, H. A Novel System for Measuring Alternating Current Impedance Spectra of Series-Connected Lithium-Ion Batteries With a High-Power Dual Active Bridge Converter and Distributed Sampling Units. IEEE Trans. Ind. Electron. 2021, 68, 7380–7390.
  79. Meissner, E.; Richter, G. Vehicle Electric Power Systems Are under Change!: Implications for Design, Monitoring and Management of Automotive Batteries. J. Power Sources 2001, 95, 13–23.
  80. Schwenzel, J.; Thangadurai, V.; Weppner, W. Developments of High-Voltage All-Solid-State Thin-Film Lithium Ion Batteries. J. Power Sources 2006, 154, 232–238.
  81. Wang, X.Y.; Mahinda Vilathgamuwa, D.; Choi, S.S. Determination of Battery Storage Capacity in Energy Buffer for Wind Farm. IEEE Trans. Energy Convers. 2008, 23, 868–878.
  82. Xiong, R.; Zhang, Y.; Wang, J.; He, H.; Peng, S.; Pecht, M. Lithium-Ion Battery Health Prognosis Based on a Real Battery Management System Used in Electric Vehicles. IEEE Trans. Veh. Technol. 2019, 68, 4110–4121.
  83. Bi, Y.; Yin, Y.; Choe, S.-Y. Online State of Health and Aging Parameter Estimation Using a Physics-Based Life Model with a Particle Filter. J. Power Sources 2020, 476, 228655.
  84. Li, X.; Yuan, C.; Li, X.; Wang, Z. State of Health Estimation for Li-Ion Battery Using Incremental Capacity Analysis and Gaussian Process Regression. Energy 2020, 190, 116467.
  85. Li, X.; Yuan, C.; Wang, Z. State of Health Estimation for Li-Ion Battery via Partial Incremental Capacity Analysis Based on Support Vector Regression. Energy 2020, 203, 117852.
  86. Zheng, L.; Zhu, J.; Lu, D.D.-C.; Wang, G.; He, T. Incremental Capacity Analysis and Differential Voltage Analysis Based State of Charge and Capacity Estimation for Lithium-Ion Batteries. Energy 2018, 150, 759–769.
  87. Isermann, R. Model-Based Fault-Detection and Diagnosis—Status and Applications. Annu. Rev. Control 2005, 29, 71–85.
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Subjects: Energy & Fuels
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Edurne Jaime-Barquero
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Emilie Bekaert
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Javier Olarte
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Ekaitz Zulueta
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Jose Manuel Lopez-Guede
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Update Date: 02 Aug 2023
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