Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1 + 2591 word(s) 2591 2021-09-30 08:55:02 |
2 update references and layout Meta information modification 2591 2021-10-08 05:13:15 |

Video Upload Options

Do you have a full video?

Confirm

Are you sure to Delete?
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Samanta, A. Lithium-Ion Cell Temperature Estimation Techniques. Encyclopedia. Available online: https://encyclopedia.pub/entry/14782 (accessed on 29 March 2024).
Samanta A. Lithium-Ion Cell Temperature Estimation Techniques. Encyclopedia. Available at: https://encyclopedia.pub/entry/14782. Accessed March 29, 2024.
Samanta, Akash. "Lithium-Ion Cell Temperature Estimation Techniques" Encyclopedia, https://encyclopedia.pub/entry/14782 (accessed March 29, 2024).
Samanta, A. (2021, September 30). Lithium-Ion Cell Temperature Estimation Techniques. In Encyclopedia. https://encyclopedia.pub/entry/14782
Samanta, Akash. "Lithium-Ion Cell Temperature Estimation Techniques." Encyclopedia. Web. 30 September, 2021.
Lithium-Ion Cell Temperature Estimation Techniques
Edit

A solution is to develop a suitable estimation strategy which led scholars to propose different temperature estimation schemes aiming to establish a balance among accuracy, adaptability, modelling complexity and computational cost. This article presented an exhaustive review of these estimation strategies covering recent developments, current issues, major challenges, and future research recommendations. The prime intention is to provide a detailed guideline to researchers and industries towards developing a highly accurate, intelligent, adaptive, easy-to-implement and computationally efficient online temperature estimation strategy applicable to health-conscious fast charging and smart onboard BMS. Full Paper:https://doi.org/10.3390/en14185960

electric vehicles machine learning Kalman filter thermal modelling online prediction electromagnetic impedance spectroscopy computational cost

1. Introduction

Lithium-ion batteries (LIBs) are widely used in electric vehicles (EVs), grid-tied stationary energy storage systems, and several other consumer electronics primarily due to their high voltage rating (>4 V/cell) and high energy density (~265 (W h) L −1 ) and longer operational life. The use of LIBs in automotive and aerospace applications has led to larger cell sizes and large battery packs for a higher driving range and the requirement for more aggressive charging and discharging. However, thermal instability and temperature-dependent nonlinear behavior is some of the common concerns behind the safe and reliable operation of LIB systems. It is noticed that the operation of batteries outside the safe operating temperature directly affects the performance of LIBs, such as cycle life, efficiency, reliability and safety. Researchers investigating the thermal performance of LIB showed that the best operating temperature range is from 25 °C to 40 °C [1][2]. Richardson et al. [3] demonstrated that the difference between the core and surface temperature could reach more than 10 °C during real-life applications, especially during the high discharging condition and fluctuating load current demand. The excessive temperature difference and the accumulation of a large amount of heat inside the cell could lead to thermal runaway or even explosions and fire [4]. That necessitates the employment of a battery management system (BMS) for effective monitoring of battery parameters (current, voltage, temperature), estimation of battery states (state of charge (SOC), state of health (SOH), remaining useful life (RUL), state of temperature (SOT) [5]). Research studies demonstrated that SOC [6], SOH [7], and remaining storage capacity [8] are a function of temperature; thus, the estimation of the battery states also depends on the accurate estimation of cell temperature. The Columbic efficiency of a cell is greatly affected by the cell temperature during the charging and discharging period. Few other popular functionalities of BMS include cell balancing [9] and fault detection/diagnosis [10] to ensure optimum capacity utilization, operational safety, reliability, and longer battery life often requires temperature information of an individual cell and battery pack as well. Therefore, accurate information of core and surface temperature is highly crucial for effective thermal management and safety of a LIB pack. Moreover, in cold climate areas, the battery capacity is drastically reduced due to low-temperature operation that requires preheating the battery to a suitable range for optimum performance [11][12]. It is also evidenced that for every 0.1 °C beyond the safe operating region the battery capacity degrades by about 5% [13]. It is evidenced that maximum heat is generated during the discharging period especially with fast discharging [14]. Therefore, accurate temperature estimation is essential for effective thermal management and safety during fast charging and discharging and preheating of the cell to minimize capacity fade.

In summary, it could be stated that the accurate information of cell temperature is undoubtedly serving as the essential basis for the thermal management and safety of LIB. While the surface temperature of each cell can be measured by installing a temperature sensor on each cell, the core or internal temperature measurement directly using physical sensors is challenging. Moreover, installing a temperature sensor on each cell surface is not practically feasible from a system cost, space and weight point of view as any high-capacity battery pack used in EVs and grid-tied systems essentially consists of thousands of individual cells. Researchers have also incorporated multi-dimensional sensing and self-healing functions into a single battery cell to develop a smart battery [15][16][17][18]. Smart cells are typically capable of parameter measurements and estimation of cell states including the state of temperature. Despite the modularized application of BMS in smart batteries, accurate temperature estimation is still required, as otherwise installing sensors in each cell results in high implementation cost and complexity. Therefore, researchers are struggling hard to develop a high-fidelity, accurate, easy-to-implement, and computationally inexpensive online temperature estimation strategy suitable for low-cost onboard BMS. Several temperature estimation techniques have been proposed by researchers so far. Each different type of method has its advantages and limitations with respect to the above-mentioned features of an optimum BMS. Therefore, a summary of all the prominent techniques would be very helpful to researchers and developers serving as a baseline for further research and as a guideline for selecting appropriate techniques suitable for a specific requirement. However, such a summary with detailed discussion on current progress and explanation of the existing issues, challenges and future research scopes has not yet been presented in the literature. Therefore, this article covered the research gap by conducting a comprehensive review of the state-of-the-art temperature estimation strategies reported in the literature so far.

2. Generic Temperature Estimation Strategy

Irrespective of battery chemistry, heat is accumulated inside the battery during the charging/discharging even during idle conditions, majorly due to several largely exothermic chemical and electrochemical reactions as well as transport processes. If the heat transfer from the battery to the surroundings is not sufficient, then the heat gets accumulated inside the battery resulting in an increase in core and surface temperature, thereby risking thermal runaway. This phenomenon is even more prominent in the case of hard-cased insulated batteries (as used in EVs), under fast charging/discharging and the operation in hot environments. Heat dissipation is worse in cylindrical LIBs that are extensively used in high-capacity LIB packs. Therefore, a typical temperature estimation scheme consists of two models, namely, a heat generation model and a heat transfer model [19]. Often, a battery electrical model is also used to estimate the total heat generation using Bernardi’s [20] heat generation model whereas few other models use a mathematical form of battery electrochemistry to calculate the heat generation. Adaptive estimation strategies also consider the influence of different battery states, such as SOC and SOH, as the battery temperature is a function of these battery states. Then, the heat transfer model takes the estimated total heat quantity as well as few other external measurements such as ambient temperature to predict the temperature of that cell. Closed-loop estimation schemes use the measured or the estimation temperature as feedback to improve the prediction accuracy. A schematic layout of a generic temperature estimation strategy for LIB is shown in Figure 1 .

3. Classification of Temperature Estimation Strategies

As shown in Figure 1 , typically, a temperature estimation scheme consists of a heat generation model and a heat transfer model. The heat generation models reported in the literature can be broadly classified from two different aspects; based on modelling strategy and based on the source of heat generation. Heat generation models based on modelling strategy can be classified into three groups, physics-based electrochemical models [21][22][23][24], equivalent circuit models (ECM) [25][26][27], black-box models [28][29][30]. In contrast, based on the source of heat generation, these models can be grouped as a concentrated model, distributed model [31] and heterogeneous model [25][32]. The concentrated heat generation model considers that all heat is generated at the core only, usually considered to reduce the modelling complexity. The distributed heat generation model considers that uniform heat is generated throughout the entire cell geometry whereas the heterogeneous model can capture different heat generation from difference cell layers usually resulting in temperature and current density gradients inside the cell. The heterogeneous models are more detailed thus can produce highly accurate predictions; however, these are most complex and require extensive experiments for modelling. Distributed heat generation models are a balance between the concentrated and heterogeneous models. The heat transfer models can be classified into finite element analysis (FEA)-based models [27][33][34][35][36], heat capacitor-resistor models (lumped or distributed parameter) [28][37][38][39][40], and data-driven techniques. Heat capacitor–resistor-based models use the analogy between electrical and thermal systems. A heat capacitor–resistor can be further classified as mentioned in Figure 1 . Lumped parameter models are simple and useful for online applications, however, only one or two average temperatures can be predicted with these models whilst the battery temperature distribution is not spatially uniform, especially in larger capacity cylindrical LIB cells. On the other hand, complex distributed models [41][42] can describe the detailed temperature distribution in a cell, however, they are not suitable for online application due to their computational complexity. Several other detailed models of LIB accounting for the thermal characteristics of different layers are studied in [43][44][45][46][47][48]. A two-state/node model provides information on core and surface temperature whereas a one-state/node model can provide only core temperature.

Figure 1. Family of (A) Heat generation model, (B) Heat transfer model, (C) Temperature estimation strategy.

The heat transfer model where the total heat generation is one of the input parameters is collectively called the battery thermal model where the total heat generation is estimated by the battery heat generation model. The thermal modelling of LIB is a separate area of study and is not under the scope of this study. It deals only with the temperature estimation strategies. However, as most of the temperature estimation strategies are extensively depending on thermal modelling, an overview of each modelling technique is also discussed with the respective temperature estimation strategy for better understanding. Researchers employed different types of heat generation models with different kinds of heat transfer models to come up with a temperature estimation scheme. Therefore, it is challenging to classify these estimation strategies. Broadly, the temperature estimation schemes can be grouped into electrochemical thermal modelling-based, equivalent electric circuit model (EECM)-based, machine learning (ML)-based, numerical-model based, direct impedance measurement-based, magnetic nanoparticles-based schemes. The families of the LIB heat generation model, heat transfer model and temperature estimation strategy are illustrated in Figure 2 .

4. Comprehensive Review of Temperature Estimation Strategies

Further, depending on the modelling complexity, EECM could be also classified as lumped-parameter and distributed parameter models. Lumped-parameter models are used for simplification and thus low computational cost compared to detailed distributed models. Computationally efficient lumped thermal models are developed using single temperature as input to capture the model parameters [49] while some researchers used both surface and core temperatures of the cell to construct the lumped thermal models. Some also considered the correlation between cell geometry and other physical properties with thermal modelling [50]. However, several assumptions were made during modelling leading to inaccurate temperature estimation compared to detailed thermal modelling. Further, thermal models that only estimate the core temperature are considered as single-state/node [51], whereas if the model can estimate both surface and core temperature then it is termed as two-state/node [52] thermal model. The parameters of the EECM are identified through ranges of experimental studies such as electrochemical impedance spectroscopy (EIS) or utilizing externally measurable quantities, such as voltage, current, and temperature. Few studies also considered various conditions of SOC, SOH and estimated surface/core temperatures to make the model more robust. It is very difficult to group those thermal models because lumped models are used in both single-state and dual-state modelling and the model could be first-order and second-order. Therefore, the literature is grouped into cell-level and pack-level temperature estimation schemes that are discussed below.

Cell internal temperature estimation using a lumped-parameter thermal model and an approximate distributed thermal model have several drawbacks. Firstly, accurate determination of thermal model parameters such as heat generation and cell thermal properties is highly challenging. Heat generation inside the cell is typically approximated by measuring the cell operating current, voltage and the internal resistance that are again functions of SOC, cell internal temperature and SOH. Moreover, a cell is constructed using many different materials combined into a layered structure and thermal contact resistances between these layers are often unknown. Temperature estimation methods use surface temperature measurements and even the combination of surface-mounted temperature sensor and thermal model typically failed to detect the thermal runaway as rapid fluctuations in the internal temperature is difficult to capture using surface-mounted sensors because the heat conduction between the core and battery surface takes a considerable amount of time [53]. Furthermore, embedding micro-temperature sensors within the cell [54][55] is not practically possible for a large capacity LIB pack from a manufacturing complexity and system cost point of view. Hence, the core temperature measurement using a physical sensor is not an appropriate method for industrial applications.

Online EIS-based temperature estimation strategy termed impedance-temperature detection (ITD) was proposed by Richardson and Howey [56] for sensorless temperature estimation which is adaptive to cell ageing and practical uncertainties. However, ITD cannot provide a general solution alone, thus, such a strategy combines surface-mounted sensors with ITD for accurate online temperature estimation [3]. Still, temperature sensors are required to be installed. Further to this study, they integrated ITD with an electric-thermal model along with a DEKF for online core temperature estimation of a LIB cell even with unknown convection coefficient. They also demonstrated that the performance of the thermal model plus ITD is almost similar to the ITD with surface thermal sensors. Despite the advantages, the major limitations of the strategy are online impedance determination and the requirement of an accurate electric thermal model, thus encompassing the same drawback of conventional thermal modelling-based strategies. Moreover, although the strategy can estimate both core and surface temperature of an individual cell, the pack-level estimation strategy was not illustrated in this study.

The influence of cell temperature, SOC and SOH on the impedance spectrum, excitation frequency and thereby estimation accuracy of cell internal temperature was investigated by Zhu et al. [57]. Here, the temperature estimation was made based on an impedance response matrix analysis which was developed using EIS measurements. Despite high accuracy, the effect of the nonuniformity of the cell temperature and the correction method was not considered. Moreover, an extensive experimental study is required for modelling and the computational cost is also very high. Thus, the online application of the strategy is challenging. Identification of suitable frequency and other EIS parameters is very difficult whilst the estimation accuracy significantly depends on these parameters. Moreover, accurate determination of the real and imaginary parts of the impedance is highly challenging, whilst different decisions for these two parts lead to inaccurate temperature estimation. A combination of Linear Parameter Varying (LPV) thermal model and a polytopic observer-based battery-cell temperature estimation algorithm was proposed by Debert et al. [58]. The EIS-based strategy was also employed in references [3][59][60][61][62] to estimate the core temperature. Despite high accuracy, the major limitation is the determination of accurate impedance-temperature characteristics and it should be acquired in advance through tedious preliminary tests. In addition, the impedance-temperature characteristic of a cell is influenced by cell ageing leading to inaccurate prediction due to SOH deterioration. A summary of direct impedance measurement-based temperature estimation strategies is presented in Table 1 .

Table 1. Summary of direct impedance measurement-based strategies.
Reference Types of Models Important Note
Srinivasan et al. [63][64] Direct measurement of electrochemical impedance Experimental validation with EIS data
Schmidt et al. [65] Direct measurement of electrochemical impedance Temperature non-uniformity was not considered, experimentally validated
Richardson et al. [3] Thermal-impedance model + EIS measurement at single frequency + surface temperature feedback Independent of cell thermal properties, heat generation or thermal boundary conditions, experimental validation with EIS data
Richardson and Howey [56] Online EIS measurement (impedance-temperature detection (ITD) + dual-extended Kalman filter (DEKF) Unknown convection coefficient is considered, experimentally validated
Zhu et al. [57] Impedance response matrix analysis, developed using EIS measurements Influence of cell temperature, SOC and SOH on the impedance spectrum, experimental validation with EIS data

References

  1. Jilte, R.D.; Kumar, R.; Ma, L. Thermal performance of a novel confined flow Li-ion battery module. Appl. Therm. Eng. 2019, 146, 1–11.
  2. Yang, X.-H.; Tan, S.-C.; Liu, J. Thermal management of li-ion battery with liquid metal. Energy Convers. Manag. 2016, 117, 577–585.
  3. Richardson, R.R.; Ireland, P.T.; Howey, D. Battery internal temperature estimation by combined impedance and surface temperature measurement. J. Power Sources 2014, 265, 254–261.
  4. Wang, Y.-F.; Wu, J.-T. Performance improvement of thermal management system of lithium-ion battery module on purely electric AUVs. Appl. Therm. Eng. 2019, 146, 74–84.
  5. Surya, S.; Samanta, A.; Marcis, V.; Williamson, S.S. Smart core and surface temperature estimation techniques for health-conscious lithium-ion battery management systems: A model-to-model comparison. Preprints 2021, 1–21.
  6. Tanim, T.R.; Rahn, C.D.; Wang, C.-Y. State of charge estimation of a lithium ion cell based on a temperature dependent and electrolyte enhanced single particle model. Energy 2015, 80, 731–739.
  7. Farmann, A.; Sauer, D.U. A study on the dependency of the open-circuit voltage on temperature and actual aging state of lithium-ion batteries. J. Power Sources 2017, 347, 1–13.
  8. Zheng, F.; Jiang, J.; Sun, B.; Zhang, W.; Pecht, M. Temperature dependent power capability estimation of lithium-ion batteries for hybrid electric vehicles. Energy 2016, 113, 64–75.
  9. Samanta, A.; Chowdhuri, S. Active cell balancing of lithium-ion battery pack using dual dc-dc converter and auxiliary lead-acid battery. J. Energy Storage 2021, 33, 102109.
  10. Samanta, A.; Chowdhuri, S.; Williamson, S. Machine learning-based data-driven fault detection/diagnosis of lithium-ion battery: A critical review. Electronics 2021, 10, 1309.
  11. Zhang, S.; Xu, K.; Jow, T. The low temperature performance of li-ion batteries. J. Power Sources 2003, 115, 137–140.
  12. Mohan, S.; Kim, Y.; Stefanopoulou, A.G. Energy-conscious warm-up of li-ion cells from subzero temperatures. IEEE Trans. Ind. Electron. 2016, 63, 2954–2964.
  13. Surya, S.; Marcis, V.; Williamson, S. Core temperature estimation for a lithium ion 18650 cell. Energies 2020, 14, 87.
  14. Surya, S.; Mn, A. Effect of fast discharge of a battery on its core temperature. In Proceedings of the 2020 International Conference on Futuristic Technologies in Control Systems & Renewable Energy (ICFCR), Kerala, India, 23–24 September 2020.
  15. Wei, Z.; Zhao, J.; He, H.; Ding, G.; Cui, H.; Liu, L. Future smart battery and management: Advanced sensing from external to embedded multi-dimensional measurement. J. Power Sources 2021, 489, 229462.
  16. Steinhorst, S.; Lukasiewycz, M.; Narayanaswamy, S.; Kauer, M.; Chakraborty, S. Smart cells for embedded battery management. In Proceedings of the 2014 IEEE International Conference on Cyber-Physical Systems, Networks, and Applications, Hong Kong, China, 25–26 August 2014.
  17. Huang, X.; Sui, X.; Stroe, D.-I.; Teodorescu, R. A review of management architectures and balancing strategies in smart batteries. In Proceedings of the IECON 2019—45th Annual Conference of the IEEE Industrial Electronics Society, Lisbon, Portugal, 14–17 October 2019.
  18. Fleming, J.; Amietszajew, T.; Charmet, J.; Roberts, A.J.; Greenwood, D.; Bhagat, R. The design and impact of In Situ and operando thermal sensing for smart energy storage. J. Energy Storage 2019, 22, 36–43.
  19. Pan, Y.-W.; Hua, Y.; Zhou, S.; He, R.; Zhang, Y.; Yang, S.; Liu, X.; Lian, Y.; Yan, X.; Wu, B. A computational multi-node electro-thermal model for large prismatic lithium-ion batteries. J. Power Sources 2020, 459, 228070.
  20. Bernardi, D.M.; Pawlikowski, E.M.; Newman, J. A general energy balance for battery systems. J. Electrochem. Soc. 1985, 132, 5–12.
  21. Sun, F.; Xiong, R.; He, H.; Li, W.; Aussems, J.E.E. Model-based dynamic multi-parameter method for peak power estimation of lithium-ion batteries. Appl. Energy 2012, 96, 378–386.
  22. Lu, L.; Han, X.; Li, J.; Hua, J.; Ouyang, M. A review on the key issues for lithium-ion battery management in electric vehicles. J. Power Sources 2013, 226, 272–288.
  23. Ghalkhani, M.; Bahiraei, F.; Nazri, G.-A.; Saif, M. Electrochemical-thermal model of pouch-type lithium-ion batteries. Electrochim. Acta 2017, 247, 569–587.
  24. Yang, X.-G.; Leng, Y.; Zhang, G.; Ge, S.; Wang, C.-Y. Modeling of lithium plating induced aging of lithium-ion batteries: Transition from linear to nonlinear aging. J. Power Sources 2017, 360, 28–40.
  25. Chen, M.; Bai, F.; Song, W.; Lv, J.; Lin, S.; Feng, Z.; Li, Y.; Ding, Y. A multilayer electro-thermal model of pouch battery during normal discharge and internal short circuit process. Appl. Therm. Eng. 2017, 120, 506–516.
  26. Zhao, Y.; Diaz, L.B.; Patel, Y.; Zhang, T.; Offer, G.J. How to cool lithium ion batteries: Optimising cell design using a thermally coupled model. J. Electrochem. Soc. 2019, 166, A2849–A2859.
  27. Damay, N.; Forgez, C.; Bichat, M.-P.; Friedrich, G. Thermal modeling of large prismatic LiFePO4/graphite battery. Coupled thermal and heat generation models for characterization and simulation. J. Power Sources 2017, 283, 37–45.
  28. Allafi, W.; Zhang, C.; Uddin, K.; Worwood, D.; Dinh, T.Q.; Ormeno, P.A.; Li, K.; Marco, J. A lumped thermal model of lithium-ion battery cells considering radiative heat transfer. Appl. Therm. Eng. 2018, 143, 472–481.
  29. Esmaeili, J.; Jannesari, H. Developing heat source term including heat generation at rest condition for lithium-ion battery pack by up scaling information from cell scale. Energy Convers. Manag. 2017, 139, 194–205.
  30. Arora, S.; Shen, W.; Kapoor, A. Neural network based computational model for estimation of heat generation in LiFePO4 pouch cells of different nominal capacities. Comput. Chem. Eng. 2017, 101, 81–94.
  31. Hu, X.; Liu, W.; Lin, X.; Xie, Y. A comparative study of control-oriented thermal models for cylindrical li-ion batteries. IEEE Trans. Transp. Electrif. 2019, 5, 1237–1253.
  32. Xie, Y.; Li, W.; Hu, X.; Zou, C.; Feng, F.; Tang, X. Novel mesoscale electrothermal modeling for lithium-ion batteries. IEEE Trans. Power Electron. 2019, 35, 2595–2614.
  33. Friesen, A.; Mönnighoff, X.; Börner, M.; Haetge, J.; Schappacher, F.M.; Winter, M. Influence of temperature on the aging behavior of 18650-type lithium ion cells: A comprehensive approach combining electrochemical characterization and post-mortem analysis. J. Power Sources 2017, 342, 88–97.
  34. Fan, Y.; Bao, Y.; Ling, C.; Chu, Y.; Tan, X.; Yang, S. Experimental study on the thermal management performance of air cooling for high energy density cylindrical lithium-ion batteries. Appl. Therm. Eng. 2019, 155, 96–109.
  35. Saw, L.H.; Poon, H.M.; Thiam, H.S.; Cai, Z.; Chong, W.T.; Pambudi, N.A.; King, Y.J. Novel thermal management system using mist cooling for lithium-ion battery packs. Appl. Energy 2018, 223, 146–158.
  36. Liu, B.; Yin, S.; Xu, J. Integrated computation model of lithium-ion battery subject to nail penetration. Appl. Energy 2016, 183, 278–289.
  37. Doyle, M.; Fuller, T.F.; Newman, J.S. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. J. Electrochem. Soc. 1993, 140, 1526–1533.
  38. Xiao, Y.; Fahimi, B. State-space based multi-nodes thermal model for lithium-ion battery. In Proceedings of the 2014 IEEE Transportation Electrification Conference and Expo (ITEC), Beijing, China, 31 August 2014.
  39. Tian, N.; Fang, H.; Wang, Y. 3-D Temperature field reconstruction for a lithium-ion battery pack: A distributed kalman filtering approach. IEEE Trans. Control. Syst. Technol. 2019, 27, 847–854.
  40. Ruan, H.; Jiang, J.; Sun, B.; Gao, W.; Wang, L.; Zhang, W. Online estimation of thermal parameters based on a reduced wide-temperature-range electro-thermal coupled model for lithium-ion batteries. J. Power Sources 2018, 396, 715–724.
  41. Gu, W.B.; Wang, C.Y. Thermal-electrochemical modeling of battery systems. J. Electrochem. Soc. 2000, 147, 2910–2922.
  42. Kumaresan, K.; Sikha, G.; White, R.E. Thermal model for a li-ion cell. J. Electrochem. Soc. 2008, 155, A164–A171.
  43. Forgez, C.; Do, D.V.; Friedrich, G.; Morcrette, M.; Delacourt, C. Thermal modeling of a cylindrical LiFePO4/graphite lithium-ion battery. J. Power Sources 2010, 195, 2961–2968.
  44. Dees, D.W.; Battaglia, V.S.; Bélanger, A. Electrochemical modeling of lithium polymer batteries. J. Power Sources 2002, 110, 310–320.
  45. Lin, X.; Perez, H.E.; Siegel, J.; Stefanopoulou, A.G.; Li, Y.; Anderson, R.D.; Ding, Y.; Castanier, M. Online parameterization of lumped thermal dynamics in cylindrical lithium ion batteries for core temperature estimation and health monitoring. IEEE Trans. Control Syst. Technol. 2012, 21, 1745–1755.
  46. Choi, J.W.; Aurbach, D. Promise and reality of post-lithium-ion batteries with high energy densities. Nat. Rev. Mater. 2016, 1, 16013.
  47. Yang, F.; Wang, D.; Zhao, Y.; Tsui, K.-L.; Bae, S.J. A study of the relationship between coulombic efficiency and capacity degradation of commercial lithium-ion batteries. Energy 2018, 145, 486–495.
  48. Wang, Q.; Zhao, X.; Ye, J.; Sun, Q.; Ping, P.; Sun, J. Thermal response of lithium-ion battery during charging and discharging under adiabatic conditions. J. Therm. Anal. Calorim. 2016, 124, 417–428.
  49. Mahamud, R.; Park, C. Reciprocating air flow for li-ion battery thermal management to improve temperature uniformity. J. Power Sources 2011, 196, 5685–5696.
  50. Park, C.; Jaura, A.K. Dynamic Thermal Model of Li-Ion Battery for Predictive Behavior in Hybrid and Fuel Cell Vehicles; SAE Technical Paper Series; SAE International: Warrendale, PA, USA, 2003.
  51. Chen, L.; Hu, M.; Cao, K.; Li, S.; Su, Z.; Jin, G.; Fu, C. Core temperature estimation based on electro-thermal model of lithium-ion batteries. Int. J. Energy Res. 2020, 44, 5320–5333.
  52. Zhang, J.; Yang, X.-G.; Sun, F.; Wang, Z.; Wang, C.-Y. An online heat generation estimation method for lithium-ion batteries using dual-temperature measurements. Appl. Energy 2020, 272, 115262.
  53. Santhanagopalan, S.; Ramadass, P.; Zhang, J.Z. Analysis of internal short-circuit in a lithium ion cell. J. Power Sources 2009, 194, 550–557.
  54. Li, Z.; Zhang, J.; Wu, B.; Huang, J.; Nie, Z.; Sun, Y.; An, F.; Wu, N. Examining temporal and spatial variations of internal temperature in large-format laminated battery with embedded thermocouples. J. Power Sources 2013, 241, 536–553.
  55. Mutyala, M.S.K.; Zhao, J.; Li, J.; Pan, H.; Yuan, C.; Li, X. In Situ temperature measurement in lithium ion battery by transferable flexible thin film thermocouples. J. Power Sources 2014, 260, 43–49.
  56. Richardson, R.R.; Howey, D.A. Sensorless battery internal temperature estimation using a kalman filter with impedance measurement. IEEE Trans. Sustain. Energy 2015, 6, 1190–1199.
  57. Zhu, J.; Sun, Z.; Wei, X.; Dai, H. A new lithium-ion battery internal temperature on-line estimate method based on electrochemical impedance spectroscopy measurement. J. Power Sources 2015, 274, 990–1004.
  58. Debert, M.; Colin, G.; Bloch, G.; Chamaillard, Y. An observer looks at the cell temperature in automotive battery packs. Control Eng. Pract. 2013, 21, 1035–1042.
  59. Hu, X.; Yuan, H.; Zou, C.; Li, Z.; Zhang, L. Co-estimation of state of charge and state of health for lithium-ion batteries based on fractional-order calculus. IEEE Trans. Veh. Technol. 2018, 67, 10319–10329.
  60. Wang, Z.; Ma, J.; Zhang, L. State-of-health estimation for lithium-ion batteries based on the multi-island genetic algorithm and the gaussian process regression. IEEE Access 2017, 5, 21286–21295.
  61. Hande, A. Internal battery temperature estimation using series battery resistance measurements during cold temperatures. J. Power Sources 2006, 158, 1039–1046.
  62. Howey, D.; Mitcheson, P.D.; Yufit, V.; Offer, G.; Brandon, N.P. Online measurement of battery impedance using motor controller excitation. IEEE Trans. Veh. Technol. 2014, 63, 2557–2566.
  63. Srinivasan, R.; Carkhuff, B.G.; Butler, M.H.; Baisden, A.C. Instantaneous measurement of the internal temperature in lithium-ion rechargeable cells. Electrochim. Acta 2011, 56, 6198–6204.
  64. Srinivasan, R. Monitoring dynamic thermal behavior of the carbon anode in a lithium-ion cell using a four-probe technique. J. Power Sources 2012, 198, 351–358.
  65. Schmidt, J.P.; Arnold, S.; Loges, A.; Werner, D.; Wetzel, T.; Ivers-Tiffée, E. Measurement of the internal cell temperature via. impedance: Evaluation and application of a new method. J. Power Sources 2013, 243, 110–117.
More
Information
Contributor MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
View Times: 560
Revisions: 2 times (View History)
Update Date: 08 Oct 2021
1000/1000