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| Lithium-Ion Battery Intelligent Sensing Monitoring and Early Warning Technology |
| Ma Jingxuan1, Lai Yilin2, Lü Nawei1, Jiang Xin1, Jin Yang1 |
1. School of Electrical and Information Engineering Zhengzhou University Zhengzhou 450001 China;
2. China Electric Power Research Institute Beijing 100192 China |
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Abstract Advanced battery technology is an important technical means for human beings to cope with the global climate change challenges and energy crisis, especially in recent years, the rapid development of the electric vehicle industry and large-scale energy storage has brought a huge market for the lithium-ion battery industry. However, the rapid development of lithium batteries is accompanied by many problems and challenges. The high energy density of electrode materials creates poor thermal stability, resulting in a certain probability of failure of lithium batteries during use or storage, including capacity degradation, accelerated self-discharge, shortened cycle life, thermal runaway, etc., which seriously affects the consistency, reliability, and safety of the batteries in use. In recent years, there have been a number of fire and explosion accidents in various scales of energy storage systems, such as electric vehicles and energy storage power stations at home and abroad, which indicates that the existing means of monitoring the safety status of batteries are insufficient, and there is an urgent need for intelligent sensing and early warning technologies with a wider range of monitoring dimensions and more reliable detection strength. Therefore, the use of intelligent sensing technology to identify and sense the multi-dimensional physical and chemical characteristics (electric, thermal, gas, acoustic, optical, pressure, magnetic, etc.) of the early characterisation of battery failures, online monitoring and diagnosis of the battery safety status, and early warning of battery failures can greatly reduce the rate of battery failures and the incidence of accidents.
Firstly, based on the investigation of lithium-ion battery degradation and failure process, the generation and evolution mechanism of multi-dimensional characteristic signals of lithium batteries are outlined. Li-ion batteries are prone to a series of failures such as lithium precipitation, temperature rise, gas production, diaphragm puncture, short circuit, etc., and exhibit typical failure behaviours such as external battery deformation, temperature rise, gas production, liquid leakage, safety valve opening, thermal runaway and other typical failure behaviours under the abusive conditions such as overcharging, overheating, short-circuiting, and collision, which result in the uninterrupted failure of the battery's internal material structure with the continuous degradation and aging of the battery's performance. Taking the thermal runaway overcharge of commercial lithium-ion batteries as an example, the deterioration process of lithium batteries is described in four stages and the generation mechanism of multi-dimensional physical and chemical characteristic parameters, such as electrical signals, temperature signals, gas signals, sound signals, light signals, pressure signals, magnetic signals, etc., is elaborated.
Based on this, multi-dimensional sensing technologies such as electrical, temperature, gas, sound, optical, pressure, electromagnetic, etc. are introduced, existing research results are comprehensively researched and reviewed, and their advantages and disadvantages in terms of lithium-ion battery fault early warning time, realisation technological difficulty, identification accuracy, monitoring range, cost, etc. are compared.
From the above analysis and discussion, the main challenges facing the development of intelligent sensing monitoring and early warning technology for lithium-ion batteries are summarised, such as the difficulty of sensor implantation, the vulnerability of sensors to damage, the integration of multi-parameter sensing technology, etc. Meanwhile, it is proposed that the future development of lithium-ion batteries' intelligent sensing monitoring and early warning technology should be directed towards the development of multi-parameter integration, the development of smarter sensors, the enhancement of the implantation of sensors and the packaging of batteries, and the strengthening of the implementation of multi-parameter sensing technology. We also propose that the future development of lithium-ion battery intelligent sensing monitoring and early warning technology should be towards multi-parameter integration, develop smarter sensors, improve sensor implantation and battery packaging technology, and strengthen the implementation of multi-parameter sensing technology.
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Received: 21 December 2023
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[1] Wennersten R, Sun Qie, Li Hailong.The future potential for Carbon Capture and Storage in climate change mitigation-an overview from perspectives of technology, economy and risk[J]. Journal of Cleaner Production, 2015, 103: 724-736.
[2] Xu Jiyang, Ma Jian, Zhao Xuan, et al.Detection technology for battery safety in electric vehicles: a review[J]. Energies, 2020, 13(18): 4636.
[3] Zhang Guangxu, Wei Xuezhe, Chen Siqi, et al.Revealing the impact of slight electrical abuse on the thermal safety characteristics for lithium-ion batteries[J]. ACS Applied Energy Materials, 2021, 4(11): 12858-12870.
[4] Amici J, Asinari P, Ayerbe E, et al.A roadmap for transforming research to invent the batteries of the future designed within the European large scale research initiative BATTERY 2030+[J]. Advanced Energy Materials, 2022, 12(17): 2102785.
[5] Ouyang Dongxu, Chen Mingyi, Huang Que, et al.A review on the thermal hazards of the lithium-ion battery and the corresponding countermeasures[J]. Applied Sciences, 2019, 9(12): 2483.
[6] Belov D, Yang Mohua.Failure mechanism of Li-ion battery at overcharge conditions[J]. Journal of Solid State Electrochemistry, 2008, 12(7): 885-894.
[7] Maleki H, Howard J N.Effects of overdischarge on performance and thermal stability of a Li-ion cell[J]. Journal of Power Sources, 2006, 160(2): 1395-1402.
[8] Jin Yang, Zhao Zhixing, Miao Shan, et al.Explosion hazards study of grid-scale lithium-ion battery energy storage station[J]. Journal of Energy Storage, 2021, 42: 102987.
[9] Finegan D P, Darcy E, Keyser M, et al.Identifying the cause of rupture of Li-ion batteries during thermal runaway[J]. Advanced Science, 2017, 5(1): 1700369.
[10] Lamb J, Orendorff C J, Steele L A M, et al. Failure propagation in multi-cell lithium ion batteries[J]. Journal of Power Sources, 2015, 283: 517-523.
[11] 孙金磊, 唐传雨, 李磊, 等. 基于状态与模型参数联合估计的老化电池可充入电量估计方法[J]. 电工技术学报, 2022, 37(22): 5886-5898.
Sun Jinlei, Tang Chuanyu, Li Lei, et al.An estimation method of rechargeable electric quantity for aging battery based on joint estimation of state and model parameters[J]. Transactions of China Electrotechnical Society, 2022, 37(22): 5886-5898.
[12] Rahimi-Eichi H, Ojha U, Baronti F, et al.Battery management system: an overview of its application in the smart grid and electric vehicles[J]. IEEE Industrial Electronics Magazine, 2013, 7(2): 4-16.
[13] Feng Xuning, Ren Dongsheng, He Xiangming, et al.Mitigating thermal runaway of lithium-ion batteries[J]. Joule, 2020, 4(4): 743-770.
[14] 张家琪, 刘朋印, 谢小荣, 等. 适用于故障特性分析的锂离子电池储能电磁暂态建模方法[J]. 电力系统自动化, 2023, 47(7): 166-173.
Zhang Jiaqi, Liu Pengyin, Xie Xiaorong, et al.Electromagnetic transient modeling method of lithium-ion battery energy storage system for fault characteristic analysis[J]. Automation of Electric Power Systems, 2023, 47(7): 166-173.
[15] 宿磊, 余嘉川, 杨帆, 等. 磷酸铁锂储能电池过充热失效特征参量研究[J]. 电工技术学报, 2023, 38(21): 5913-5922.
Su Lei, Yu Jiachuan, Yang Fan, et al.Study on characteristic parameters of LFP battery under the condition of overcharge thermal failure[J]. Transactions of China Electrotechnical Society, 2023, 38(21): 5913-5922.
[16] 郑志坤. 磷酸铁锂储能电池过充热失控及气体探测安全预警研究[D]. 郑州: 郑州大学, 2020.
Zheng Zhikun.Study on overcharging out-of-control and gas detection safety warning of ferrous lithium phosphate energy storage battery[D]. Zhengzhou: Zhengzhou University, 2020.
[17] 王文伟, 刘帅邦, 杨晓光, 等. 锂离子电池智能传感技术综述[J]. 电源技术, 2023, 47(9): 1107-1112.
Wang Wenwei, Liu Shuaibang, Yang Xiaoguang, et al.Review of smart sensing techniques for lithium ion batteries[J]. Chinese Journal of Power Sources, 2023, 47(9): 1107-1112.
[18] 陈虎, 熊辉, 厉运杰, 等. 锂离子电池产热特性研究进展[J]. 储能科学与技术, 2019, 8(增刊1): 49-55.
Chen Hu, Xiong Hui, Li Yunjie, et al.Research progress on thermogenic characteristics of lithium ion batteries[J]. Energy Storage Science and Technology, 2019, 8(S1): 49-55.
[19] 吴静云, 郭鹏宇, 张淼, 等. 基于气体检测的锂电池热失控预警研究进展[J]. 消防科学与技术, 2022, 41(2): 161-164.
Wu Jingyun, Guo Pengyu, Zhang Miao, et al.Research progress on the warning of the thermal runaway of lithium-ion battery based on the gas detection[J]. Fire Science and Technology, 2022, 41(2): 161-164.
[20] Su Tonglun, Lyu Nawei, Zhao Zhixing, et al.Safety warning of lithium-ion battery energy storage station via venting acoustic signal detection for grid application[J]. Journal of Energy Storage, 2021, 38: 102498.
[21] 唐文杰, 姜欣, 刘昊琰, 等. 基于气液逸出物图像识别的锂离子电池火灾早期预警[J]. 高电压技术, 2022, 48(8): 3295-3304.
Tang Wenjie, Jiang Xin, Liu Haoyan, et al.Early warning of lithium-ion battery fire based on image recognition of gas-liquid escape[J]. High Voltage Engineering, 2022, 48(8): 3295-3304.
[22] 梁浩斌, 杜建华, 郝鑫, 等. 锂电池膨胀形成机制研究现状[J]. 储能科学与技术, 2021, 10(2): 647-657.
Liang Haobin, Du Jianhua, Hao Xin, et al.A review of current research on the formation mechanism of lithium batteries[J]. Energy Storage Science and Technology, 2021, 10(2): 647-657.
[23] Chen Shichen, Wang Zhirong, Yan Wei, et al.Investigation of impact pressure during thermal runaway of lithium ion battery in a semi-closed space[J]. Applied Thermal Engineering, 2020, 175: 115429.
[24] Li Da, Zhang Zhaosheng, Liu Peng, et al.Battery fault diagnosis for electric vehicles based on voltage abnormality by combining the long short-term memory neural network and the equivalent circuit model[J]. IEEE Transactions on Power Electronics, 2021, 36(2): 1303-1315.
[25] Wang Zhenpo, Hong Jichao, Liu Peng, et al.Voltage fault diagnosis and prognosis of battery systems based on entropy and Z-score for electric vehicles[J]. Applied Energy, 2017, 196: 289-302.
[26] Jiang Lulu, Deng Zhongwei, Tang Xiaolin, et al.Data-driven fault diagnosis and thermal runaway warning for battery packs using real-world vehicle data[J]. Energy, 2021, 234: 121266.
[27] Zhang Mingxuan, Du Jiuyu, Liu Lishuo, et al.Internal short circuit detection method for battery pack based on circuit topology[J]. Science China Technological Sciences, 2018, 61(10): 1502-1511.
[28] 冷晓伟, 戴作强, 郑莉莉, 等. 锂离子电池电化学阻抗谱研究综述[J]. 电源技术, 2018, 42(11): 1749-1752.
Leng Xiaowei, Dai Zuoqiang, Zheng Lili, et al.Review on electrochemical impedance spectroscopy of lithium-ion batteries[J]. Chinese Journal of Power Sources, 2018, 42(11): 1749-1752.
[29] Luo Yifeng, Gong C S A, Chang Longxi, et al. AC impedance technique for dynamic and static state of charge analysis for Li-ion battery[C]//2013 IEEE International Symposium on Consumer Electronics (ISCE), Hsinchu, Taiwan, China, 2013: 9-10.
[30] Koleti U R, Dinh T Q, Marco J.A new on-line method for lithium plating detection in lithium-ion batteries[J]. Journal of Power Sources, 2020, 451: 227798.
[31] Straer A, Adam A, Li Jiahao.In operando detection of Lithium plating via electrochemical impedance spectroscopy for automotive batteries[J]. Journal of Power Sources, 2023, 580: 233366.
[32] 张闯, 杨浩, 刘素贞, 等. 基于阻抗在线测量的锂离子电池过放电诱发内短路识别研究[J]. 电工技术学报, 2024, 39(6): 1656-1670.
Zhang Chuang, Yang Hao, Liu Suzhen, et al.Research on overdischarge-induced internal short circuit identification of lithium-ion battery based on impedance online measurement[J]. Transactions of China Electrotechnical Society, 2024, 39(6): 1656-1670.
[33] Lyu Nawei, Jin Yang, Xiong Rui, et al.Real-time overcharge warning and early thermal runaway prediction of Li-ion battery by online impedance measurement[J]. IEEE Transactions on Industrial Electronics, 2022, 69(2): 1929-1936.
[34] 孙丙香, 宋东林, 阮海军, 等. 基于自产热和外传热的锂离子电池热学模型参数辨识方法[J]. 电工技术学报, 2024, 39(1): 278-288.
Sun Bingxiang, Song Donglin, Ruan Haijun, et al.Parameter identification method of thermal model of lithium-ion battery based on self-generated heat and external heat transfer[J]. Transactions of China Electrotechnical Society, 2024, 39(1): 278-288.
[35] Gulsoy B, Vincent T A, Sansom J E H, et al. In-situ temperature monitoring of a lithium-ion battery using an embedded thermocouple for smart battery applications[J]. Journal of Energy Storage, 2022, 54: 105260.
[36] Mei Wenxin, Liu Zhi, Wang Chengdong, et al.Operando monitoring of thermal runaway in commercial lithium-ion cells via advanced lab-on-fiber technologies[J]. Nature Communications, 2023, 14(1): 5251.
[37] Li Hanyang, Wei Feng, Li Yanzeng, et al.Optical fiber sensor based on upconversion nanoparticles for internal temperature monitoring of Li-ion batteries[J]. Journal of Materials Chemistry C, 2021, 9(41): 14757-14765.
[38] Yu Yifei, Vincent T, Sansom J, et al.Distributed internal thermal monitoring of lithium ion batteries with fibre sensors[J]. Journal of Energy Storage, 2022, 50: 104291.
[39] Yang S O, Lee S, Song S H, et al.Development of a distributed optical thermometry technique for battery cells[J]. International Journal of Heat and Mass Transfer, 2022, 194: 123020.
[40] Li Marui, Dong Chaoyu, Yu Xiaodan, et al.Multi-step ahead thermal warning network for energy storage system based on the core temperature detection[J]. Scientific Reports, 2021, 11(1): 15332.
[41] Zou Dongyao, Li Ming, Wang Dandan, et al.Temperature estimation of lithium-ion battery based on an improved magnetic nanoparticle thermometer[J]. IEEE Access, 2020, 8: 135491-135498.
[42] Zhu J G, Sun Z C, Wei X Z, et al.A new lithium-ion battery internal temperature on-line estimate method based on electrochemical impedance spectroscopy measurement[J]. Journal of Power Sources, 2015, 274: 990-1004.
[43] Srinivasan R, Carkhuff B G, Butler M H, et al.Instantaneous measurement of the internal temperature in lithium-ion rechargeable cells[J]. Electrochimica Acta, 2011, 56(17): 6198-6204.
[44] Srinivasan R, Demirev P A, Carkhuff B G.Rapid monitoring of impedance phase shifts in lithium-ion batteries for hazard prevention[J]. Journal of Power Sources, 2018, 405: 30-36.
[45] 杨梦洁, 杨爱军, 叶奕君, 等. 基于气体分析的锂离子电池热失控早期预警研究进展[J]. 电工技术学报, 2023, 38(17): 4507-4538.
Yang Mengjie, Yang Aijun, Ye Yijun, et al.Research progress on early warning of thermal runaway of Li-ion batteries based on gas analysis[J]. Transactions of China Electrotechnical Society, 2023, 38(17): 4507-4538.
[46] Qin Peng, Jia Zhuangzhuang, Wu Jingyun, et al.The thermal runaway analysis on LiFePO4 electrical energy storage packs with different venting areas and void volumes[J]. Applied Energy, 2022, 313: 118767.
[47] Golubkov A W, Scheikl S, Planteu R, et al.Thermal runaway of commercial 18650 Li-ion batteries with LFP and NCA cathodes-impact of state of charge and overcharge[J]. RSC Advances, 2015, 5(70): 57171-57186.
[48] Yuan Liming, Dubaniewicz T, Zlochower I, et al.Experimental study on thermal runaway and vented gases of lithium-ion cells[J]. Process Safety and Environmental Protection, 2020, 144: 186-192.
[49] Larsson F, Bertilsson S, Furlani M, et al.Gas explosions and thermal runaways during external heating abuse of commercial lithium-ion graphite-LiCoO2 cells at different levels of ageing[J]. Journal of Power Sources, 2018, 373: 220-231.
[50] Essl C, Golubkov A W, Gasser E, et al.Comprehensive hazard analysis of failing automotive lithium-ion batteries in overtemperature experiments[J]. Batteries, 2020, 6(2): 30.
[51] Lammer M, Königseder A, Hacker V.Holistic methodology for characterisation of the thermally induced failure of commercially available 18650 lithium ion cells[J]. RSC Advances, 2017, 7(39): 24425-24429.
[52] Jin Yang, Zheng Zhikun, Wei Donghui, et al.Detection of micro-scale Li dendrite via H2 gas capture for early safety warning[J]. Joule, 2020, 4(8): 1714-1729.
[53] 石爽, 吕娜伟, 马敬轩, 等. 不同类型气体探测对磷酸铁锂电池储能舱过充安全预警有效性对比[J]. 储能科学与技术, 2022, 11(8): 2452-2462.
Shi Shuang, Lyu Nawei, Ma Jingxuan, et al.Comparative study on the effectiveness of different types of gas detection on the overcharge safety early warning of a lithium iron phosphate battery energy storage compartment[J]. Energy Storage Science and Technology, 2022, 11(8): 2452-2462.
[54] Cai Ting, Valecha P, Tran V, et al.Detection of Li-ion battery failure and venting with Carbon Dioxide sensors[J]. eTransportation, 2021, 7: 100100.
[55] Liao Zhenghai, Zhang Jiangong, Gan Zheyuan, et al.Thermal runaway warning of lithium-ion batteries based on photoacoustic spectroscopy gas sensing technology[J]. International Journal of Energy Research, 2022, 46(15): 21694-21702.
[56] Kaur P, Bagchi S, Gribble D, et al.Impedimetric chemosensing of volatile organic compounds released from Li-ion batteries[J]. ACS Sensors, 2022, 7(2): 674-683.
[57] Teng Xin, Zhan Chun, Bai Ying, et al.In situ analysis of gas generation in lithium-ion batteries with different carbonate-based electrolytes[J]. ACS Applied Materials & Interfaces, 2015, 7(41): 22751-22755.
[58] Kim J, Gerelt-Od B, Shin E, et al.State of health monitoring by gas generation patterns in commercial 18, 650 lithium-ion batteries[J]. Journal of Electroanalytical Chemistry, 2022, 907: 115892.
[59] Wasylowski D, Kisseler N, Ditler H, et al.Spatially resolving lithium-ion battery aging by open-hardware scanning acoustic imaging[J]. Journal of Power Sources, 2022, 521: 230825.
[60] Ju Lingling, Li Xining, Geng Guangchao, et al.Degradation diagnosis of lithium-ion batteries considering internal gas evolution[J]. Journal of Energy Storage, 2023, 71: 108084.
[61] 杨元威, 关永刚, 陈士刚, 等. 基于声音信号的高压断路器机械故障诊断方法[J]. 中国电机工程学报, 2018, 38(22): 6730-6737.
Yang Yuanwei, Guan Yonggang, Chen Shigang, et al.Mechanical fault diagnosis method of high voltage circuit breaker based on sound signal[J]. Proceedings of the CSEE, 2018, 38(22): 6730-6737.
[62] 符劲松. 基于可听声的变压器内部火花放电故障诊断研究[D]. 武汉: 华中科技大学, 2013.
Fu Jinsong.Research on fault diagnosis of transformer internal spark discharge based on audible sound[D]. Wuhan: Huazhong University of Science and Technology, 2013.
[63] 刘素贞, 袁路航, 张闯, 等. 基于超声时域特征及随机森林的磷酸铁锂电池荷电状态估计[J].电工技术学报, 2022, 37(22):5873-5885.
Liu Suzhen, Yuan Luhang, Zhang Chuang, et al.State of charge estimation of LiFeO4 batteries based on time domain features of ultrasonic waves and random forest[J]. Transactions of China Electrotechnical Society, 2022, 37(22): 5873-5885.
[64] Deng Zhe, Huang Zhenyu, Shen Yue, et al.Ultrasonic scanning to observe wetting and “unwetting” in Li-ion pouch cells[J]. Joule, 2020, 4(9): 2017-2029.
[65] Huang Zhenyu, Zhou Yu, Deng Zhe, et al.Precise state-of-charge mapping via deep learning on ultrasonic transmission signals for lithium-ion batteries[J]. ACS Applied Materials & Interfaces, 2023, 15(6): 8217-8223.
[66] Sood B, Osterman M, Pecht M.Health monitoring of lithium-ion batteries[C]//2013 IEEE Symposium on Product Compliance Engineering (ISPCE), Austin, TX, USA, 2013: 1-6.
[67] Bommier C, Chang W, Li Jianlin, et al.operando acoustic monitoring of SEI formation and long-term cycling in NMC/SiGr composite pouch cells[J]. Journal of the Electrochemical Society, 2020, 167(2): 020517.
[68] Zhang Yeshui, Pallipurath Radhakrishnan A N, Robinson J B, et al. In situ ultrasound acoustic measurement of the lithium-ion battery electrode drying process[J]. ACS Applied Materials & Interfaces, 2021, 13(30): 36605-36620.
[69] Li Honggang, Zhou Zhenggan.Numerical simulation and experimental study of fluid-solid coupling-based air-coupled ultrasonic detection of stomata defect of lithium-ion battery[J]. Sensors, 2019, 19(10): 2391.
[70] Robinson J B, Owen R E, Kok M D R, et al. Identifying defects in Li-ion cells using ultrasound acoustic measurements[J]. Journal of the Electrochemical Society, 2020, 167(12): 120530.
[71] Wu Yi, Wang Youren, Yung W K C, et al. Ultrasonic health monitoring of lithium-ion batteries[J]. Electronics, 2019, 8(7): 751.
[72] Alamgir N, Nguyen K, Chandran V, et al.Combining multi-channel color space with local binary co-occurrence feature descriptors for accurate smoke detection from surveillance videos[J]. Fire Safety Journal, 2018, 102: 1-10.
[73] Vijayalakshmi S R, Muruganand S.Fire alarm based on spatial temporal analysis of fire in video[C]//2018 2nd International Conference on Inventive Systems and Control (ICISC), Coimbatore, 2018: 104-109.
[74] Emmy Prema C, Vinsley S S, Suresh S.Multi feature analysis of smoke in YUV color space for early forest fire detection[J]. Fire Technology, 2016, 52(5): 1319-1342.
[75] Chen Zhiwen, Lin Jiawei, Zhu Cuicui, et al.Detection of jelly roll pressure evolution in large-format Li-ion batteries via in situ thin film flexible pressure sensors[J]. Journal of Power Sources, 2023, 566: 232960.
[76] Peng Xiaoli, Han Jiang, Zhang Qian, et al.Real-time mechanical and thermal monitoring of lithium batteries with PVDF-TrFE thin films integrated within the battery[J]. Sensors and Actuators A: Physical, 2022, 338: 113484.
[77] Jia Zhuangzhuang, Song Laifeng, Mei Wenxin, et al.The preload force effect on the thermal runaway and venting behaviors of large-format prismatic LiFePO4 batteries[J]. Applied Energy, 2022, 327: 120100.
[78] Atchison H L, Bailey Z R, Wetz D A, et al.Fiber optic based thermal and strain sensing of lithium-ion batteries at the individual cell level[J]. Journal of the Electrochemical Society, 2021, 168(4): 040535.
[79] 史雯慧, 王浩, 曹慧, 等. 基于光纤布拉格光栅传感的锂电池内部状态原位监测[J]. 光子学报, 2023, 52(9): 0906002.
Shi Wenhui, Wang Hao, Cao Hui, et al.In-situ monitoring of the internal status of lithium batteries based on fiber Bragg grating sensors[J]. Acta Photonica Sinica, 2023, 52(9): 0906002.
[80] Zhu Shengxin, Yang Le, Fan Jinbao, et al.In-situ obtained internal strain and pressure of the cylindrical Li-ion battery cell with silicon-graphite negative electrodes[J]. Journal of Energy Storage, 2021, 42: 103049.
[81] Tan Ke, Li Wei, Lin Zhen, et al.operando monitoring of internal gas pressure in commercial lithium-ion batteries via a MEMS-assisted fiber-optic interferometer[J]. Journal of Power Sources, 2023, 580: 233471.
[82] Li Weifeng, Zhang Yajun, Gao Zhenhai, et al.Experimental and theoretical analysis of the eruption processes of abused prismatic Ni-rich automotive batteries based on multi-parameters[J]. Journal of Energy Storage, 2022, 52: 105012.
[83] Song Yuhang, Lyu Nawei, Shi Shuang, et al.Safety warning for lithium-ion batteries by module-space air-pressure variation under thermal runaway conditions[J]. Journal of Energy Storage, 2022, 56: 105911.
[84] Krachkovskiy S A, Reza M, Aguilera A R, et al.Real-time quantitative detection of lithium plating by in situ NMR using a parallel-plate resonator[J]. Journal of the Electrochemical Society, 2020, 167(13): 130514.
[85] Ishigaki M, Ishikawa K, Usuki T, et al.operando Li metal plating diagnostics via MHz band electromagnetics[J]. Nature Communications, 2023, 14(1): 7275. |
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2025, Vol. 40 
