章节

锂离子电池性能衰退机理与故障预警策略研究

摘要

本文基于新能源汽车用锂离子动力电池在车载复杂工况下由于老化过程造成整车性能下降以及产生安全隐患的问题开展研究,首先分析锂离子电池老化机理,研究老化路径对衰减过程的影响;其次对当前常用的电池模型方法进行了总结,并着重介绍了动力电池电化学-热-机耦合仿真模型;再次分析了实际应用下锂离子电池伴随老化过程可能出现的风险,归纳了主要容量衰退分析方法;最后阐明了基于动力电池云端控制的故障预警方案将是动力电池整车应用未来的发展方向。

作者

杨世春 ,北京航空航天大学,交通科学与工程学院院长,博士,教授,主要研究方向为电动汽车能源动力系统优化与控制、智能无人驾驶等。
华旸 ,博士,北京航空航天大学,主要研究方向为电动汽车能源动力系统优化与控制等。
周思达
周新岸

参考文献 查看全部 ↓
  • Waldmann T,Wilka M,Kasper M,et al. Temperature dependent ageing mechanisms in Lithium-ion batteries-A Post-Mortem study[J]. Journal of Power Sources. 2014,262:129-135.
  • Feng X,Fang M,He X,et al. Thermal runaway features of large format prismatic lithium ion battery using extended volume accelerating rate calorimetry[J]. Journal of Power Sources. 2014,255.
  • 吴正国,张剑波,李哲,等. 锂离子电池加速老化温度应力的滥用边界[J]. 汽车安全与节能学报. 2018,9(01):99-109.
  • Petzl M,Kasper M,Danzer M A. Lithium plating in a commercial lithium-ion battery-A low-temperature aging study[J]. Journal of Power Sources. 2015,275:799-807.
  • Zhang Y,Ge H,Huang J,et al. A comparative degradation study of commercial lithium-ion cells under low-temperature cycling[J]. RSC Advances. 2017.
  • Dubarry M,Truchot C,Liaw B Y,et al. Evaluation of commercial lithium-ion cells based on composite positive electrode for plug-in hybrid electric vehicle applications. Part II. Degradation mechanism under 2C cycle aging[J]. Journal of Power Sources. 2011,196(23):10336-10343.
  • Cheng J,Li X,Wang Z,et al. Mechanism for capacity fading of 18650 cylindrical lithium ion batteries[J]. Transactions of Nonferrous Metals Society of China. 2017,27(7):1602-1607.
  • Wang J,Liu P,Hicks-Garner J,et al. Cycle-life model for graphite-LiFePO4 cells[J]. Journal of Power Sources. 2011,196(8):3942-3948.
  • Klett M,Eriksson R,Groot J,et al. Non-uniform aging of cycled commercial LiFePO4//graphite cylindrical cells revealed by post-mortem analysis[J]. Journal of Power Sources. 2014,257:126-137.
  • Lei Y,Zhang C,Gao Y,et al. Charging optimization of lithium-ion batteries based on capacity degradation speed and energy loss[J]. Energy Procedia. 2018,152:544-549.
  • Dung L,Yuan H,Yen J,et al. A lithium-ion battery simulator based on a diffusion and switching overpotential hybrid model for dynamic discharging behavior and runtime predictions[J]. Energies. 2016,9(1):51.
  • Xiong R,Shen W. Advanced battery management technologies for electric vehicles[M]. Wiley,2019.
  • He H,Xiong R,Fan J. Evaluation of lithium-ion battery equivalent circuit models for state of charge estimation by an experimental approach[J]. Energies. 2011,4(4):582-598.
  • 雷肖,陈清泉,刘开培,等. 电动车蓄电池荷电状态估计的神经网络方法[J]. 电工技术学报. 2007(08):155-160.
  • Kang L,Zhao X,Ma J. A new neural network model for the state-of-charge estimation in the battery degradation process[J]. Applied Energy. 2014,121:20-27.
  • Chemali E,Kollmeyer P J,Preindl M,et al. State-of-charge estimation of Li-ion batteries using deep neural networks:A machine learning approach[J]. Journal of Power Sources. 2018,400:242-255.
  • Wang J,Chen Q,Cao B. Support vector machine based battery model for electric vehicles[J]. Energy Conversion and Management. 2006,47(7):858-864.
  • Klass V,Behm M,Lindbergh G. A support vector machine-based state-of-health estimation method for lithium-ion batteries under electric vehicle operation[J]. Journal of Power Sources. 2014,270:262-272.
  • 熊瑞,何洪文,许永莉,等. 电动汽车用动力电池组建模和参数辨识方法[J]. 吉林大学学报(工学版). 2012,42(04):809-815.
  • 康鑫,时玮,陈洪涛. 基于锂离子电池简化电化学模型的参数辨识[J]. 储能科学与技术. 2020:1-16.
  • Doyle M. Modeling of Galvanostatic Charge and Discharge of the Lithium/Polymer/Insertion Cell[J]. Journal of The Electrochemical Society. 1993,140(6):1526.
  • Ning G,Popov B N. Cycle life modeling of lithium-ion batteries[J]. Journal of The Electrochemical Society. 2004,151(10):A1584.
  • Farkhondeh M,Safari M,Pritzker M,et al. Full-range simulation of a commercial LiFePO4 electrode accounting for bulk and surface effects:a comparative analysis[J]. Journal of The Electrochemical Society. 2014,161:A201-A212.
  • 庞辉. 基于电化学模型的锂离子电池多尺度建模及其简化方法[J]. 物理学报. 2017,66(23):312-322.
  • Bernardi D. A general energy balance for battery systems[J]. Journal of The Electrochemical Society. 1985,132(1):5.
  • Zhao K,Pharr M,Vlassak J,et al. Fracture of electrodes in lithium-ion batteries caused by fast charging[J]. Journal of Applied Physics. 2010,108:73517.
  • Lim C,Yan B,Yin L,et al. Simulation of diffusion-induced stress using reconstructed electrodes particle structures generated by micro/nano-CT[J]. Electrochimica Acta. 2012,75:279-287.
  • Cheng Y,Verbruggeb M W. Evolution of stress within a spherical insertion electrode particle under potentiostatic and galvanostatic operation[J]. Jounal of Power Sources. 2009,190:453-460.
  • 徐兴,王位,陈龙. 基于GA的车用锂离子电池电化学模型参数辨识[J]. 汽车工程. 2017,39(07):813-821.
  • Pang H,Mou L,Guo L,et al. Parameter identification and systematic validation of an enhanced single-particle model with aging degradation physics for Li-ion batteries[J]. Electrochimica Acta. 2019,307:474-487.
  • Li Y,Chattopadhyay P,Ray A. Dynamic data-driven identification of battery state-of-charge via symbolic analysis of input-output pairs[J]. Applied Energy. 2015,155:778-790.
  • Li Y,Chen J,Lan F. Enhanced online model identification and state of charge estimation for lithium-ion battery under noise corrupted measurements by bias compensation recursive least squares[J]. Journal of Power Sources. 2020,456:227984.
  • Guan W,Dong L L,Zhou J M,et al. Data-driven methods for operational modal parameters identification:A comparison and application[J]. Measurement. 2019,132:238-251.
  • Xiong R,Sun F,Chen Z,et al. A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion polymer battery in electric vehicles[J]. Applied Energy. 2014,113:463-476.
  • Li Y,Chattopadhyay P,Ray A,et al. Identification of the battery state-of-health parameter from input-output pairs of time series data[J]. Journal of Power Sources. 2015,285:235-246.
  • Cadini F,Sbarufatti C,Cancelliere F,et al. State-of-life prognosis and diagnosis of lithium-ion batteries by data-driven particle filters[J]. Applied Energy. 2019,235:661-672.
  • Ren D,Hsu H,Li R,et al. A comparative investigation of aging effects on thermal runaway behavior of lithium-ion batteries[J]. eTransportation. 2019,2:100034.
  • Liu J,Duan Q,Ma M,et al. Aging mechanisms and thermal stability of aged commercial 18650 lithium ion battery induced by slight overcharging cycling[J]. Journal of Power Sources. 2020,445:227263.
  • Li H,Kong X,Liu C,et al. Study on thermal stability of nickel-rich/silicon-graphite large capacity lithium ion battery[J]. Applied Thermal Engineering. 2019,161:114144.
  • Yang C,Wang X,Fang Q,et al. An online SOC and capacity estimation method for aged lithium-ion battery pack considering cell inconsistency[J]. Journal of Energy Storage. 2020,29:101250.
  • Zheng Y,Gao W,Ouyang M,et al. State-of-charge inconsistency estimation of lithium-ion battery pack using mean-difference model and extended Kalman filter[J]. Journal of Power Sources. 2018,383:50-58.
  • Hu X,Xu L,Lin X,et al. Battery Lifetime Prognostics[J]. Joule. 2020,4(2):310-346.
  • Han X,Lu L,Zheng Y,et al. A review on the key issues of the lithium ion battery degradation among the whole life cycle[J]. eTransportation. 2019,1:100005.
  • 姜久春,高洋,张彩萍,等. 电动汽车锂离子动力电池健康状态在线诊断方法木[J]. 机械工程学报. 2019,55(20):60-72.
  • Wang J,Liu P,Hicks-Garner J,et al. Cycle-life model for graphite-LiFePO4 cells[J]. Journal of Power Sources. 2011,196(8):3942-3948.
  • Petit M,Prada E,Sauvant-Moynot V. Development of an empirical aging model for Li-ion batteries and application to assess the impact of Vehicle-to-Grid strategies on battery lifetime[J]. Applied Energy. 2016,172:398-407.
  • de Hoog J,Timmermans J,Ioan-Stroe D,et al. Combined cycling and calendar capacity fade modeling of a Nickel-Manganese-Cobalt Oxide Cell with real-life profile validation[J]. Applied Energy. 2017,200:47-61.
  • 马彦,陈阳,张帆,等. 基于扩展H_∞粒子滤波算法的动力电池寿命预测方法[J]. 机械工程学报. 2019,55(20):36-43.
  • 张雅琨,苏来锁,王彩娟,等. 多应力作用下锂离子电池老化模型[J]. 电源技术. 2018,42(01):32-36.
  • Roscher M A,Assfalg J,Bohlen O S. Detection of utilizable capacity deterioration in battery systems[J]. IEEE Transactions on Vehicular Technology. 2011,60(1):98-103.
  • Wassiliadis N,Adermann J,Frericks A,et al. Revisiting the dual extended kalman filter for battery state-of-charge and state-of-health estimation:A use-case life cycle analysis[J]. Journal of Energy Storage. 2018,19:73-87.
  • 杨世春,顾启蒙,华旸,等. 锂离子电池SOC及容量的多尺度联合估计[J]. 北京航空航天大学学报. 2019.
  • 林娅,陈则王. 锂离子电池剩余寿命预测研究综述[J]. 电子测量技术. 2018,41(4):29-35.
  • 杨杰,王婷,杜春雨,等. 锂离子电池模型研究综述[J]. 储能科学与技术. 2019,8(01):58-64.
  • Li J,Adewuyi K,Lotfi N,et al. A single particle model with chemical/mechanical degradation physics for lithium ion battery State of Health (SOH) estimation[J]. Applied Energy. 2018,212:1178-1190.
  • Yang S,Hua Y,Qiao D,et al. A coupled electrochemical-thermal-mechanical degradation modelling approach for lifetime assessment of lithium-ion batteries[J]. Electrochimica Acta. 2019,326:134928.
  • Richardson R R,Osborne M A,Howey D A. Gaussian process regression for forecasting battery state of health[J]. Journal of Power Sources. 2017,357:209-219.
  • 来鑫,秦超,郑岳久,等. 基于恒流充电曲线电压特征点的锂离子电池自适应容量估计方法[J]. 汽车工程. 2019,41(01):1-6.
  • Ouyang M,Feng X,Han X,et al. A dynamic capacity degradation model and its applications considering varying load for a large format Li-ion battery[J]. Applied Energy. 2016,165:48-59.
  • 陈泽宇,熊瑞,孙逢春. 电动汽车电池安全事故分析与研究现状[J]. 机械工程学报. 2019,55(24):93-104.
  • Quirama L F,Giraldo M,Huertas J I,et al. Driving cycles that reproduce driving patterns,energy consumptions and tailpipe emissions[J]. Transportation Research Part D:Transport and Environment. 2020,82:102294.
  • Ma R,He X,Zheng Y,et al. Real-world driving cycles and energy consumption informed by large-sized vehicle trajectory data[J]. Journal of Cleaner Production. 2019,223:564-574.
  • Rechkemmer S K,Zang X,Zhang W,et al. Lifetime optimized charging strategy of Li-ion cells based on daily driving cycle of electric two-wheelers[J]. Applied Energy. 2019,251:113415.

锂离子电池性能衰退机理与故障预警策略研究

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章节目录

  • 一 绪论
  • 二 锂离子电池老化机理研究
    1. (一)温度影响
      1. 1.高温环境影响
      2. 2.低温环境影响
    2. (二)充放电倍率与循环次数
    3. (三)SOC影响及放电区间
  • 三 锂离子电池建模方法
    1. (一)锂离子电池等效模型
      1. 1.等效电路模型
      2. 2.数据驱动模型
      3. 3.电化学模型
    2. (二)电化学-热-机耦合模型在动力电池老化中的应用
      1. 1.P2D模型
      2. 2.热模型
      3. 3.机械模型
    3. (三)仿真模型在大数据应用环境下的改进
  • 四 电池故障预警策略优化研究
    1. (一)衰退引发的安全性风险
      1. 1.热稳定性下降导致的风险
      2. 2.容量不一致性导致的风险
    2. (二)容量衰退识别方法
      1. 1.基于模型驱动的方法
      2. 2.数据驱动方法
      3. 3.联合驱动方法
    3. (三)基于云端模型分析的故障预警方案
      1. 1.动力电池实际工况研究
      2. 2.基于大数据的时变工况提取
      3. 3.云端预警模型建立
  • 五 结论

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