动力锂电池健康状态评估方法综述
Review of the State of Health Estimation Methods for Lithium-Ion Battery
DOI: 10.12677/SG.2020.104024, PDF,   
作者: 熊 平, 陶 骞：国网湖北省电力有限公司电力科学研究院，湖北 武汉
关键词: 锂离子电池；电动汽车；健康状态；预测；Lithium-Ion Battery； Electric Vehicle； State of Health； Estimate
摘要: 由于不可再生能源的日益减少，而电动汽车(Electric vehicles, EV)由于其具有零碳排放，乘坐舒适和轻便等特点变得越来越受欢迎。然而，由于各种内部和外部因素，要准确预测电动汽车的锂电池健康状况(state of health, SOH)等并不是一件容易的事情。基于此目的，本文全面的回顾目前各种不同的SOH预测模型，并进行比较，此外本文还分析了影响电池状态和寿命的预测的一些因素。本文为锂离子电池的SOH估算的技术发展提供了一些选择性的建议。
Abstract: Due to the declining non-renewable energy sources, electric vehicles (EVs) are becoming more and more popular due to their zero carbon emissions and comfortable and light transportation. However, due to various internal and external factors, it is not easy to accurately predict the state of health (SOH) of lithium-ion battery employed in electric vehicles. For this purpose, this paper will comprehensively review and compare the current various SOH prediction models. This paper also examines some of the factors and possible solutions that affect the prediction of battery status and lifetime. Finally, this paper provides some suggestions for further technological developments in the SOH estimates for lithium-ion batteries and provides some ideas for developing advanced SOH methods for future electric vehicles.
文章引用：熊平, 陶骞. 动力锂电池健康状态评估方法综述[J]. 智能电网, 2020, 10(4): 211-224. https://doi.org/10.12677/SG.2020.104024

参考文献

[1] Hofmann, J., Guan, D., Chalvatzis, K. and Huo, H. (2016) Assessment of Electrical Vehicles as a Successful Driver for Reducing CO2 Emissions in China. Applied Energy, 184, 995-1003.
[Google Scholar] [CrossRef] 
[2] Sulaiman, N., Hannan, M.A., Mohamed, A., Majlan, E.H. and Wan Daud, W.R. (2015) A Review on Energy Management System for Fuel Cell Hybrid Electric Vehicle: Issues and Challenges. Renewable & Sustainable Energy Reviews, 52, 802-814.
[Google Scholar] [CrossRef] 
[3] Opitz, A., Badami, P., Shen, L., Vignarooban, K. and Kannan, A.M. (2017) Can Li-Ion Batteries Be the Panacea for Automotive Applications. Renewable & Sustainable Energy Reviews, 68, 685-692.
[Google Scholar] [CrossRef] 
[4] Lipu, M.S.H., Hannan, M.A., Hussain, A., Saad, M.H.M., Ayob, A. and Blaabjerg, F. (2018) State of Charge Estimation for Lithium-Ion Battery Using Recurrent NARX Neural Network Model Based Lighting Search Algorithm. IEEE Access, 6, 28150-28161.
[Google Scholar] [CrossRef] 
[5] Chen, Y., Miao, Q., Zheng, B., Wu, S. and Pech, M. (2013) Quantitative Analysis of Lithium-Ion Battery Capacity Prediction via Adaptive Bathtub-Shaped Function. Energies, 6, 3082-3096.
[Google Scholar] [CrossRef] 
[6] Ng, K.S., Moo, C.S., Chen, Y.P. and Hsieh, Y.C. (2009) Enhanced Coulomb Counting Method for Estimating State-of-Charge and State-of-Health of Lithium-Ion Batteries. Applied Energy, 86, 1506-1511.
[Google Scholar] [CrossRef] 
[7] Waag, W., Fleischer, C. and Sauer, D.U. (2014) Critical Review of the Methods for Monitoring of Lithium-Ion Batteries in Electric and Hybrid Vehicles. Journal of Power Sources, 258, 321-339.
[Google Scholar] [CrossRef] 
[8] Xing, Y.J., He, W., Pecht, M. and Tsui, K.L. (2014) State of Charge Estimation of Lithium-Ion Batteries Using the Open Circuit Voltage at Various Ambient Temperatures. Applied Energy, 113, 106-115.
[Google Scholar] [CrossRef] 
[9] Guo, Z., Qiu, X.P., Hou, G.D., Liaw, B.Y. and Zhang, C.S. (2014) State of Health Estimation for Lithium Ion Batteries Based on Charging Curves. Power Sources, 249, 457-462.
[Google Scholar] [CrossRef] 
[10] Weng, C.H., Sun, J. and Peng, H. (2014) A Unified Open-Circuit-Voltage Model of Lithium-Ion Batteries for State-of-Charge Estimation and State-of-Health Monitoring. Power Sources, 258, 228-237.
[Google Scholar] [CrossRef] 
[11] Li, S.E., Wang, B., Peng, H. and Hu, X. (2014) An Electro-Chemistry Based Impedance Model for Lithium-Ion Batteries. Journal of Power Sources, 258, 9-18.
[Google Scholar] [CrossRef] 
[12] Eddahech, A., Briat, O., Bertrand, N., Deletage, J.Y. and Vinassa, J.M. (2012) Behavior and State-of-Health Monitoring of Li-Ion Batteries Using Impedance Spectroscopy and Recurrent Neural Networks. International Journal of Electrical & Power Energy Systems, 42, 487-494.
[Google Scholar] [CrossRef] 
[13] Ting, T.O., Man, K.L., Lim, E.G. and Leach, M. (2014) Tuning of Kalman Filter Parameters via Genetic Algorithm for State-of-Charge Estimation in Battery Management System. The Scientific World Journal, 2014, Article ID: 176052.
[Google Scholar] [CrossRef] [PubMed]
[14] Kim, J., Lee, S. and Cho, B.H. (2012) Complementary Cooperation Algorithm Based on DEKF Combined with Pattern Recognition for SOC/Capacity Estimation and SOH Prediction. IEEE Transactions on Power Electronics, 27, 436-451.
[Google Scholar] [CrossRef] 
[15] 谷苗, 夏超英, 田聪颖. 基于综合型卡尔曼滤波的锂离子电池荷电状态估算[J]. 电工技术学报, 2019, 34(2): 419-426.
[16] Schwunk, S., Armbruster, N., Straub, S., Kehl, J. and Vetter, M. (2013) Particle Filter for State of Charge and State of Health Estimation for Lithium-Iron Phosphate Batteries. Journal of Power Sources, 239, 705-710.
[Google Scholar] [CrossRef] 
[17] Bi, J., Zhang, T., Yu, H.Y. and Kang, Y.Q. (2016) State-of-Health Estimation of Lithium-Ion Battery Packs in Electric Vehicles Based on Genetic Resampling Particle Filter. Applied Energy, 182, 558-568.
[Google Scholar] [CrossRef] 
[18] 林娜, 朱武, 邓安全. 基于引力场粒子滤波算法估算锂电池健康状态[J]. 科技创新与应用, 2019(25): 32-33.
[19] Duong, V., Bastawrous, H.A., Lim, K., See, K.W., Zhang, P. and Dou, S.X. (2015) Online State of Charge and Model Parameters Estimation of the LiFePO4 Battery in Electric Vehicles Using Multiple Adaptive Forgetting Factors Recursive Least-Squares. Journal of Power Sources, 296, 215-224.
[Google Scholar] [CrossRef] 
[20] 朱瑞, 段彬, 温法政, 张君鸣, 张承慧. 基于分布式最小二乘法的锂离子电池建模及参数辨识[J]. 机械工程学报, 2019(20): 1-9.
[21] Salkind, A.J., Fennie, C., Singh, P., Atwater, T. and Reisner, D.E. (1999) Determination of State-of-Charge and State-of-Health of Batteries by Fuzzy Logic Methodology. Journal of Power Sources, 80, 293-300.
[Google Scholar] [CrossRef] 
[22] Landi, M. and Gross, G. (2014) Measurement Techniques for Online Battery State of Health Estimation in Vehicle-to-Grid Applications. IEEE Transactions on Instrumentation and Measurement, 63, 1224-1234.
[Google Scholar] [CrossRef] 
[23] Hannan, M.A., Hoque, M.M., Hussain, A., Yusof, Y. and Ker, P.J. (2018) State-of-the-Art and Energy Management System of Lithium-Ion Batteries in Electric Vehicle Applications: Issues and Recommendations. IEEE Access, 6, 19362-19378.
[Google Scholar] [CrossRef] 
[24] Hannan, M.A., Lipu, M.S.H., Hussain, A., Saad, M.H. and Ayob, A. (2018) Neural Network Approach for Estimating State of Charge of Lithium-Ion Battery Using Backtracking Search Algorithm. IEEE Access, 6, 10069-10079.
[Google Scholar] [CrossRef] 
[25] Shahriari, M. and Farrokhi, M. (2013) Online State-of-Health Estimation of VRLA Batteries Using State of Charge. IEEE Transactions on Industrial Electronics, 60, 191-202.
[Google Scholar] [CrossRef] 
[26] 刘婉晴. 电池健康状态估算[J]. 华南理工大学学报(自然科学版), 2017, 39(1): 91-95.
[27] 韩丽, 戴广剑, 李宁. 基于GA-Elman神经网络的电池劣化程度预测研究[J]. 电源技术, 2013, 37(2): 249-250.
[28] Li, H.T., Liang, T.J. and Chen, S.M. (2013) Estimation of Battery State of Health Using Probabilistic Neural Network. IEEE Transactions on Industrial Informatics, 9, 679-685.
[Google Scholar] [CrossRef] 
[29] Zhang, R., Xu, F., Chen, J.L., et al. (2016) Li-Ion Battery SOH Prediction Based on PSO-RBF Neural Network. China Mechanical Engineering, 27, 2975-2981.
[30] Basak, D., Srimanta, P. and Patranbis, D.C. (2007) Support Vector Regression. Neural Information Processing Letters & Reviews, 11, 203-224.
[31] 李睿琪, 汪玉洁, 陈宗海. 一种基于支持向量机的锂电池健康状态评估方法[M]. 合肥: 中国科技大学出版社, 2016: 55-60.
[32] 刘皓, 胡明昕, 朱一亨, 等. 基于遗传算法和支持向量回归的锂电池健康状态预测[J]. 南京理工大学学报, 2018, 42(3): 329-334.
[33] 卢明哲. 动力电池SOH估计及故障预测方法研究[D]: [硕士学位论文]. 北京: 北京工业大学, 2015.
[34] 孙猛猛. 基于数据驱动方法的锂离子电池健康状态估计[D]: [硕士学位论文]. 昆明: 昆明理工大学, 2018.
[35] Nuhic, A., Terzimehic, T., Soczka-Guth, T., Buchholz, M. and Dietmayer, K. (2013) Health Diagnosis and Remaining Useful Life Prognostics of Lithium-Ion Batteries Using Data-Driven Methods. Journal of Power Sources, 239, 680-688.
[Google Scholar] [CrossRef] 
[36] Widodo, A., Shim, M.-C., Caesarendra, W. and Yang, B.-S. (2011) Intelligent Prognostics for Battery Health Monitoring Based on Sample Entropy. Expert Systems with Application, 38, 11763-11769.
[Google Scholar] [CrossRef] 
[37] Hu, X., Li, S.E., Jia, Z. and Egardt, B. (2014) Enhanced Sample Entropy-Based Health Management of Li-Ion Battery for Electrified Vehicles. Energy, 64, 953-960.
[Google Scholar] [CrossRef] 
[38] Feng, X., Li, J., Ouyang, M., Lu, L., Li, J. and He, X. (2013) Using Probability Density Function to Evaluate the State of Health of Lithium-Ion Batteries. Journal of Power Sources, 232, 209-218.
[Google Scholar] [CrossRef] 
[39] Kim, M.Y., Kim, J.H. and Moon, G.W. (2014) Center-Cell Concentration Structure of a Cellto-Cell Balancing Circuit with a Reduced Number of Switches. IEEE Transactions on Power Electronics, 29, 5285-5297.
[Google Scholar] [CrossRef] 
[40] Omar, N., Verbrugge, B., Mulder, G., Van Den Bossche, P., Van Mierlo, J., Daowd, M., Dhaens, M. and Pauwels, S. (2010) Evaluation of Performance Characteristics of Various Lithium-Ion Batteries for Use in BEV Application. IEEE Vehicle Power and Propulsion Conference, Lille, 1-6.
[Google Scholar] [CrossRef] 
[41] Jaguemont, J., Boulon, L., Dube, Y. and Poudrier, D. (2014) Low Temperature Discharge Cycle Tests for a Lithium- Ion Cell. 2014 IEEE Vehicle Power and Propulsion Conference (VPPC), Coimbra, 1-6.
[Google Scholar] [CrossRef] 
[42] Leng, F., Tan, C.M. and Pecht, M. (2015) Effect of Temperature on the Aging Rate of Li-Ion Battery Operating above Room Temperature. Scientific Reports, 5, Article No. 12967.
[Google Scholar] [CrossRef] [PubMed]
[43] Bragard, M., Soltau, N., Thomas, S. and De Doncker, R.W. (2010) The Balance of Renewable Sources and User Demands in Grids: Power Electronics for Modular Battery Energy Storage Systems. IEEE Transactions on Power Electronics, 25, 3049-3056.
[Google Scholar] [CrossRef] 
[44] Cao, J. and Emadi, A. (2012) A New Battery/Ultracapacitor Hybrid Energy Storage System for Electric, Hybrid, and Plug-In Hybrid Electric Vehicles. IEEE Transactions on Power Electronics, 27, 122-132.
[Google Scholar] [CrossRef] 
[45] Kim, J., Shin, J., Chun, C. and Cho, B.H. (2012) Stable Configuration of a Li-Ion Series Battery Pack Based on a Screening Process for Improved Voltage/SOC Balancing. IEEE Transactions on Power Electronics, 27, 411-424.
[Google Scholar] [CrossRef] 
[46] Belov, D. and Yang, M.-H. (2008) Failure Mechanism of Li-Ion Battery at Overcharge Conditions. Journal of Solid State Electrochemistry, 12, 885-894.
[Google Scholar] [CrossRef] 
[47] Maleki, H. and Howard, J.N. (2006) Effects of Overdischarge on Performance and Thermal Stability of a Li-Ion Cell. Journal of Power Sources, 160, 1395-1402.
[Google Scholar] [CrossRef] 
[48] Danzer, M.A., Liebau, V. and Maglia, F. (2015) Aging of Lithium-Ion Batteries for Electric Vehicles. In: Advances in Battery Technologies for Electric Vehicles, Elsevier, Amsterdam, 359-387.
[Google Scholar] [CrossRef] 
[49] Marongiu, A., Nußbaum, F.G.W., Waag, W., Garmendia, M. and Sauer, D.U. (2016) Comprehensive Study of the Influence of Aging on the Hysteresis Behavior of a Lithium Iron Phosphate Cathode-Based Lithium Ion Battery—An Experimental Investigation of the Hysteresis. Applied Energy, 171, 629-645.
[Google Scholar] [CrossRef] 
[50] Roscher, M.A. and Sauer, D.U. (2011) Dynamic Electric Behavior and Open-Circuit-Voltage Modeling of LiFePO4-Based Lithium Ion Secondary Batteries. Journal of Power Sources, 196, 331-336.
[Google Scholar] [CrossRef] 
[51] Barai, A., Widanage, W.D., Marco, J., McGordon, A. and Jennings, P. (2015) A Study of the Open Circuit Voltage Characterization Technique and Hysteresis Assessment of Lithium-Ion Cells. Journal of Power Sources, 295, 99-107.
[Google Scholar] [CrossRef] 
[52] Zhu, L., Sun, Z., Dai, H. and Wei, X. (2015) A Novel Modeling Methodology of Open Circuit Voltage Hysteresis for LiFePO4 Batteries Based on an Adaptive Discrete Preisach Model. Applied Energy, 155, 91-109.
[Google Scholar] [CrossRef] 

为你推荐