Research on Multi-Parameter Evaluation of Electric Vehicle Power Battery Consistency Based on Principal Component Analysis

  • Liye Wang (王立业)
  • Lifang Wang (王丽芳)
  • Chenglin Liao (廖承林)
  • Wenjie Zhang (张文杰)


Electric vehicle power battery consistency is the key factor affecting the performance of power batteries. it is not scientific to evaluate the consistency of the battery depending on voltage or capacity. In this paper, multi-parameter evaluation method for battery consistency based on principal component analysis is proposed. Firstly, the characteristic parameters of battery consistency are analyzed, the principal component score can be used as the basis for evaluating the consistency of the battery. Then, the function that multi-parameter evaluation of battery consistency is established. Finally, battery balancing strategy based on fuzzy control is developed. The basic principle of fuzzy control is to fuzzy the input quantity based on expert knowledge, and the fuzzy control quantity is obtained by fuzzy control rule. The results are verified by test.

Key words

electric vehicle principal component analysis battery consistency multi-parameter evaluation 

CLC number

TM 912.1 


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  1. [1]
    KIM C H, KIM M Y, MOON G W. A modularized charge equalizer using a battery monitoring IC for series-connected Li-ion battery strings in electric vehicles [J]. IEEE Transactions on Power Electronics, 2013, 28(8): 3779–3787.CrossRefGoogle Scholar
  2. [2]
    CHEN Y, LIU X F, CUI Y Y, et al. A multiwinding transformer cell-to-cell active equalization method for Lithium-ion batteries with reduced number of driving circuits [J]. IEEE Transactions on Power Electronics, 2016, 31(7): 4916–4929.Google Scholar
  3. [3]
    XIONG R, ZHANG Y, HE H, et al. A double-scale, particle-filtering, energy state prediction algorithm for lithium-ion batteries [J]. IEEE Transactions on Industrial Electronics, 2018, 65(2): 1526–1538.CrossRefGoogle Scholar
  4. [4]
    JIN Y D, SONG Q, LIU W H. Large scaled cascaded battery energy storage system with charge/discharge balancing [J]. Electric Power Automation Equipment, 2011, 31(13): 6–11 (in Chinese).Google Scholar
  5. [5]
    SANG B Y, TAO Y B, ZHENG G, et al. Research on topology and control strategy of the super-capacitor and battery hybrid energy storage [J]. Power System Protection and Control, 2014, 42(2): 1–6 (in Chinese).Google Scholar
  6. [6]
    DANG J, TANG Y, NING J, et al. A strategy for distribution of electric vehicles charging load based on user intention and trip rule [J]. Power System Protection and Control, 2015, 43(16): 8–15 (in Chinese).Google Scholar
  7. [7]
    SUN B X, GAO K, JIANG J C, et al. Research on discharge peak power prediction of battery based on ANFIS and subtraction clustering [J]. Transactions of China Electrotechnical Society, 2015, 30(4): 272–280 (in Chinese).Google Scholar
  8. [8]
    LI Y, WANG L F, LIAO C L, et al. Research on subspace-based identification of battery model for electric vehicles [J]. Advanced Technology of Electrical Engineering and Energy, 2015, 34(1): 1–6 (in Chinese).Google Scholar
  9. [9]
    KALIKMANOV V I, KOUDRIACHOVA M V, DE LEEUWSW. Lattice-gas model for intercalation compounds [J]. Solid State Ionics, 2000, 136/137: 1373–1378.CrossRefGoogle Scholar
  10. [10]
    WAAG W, FLEISCHER C, SAUER D U. On-line estimation of lithium-ion battery impedance parameters using a novel varied-parameters approach [J]. Journal of Power Sources, 2013, 237: 260–269.CrossRefGoogle Scholar
  11. [11]
    WAAG W, SAUER D U. Adaptive estimation of the electromotive force of the lithium-ion battery after current interruption for an accurate state-of-charge and capacity determination [J]. Applied Energy, 2013, 111: 416–427.CrossRefGoogle Scholar
  12. [12]
    HE H W, XIONG R, GUO H Q. Online estimation of model parameters and state-of-charge of LiFePO4 batteries in electric vehicles [J]. Applied Energy, 2012, 89(1): 413–420.CrossRefGoogle Scholar
  13. [13]
    LUO S H, HUANG J, LI Z L. Prediction for silicon content in molten iron using unsupervised optimal fuzzy clustering [C]//Proceedings of the 7th World Congress on Intelligent Control and Automation. Chongqing, China: IEEE, 2008: 6503–6506.Google Scholar
  14. [14]
    NISHIJIMA K, SAKAMOTO H, HARADA K. A PWM controlled simple and high performance battery balancing system [C]//Proceedings of the 31st Annual Power Electronics Specialists Conference. Galway, Ireland: IEEE, 2000: 517–520.Google Scholar
  15. [15]
    SMITH K A, RAHN C D, WANG C Y. Model-based electrochemical estimation and constraint management for pulse operation of lithium ion batteries [J]. IEEE Transactions on Control Systems Technology, 2010, 18(3): 654–663.CrossRefGoogle Scholar

Copyright information

© Shanghai Jiaotong University and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Liye Wang (王立业)
    • 1
    • 2
  • Lifang Wang (王丽芳)
    • 1
    • 2
  • Chenglin Liao (廖承林)
    • 1
    • 2
  • Wenjie Zhang (张文杰)
    • 1
  1. 1.Key Laboratory of Power Electronics and Electric Drive, Institute of Electrical EngineeringChinese Academy of SciencesBeijingChina
  2. 2.Beijing Co-Innovation Center for Electric VehiclesBeijingChina

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