臺灣博碩士論文加值系統

在本篇論文中將考慮基於溫度變因下之鋰離子電池的殘電量估測。所用的方法包括參數更新法及電池模型。其中參數更新法可以藉由更新等效電路模型中的參數以達到在不同溫度條件下的殘電量估測；溫度模型則是基於先建立不同溫度條件下的一階RC等效電路模型進行殘電量估測。且等效模型是藉由開路電壓測試法、直流內電阻法及模糊c回歸模型法所建立。上述所提的兩種建立模型的方式將會應用到自適應擴展式卡爾曼濾波器中進行電池殘電量的估測。另外，在本篇論文中會額外應用粒子濾波器進行電池殘電量的估測而不進行模型的建立。
實驗的結果顯示了上述所提出的方法皆在基於不同溫度條件下的電池殘電量估測上有好的成效。

In this thesis, we considered that the ambient temperature plays a significant factor for the state-of-charge (SOC) estimation of lithium-ion (Li-ion) battery. The proposed approach including of a parameter updating method which can update the parameter values of the equivalent circuit model (ECM) of the Li-ion battery under different temperature conditions, battery models which are built based on the first-order RC network equivalent circuit model under different ambient temperature conditions. Also, the battery models are established by the open circuit voltage (OCV) test, direct current internal resistance (DCIR) test, and fuzzy c-regression models (FCRMs) method. Each of them is applied in the SOC estimation with the algorithms, Adaptive Extended Kalman Filter (AEKF). On the other hand, the Particle Filter (PF) will be applied in the SOC estimation without the ECM of the Li-ion battery.
The experimental results show that the proposed method has good performance for the SOC estimation of the Li-ion battery with different temperature conditions.

ABSTRACT II
摘 要 III
LIST OF FIGURES VII
LIST OF TABLES XIII
Chapter
1. INTRODUCTION 1
2. LITHIUM-ION BATTERY MODEL 4
2.1 THE LITHIUM-ION BATTERY MODEL 4
2.2 THE DEFINITION OF SOC 4
2.3 EQUIVALENT CIRCUIT MODEL 5
3. PARAMETER ESTIMATION 8
3.1 EXPERIMENTAL SETUP 8
3.2 THE PARAMETERS OF THE BATTERY MODEL 13
3.2.1 the rated capacity of the battery 13
3.2.2 Open Circuit Voltage Test 15
3.2.3 Direct Current Internal Resistance (DCIR) test 18
3.2.4 Fuzzy C-Regression Models Clustering Algorithm 27
3.2.5 the parameter updating method 32
4. SOC ESTIMATION USING ADAPTIVE EXTENDED KALMAN FILTERING 34
4.1 ADAPTIVE EXTENDED KALMAN FILTERING 34
4.2 SOC ESTIMATION BY AEKF 37
5. PARTICLE FILTER ALGORITHM 39
5.1 NONLINEAR BAYESIAN TRACKING 39
5.2 SEQUENTIAL IMPORTANCE SAMPLING (SIS) ALGORITHM 40
5.3 IMPROVEMENT IN DEGENERACY PHENOMENON 42
5.3.1 Choice of the Proposed Importance Density 42
5.3.2 Resampling Algorithm 42
5.4 DETERMINATION OF THE SOC BY PF 43
6. EXPERIMENTAL AND SIMULATION RESULITS 44
6.1 THE EXPERIMENT RESULTS BY USING AEKF 46
6.1.1 SOC estimation with the battery models 47
6.1.2 SOC estimation with the parameter updating method 61
6.2 THE EXPERIMENTAL RESULTS BY USING PF 70
7. CONCLUSIONS 78
REFERENCES

[1]P. Weicker, A systems approach to lithium-ion battery management, Artech House, 2014.
[2]C. D. Rahn and C. Y. Wang, Battery systems engineering, Wiley, 2013.
[3]D. Andrea, Battery management systems for large lithium-ion battery packs, Artech House, 2010.
[4]T. B. Reddy, Linden’s handbook of batteries, Mcgraw-Hill, 2011.
[5]B. Kenndey, D. Patterson, S. Camilleri, “Use of lithium-ion in electric vehicles,” Journal of Power Sources, vol. 90, pp.156-162, 2000.
[6]T. Horiba, T. Maeshima, T. Matsumura, M. Koseki, J. Arai, Y. Muranaka, “Applications of high power density lithium ion batteries,” Journal of Power Sources , vol. 146, pp. 107-110, 2005.
[7]S. Piller, M. Perrin and A. Jossen, “Methods for state-of-charge determination their applications,” J.Power Sources, vol. 96, pp. 113-120, 2001.
[8]N. A. Chaturvedi, R. Klein, J. Christensen, J. Ahmed and A. Kojic, “Algorithms for Advanced Battery-Management Systems,” in IEEE Control Systems, vol. 30, no. 3, pp. 49-68, Jun. 2010.
[9]S. I. Tobishima, K. J. Takei, Y.J. Sakurai, J .I. Yamaki, “Lithium ion cell safety,” Journal of Power Sources, Vol. 90, Issue 2, pp. 188-195, 2000.
[10]S Passerini, F Coustier, B.B Owens, “Lithium-ion batteries for hearing aid applications,” Journal of Power Sources, Vol. 90, Issue 2, pp. 144-152, 2000.
[11]C. Wu, J. Sun, C. Zhu, Y. Ge and Y. Zhao, "Research on Overcharge and Overdischarge Effect on Lithium-Ion Batteries," 2015 IEEE Vehicle Power and Propulsion Conference (VPPC), pp. 1-6, Montreal, QC, 2015.
[12]J. Kim, S. Lee and B. Cho, “The State of Charge estimation employing empirical parameters measurements for various temperatures,” 2009 IEEE 6th International Power Electronics and Motion Control Conference, pp. 939-944, Wuhan, 2009
[13]L. Liao, P. Zuo, Y. Ma, X. Q. Chen, Y. An, Y. Gao, G. Yin, “Effects of temperature on charge/discharge behaviors of LiFePO4 cathode for Li-ion batteries,” Electrochimica Acta, vol. 60, pp. 269-273, Jan. 2012.
[14]D. Jiani, L. Zhitao, W. Youyi and W. Changyun, “A fuzzy logic-based model for Li-ion battery with SOC and temperature effect," 11th IEEE International Conference on Control & Automation (ICCA), pp. 1333-1338, Taichung, 2014.
[15]W. Y. Low, M. J. A. Aziz, , N. R. N. Idris, “Modelling of lithium-titanate battery with ambient temperature effect for charger design,” IET Power Electronics, 9, (6), pp. 1204-1212, 2016.
[16]F. Feng, R. Lu, G. Wei, C. Zhu, “Identification and analysis of model parameters used for LiFePO4 cells series battery pack at various ambient temperature,” IET Electrical Systems in Transportation, 6, (2), pp. 50-55, 2016.
[17]A. El Mejdoubi, A. Oukaour, H. Chaoui, H. Gualous, J. Sabor and Y. Slamani, "State-of-Charge and State-of-Health Lithium-Ion Batteries’ Diagnosis According to Surface Temperature Variation," in IEEE Transactions on Industrial Electronics, vol. 63, no. 4, pp. 2391-2402, Apr. 2016.
[18]S. N. Motapon, A. Lupien-Bedard, L. A. Dessaint, H. Fortin-Blanchette and K. Al-Haddad, “A Generic Electrothermal Li-ion Battery Model for Rapid Evaluation of Cell Temperature Temporal Evolution,” in IEEE Transactions on Industrial Electronics, vol. 64, no. 2, pp. 998-1008, Feb. 2017.
[19]S. Duryea, S.Islam, and W. Lawrance,“A battery management system for stand-alone photovoltaic energy systems,” IEEE Ind. Appl. Mag., vol. 7, no. 3, pp. 67–72, Jun. 2001.
[20]T. Hansen and C.-J. Wang, “Support vector based battery state of charge estimator,” J. Power Sources, vol. 141, no. 2, pp. 351–358, Mar. 2005.
[21]I. Snihir, W. Rey, E. Verbitskiy, A. Belfadhel-Ayeb, P. H.L. Notten, “Battery open-circuit voltage estimation by a method of statistical analysis,” J. Power Sources, vol. 159, Issue 2, pp. 1484-1487, Sep. 2006.
[22]S. Lee, J. Kim, J. Lee, B. H. Cho, “State-of-charge and capacity estimation of lithium-ion battery using a new open-circuit voltage versus state-of-charge,” Journal of Power Sources, vol. 185, Issue 2, pp. 1367-1373, 2008.
[23]C. R. Birkl, E. McTurk, M. R. Roberts, P. G. Bruce, an D. A. Howey, “A parametric Open Circuit Voltage Model for Lithium Ion Batteries,” Journal of The Electrochemical Society, 162(12), A2271-A2280, 2015.
[24]A. J. Salkind, C. Fennie, P. Singh, T. Atwater, “David E Reisner, Determination of state-of-charge and state-of-health of batteries by fuzzy logic methodology,” Journal of Power Sources, vol. 80, Issue 1, pp. 293-300, 1999.
[25]P. Singh, R. Vinjamuri, X. Wang, D. Reisner, “Design and implementation of a fuzzy logic-based state-of-charge meter for Li-ion batteries used in portable defibrillators,” Journal of Power Sources, vol. 162, Issue 2, pp. 829-836, 2006.
[26]M. H. Yazdanpanah lari, M. Jafari, M. Salehi, M. Imanieh and Z. Malekjamshidi, “Simulation and implementation of a fuzzy controlled charger for Ni-Cd batteries," 2010 IEEE International Conference on Wireless Communications, Networking and Information Security, pp. 653-658, Beijing, China, 2010.
[27]X. Hu, S. Li, H. Peng, “A comparative study of equivalent circuit models for Li-ion batteries,” J. Power Sources, vol. 198, pp. 359-367, Jan. 2012.
[28]J. Kim, J. Shin, C. Chun, and B. Cho, “Stable configuration of a LiIon series battery pack based on a screening process for improved voltage/SOC balancing,” IEEE Trans. Power Electron, vol. 27, no. 1, pp. 411-424, Jan. 2012.
[29]M. Dubarry, N. Vuillaume, B. Y. Liaw, “From single cell model to battery pack simulation for Li-ion batteries,” J. Power Sources, vol. 186, Issue 2, pp. 500-507, Jan. 2009.
[30]M. A. Roscher, O. S. Bohlen and D. U. Sauer, “Reliable State Estimation of Multicell Lithium-Ion Battery Systems,” in IEEE Transactions on Energy Conversion, vol. 26, no. 3, pp. 737-743, Sept. 2011.
[31]M. Dubarry, B. Y. Liaw, “Development of a universal modeling tool for rechargeable lithium batteries,” J. Power Sources, vol. 174, Issue 2, Dec. 2007.
[32]Y. Hu, S. Yurkovich, Y. Guezennec, B.J. Yurkovich, “A technique for dynamic battery model identification in automotive applications using linear parameter varying structures,” Control Engineering Practice, vol. 17, Issue 10, Oct. 2009.
[33]Y. Hu, S. Yurkovich, “Linear parameter varying battery model identification using subspace methods,” J. Power Sources, vol. 196, Issue 5, pp. 2913-2923, Mar. 2011.
[34]J. H. Kim, S. J. Lee, J. M. Lee, B. H. Cho, “A New Direct Current Internal Resistance and State of Charge Relationship for the Li-Ion Battery Pulse Power Estimation,” The 7th International Conference on Power Electronics, pp.1173-1178, Oct. 2007.
[35]R. J. Hathaway and J. C. Bezdek, “Switching regression models and fuzzy clustering,” in IEEE Transactions on Fuzzy Systems, vol. 1, no. 3, pp. 195-204, Aug. 1993.
[36]Euntai Kim, Minkee Park, Seunghwan Ji and Mignon Park,“A new approach to fuzzy modeling,” in IEEE Transactions on Fuzzy Systems, vol. 5, no. 3, pp. 328-337, Aug. 1997.
[37]C. C. Kung and J. Y. Su, “Affine Takagi-Sugeno fuzzy modelling algorithm by fuzzy c-regression models clustering with a novel cluster validity criterion,” in IET Control Theory & Applications, vol. 1, no. 5, pp. 1255-1265, Sep. 2007.
[38]C. C. Kung and S. C. Chang, “The estimation of the state of charge for lithium-ion battery by fuzzy c-regression model (FCRM) clustering algorithm,” 2011 IEEE International Conference on Systems, Man, and Cybernetics, Anchorage, AK, 2011, pp. 1568-1573.
[39]C. C. Kung, C. C. Weng, and T. H. Chen, “Fuzzy c-regression model based state of charge estimation for lithium battery using adaptive extended Kalman filter,” Proc. of the 2016 International Conference on Machine Learning and Cybernetics, pp. 958-963, Jul. 2016.
[40]C. J. Chiang, J. L. Yang, W. C. Cheng, “Temperature and state-of-charge estimation in ultracapacitors based on extended Kalman filter,” J. Power Sources, vol. 234, pp. 234-243, Jul. 2013.
[41]G. L. Plett, “Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs Part1. Background,” J. Power Sources, vol. 134, pp. 252-261, 2004.
[42]G. L. Plett, “Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs Part2. Modeling and identification,” J. Power Sources, vol. 134, pp. 262-276, 2004.
[43]G. L. Plett, “Extended Kalman filtering for battery management systems of LiPB-basedHEV battery packs Part3. State and parameter estimation,” J. Power Sources, vol. 134, pp. 277-292, 2004.
[44]F. Sun, X. Hu, Y. Zou, S. Li, “Adaptive unscented Kalman filtering for state of charge estimation of a lithium-ion battery for electric vehicles,” Energy, vol. 36, Issue 5, pp. 3531-3540, 2011.
[45]J. Han, D. Kim and M. Sunwoo, “State-of-charge estimation of lead-acid batteries using an adaptive extended Kalman filter,” J. Power Sources, vol. 188, pp. 606–612, 2009.
[46]J. Han, D. Kim and M. Sunwoo, “State-of-charge estimation of lead-acid batteries using an adaptive extended Kalman filter,” J. Power Sources, vol. 188, pp. 606–612, 2009.
[47]R. Xiong, X. Gong, C. C. Mi, and F. Sun, “A robust state-of-charge estimator for multiple types of lithium-ion batteries using adaptive extended Kalman filter,” J. Power Sources, vol. 243, pp. 805–816, 2013.
[48]R. Xiong, H. He, F. Sun and K. Zhao, “Evaluation on State of Charge Estimation of Batteries With Adaptive Extended Kalman Filter by Experiment Approach,” in IEEE Transactions on Vehicular Technology, vol. 62, no. 1, pp. 108-117, Jan. 2013.
[49]S. Sepasi, R. Ghorbani and B. Y. Liaw, “SOC estimation for aged lithium-ion batteries using model adaptive extended Kalman filter,” 2013 IEEE Transportation Electrification Conference and Expo (ITEC), Detroit, MI, pp. 1-6, 2013.
[50]N. J. Gordon, D. J. Salmond, A. F. M. Smith, “Novel approach to nonlinear/non-Gaussian Bayesian state estimation,” Proc IEEE F,vol. 140, pp. 107-113, 1993.
[51]D. Jiani, W. Youyi and W. Changyun, “Li-ion battery SOC estimation using particle filter based on an equivalent circuit model,” 2013 10th IEEE International Conference on Control and Automation (ICCA), pp. 580-585, Hangzhou, 2013.
[52]M. E. Orchard, P. Hevia-Koch, B. Zhang and L. Tang, “Risk Measures for Particle-Filtering-Based State-of-Charge Prognosis in Lithium-Ion Batteries,” in IEEE Transactions on Industrial Electronics, vol. 60, no. 11, pp. 5260-5269, Nov. 2013.
[53]J. Li, C. Lyu, L. Wang, L. Zhang, C. Li, “Remaining capacity estimation of Li-ion batteries based on temperature sample entropy and particle filter,” Journal of Power Sources, vol. 268, pp. 895-903, 2014.
[54]B. Mo, J. Yu, D. Tang, H. Liu and J. Yu, “A remaining useful life prediction approach for lithium-ion batteries using Kalman filter and an improved particle filter,” 2016 IEEE International Conference on Prognostics and Health Management (ICPHM), pp. 1-5, Ottawa, Nov, 2016.
[55]D. D. MacNeil, T. D. Hatchard, and J. R. Dahn, “A comparison between the High Temperature Electrode /Electrolyte Reactions of LixCoO2 and LixMn2O4,” J. Electrochem. vol.148, Soc. 2001.
[56]W. B. Gu and C. Y. Wang, “Thermal‐Electrochemical Modeling of Battery Systems,” J. Electrochem, vol.148, Soc. 2000.
[57]L.X. Wang, A Course in Fuzzy Systems and Control, Pearson Education Taiwan Ltd, 2005.
[58]T. C. Hsia, System Identification, 1st ed., Lexington Books, 1977.
[59]J. W. Woods and C. Radewan, “Kalman filtering in two dimensions,” Information Theory, IEEE Transactions on, vol. 23, no. 4, pp. 473 - 482,1977.
[60]United States Advanced Battery Consortium. Electric vehicle battery test procedures manual USABC, pp. 13–14, 1996.
[61]A. Samba, N. Omar, H. Gualous, Y. Firouz, P. Van den Bossche, J. Van Mierlo, T. I. Boubekeur, “Development of an Advanced TwoDimensional Thermal Model for Large size Lithium-ion Pouch Cells,” Electrochimica Acta, vol. 117, pp. 246-254, Jan. 2014.