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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/1134完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 詹魁元 | |
| dc.contributor.author | John Chong | en |
| dc.contributor.author | 張顯主 | zh_TW |
| dc.date.accessioned | 2021-05-12T09:33:05Z | - |
| dc.date.available | 2018-08-07 | |
| dc.date.available | 2021-05-12T09:33:05Z | - |
| dc.date.copyright | 2018-08-07 | |
| dc.date.issued | 2018 | |
| dc.date.submitted | 2018-08-03 | |
| dc.identifier.citation | [1] “Taiwan ministry of economic affairs: Energy statistics report 2015.” http://web3.moeaboe.gov.tw/ecw/populace/content/ContentLink.aspx?menu_id=378
[2] M. A. Miller, A. G. Holmes, B. M. Conlon, and P. J. Savagian, “The gm“voltec” 4et50 multi-mode electric transaxle,” SAE Int. J. Engines, vol. 4, pp. 1102–1114, 04 2011. [3] T. B. C. Group, “Batteries for electric cars: Challenges, opportunities, and the outlook to 2020.” https://www.bcg.com/documents/file36615.pdf [4] “News: The price of an electric vehicle.” http://www.chinatimes.com/newspapers/20140925000033-260202 [5] J. F. K. Nadine Rauh, Thomas Franke, “Understanding the impact of electric vehicle driving experience on range anxiety,” Human Factors: The Journal of the Human Factors and Ergonomics Society, 2014. [6] T. A. Fund, “Fleetwise ev300: Findings report on ev usage in sixteen gta fleets.” http://taf.ca/wp-content/uploads/2014/09/FleetWise-EV300-Findings-Report-16-June-2015.pdf [7] “News: Gogoro energy interview.” http://chinese.engadget.com/2016/05/10/gogoro-interview-alan-pan/ [8] W.-Y. Chang, “The State of Charge Estimating Methods for Battery: A Review,” ISRN Applied Mathematics, vol. 2013, 2013. [9] B. B. Nicolas Watrin and A. Miraoui, “Review of adaptive systems for lithium batteries state-of-charge and state-of-health estimation,” IEEE, 2012. [10] V. Prajapati, H. Hess, E. J. William, V. Gupta, M. Huff, M. Manic, F. Rufus, A.Thakker, and J. Govar, “A literature review of state of-charge estimation techniques applicable to lithium poly-carbon monoflouride (li/cfx) battery,” in India International Conference on Power Electronics 2010 (IICPE2010), pp. 1–8, Jan 2011. [11] H. He, X. Zhang, R. Xiong, Y. Xu, and H. Guo, “Online model-based estimation of state-of-charge and open-circuit voltage of lithium-ion batteries in electric vehicles,” Energy, vol. 39, no. 1, pp. 310 – 318, 2012. Sustainable Energy and Environmental Protection 2010. [12] C. S. Moo, K. S. Ng, Y. P. Chen, and Y. C. Hsieh, “State-of-charge estimation with open-circuit-voltage for lead-acid batteries,” in 2007 Power Conversion Conference-Nagoya, pp. 758–762, April 2007. [13] K. S. Ng, C. S. Moo, Y.-P. Chen, and Y. C. Hsieh, “State-of-charge estimation for lead-acid batteries based on dynamic open-circuit voltage,” in 2008 IEEE 2nd International Power and Energy Conference, pp. 972–976, Dec 2008. [14] Y. Xing,W. He, M. Pecht, and K. L. Tsui, “State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures,” Applied Energy, vol. 113, pp. 106 – 115, 2014. [15] S. Lee, J. Kim, J. Lee, and B. 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, no. 2, pp. 1367 – 1373, 2008. [16] F. Huet, “A review of impedance measurements for determination of the state-of charge or state-of-health of secondary batteries,” Journal of Power Sources, vol. 70, no. 1, pp. 59 – 69, 1998. [17] J. Xu, C. C. Mi, B. Cao, and J. Cao, “A new method to estimate the state of charge of lithium-ion batteries based on the battery impedance model,” Journal of Power Sources, vol. 233, pp. 277 – 284, 2013. [18] S. Rodrigues, N. Munichandraiah, and A. Shukla, “A review of state-of-charge indication of batteries by means of a.c. impedance measurements,” Journal of Power Sources, vol. 87, no. 1–2, pp. 12 – 20, 2000. [19] M. Coleman, C. K. Lee, C. Zhu, and W. G. Hurley, “State-of-charge determination from emf voltage estimation: Using impedance, terminal voltage, and current for lead-acid and lithium-ion batteries,” IEEE Transactions on Industrial Electronics, vol. 54, pp. 2550–2557, Oct 2007. [20] W. He, N. Williard, C. Chen, and M. Pecht, “State of charge estimation for electric vehicle batteries using unscented kalman filtering,” Microelectronics Reliability, vol. 53, no. 6, pp. 840 – 847, 2013. [21] K. S. Ng, C.-S. Moo, Y.-P. Chen, and Y.-C. Hsieh, “Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries,” Applied Energy, vol. 86, no. 9, pp. 1506 – 1511, 2009. [22] K. S. Ng, Y. F. Huang, C. S. Moo, and Y. C. Hsieh, “An enhanced coulomb counting method for estimating state-of-charge and state-of-health of lead-acid batteries,” in INTELEC 2009 - 31st International Telecommunications Energy Conference, pp. 1–5, Oct 2009. [23] J. Wang, B. Cao, Q. Chen, and F. Wang, “Combined state of charge estimator for electric vehicle battery pack,” Control Engineering Practice, vol. 15, no. 12, pp.1569 – 1576, 2007. [24] W. He, N. Williard, C. Chen, and M. Pecht, “State of charge estimation for li-ion batteries using neural network modeling and unscented kalman filter-based error cancellation,” International Journal of Electrical Power and Energy Systems, vol.62, pp. 783 – 791, 2014. [25] C. Bo, B. Zhifeng, and C. Binggang, “State of charge estimation based on evolutionary neural network,” Energy Conversion and Management, vol. 49, no. 10, pp.2788 – 2794, 2008. [26] I. H. Li, W. Y. Wang, S. F. Su, and Y. S. Lee, “A merged fuzzy neural network and its applications in battery state-of-charge estimation,” IEEE Transactions on Energy Conversion, vol. 22, pp. 697–708, Sept 2007. [27] Y. Shen, “Adaptive online state-of-charge determination based on neuro-controller and neural network,” Energy Conversion and Management, vol. 51, no. 5, pp. 1093– 1098, 2010. [28] M. Charkhgard and M. Farrokhi, “State-of-charge estimation for lithium-ion batteries using neural networks and ekf,” IEEE Transactions on Industrial Electronics, vol. 57, pp. 4178–4187, Dec 2010. [29] L. Xu, J.Wang, and Q. Chen, “Kalman filtering state of charge estimation for battery management system based on a stochastic fuzzy neural network battery model,” Energy Conversion and Management, vol. 53, no. 1, pp. 33 – 39, 2012. [30] T. Hansen and C.-J. Wang, “Support vector based battery state of charge estimator,” Journal of Power Sources, vol. 141, no. 2, pp. 351 – 358, 2005. [31] J. Hu, J. Hu, H. Lin, X. Li, C. Jiang, X. Qiu, and W. Li, “State-of-charge estimation for battery management system using optimized support vector machine for regression,” Journal of Power Sources, vol. 269, pp. 682 – 693, 2014. [32] W. Junping, C. Quanshi, and C. Binggang, “Support vector machine based battery model for electric vehicles,” Energy Conversion and Management, vol. 47, no. 7–8, pp. 858 – 864, 2006. [33] J. C. Álvarez Antón, P. J. G. Nieto, C. B. Viejo, and J. A. V. Vilán, “Support vector machines used to estimate the battery state of charge,” IEEE Transactions on Power Electronics, vol. 28, pp. 5919–5926, Dec 2013. [34] A. J. Salkind, C. Fennie, P. Singh, T. Atwater, and D. E. Reisner, “Determination of state-of-charge and state-of-health of batteries by fuzzy logic methodology,” Journal of Power Sources, vol. 80, no. 1–2, pp. 293 – 300, 1999. [35] P. Singh, C. F. Jr., and D. Reisner, “Fuzzy logic modelling of state-of-charge and available capacity of nickel/metal hydride batteries,” Journal of Power Sources, vol. 136, no. 2, pp. 322 – 333, 2004. Selected papers presented at the International Power Sources Symposium. [36] P. Singh, R. Vinjamuri, X. Wang, and 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, no. 2, pp. 829 – 836, 2006. Special issue including selected papers from the International Power Sources Symposium 2005 together with regular papers. [37] H. He, R. Xiong, X. Zhang, F. Sun, and J. Fan, “State-of-charge estimation of the lithium-ion battery using an adaptive extended kalman filter based on an improved thevenin model,” IEEE Transactions on Vehicular Technology, vol. 60, pp. 1461–1469, May 2011. [38] J. Han, D. Kim, and M. Sunwoo, “State-of-charge estimation of lead-acid batteries using an adaptive extended kalman filter,” Journal of Power Sources, vol. 188, no. 2, pp. 606 – 612, 2009. [39] F. Sun, X. Hu, Y. Zou, and S. Li, “Adaptive unscented kalman filtering for state of charge estimation of a lithium-ion battery for electric vehicles,” Energy, vol. 36, no. 5, pp. 3531 – 3540, 2011. [40] J. Lee, O. Nam, and B. Cho, “Li-ion battery {SOC} estimation method based on the reduced order extended kalman filtering,” Journal of Power Sources, vol. 174, no. 1, pp. 9 – 15, 2007. Hybrid Electric Vehicles. [41] A. Vasebi, M. Partovibakhsh, and S. M. T. Bathaee, “A novel combined battery model for state-of-charge estimation in lead-acid batteries based on extended kalman filter for hybrid electric vehicle applications,” Journal of Power Sources, vol. 174, no. 1, pp. 30 – 40, 2007. Hybrid Electric Vehicles. [42] L. Lu, X. Han, J. Li, J. Hua, and M. Ouyang, “A review on the key issues for lithium ion battery management in electric vehicles,” Journal of Power Sources, vol. 226, pp. 272 – 288, 2013. [43] Y. Zou, X. Hu, H. Ma, and S. E. Li, “Combined state of charge and state of health estimation over lithium-ion battery cell cycle lifespan for electric vehicles,” Journal of Power Sources, vol. 273, pp. 793 – 803, 2015. [44] H. Rahimi-Eichi and M. Y. Chow, “Adaptive parameter identification and state-of-charge estimation of lithium-ion batteries,” in IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society, pp. 4012–4017, Oct 2012. [45] C. Antaloae, J. Marco, and F. Assadian, “A novel method for the parameterization of a li-ion cell model for ev/hev control applications,” IEEE Transactions on Vehicular Technology, vol. 61, pp. 3881–3892, Nov 2012. [46] A. Devices, “A closer look at state of charge(soc) and state of health(soh) estimation techniques for batteries.” http://www.analog.com/media/en/technical-documentation/technical-articles/A-Closer-Look-at-State-Of-Charge-and-State-Health-Estimation-Techniques-....pdf [47] M. A. Roscher and D. U. Sauer, “Dynamic electric behavior and open-circuit voltage modeling of lifepo4-based lithium ion secondary batteries,” Journal of Power Sources, vol. 196, no. 1, pp. 331 – 336, 2011. [48] Y. Zhang, W. Song, S. Lin, and Z. Feng, “Multiparameters model of the initial soc considering the relaxation effect,” ACS Sustainable Chemistry & Engineering, vol. 2, no. 4, pp. 599–605, 2014 [49] H. W. Z. Huang and R. Zhang, “An improved kriging interpolation technique based on svm and its recovery experiment in oceanic missing data,” American Journal of Computational Mathematics, vol. 2, no. 1, pp. 56–60, 2012. [50] “Wiki: Peukert’s law.” https://en.wikipedia.org/wiki/Peukert%27s_law [51] “Characteristics of battery due to peukert’s law.” http://makezine.com/2013/03/05/component-of-the-month-batteries/ [52] F. Feng, R. Lu, and C. Zhu, “A combined state of charge estimation method for lithium-ion batteries used in a wide ambient temperature range,” Energies, vol. 7, no. 5, pp. 3004–3032, 2014. [53] Q. Xie, S. Yue, M. Pedram, D. Shin, and N. Chang, “Adaptive thermal management for portable system batteries by forced convection cooling,” in 2013 Design, Automation Test in Europe Conference Exhibition (DATE), pp. 1225–1228, March 2013. [54] “Battery internal resistance temperature dependence.” http://www.avdweb.nl/solar-bike/batteries/ebike-battery.html | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/handle/123456789/1134 | - |
| dc.description.abstract | 本研究旨在發展可應用於電動車電池電量(State of Charge, SOC)估測的演算法。演算方法必須滿足無需高運算能力硬體，考量電池SOC影響因子以及可應用於電動車動態的行車行為中。為此，本研究發展了一套結合開路電壓查表法(OCV method)與庫倫積分法(Coulomb counting method)的優化方法用以估測鋰電池SOC。此優化方法藉由實驗數據建立反應曲面，加入溫度、電池電流等電池SOC估測影響因子以改善並提升電池SOC估測精準度。除此，本研究提出了一套可用於電動車動態充電以及放電環境的電池SOC估測方法，以改善非靜置狀態無法使用開路電壓查表法的問題。更全面的電池SOC估測，不僅可在靜置狀態修正電池SOC，也可在充放電池狀態進行準確電池SOC估測，致使電池在整體使用過程中擁有更可靠的電池SOC。實驗結果顯示優化方法的預測準確性高於原有的開路電壓查表法與庫倫積分法的應用。 | zh_TW |
| dc.description.abstract | The goal of the thesis is to come up with an algorithm that is adequate for real-time electric vehicle battery state of charge(SOC) estimation. Therefore, the algorithm should meet the requirement of not hardware performance demanding, considering factors that influence battery SOC estimation and most importantly able to perform in dynamic operating state of electric vehicle. To cope with this, the study developed an improved algorithm based on combination of open-circuit voltage (OCV) method and coulomb counting method to estimate the SOC of lithium-ion battery. The proposed algorithm builds several surrogate models based on experimental data, and considers various influential issues such as temperature influence, battery current to improve SOC estimation. In addition, a methodology to estimate battery initial SOC during more realistic charging and discharging dynamic environment is proposed to cope with the unavailability of OCV method when not in rest state. In other words, the estimation of battery SOC is more comprehensive and can be corrected more often resulting in a more reliable battery SOC throughout battery usage. Experimental results of the algorithm shown better accuracy compared to basic OCV-Coulomb counting method. | en |
| dc.description.provenance | Made available in DSpace on 2021-05-12T09:33:05Z (GMT). No. of bitstreams: 1 ntu-107-R04522632-1.pdf: 5662758 bytes, checksum: e4eca2a7fda0bf41177767b6c0ca9ef2 (MD5) Previous issue date: 2018 | en |
| dc.description.tableofcontents | 目錄
誌謝................................................................................................................................ ii 摘要................................................................................................................................ iii Abstract .......................................................................................................................... iv 圖目錄............................................................................................................................ x 表目錄............................................................................................................................ xii 符號列表........................................................................................................................ xiii 第一章緒論.................................................................................................................. 1 1.1 前言.............................................................................................................. 1 1.2 研究動機與目的.......................................................................................... 3 1.3 論文架構...................................................................................................... 3 第二章文獻回顧.......................................................................................................... 5 2.1 直接量測法Direct Measurement Method .................................................. 5 2.1.1 開路電壓查表法Open Circuit Voltage........................................ 6 2.1.2 阻抗法Impedance Method........................................................... 7 2.2 簿記法Book-Keeping Method.................................................................... 8 2.3 適應系統法Adaptive System Method........................................................ 9 2.3.1 類神經網路Artificial Neural Network ........................................ 9 2.3.2 輔助向量機Support Vector Machine .......................................... 10 2.3.3 模糊理論Fuzzy Logic ................................................................. 12 2.3.4 卡爾曼濾波器Kalman Filter ....................................................... 13 2.4 方法優缺點比較.......................................................................................... 14 2.4.1 庫倫積分法缺點........................................................................... 15 第三章研究方法.......................................................................................................... 20 3.1 電池SOC估算與更新流程.......................................................................... 21 3.2 實驗設備Experimental Equipment ............................................................ 22 3.3 實驗內容Experiments Detail...................................................................... 23 3.4 初始電池電量Initial SOC .......................................................................... 24 3.4.1 靜置狀態Rest Stage..................................................................... 24 3.4.2 充電狀態&放電狀態Charging Stage & Discharging Stage ..... 26 3.4.3 Kriging Fit..................................................................................... 28 3.5 庫倫效率Columbic Efficiency ................................................................... 29 3.6 電流影響Current Effect.............................................................................. 31 3.7 溫度影響Temperature Effect...................................................................... 32 3.8 汽車行駛循環Driving Cycle...................................................................... 34 第四章研究結果.......................................................................................................... 37 4.1 初始電池電量Initial SOC .......................................................................... 37 4.1.1 靜置狀態Rest Stage..................................................................... 37 4.1.2 鬆弛效應Relaxation Effect ......................................................... 39 4.1.3 充電與放電狀態Charging and Discharging Stage...................... 48 4.1.4 小結............................................................................................... 58 4.2 電流影響& 溫度影響Current Effect & Temperature Effect..................... 58 4.2.1 電池容量....................................................................................... 58 4.2.2 溫度修正項目............................................................................... 65 4.3 庫倫效率Coulombic Efficiency ................................................................. 70 第五章驗證實驗與結果討論...................................................................................... 76 5.1 靜置狀態驗證實驗...................................................................................... 76 5.2 充電狀態驗證實驗...................................................................................... 77 5.3 放電狀態驗證實驗...................................................................................... 79 第六章結論.................................................................................................................. 83 6.1 研究貢獻...................................................................................................... 83 6.2 未來工作...................................................................................................... 84 參考文獻........................................................................................................................ 86 | |
| dc.language.iso | zh-TW | |
| dc.subject | 電池電量 | zh_TW |
| dc.subject | 電池SOC估測 | zh_TW |
| dc.subject | 開路電壓查表法 | zh_TW |
| dc.subject | 庫倫積分法 | zh_TW |
| dc.subject | 溫度 | zh_TW |
| dc.subject | OCV method | en |
| dc.subject | Battery SOC estimation | en |
| dc.subject | Battery Current | en |
| dc.subject | Temperature | en |
| dc.subject | Coulomb Counting method | en |
| dc.title | 使用反應曲面以提升電池電量在動態運行下的預測精度 | zh_TW |
| dc.title | Improved State of Charge Estimation of Lithium-Ion Cells via Surrogate Modeling under Dynamic Operating Conditions | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 106-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 鄭榮和,傅增棣 | |
| dc.subject.keyword | 電池SOC估測,開路電壓查表法,庫倫積分法,溫度,電池電量, | zh_TW |
| dc.subject.keyword | Battery SOC estimation,OCV method,Coulomb Counting method,Temperature,Battery Current, | en |
| dc.relation.page | 92 | |
| dc.identifier.doi | 10.6342/NTU201700840 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2018-08-03 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 機械工程學研究所 | zh_TW |
| 顯示於系所單位： | 機械工程學系 | |
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