臺灣博碩士論文加值系統

近年來，二次電池應用於消費性產品、不斷電系統、儲能系統與電動車，因此，對於電池健康狀態(State of Health，SOH)管理變得非常重要。目前電池健康狀態大部分採用電池容量進行估測，以現在電池容量除以全新電池容量。該方法通常要讓電池完整充滿再放電至截止電壓，花上時間約數小時，因此，耗費時間較長才能估測出SOH。

本論文針對鉛酸電池與鋰離子電池進行實驗，建置一套電池SOH估測系統，該系統包含電池內阻量測系統、變動式電流充電策略與SOH演算法。電池內阻量測系統搭配變動式電流充電策略對兩款電池進行充放電，取得全新電池之內阻數據(Rbat_ new)，接著電池進行數次標準充放電(步驟1)，並重複使用變動式電流充電策略記錄目前電池內阻之變化(Rbat _75%、Rbat_ 50%、Rbat _25%) (步驟2)。兩款電池重複上述(1)到(2)步驟直到電池老化至一定程度(Rbat _ aged)。

根據SOH演算法計算，各個電池內阻之數值形成一曲線，則曲線經由複合梯形法計算成面積，並從數個面積中取前三次的平均值作為估測電池健康狀態的參數。依序為全新電池的參數為Rbat_avg_new，經過BAPSOH¬_75%、BAPSOH¬_50%、BAPSOH¬_25%的電池老化程(Battery aging procedure，BAP)後，分別得到Rbat _avg_75%、Rbat_ avg_50%、Rbat _avg_25%，電池完全老化的參數為Rbat _avg_ aged，最終，上述參數帶入演算法可得到電池的SOH。實驗結果顯示，兩款電池模擬實際情況，當電池隨著循環充放電老化，估測出SOH也逐漸降低，再以電池容量加以驗證，當SOH降低的同時，電池容量也相對下降，因此，驗證本文提出的電池SOH估測系統之可行性。

In recent years, secondary batteries have been used in consumer products, uninterruptible power supplies, energy storage systems, and electric vehicles. Thus, the estimation of the battery’s state of health (SOH) has become a highly important problem. At present, SOH is measured in terms of battery capacity (BC), where the battery’s SOH is its present BC divided by the BC when the battery was new. This method typically requires the battery to be fully charged before being discharged to the cut-off voltage; this takes several hours and makes SOH estimation time intensive.

Thus, this paper presents a method of SOH estimation for lead-acid batteries and lithium-ion batteries. The method estimates SOH using the battery internal resistance (BIR), and it features a variable-current charging strategy and an average-area algorithm. Specifically, the variable-current charging strategy is used to charge and discharge two batteries before their BIR (Rbat_new) is measured. Subsequently, the battery is charged and discharged several times (to obtain Rbat_75%, Rbat_50%, and Rbat_25%; Step 1), and the variation in their BIR (i.e., between Rbat_75%, Rbat_50%, and Rbat_25%) is recorded using the variable-current charging strategy (Step 2). Thereafter, steps 1 and 2 are repeated to age both batteries, and their BIR (Rbat_aged) is obtained.

Subsequently, the average-area algorithm is applied to plot the BIR values on a curve; the area under the curve (Acurve) is then computed using the composite numerical, and the mean of the first three times from Acurve is taken as the parameter to estimate the SOH. According to the sequence of Acurve, the parameter of the new battery is Rbat_avg_new, where Rbat_avg_75%, Rbat_avg_50%, and Rbat_avg_25% are obtained after the BAPSOH¬_75%, BAPSOH-_50%, and BAPSOH¬_25% battery aging procedures(BAP) (Step 1 and step 2), respectively, and the parameter for the completely aged battery is Rbat _avg_ aged. Finally, these parameters are input into the algorithm to obtain the SOH of the battery. In experiments, the SOH of the two batteries decreased gradually as they aged, and the accuracy of BC estimation was verified using the charging and discharging process. The experimental results indicate the practicability of the average-area BIR algorithm.

摘要.....................................i
Abstract................................ii
誌謝.....................................iv
目錄.....................................v
表目錄...................................vii
圖目錄...................................viii
第一章 緒論.............................1
1-1 研究背景..............................1
1-2 研究目的..............................3
1-3 論文大綱..............................4
第二章 電池特性與內阻量測與充電技術........5
2-1 電池之構造與化學原理...................6
2-2 電池充電技術..........................8
2-3 電池內阻量測技術......................10
2-4 電池SOH之定義........................12
2-5 演算法...............................12
2-5-1 多項式回歸分析..................12
2-5-2 決定係數.......................13
2-5-3 數值分析.......................14
第三章 電池SOH估測之系統架構.............18
3-1 系統架構.............................18
3-1-1 內阻量測系統...................19
3-2 電流量測電路.........................19
3-3 實驗規劃.............................20
第四章 電池內阻之SOH估測實驗.............25
4-1 鉛酸蓄電池之SOH估測...................26
4-2 鋰電池之SOH估測......................35
第五章 結論與未來展望...................45
5-1 結論................................45
5-2未來展望.............................45
參考文獻................................47

[1]H. Oman, “Battery Development that will make Electric Vehicles practical,” IEEE AESM’00, vol.1, no. 8, pp. 11-21, Aug. 2000.
[2] Y. Nishi, K. Katayama, J. Shigetomi and H. Horie, “The development of lithium-ion secondary battery systems for EV and HEV,” in Proc. Thirteenth Annual Battery Conference on Applications and Advances. Proceedings of the Conference, Jan. 1998, pp. 31-36.
[3] V. Pop, H. J. Bergveld, P. H. L. Notten and P. P. L. Regtien, “Smart and Accurate State-of-Charge Indication In Portable Applications,” in Proc. International Conference on Power Electronics and Drives Systems, Nov. 2005, pp. 262-267.
[4] Fletcher, Seth. Bottled Lightning: Superbatteries, Electric Cars, and the New Lithium Economy. Hill and Wang, NY, USA, 2011.
[5] M.M. Thackeray, W.I.F. David, P.G. Bruce, J.B. Goodenough, “Lithium insertion into manganese spinels ,” Materials Research Bulletin,vol. 18, no. 4 , pp 461-472, 1983.
[6] 孫清華，最新可充電電池技術大全，全華科技圖書，2001年9月，初版。
[7]唐聖億，“研究電動車可行駛中充電的增程式系統”，科技部專題研究計畫，2020年12月。
[8] N. Meena et al., “Charging and discharging characteristics of Lead acid and Li-ion batteries,” in Proc. POWER AND ENERGY SYSTEMS: TOWARDS SUSTAINABLE ENERGY, March 2014, pp. 1-3.
[9] J. Chiasson and B. Vairamohan, “Estimating the state of charge of a battery,” IEEE Transactions on Control Systems Technology, vol. 13, no. 3, pp. 465-470, 2005.
[10]Q. Guo et al., “Estimation of electric vehicle battery state of health based on relative state of health evaluation,” in Proc. IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference , March 2017, pp. 1998-2002.
[11]A. Zenati, P. Desprez, H. Razik and S. Rael, “A methodology to assess the State of Health of lithium-ion batteries based on the battery's parameters and a Fuzzy Logic System,” in Proc. 2012 IEEE International Electric Vehicle Conference, March 2 012, pp. 1-6.
[12]IEEE Power Engineering Society, IEEE Recommended Practice for Maintenance, Testing, and Replacement of Valve-Regulated Lead-Acid (VRLA) Batteries for Stationary Applications, NY, USA, 2006.
[13]B.V. Ratnakumar, M.C. Smart, L.D. Whitcanack, R.C. Ewell, “The impedance characteristics of Mars Exploration Rover Li-ion batteries,” Journal of Power Sources, vol. 159, no.2, pp.1428-1439, 2006.
[14]Shuhong Zhao, Feng Wu, Liuxiang Yang, Lijun Gao, Andrew F. Burke, “A measurement method for determination of dc internal resistance of batteries and supercapacitors,” Electrochemistry Communications, vol. 12, no. 2, 12, pp.242-245 , 2010.
[15]J. Zhang and X. Zhang, “A Novel Internal Resistance Curve Based State of Health Method to Estimate Battery Capacity Fade and Resistance Rise,” in Proc. IEEE Transportation Electrification Conference & Expo , June 2020, pp. 575-578.
[16]Hyun-jun Lee, Joung-hu Park and J. Kim, “Comparative analysis of the SOH estimation based on various resistance parameters for LiCoO2 cells,” in Proc. IEEE Transportation Electrification Conference and Expo, June 2016, pp. 788-792.
[17]陳怡萍，「鉛酸與鋰離子蓄電池之電量估測」，國立中山大學電機工程學系碩士論文，2007年6月。
[18]R. Ahmed, M. E. Sayed, I. Arasaratnam, J. Tjong and S. Habibi, “Reduced-Order Electrochemical Model Parameters Identification and SOC Estimation for Healthy and Aged Li-Ion Batteries Part I: Parameterization Model Development for Healthy Batteries,” IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 2, no. 3, pp. 659-677, Sep. 2014.
[19]吳志剛，「多模式充電電路」，國立中山大學電機工程學系碩士論文，2004年6月。
[20]Jian Feng ; Jian Sun ; Zhiwei Li ; Yinlai Yang, “AC Internal Resistance Measurement of Batteries,” in Proc. IEEE Precision Electromagnetic Measurements, July 2018, pp. 1-2.
[21]B. Celen, A. C. Aras, M. Dohr and T. Sivaraman, “Equivalent Circuit Modeling of All-Solid-State Battery by using DC-IR Data,” in Proc IEEE International Systems Conference, May 2021, pp. 1-6.
[22]Z. Feng, Z. Li and W. Bin, “Research on Integrated Method to Detect Internal Resistance of Substation Battery,” in Proc. Asia-Pacific Power and Energy Engineering Conference, March 2012, pp. 1-4.
[23]X. Tan et al., “Real-Time State-of-Health Estimation of Lithium-Ion Batteries Based on the Equivalent Internal Resistance,” IEEE Access, vol. 8, pp. 56811-56822, 2020.
[24]P. A. Topan, M. N. Ramadan, G. Fathoni, A. I. Cahyadi and O. Wahyunggoro, “State of Charge (SOC) and State of Health (SOH) estimation on lithium polymer battery via Kalman filter,” in Proc. 2nd International Conference on Science and Technology-Computer, Oct. 2016, pp. 93-96.
[25]Steven C. Chapra，應用數值方法，滄海圖書，2005年，初版。
[26]Mouser Electronics Inc. BA6010 Series Datasheet-1290906; Mouser Electronics Inc.: Mansfield, TX, USA, 2018.
[27]ITECH Inc. IT6302 User Manual; ITECH Inc.: USA, 2019.
[28]Analog Devices Inc. AD8212 Series Current Sense Amplifiers, Analog Devices Inc.: Cambridge, MA, USA, 2017.
[29]DATAQ Instruments Inc. GL240 Data Logger, DATAQ Instruments Inc.: Akron, Ohio, USA, 2015.
[30]ITECH Inc. Series IT8700 User Manual; ITECH Inc.: USA, 2019.