當前儲能系統(Energy Storage Systems, ESS)以及鋰離子電池(Li-ion Battery)的重要性及數量逐漸上升。鋰離子電池的健康狀態(State of Health, SOH)以及電荷狀態(State of Charge, SOC)是非線性變化的，很容易受到外在因素影響。因此如何線上估測電池狀態成為一大課題。
本文提出使用一降壓型轉換器(Buck Converter)在不新增任何電路元件的前提下，產生可變頻的弦波電流對電池進行主動交流擾動(Perturbation)，並藉由此擾動量測其電池在各個頻率下的交流阻抗(AC Impedance)。接著利用事先訓練好且已經寫入微控制器(Microprocessor)中的類神經網路(Artificial Neural Network, ANN)進行線上SOC/SOH估測。在電池充滿電後，量測電池交流阻抗，更新電池目前狀態之SOH。而當電池處在休息狀態超過一小時後，也會量測電池之交流阻抗，更新靜態SOC。最後當電池處於放電狀態時，會依據放電電流、交流阻抗以及電池表面溫升來估測電池的動態SOC。
本文依據上述三種不同情況訓練三組類神經網路，分別以不同測試資料進行驗證，第一組類神經網路以電池老化之交流阻抗估測SOH時平均絕對誤差為0.32%以及均方誤差為0.14。第二組類神經網路以靜態交流阻抗及放電電流大小估測SOC時平均絕對誤差為1.37%以及均方誤差為2.73。第三組類神經網路則是以動態交流阻抗、放電電流大小以及電池表面溫升估測SOC，其平均絕對誤差為1.06%以及均方誤差為1.76。這三種類神經網路於微控制器之估測結果皆與MATLAB平台上之執行結果相同。

The importance and quantity of Energy Storage Systems (ESS) and Li-ion batteries are increasing. The State of Health (SOH) and State of Charge (SOC) of Li-ion batteries are non-linear and can be easily affected by external factors. Therefore, how to estimate the battery status online is a major issue.
This paper proposes to use a Buck converter to perturb the battery by generating a variable frequency sinusoidal current to the battery without adding any new circuit components and measuring the AC Impedance of the battery at each frequency. Then, an Artificial Neural Network (ANN) is written into the microprocessor for online SOC/SOH estimation. When the battery is fully charged, the AC impedance of the battery is measured to update the SOH at the current state, and when the battery is rested for more than an hour, the AC impedance is measured to update the static SOC. Finally, when the battery is discharged, the dynamic SOC is estimated based on the discharge current, AC impedance, and surface temperature rise of the battery.
In this paper, three groups of neural networks are trained based on the above three different situations and verified with different test data. The first neural network uses the AC impedance of the battery to estimate SOH with an average absolute error of 0.32% and a mean square error of 0.14. The average absolute error of the second neural network is 1.37% and the mean square error is 2.73 when estimating static SOC with static AC impedance and discharge current. The third neural network is based on dynamic AC impedance, discharge current, and surface temperature rise of the battery to estimate dynamic SOC, with an average absolute error of 1.06% and a mean square error of 1.76. The estimation results of these three neural networks on the microprocessor are the same as the results on the MATLAB platform.

[1] 再生能源資訊網, “國際再生能源發展趨勢與政策,” https://www.re.org.tw/knowledge/more.aspx?cid=201&id=3966, 2021.
[2] BloombergNEF, “Global Energy Storage Market Set to Hit One Terawatt-Hour by 2030,” https://about.bnef.com/blog/global-energy-storage-market-set-to-hit-one-terawatt-hour-by-2030/, 2021.
[3] M. Berecibar, I. Gandiaga, I. Villarreal, N. Omar, J. V. Mierlo, P. V. den Bossche “Critical review of state of health estimation methods of Li-ion batteries for real applications,” Renewable and Sustainable Energy Reviews, 2016.
[4] M. Wohlfahrt-Mehrens, C. Vogler, J. Garche, “Aging mechanisms of lithium cathode materials,” Journal of Power Sources, 2004.
[5] C. Pastor-Fernndez, W. D. Widanage, J. Marco, M. A. Gama-Valdez,and G. H. Chouchelamane, “Identification and quantification of ageing mechanisms in Lithium-ion batteries using the EIS technique,” IEEE Transportation Electrification Conference and Expo (ITEC), 2016.
[6] Gamry Instruments, “Basics of Electrochemical Impedance Spectroscopy,” https://www.gamry.com/application-notes/EIS/basics-of-electrochemical-impedance-spectroscopy/
[7] Y. -D. Lee, S. -Y. Park and S. -B. Han, “Online Embedded Impedance Measurement Using High-Power Battery Charger,” IEEE Transactions on Industry Applications, 2015.
[8] Z. Xia and J. A. A. Qahouq, “An online battery impedance spectrum measurement method with increased frequency resolution,” IEEE Applied Power Electronics Conference and Exposition (APEC), 2018.
[9] E. Locorotondo, V. Cultrera, L. Pugi, L. Berzi, M. Pierini, and G. Lutzemberger, “Development of a battery real-time state of health diagnosis based on fast impedance measurements,” Journal of Energy Storage, 2021.
[10] E. Din, C. Schaef, K. Moffat and J. T. Stauth, “A Scalable Active Battery Management System With Embedded Real-Time Electrochemical Impedance Spectroscopy,” IEEE Transactions on Power Electronics, 2017.
[11] A. Cuadras and O. Kanoun, “SoC Li-ion battery monitoring with impedance spectroscopy,” 6th International Multi-Conference on Systems, Signals and Devices, 2009.
[12] D. A. Howey, P. D. Mitcheson, V. Yufit, G. J. Offer and N. P. Brandon, “Online Measurement of Battery Impedance Using Motor Controller Excitation,” IEEE Transactions on Vehicular Technology, 2014.
[13] Samsung SDI, “The Four Components of a Li-ion Battery,” https://www.samsungsdi.com/column/technology/detail/55272.html
[14] Gold Peak Industries Ltd. “Lithium Ion technical handbook,” http://large.stanford.edu/courses/2015/ph240/uang2/docs/li-handbook.pdf, 2003.
[15] H. C. Choi, Y. M. Jung, I. Noda, and S. B. Kim, “A Study of the Mechanism of the Electrochemical Reaction of Lithium with CoO by Two-Dimensional Soft X-ray Absorption Spectroscopy (2D XAS), 2D Raman, and 2D Heterospectral XAS−Raman Correlation Analysis,” The Journal of Physical Chemistry B (American Chemical Society (ACS)). 2003
[16] L. Liua, M. Lia, L. Chua, B. Jianga, L. Ruoxub, Z. Xiaopeib, and G. Cao, “Layered ternary metal oxides: Performance degradation mechanisms as cathodes, and design strategies for high-performance batteries,” Progress in Materials Science, 2020.
[17] C. S. Moo, K. S. Ng, Y. P. Chen, and Y. C. Hsieh, “State-of-Charge Estimation with Open-CircuitVoltage for Lead-Acid Batteries,” 2007 Power Conversion Conference – Nagoya, 2007.
[18] C. Duan, C. Wang, and M. Liu, “A fast approach for predicting battery open circuit voltage based on exponential recovery voltage,” 2017 North American Power Symposium (NAPS), 2017.
[19] M. Messing, T. Shoa and S. Habibi, “Electrochemical Impedance Spectroscopy With Practical Rest-Times for Battery Management Applications,” IEEE Access, 2021.
[20] D. Saji, P. S. Babu, and K Ilango, “SoC Estimation of Lithium Ion Battery Using Combined Coulomb Counting and Fuzzy Logic Method,” 2019 4th International Conference on Recent Trends on Electronics, Information, Communication & Technology, 2019.
[21] Texas Instruments, “TI電池電量監測基礎知識培訓-TI培訓電子書籍系列”， https://www.dianyuan.com/index.php?do=ti_lesson_list&tid=15, 2013.
[22] Texas Instruments, “Theory and Implementation of Impedance Track™ Battery Fuel-Gauging Algorithm in bq20zxx Product Family,” https://www.ti.com/lit/an/slua364b/slua364b.pdf, 2006.
[23] Texas Instruments, “Impedance Track™ Gas Gauge for Novices,” https://www.ti.com/lit/an/slua375/slua375.pdf, 2006.
[24] R. Xiong, L. Li, and J. Tian, “Towards a Smarter Battery Management System: A Critical Review on Battery State of Health Monitoring Methods,” Journal of Power Sources, 2018.
[25] M. Berecibar, I. Gandiaga, I. Villarreal, N. Omar, J. Van Mierlo and P. Van Den Bossche, “Critical review of state of health estimation methods of Li-ion batteries for real applications,” Renewable and Sustainable Energy Reviews, 2016.
[26] X. Han, M. Ouyang, L. Lu, J. Li, Y. Zheng and Z. Li, “A comparative study of commercial lithium ion battery cycle life in electrical vehicle: Aging mechanism identification,” Journal of Power Sources, 2014.
[27] M. Dubarry and B.Y. Liaw, “Identify capacity fading mechanism in a commercial LiFePO4 cell,” Journal of Power Sources, 2009.
[28] M. Dubarry, B.Y. Liaw, M.S. Chen, S.S. Chyan, K.C. Han, W.T. Sie, et al., “Identifying battery aging mechanisms in large format Li ion cells,” Journal of Power Sources, 2011.
[29] B. Sood, M. Osterman, and M. Pecht, “Health Monitoring of Lithium-Ion Batteries,” 10th Annual IEEE Symposium on Product Compliance Engineering (ISPCE), 2013.
[30] J. Remmlinger, M. Buchholz, M. Meiler, P. Bernreuter and K. Dietmayer, “State-of health monitoring of lithium-ion batteries in electric vehicles by on-board internal resistance estimation,” Journal of Power Sources, 2011.
[31] R. Xiong, F. Sun, X. Gong and C. Gao, “A data-driven based adaptive state of charge estimator of lithium-ion polymer battery used in electric vehicles,” Applied Energy, 2014.
[32] R. Xiong, Q.Q. Yu, L.Y. Wang and C. Lin, “A novel method to obtain the open circuit voltage for the state of charge of lithium ion batteries in electric vehicles by using H infinity filter,” Applied Energy, 2017.
[33] A. Widodo, M.C. Shim, W. Caesarendra and B.S. Yang, “Intelligent prognostics for battery health monitoring based on sample entropy,” Expert Systems with Applications, 2011.
[34] D. Andre, C. Appel, T. Soczka-Guth and D. Uwe, “Advanced mathematical methods of SOC and SOH estimation for lithium-ion batteries,” Journal of Power Sources, 2013.
[35] V. Klass, M. Behm and G. Lindbergh, “A support vector machine-based state-of-health estimation method for lithium-ion batteries under electric vehicle operation,” Journal of Power Sources, 2014.
[36] R. Xiong, Y. Zhang, J. Wang, H. He, S. Peng and M. Pecht, “Lithium-ion battery health prognosis based on a real battery management system used in electric vehicles,” IEEE Transactions on Vehicular Technology, 2018.
[37] Y. Zhang, R. Xiong, H. He and M. Pecht, “Lithium-ion battery remaining useful life prediction with Box-Cox transformation and Monte Carlo simulation,” IEEE Transactions on Industrial Electronics, 2018.
[38] Bio-Logic Inc., “EIS measurements: Potentio or Galvano mode?,” https://www.biologic.net/wp-content/uploads/2019/08/battery-potentio-or-galvano-eis_electrochemistry-an49.pdf, 2013.
[39] M. Messing, T. Shoa, and S. Habibi, “Electrochemical Impedance Spectroscopy With Practical Rest-Times for Battery Management Applications,” IEEE Access, 2021.
[40] Y. Zhang, and C.-Y. Wang, “Cycle-life characterization of automotive lithium-ion batteries with LiNiO2 cathode,” Journal of The Electrochemical Society, 2009.
[41] G. Nagasubramanian, E. P. Roth, and D. Ingersoll, “Electrical and Electrochemical Performance Characteristics of Small Commercial Li-Ion Cells,” Battery Conference on Applications and Advances, 1999.
[42] F. Berthier, J.-P. Diard, and R. Michel, “Distinguishability of equivalent circuits containing CPEs: Part I. Theoretical part,” Journal of Electroanalytical Chemistry, 2001.
[43] E. Locorotondc, L. Pugi, L. Berzi, M. Pierini, S. Scavuzzc, A. Ferraris, A. G. Airale and M. Carello, “Modeling and simulation of Constant Phase Element for battery Electrochemical Impedance Spectroscopy,” IEEE 5th International forum on Research and Technology for Society and Industry (RTSI), 2019.
[44] Jürgen Schmidhuber, “Deep learning in neural networks: An overview,” Neural Networks, 2015.
[45] I. Goodfellow, Y. Bengio and A. Courville, “6.5 Back-Propagation and Other Differentiation Algorithms,” Deep Learning, MIT Press, 2016.
[46] A. Hussein, “Adaptive Artificial Neural Network-Based Models for Instantaneous Power Estimation Enhancement Electric Vehicles’ Li-Ion Batteries,” IEEE Transactions on Industry Applications, 2019.
[47] A. Hussein, “Capacity Fade Estimation in Electric Vehicle Li-Ion Batteries using Artificial Neural Network,” IEEE Transactions on Industry Applications, 2015.
[48] M. M. Gupta, L. Jin, and N. Homma, “Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory,” Wiley-IEEE Press, 2003.
[49] 鄭偉呈，「基於類神經網路之鋰離子電池健康狀態估測技術研究」，台灣科技大學電機工程系碩士學位論文，2021年。
[50] 陳重諺，「以交流阻抗為基礎之鋰離子電池殘餘容量估測技術研究」，台灣科技大學電機工程系碩士學位論文，2014年。
[51] 鍾顏澤，「基於類神經網路之鋰離子電池動態交流阻抗估測剩餘容量之研究」，台灣科技大學電機工程系碩士學位論文，2022年。
[52] Texas Instruments, “TMS320F28004x Microcontrollers datasheet (Rev. F),” SPRS945F datasheet, Jan. 2017 [Revised Feb. 2021].