本文利用遞迴歸類神經網路實現無線充電電池管理。
隨著電動車科技的發展，使得鉛酸電池與鋰電池廣泛的使用與電動車產業中，顯現出充電電池管理的重要性。本論文將發展鉛酸電池與鋰電池充電管理系統，以電池製造商的資料規格與兩段定電壓-電流充電法建立充電系統模型，目的是為建立類神經網路訓練所需要資料庫，在充電管理系統將利用量測電池充電電壓與充電電流，並透過遞迴歸神經網路來估測電池的開路電壓與瞬時效率。
本文除了建立鉛酸電池充電系統模型之外，還透過實驗來驗證遞迴類神經網路對電池充電狀態的估測，在微控制器上處理充電資訊以及演算充電策略找出最佳充電曲線，期望能有效地控制電池充電。

In this paper, utilize the recurrent neural network to implement wireless charging management of battery.
As the development of electric vehicle technology lead to lead-acid battery and lithium battery are used widely in electric vehicle industry. Obviously it is important for battery charging management. This paper will develop a lead-acid battery charging management system. Establish a charging system model based on the battery manufacturer's data specifications and a two-stage constant voltage-current charging method. The purpose is to establish a database for the neural network training needs in charge management. The system will measure the battery charging voltage and current and estimate the open circuit voltage and instantaneous efficiency of the battery by recurrent neural network.
In addition to establishing a lead-acid battery charging system model, this paper observes whether the state of battery charging can be estimated by using the recurrent neural network. It is expected to do the charging information and calculating charging strategy to find the optimized charging curve on the microcontroller. Expect to effectively control battery charging.

[1] K.I. Funahashi, “On the Approximate Realization of Continuous
Mappings by Neural Networks,” Neural Networks, vol.2, pp. 183-192, 1989.
[2] S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural
computation, vol. 9, no.8, pp. 1735–1780, 1997.
[3] A.K. Jain and J. “Mao. Artificial neural networks: A tutorial,” IEEE Computer,
vol. 29, no. 3, pp. 31-44, 1996.
[4] W.S. McCulloch, “Pitts W .A logical calculus of the ideas immanent in nervous
activity,” Bulletin of Mathematical Biophysics, vol. 52, no. 1/2, pp. 99-115, 1990.
[5] J. J. Hopfield, “Neural networks and physical systems with emergent collective
computational abilities,” Proceedings of the National Academy of Sciences of the
USA, vol. 79, no. 8, pp. 2554-2558, April 1982.
[6] D.O. Hebb, The Organization of Behavior, New York: Wiley & Sons. 1949.
[7] D.E Rumelhart, G.E. Hinton and R.J. Williams, “Learning representations by
Back-propagating errors,’’ Nature 323, pp. 533-536, October 1986.
[8] L.B. Almeida, “A learning rule for asynchronous perceptrons with feedback in a
combinatorial environment,” Proceedings of the IEEE First International
conference on Neural Networks, pp. 609-618, 1987.
[9] S. Chen, C. Kang, Z. Zhang, H. Zhu, “A method for SOC estimation for lead-acid
battery based on multi-model adaptive Extended Kalman Filtering estimation”,
IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics
Society, pp. 18-24, 2016.
[10] F. Husnayain, A. R. Utomo, P. S. Priambodo, ‘‘State of charge estimation for a
lead-acid battery using backpropagation neural network method,’’ IEEE International Conference on Electrical Engineering and Computer Science(ICEECS), pp. 274-278, 2014.
[11] J.C. Alvarez Anton, P.J. Garcia Nieto, C. Blanco Viejo, J.A. VilanVilan, ‘‘Support vector machines used to estimate the battery state of charge,’’ IEEE Transaction on Power Electronics, vol. 28, no. 12, pp. 5919-5926, 2013.
[12] D. Andre, C. Appel, T. Soczka-Guth, D.U. Sauer, ‘‘Advanced
Mathematical Methods of SOC and SOH estimation for lithium-ion batteries,’’ Journal of Power Sources, vol. 224, pp.20-27, 2013.
[13] R. Feng, S. Zhao, X. Lu, ‘‘On-line estimation of Dynamic State-of-Charge for lead acid battery based on fuzzy logic,’’ 2nd International Conference on Measurement Information and Control, pp. 447-451, Aug 2013.
[14] G. Capizzi, F. Bonanno, G.M. Tina, “Recurrent neural network-based modeling and simulation of lead-acid batteries charge-discharge,” IEEE Trans Energy Convers, vol.26, pp. 435-443, 2011.
[15] R. Zhao, P. J. Kollmeyer, R.D. Lorenz, T. M. Jahns, “A compact unified methodology via a recurrent neural network for accurate modeling of lithium-ion battery voltage and state-of-charge, ” IEEE Transactions on Energy Conversion, pp. 5234-5241, November 2017.
[16] G. Capizzi, F. Bonanno, and C. Napoli, “Recurrent neural network-based control strategy for battery energy storage in generation systems with intermittent renewable energy sources,’’ International Conference on Clean Electrical Power (ICCEP), pp. 336-340, 2011.
[17] M. James, J. Grummett, M. Rowan, and J. Newman: “Application of Pulse Charging Techniques to Submarine Lead-Acid Batteries”, Journal of Power Sources, vol. 162, No. 2, pp. 878-883, 2006.

[18] T. Ikeya, “Multi-step constant-current charging method for an electric vehicle nickel/metal hydride battery with high-energy efficiency and long cycle life”, Journal of Power Sources, vol.105, pp. 6-12, 2002.
[19] D. Lee, S.Y. Park, “ Rapid charging strategy in the constant voltage mode for a high power Li-Ion battery”, IEEE Energy Conversion Congress and Exposition (ECCE), pp. 4725-4731, 2013.
[20] https://buildmedia.readthedocs.org/ micropython Data Sheet
[21] https://www.allegromicro.com/ ACS712 Data Sheet
[22] T. Chai, R.R. Draxler, ‘‘Root mean square error (RMSE) or mean absolute error
(MAE)? - arguments against avoiding RMSE in the literature,’’ Geoscientific
Model Development, vol. 7, pp. 1247-1250, 2014.
[23] Y. Bengio, P. Simard and P. Frasconi, ‘‘Learning long-term dependencies with gradient descent is difficult,’’ IEEE Transactions on Neural Networks, vol. 5, no. 2, pp. 157-166, 1994.
[24] C. C. Hua, M. Y. Lin, ‘‘A study of charging control of lead-acid battery for electric vehicle,’’ Proceedings of the 2000 IEEE International Symposium on Industrial Electronics , vol. 1, pp. 135-140, 2000.
[25] http://www.gs-battery.com.tw/ GS-battery PE12V7.2A Data Sheet
[26] W. Peng, Accurate circuit model for predicting the performance of lead acid AGM batteries. Las Vegas : University of Nevada, August 2011.
[27] A. S. Sedra, & K. C. Smith, Microelectronic circuits. New York: Holt, Rinehart and Winston, 1982.
[28] M.S.H. Lipu, A. Hussain, M.H.M. Saad, A. Ayob, M.A. Hannan, ‘‘Improved recurrent NARX neural network model for state of charge estimation of lithium-ion battery using pso algorithm,’’ IEEE Symposium on Computer Applications & Industrial Electronics, pp. 354-359, 2018.
[29] https://www.raspberrypi.org/ Raspberry pi Data Sheet
[30] Paolucci, Jonathan D.; “Novel Current-Fed Boundary-Mode Parallel Resonant Push-Pull Converter,” MS thesis. California Polytechnic State University, San Luis Obispo, June 2009.
[31] https://www.richtek.com/ 無線充電現況
[32] http://www.xlsemi.com/ XL4016 Data Sheet
[33] Tarik Rashid, Recurrent Neural Network Model. Paperback: Publisher: LAP LAMBERT Academic Publishing, March 4, 2013.
[34]王亮凱，基於E類放大器的磁共振無線電力傳輸系統參數設計時限於自走車，國立清華大學動力機械工程學系碩士論文，中華民國一百零八年五月。