1. Home
  2. Vol 7, No 3 (2023)
  3. Widjaja

State of Charge Estimation of Lead Acid Battery using Neural Network for Advanced Renewable Energy Systems

Ryo G. Widjaja, Muhammad Asrol, Iwan Agustono, Endang Djuana, Christian Harito, G. N. Elwirehardja, Bens Pardamean, Fergyanto E. Gunawan, Tim Pasang, Derrick Speaks, Eklas Hossain, Arief S. Budiman

Abstract


The Solar Dryer Dome (SDD), an independent energy system equipped with Artificial Intelligence to support the drying process, has been developed. However, inaccurate state-of-charge (SOC) predictions in each battery cell resulted in the vulnerability of the battery to over-charging and over-discharging, which accelerated the battery performance degradation. This research aims to develop an accurate neural network model for predicting the SOC of battery-cell level. The model aims to maintain the battery cell balance under dynamic load applications. It is accompanied by a developed dashboard to monitor and provide crucial information for early maintenance of the battery in the SDD. The results show that the neural network estimates the SOC with the lowest MAE of 0.175, followed by the Random Forest and support vector machine methods with MAE of 0.223 and 0.259, respectively. A dashboard was developed to help farmers monitor batteries efficiently. This research contributes to battery-cell level SOC prediction and the dashboard for battery status monitoring.

 

Doi: 10.28991/ESJ-2023-07-03-02

Full Text: PDF


Keywords


Dashboard; State-of-Charge. Lead Acid Battery; Neural Network; Solar Dryer Dome.

References


Handara, V. A., Illya, G., Tippabhotla, S. K., Shivakumar, R., & Budiman, A. S. (2016). Center for Solar Photovoltaics (CPV) at Surya University: Novel and Innovative Solar Photovoltaics System Designs for Tropical and Near-Ocean Regions (An Overview and Research Directions). Procedia Engineering, 139, 22–31. doi:10.1016/j.proeng.2015.09.211.

Illya, G., Handara, V., Yujing, L., Shivakumar, R., & Budiman, A. S. (2016). Backsheet Degradation under Salt Damp Heat Environments - Enabling Novel and Innovative Solar Photovoltaic Systems Design for Tropical Regions and Sea Close Areas. Procedia Engineering, 139, 7–14. doi:10.1016/j.proeng.2015.09.233.

Gunawan, F. E., Budiman, A. S., Pardamean, B., Djuana, E., Romeli, S., Hananda, N., Harito, C., Aji, D. P. B., Putri, D. N. N., & Stevanus. (2022). Design and energy assessment of a new hybrid solar drying dome - Enabling Low-Cost, Independent and Smart Solar Dryer for Indonesia Agriculture 4.0. IOP Conference Series: Earth and Environmental Science, 998. doi:10.1088/1755-1315/998/1/012052.

Huuskonen, J., & Oksanen, T. (2018). Soil sampling with drones and augmented reality in precision agriculture. Computers and Electronics in Agriculture, 154, 25–35. doi:10.1016/j.compag.2018.08.039.

Karim, F., Karim, F., & Frihida, A. (2017). Monitoring system using web of things in precision agriculture. Procedia Computer Science, 110, 402–409. doi:10.1016/j.procs.2017.06.083.

Radoglou-Grammatikis, P., Sarigiannidis, P., Lagkas, T., & Moscholios, I. (2020). A compilation of UAV applications for precision agriculture. Computer Networks, 172, 107148. doi:10.1016/j.comnet.2020.107148.

Pola, D. A., Navarrete, H. F., Orchard, M. E., Rabié, R. S., Cerda, M. A., Olivares, B. E., Silva, J. F., Espinoza, P. A., & Pérez, A. (2015). Particle-filtering-based discharge time prognosis for lithium-ion batteries with a statistical characterization of use profiles. IEEE Transactions on Reliability, 64(2), 710–720. doi:10.1109/TR.2014.2385069.

Mo, B., Yu, J., Tang, D., Liu, H., & Yu, J. (2016). 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). doi:10.1109/icphm.2016.7542847.

Zhang, H., Miao, Q., Zhang, X., & Liu, Z. (2018). An improved unscented particle filter approach for lithium-ion battery remaining useful life prediction. Microelectronics Reliability, 81(24), 288–298. doi:10.1016/j.microrel.2017.12.036.

Loukil, J., Masmoudi, F., & Derbel, N. (2021). A real-time estimator for model parameters and state of charge of lead acid batteries in photovoltaic applications. Journal of Energy Storage, 34, 102184. doi:10.1016/j.est.2020.102184.

Qu, J., Liu, F., Ma, Y., & Fan, J. (2019). A Neural-Network-Based Method for RUL Prediction and SOH Monitoring of Lithium-Ion Battery. IEEE Access, 7, 87178–87191. doi:10.1109/ACCESS.2019.2925468.

Liu, J., Saxena, A., Goebel, K., Saha, B., & Wang, W. (2010). An adaptive recurrent neural network for remaining useful life prediction of lithium-ion batteries. Annual Conference of the Prognostics and Health Management Society, PHM 2010, 10-14 October, 2010, Portland, United States.

Zhang, Y., Xiong, R., He, H., & Pecht, M. G. (2019). Lithium-Ion Battery Remaining Useful Life Prediction with Box-Cox Transformation and Monte Carlo Simulation. IEEE Transactions on Industrial Electronics, 66(2), 1585–1597. doi:10.1109/TIE.2018.2808918.

Hannan, M. A., Lipu, M. S. H., Hussain, A., Saad, M. H., & Ayob, A. (2018). Neural network approach for estimating state of charge of lithium-ion battery using backtracking search algorithm. IEEE Access, 6, 10069–10079. doi:10.1109/ACCESS.2018.2797976.

Xia, B., Cui, D., Sun, Z., Lao, Z., Zhang, R., Wang, W., Sun, W., Lai, Y., & Wang, M. (2018). State of charge estimation of lithium-ion batteries using optimized Levenberg-Marquardt wavelet neural network. Energy, 153, 694–705. doi:10.1016/j.energy.2018.04.085.

Yang, F., Li, W., Li, C., & Miao, Q. (2019). State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network. Energy, 175, 66–75. doi:10.1016/j.energy.2019.03.059.

Chen, C., Xiong, R., Yang, R., Shen, W., & Sun, F. (2019). State-of-charge estimation of lithium-ion battery using an improved neural network model and extended Kalman filter. Journal of Cleaner Production, 234(5), 1153–1164. doi:10.1016/j.jclepro.2019.06.273.

Huang, Z., Yang, F., Xu, F., Song, X., & Tsui, K. L. (2019). Convolutional Gated Recurrent Unit-Recurrent Neural Network for State-of-Charge Estimation of Lithium-Ion Batteries. IEEE Access, 7, 93139–93149. doi:10.1109/ACCESS.2019.2928037.

How, D. N. T., Hannan, M. A., Lipu, M. S. H., Sahari, K. S. M., Ker, P. J., & Muttaqi, K. M. (2020). State-of-Charge Estimation of Li-Ion Battery in Electric Vehicles: A Deep Neural Network Approach. IEEE Transactions on Industry Applications, 56(5), 5565–5574. doi:10.1109/TIA.2020.3004294.

Almeida, G. C. S., Souza, A. C. Z. de, & Ribeiro, P. F. (2020). A Neural Network Application for a Lithium-Ion Battery Pack State-of-Charge Estimator with Enhanced Accuracy. Proceedings, 58(1), 33. doi:10.3390/wef-06915.

Feng, X., Chen, J., Zhang, Z., Miao, S., & Zhu, Q. (2021). State-of-charge estimation of lithium-ion battery based on clockwork recurrent neural network. Energy, 236, 121360. doi:10.1016/j.energy.2021.121360.

Li, S., Ju, C., Li, J., Fang, R., Tao, Z., Li, B., & Zhang, T. (2021). State-of-charge estimation of lithium-ion batteries in the battery degradation process based on recurrent neural network. Energies, 14(2), 1–21,. doi:10.3390/en14020306.

Ee, Y. J., Tey, K. S., Lim, K. S., Shrivastava, P., Adnan, S. B. R. S., & Ahmad, H. (2021). Lithium-Ion Battery State of Charge (SoC) Estimation with Non-Electrical parameter using Uniform Fiber Bragg Grating (FBG). Journal of Energy Storage, 40, 1–9,. doi:10.1016/j.est.2021.102704.

Costa, N., Sánchez, L., Anseán, D., & Dubarry, M. (2022). Li-ion battery degradation modes diagnosis via Convolutional Neural Networks. Journal of Energy Storage, 55(PC), 105558. doi:10.1016/j.est.2022.105558.

Fasahat, M., & Manthouri, M. (2020). State of charge estimation of lithium-ion batteries using hybrid autoencoder and Long Short Term Memory neural networks. Journal of Power Sources, 469(June), 228375. doi:10.1016/j.jpowsour.2020.228375.

Sun, S., Zhang, Q., Sun, J., Cai, W., Zhou, Z., Yang, Z., & Wang, Z. (2022). Lead–Acid Battery SOC Prediction Using Improved AdaBoost Algorithm. Energies, 15(16), 5842. doi:10.3390/en15165842.

Zhang, J., & Lee, J. (2011). A review on prognostics and health monitoring of Li-ion battery. Journal of Power Sources, 196(15), 6007–6014. doi:10.1016/j.jpowsour.2011.03.101.

Tian, Y., Li, D., Tian, J., & Xia, B. (2017). State of charge estimation of lithium-ion batteries using an optimal adaptive gain nonlinear observer. Electrochimica Acta, 225, 225–234. doi:10.1016/j.electacta.2016.12.119.

Wang, S. L., Fernandez, C., Zou, C. Y., Yu, C. M., Chen, L., & Zhang, L. (2019). A comprehensive working state monitoring method for power battery packs considering state of balance and aging correction. Energy, 171, 444–455. doi:10.1016/j.energy.2019.01.020.

Nadj, M., Maedche, A., & Schieder, C. (2020). The effect of interactive analytical dashboard features on situation awareness and task performance. Decision Support Systems, 135, 113322. doi:10.1016/j.dss.2020.113322.

Yigitbasioglu, O. M., & Velcu, O. (2012). A review of dashboards in performance management: Implications for design and research. International Journal of Accounting Information Systems, 13(1), 41–59. doi:10.1016/j.accinf.2011.08.002.

El Hage, H., Herez, A., Ramadan, M., Bazzi, H., & Khaled, M. (2018). An investigation on solar drying: A review with economic and environmental assessment. Energy, 157, 815–829. doi:10.1016/j.energy.2018.05.197.

Vijayavenkataraman, S., Iniyan, S., & Goic, R. (2012). A review of solar drying technologies. Renewable and Sustainable Energy Reviews, 16(5), 2652–2670. doi:10.1016/j.rser.2012.01.007.

Pirasteh, G., Saidur, R., Rahman, S. M. A., & Rahim, N. A. (2014). A review on development of solar drying applications. Renewable and Sustainable Energy Reviews, 31, 133–148. doi:10.1016/j.rser.2013.11.052.

Sharma, S., Sharma, S., & Athaiya, A. (2020). Activation Functions in Neural Networks. International Journal of Engineering Applied Sciences and Technology, 04(12), 310–316. doi:10.33564/ijeast.2020.v04i12.054.

Oostwal, E., Straat, M., & Biehl, M. (2021). Hidden unit specialization in layered neural networks: ReLU vs. sigmoidal activation. Physica A: Statistical Mechanics and Its Applications, 564, 125517. doi:10.1016/j.physa.2020.125517.

Westerhoff, U., Kurbach, K., Lienesch, F., & Kurrat, M. (2016). Analysis of Lithium-Ion Battery Models Based on Electrochemical Impedance Spectroscopy. Energy Technology, 4(12), 1620–1630. doi:10.1002/ente.201600154.

Mishra, S., Mallick, P. K., Tripathy, H. K., Bhoi, A. K., & González-Briones, A. (2020). Performance evaluation of a proposed machine learning model for chronic disease datasets using an integrated attribute evaluator and an improved decision tree classifier. Applied Sciences (Switzerland), 10(22), 1–35. doi:10.3390/app10228137.

Redi, A. A. N. P., Widjaja, R. G., Agustono, I., Asrol, M., Budiman, A. S., & Gunawan, F. E. (2022). A Review on The Application of Machine Learning To Predict The Battery State That Enables A Smart, Low-Cost, Self-Sufficient Drying And Storage System for Agricultural Purposes. Proceedings of the International Conference on Engineering and Information Technology for Sustainable Industry. doi:10.1145/3557738.3557846.

García, S., Luengo, J., & Herrera, F. (2015). Data Preprocessing in Data Mining. Intelligent Systems Reference Library, Springer, Cham, Switzerland. doi:10.1007/978-3-319-10247-4.

Anysz, H., Zbiciak, A., & Ibadov, N. (2016). The Influence of Input Data Standardization Method on Prediction Accuracy of Artificial Neural Networks. Procedia Engineering, 153, 66–70. doi:10.1016/j.proeng.2016.08.081.

Thara, T. D. K., Prema, P. S., & Xiong, F. (2019). Auto-detection of epileptic seizure events using deep neural network with different feature scaling techniques. Pattern Recognition Letters, 128, 544–550. doi:10.1016/j.patrec.2019.10.029.

Peng, Y., & Nagata, M. H. (2020). An empirical overview of nonlinearity and overfitting in machine learning using COVID-19 data. Chaos, Solitons and Fractals, 139. doi:10.1016/j.chaos.2020.110055.

Czerwiński, A., Wróbel, J., Lach, J., Wróbel, K., & Podsadni, P. (2018). The charging-discharging behavior of the lead-acid cell with electrodes based on carbon matrix. Journal of Solid State Electrochemistry, 22(9), 2703–2714. doi:10.1007/s10008-018-3981-4.


Full Text: PDF

DOI: 10.28991/ESJ-2023-07-03-02

Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 Ryo G Widjaja, Muhammad Asrol