SOC Estimation of Li-Po Battery Using Machine Learning and Deep Learning Methods

Abstract

The aviation industry is one of the most important areas where developing technology contributes. It is important to evaluate many factors for the safe and comfortable flight of unmanned aerial vehicles (UAVs), one of the most popular areas of this industry. One of the most important of these factors is flight time estimation. Battery state of charge (SOC) plays a big role in flight time estimation. In this study, using the data obtained from the tests carried out using a lithium-polymer battery in the electric UAV engine test equipment, the SOC of the battery was estimated using deep learning like as Long-Short Term Memory (LSTM) and machine learning methods like as Support Vector Regression (SVR) and Random Forest (RF). The main reason why these methods are preferred is that they are suitable for time series analysis in the forecasting process, are trained faster, and have generalization abilities. The proposed models were compared among themselves and the simulation results were presented with graphs and tables. When the results are examined, the predicted values and true values are quite compatible. This shows that the proposed methods can be used effectively in SOC estimation.

Keywords

• Aviation
• UAV
• SOC
• Machine Learning
• LSTM

References

1. Arik, S., Turkmen, I. and Oktay, T. (2018). Redesign of Morphing UAV for Simultaneous Improvement of Directional Stability and Maximum Lift/Drag Ratio. Advances in Electrical and Computer Engineering. 18(4), 57-62.
2. Bengio, Y., Simard, P. and Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult. IEEE transactions on neural networks. 5(2), 157-166.
3. Bilgin, M. and Konar, M. (2022). Investigation of Visual Disappearance by Intelligent Illumination of Exterior Surfaces of Unmanned Aerial Vehicles. Journal of Aviation. 6(1), 26-32.
4. Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
5. Burges, C. J. C. (1998). A tutorial on support vector machines for pattern recognition, Data Mining Knowledge Discovery. 2(2), 121- 167.
6. Cai, C. H., Du, D. and Liu, Z. Y. (2003, May). Battery state-of-charge (SOC) estimation using adaptive neuro- fuzzy inference system (ANFIS). In The 12th IEEE International Conference on Fuzzy Systems, 2003. 1068-1073.
7. Chaoui, H. and Ibe-Ekeocha, C. C. (2017). State of charge and state of health estimation for lithium batteries using recurrent neural networks. IEEE Transactions on Vehicular Technology. 66(10), 8773-8783.
8. Chicco, D., Warrens, M. J. and Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science, 7, e623.

9. Coban, S. and Oktay, T. (2023). Innovative Morphing UAV Design and Manufacture. Journal of Aviation. 7(2), 184-189.
10. Cortes, C. and Vapnik, V. (1995). Support-vector networks. Machine Learning. 20, 273-297.
11. Cutler, A., Cutler, D. R. and Stevens, J. R. (2012). Random forests, Ensemble machine learning Methods and Applications. Springer, Boston, MA.
12. Daniel, K. and Wietfeld, C. (2011). Using public network infrastructures for UAV remote sensing in civilian security operations. Homeland Security Affairs, Supplement.
13. Elman, J. L. (1990). Finding structure in time. Cognitive Science. 14(2), 179-211.
14. Ersen, M and Konar, M. (2023). Obtaining Condition Monitoring Data for the Prognostics of the Flight Time of Unmanned Aerial Vehicles. Journal of Aviation. 7(2), 209-214.
15. Gupta, L., Jain, R. and Vaszkun, G. (2015). Survey of important issues in UAV communication networks. IEEE Communications Surveys & Tutorials. 18(2), 1123-1152.
16. Hannan, M. A., Lipu, M. H., Hussain, A. and Mohamed, A. (2017). A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations. Renewable and Sustainable Energy Reviews. 78, 834-854.
17. Hastie, T., Tibshirani, R. and Friedman, J. (2009). The elements of statistical learning: data mining, inference, and prediction. Springer Science & Business Media.
18. Hermawan, A. P., Kim, D. S. and Lee, J. M. (2020, September). Sensor failure recovery using multi look-back lstm algorithm in industrial internet of things. In 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 1363-1366.
19. Hochreiter, S. and Schmidhuber, J. (1997). Long short-term memory. Neural Computation. 9(8), 1735-1780.
20. Konar, M. (2019). GAO Algoritma tabanlı YSA modeliyle İHA motorunun performansının ve uçuş süresinin maksimizasyonu. Avrupa Bilim ve Teknoloji Dergisi. 15, 360-367.
21. Kotsiantis, S. B., Zaharakis, I. and Pintelas, P. (2007). Supervised machine learning: A review of classification techniques. Emerging Artificial Intelligence Applications in Computer Engineering. 160(1), 3-24.
22. LeCun, Y., Bengio, Y. and Hinton, G. (2015). Deep learning. Nature. 521(7553), 436-444.
23. Liu, X., Wu, J., Zhang, C. and Chen, Z. (2014). A method for state of energy estimation of lithium-ion batteries at dynamic currents and temperatures. Journal of Power Sources. 270, 151-157.
24. Ma, L., Hu, C. and Cheng, F. (2021). State of charge and state of energy estimation for lithium-ion batteries based on a long short-term memory neural network. Journal of Energy Storage. 37, 102440.
25. Mitchell, T. M. (1997). Machine learning. McGraw Hill.
26. Oztemel, E. (2003). Yapay Sinir Ağları. İstanbul: Papatya Yayıncılık.
27. Sahin, H., Oktay, T. and Konar, M. (2020). Anfis Based Thrust Estimation of a Small Rotary Wing Drone. European Journal of Science and Technology. 18, 738-742.
28. Sidhu, M. S., Ronanki, D. and Williamson, S. (2019). State of charge estimation of lithium-ion batteries using hybrid machine learning technique. In IECON 2019-45th Annual Conference of the IEEE Industrial Electronics Society. 1, 2732-2737.
29. Song, X., Yang, F., Wang, D. and Tsui, K. L. (2019). Combined CNN-LSTM network for state-of-charge estimation of lithium-ion batteries. IEEE Access. 7, 88894-88902.
30. Sutton, R. S. (1992). Introduction: The challenge of reinforcement learning. In Reinforcement learning. Boston, MA: Springer US.
31. Vapnik, V. N. (1998). Statistical learning theory. New York: Wiley.
32. Yang, F., Song, X., Xu, F. and Tsui, K. L. (2019a). State-of-charge estimation of lithium-ion batteries via long short-term memory network. IEEE Access. 7, 53792-53799.
33. Yang, F., Li, W., Li, C. and Miao, Q. (2019b). State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network. Energy. 175, 66-75.
34. Yao, Y., Rosasco, L. and Caponnetto, A. (2007). On Early Stopping in Gradient Descent Learning. Constructive Approximation. 26(2), 289-315.
35. Youssef, H. Y., Alkhaja, L. A., Almazrouei, H. H., Nassif, A. B., Ghenai, C. and AlShabi, M. A. (2022). A machine learning approach for state-of-charge estimation of Li-ion batteries. In Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV. 12113, 674-682).
36. Zha, W., Liu, Y., Wan, Y., Luo, R., Li, D., Yang, S. and Xu, Y. (2022). Forecasting monthly gas field production based on the CNN-LSTM model. Energy, 124889.
37. Zhang, L., Wang, S. and Liu, B. (2018). Deep learning for sentiment analysis: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 8(4), e1253.
38. Xiong, R., He, H., Sun, F., Liu, X. and Liu, Z. (2013). Model-based state of charge and peak power capability joint estimation of lithium-ion battery in plug-in hybrid electric vehicles. Journal of Power Sources. 229, 159-169.