State of Charge Estimation Method for Lithium-Ion Batteries in All-Electric Ships Based on LSTM Neural Network

1. Alnes O., Eriksen S., Vartdal B. (2017): Battery-Powered Ships: A Class Society Perspective. IEEE Electrification Mag., 5(3), 10–21.10.1109/MELE.2017.2718823Search in Google Scholar

2. Guidi G., Suul J., Jenset F., Sorfonn I. (2017): Wireless Charging for Ships: High-Power Inductive Charging for Battery Electric and Plug-In Hybrid Vessels. IEEE Electrification. Mag., 5(3),, 22–32.10.1109/MELE.2017.2718829Search in Google Scholar

3. McCoy T. (2015): Electric ships past, present, and future [technology leaders]. IEEE Electrification Mag., 3(2), 4–11.10.1109/MELE.2015.2414291Search in Google Scholar

4. Skjong E., Volden R., Rødskar E., Molinas M., Johansen T., Cunningham J. (2016): Past, Present, and Future Challenges of the Marine Vessel’s Electrical Power System. IEEE Transactions on Transportation Electrification, 2(4), 522–537.10.1109/TTE.2016.2552720Search in Google Scholar

5. Mofor L., Nuttall P., Newell A. (2015): Renewable energy options for shipping. Int. Renewable Energy Agency, Abu Dhabi, UAE, Tech. Rep.Search in Google Scholar

6. Wartsila. Wireless Charging (https://www.wartsila.com/marine/build/power-systems/shore-connections/wireless-charging), last accessed 11.07.2019.Search in Google Scholar

7. Abkenar A., Nazari A, Jayasinghe S., Kapoor A., Negnevitsky M. (2017): Fuel Cell Power Management Using Genetic Expression Programming in All-Electric Ships. IEEE Transactions on Energy Conversion, 3(2), 779‒787.10.1109/TEC.2017.2693275Search in Google Scholar

8. Boveri A., Silvestro F., Molinas M. Skjong E. (2019); Optimal Sizing of Energy Storage Systems for Shipboard Applications. IEEE Transactions on Energy Conversion, 34 (2), 801–811.10.1109/TEC.2018.2882147Search in Google Scholar

9. Fei Y., Xie C., Tang Z., Zeng C., Quan S. (2017): State-of-Charge Estimation Based on Square Root Unscented Kalman Filter Algorithm for Li-ion Batteries. Proceedings of the CSEE, 37(15), 4514‒4520.Search in Google Scholar

10. Yu H., Lu R., Zhu C., Ma R. (2012): State of Charge Estimation Calibration for Ni-MH Battery Based on Ampere-Hour Method. Transactions of China Electrotechnical Society, 27(6), 12‒18.Search in Google Scholar

11. Weng C., Sun J., Peng H. (2014): A unified open-circuit-voltage model of lithium-ion batteries for state-of-charge estimation and state-of-health monitoring. Journal of Power Sources, 258(14), 228‒237.10.1016/j.jpowsour.2014.02.026Search in Google Scholar

12. Sun D., Chen X. (2015): Charge State Estimation of Li-ion Batteries Based on Discrete-time Sliding Mode Observers. Proceedings of the CSEE, 35(1), 185‒191.Search in Google Scholar

13. Waag W., Fleischer C., Viejo B., et al. (2014): Critical review of the methods for monitoring of lithium-ion battery in electric and hybrid vehicles. Journal of Power Sources, 258, 321‒339.10.1016/j.jpowsour.2014.02.064Search in Google Scholar

14. Meng J., Luo G., Gao G. (2016): Lithium Polymer Battery State-of-Charge Estimation Based on Adaptive Unscented Kalman Filter and Support Vector Machine. IEEE Transactions on Power Electronics, 31(3), 2226‒2238.10.1109/TPEL.2015.2439578Search in Google Scholar

15. Álvarez Antón J., García Nieto P., de Cos Juez, F., et al. (2013): Battery state-of-charge estimator using the SVM technique. Applied Mathematical Modeling, 37(9), 6244‒6253.10.1016/j.apm.2013.01.024Search in Google Scholar

16. Du J., Liu Z., Wang Y. (2014): State of charge estimation for Li-ion battery based on model from extreme learning machine. Control Engineering Practice, 26, 11‒19.10.1016/j.conengprac.2013.12.014Search in Google Scholar

17. Chang W. (2013): Estimation of the state of charge for a LFP battery using a hybrid method that combines a RBF neural network, an OLS algorithm and AGA. International Journal of Electrical Power and Energy Systems, 53, 603‒611.10.1016/j.ijepes.2013.05.038Search in Google Scholar

18. Chaoui H., Ibe-Ekeocha C. C. (2016): State of Charge and State of Health Estimation for Lithium Batteries Using Recurrent Neural Networks. IEEE Transactions on Vehicular Technology, 66(10), 8773‒8783.Search in Google Scholar

19. El Mejdoubi A., Oukaour A., Chaoui H., et al. (2016): State-of-Charge and State-of-Health Lithium-Ion Batteries’ Diagnosis According to Surface Temperature Variation. IEEE Transactions on Industrial Electronics, 63(4), 2391‒2402.10.1109/TIE.2015.2509916Search in Google Scholar

20. Chen L., Wang Z., Lü Z., et al. (2018): A Novel State-of-charge Estimation Method of Lithium-ion Batteries Combining the Grey Model and Genetic Algorithms. IEEE Transactions on Power Electronics, 33(10), 8797‒8807.10.1109/TPEL.2017.2782721Search in Google Scholar

21. Liu Y., Tan G., He X. (2017): Optimized Battery Model Based Adaptive Sigma Kalman Filter for State of Charge Estimation. Transactions of China Electrotechnical Society, 32(2), 108‒118.10.1063/1.4992815Search in Google Scholar

22. Pan H., Lu Z., Li J., Chen L. (2017): Estimation of Lithium-Ion Battery State of Charge Based on Grey Prediction Model-Extended Kalman Filter. Transactions of China Electrotechnical Society, 32(21), 1‒8.Search in Google Scholar

23. Aung H., Soon Low K., Ting Goh S. (2015): State-of-charge Estimation of Lithium-Ion Battery Using Square Root Spherical Unscented Kalman Filter (Sqrt-UKFST) in Nanosatellite. IEEE Transactions on Power Electronics, 30(9), 4774‒4783.10.1109/TPEL.2014.2361755Search in Google Scholar

24. Sheng H., Xiao J., Wang P. (2017): Lithium Iron Phosphate Battery Electric Vehicle State-of-Charge Estimation Based on Evolutionary Gaussian Mixture Regression. IEEE Transactions on Industrial Electronics, 64(1), 544‒551.10.1109/TIE.2016.2606588Search in Google Scholar

25. Anton J., Nieto P., Viejo C., et al. (2013): Support vector machines used to estimate the battery state of charge. IEEE Transactions on Power Electronics, 28(12), 5919‒5926.10.1109/TPEL.2013.2243918Search in Google Scholar

26. Wang Q., Sun Y., Ni F., Luo Y. (2016): A New Method of Battery State of Charge Prediction in the Hybrid Electric Vehicle. Transactions of China Electrotechnical Society, 31(9), 189‒196.Search in Google Scholar

27. Yang F., Song X., Xu F., Tsui K. (2019): State-of-Charge Estimation of Lithium-Ion Batteries via Long Short-Term Memory Network. IEEE Access, 7, 53792–53799.10.1109/ACCESS.2019.2912803Search in Google Scholar

28. Han L., Yu C., Xiao K., Zhao X. (2019): A New Method of Mixed Gas Identification Based on a Convolutional Neural Network for Time Series Classification. Sensors, 19(9), 1‒23.10.3390/s19091960653907931027348Search in Google Scholar

29. Shrestha A., Mahmood A. (2019): Review of Deep Learning Algorithms and Architectures. IEEE Access, 7, 53040‒53065.10.1109/ACCESS.2019.2912200Search in Google Scholar

30. Kingma D., Ba J. (2015): Adam: A method for stochastic optimization. International Conference on Learning Representations, May 7-9, 2015, San Diego, 1-13.Search in Google Scholar