1. IEA. Energy Technology Perspectives--scenarios and strategies to 2050. International Energy Agency. 2006;25-33. [
Link]
2. Hannan MA, Azidin FA, Mohamed A. Hybrid electric vehicles and their challenges: A review. Renewable and Sustainable Energy Reviews. 2014;29:135-150. [
Link] [
DOI:10.1016/j.rser.2013.08.097]
3. Wang Y, Chen Z , Zhang C. On-line remaining energy prediction: A case study in embedded battery management system. Applied Energy. 2017;194:688-695. [
Link] [
DOI:10.1016/j.apenergy.2016.05.081]
4. Cheng KWE, Divakar BP, Wu H, Ding K, Ho HF. Battery-Management System (BMS) and SOC development for electrical vehicles. IEEE Transactions on Vehicular Technology. 2011;60(1):76-88. [
Link] [
DOI:10.1109/TVT.2010.2089647]
5. Li J, Tan F, Zhang C, Sun F. Capacity fade diagnosis of Lithium ion battery pack in electric vehicle base on fuzzy neural network. Energy Procedia. 2014;61:2066-2070. [
Link] [
DOI:10.1016/j.egypro.2014.12.077]
6. Gua Y, Zhao Z, Huang L. SoC estimation of Lithium battery based on improved BP neural network. Energy Procedia. 2017;105:4153-4158. [
Link] [
DOI:10.1016/j.egypro.2017.03.881]
7. Chaoui H, Ibe-Ekeocha CC. State of charge and state of health estimation for lithium batteries using recurrent neural networks. IEEE Transactions on Vehicular Technology. 2017;66(10):8773- 8783. [
Link] [
DOI:10.1109/TVT.2017.2715333]
8. Ismail M, Dlyma R, Elrakaybi A, Ahmed R, Habibi S. Battery state of charge estimation using an Artificial Neural Network. 2017 IEEE Transportation Electrification Conference and Expo (ITEC). Piscataway: IEEE; 2017. [
Link]
9. Liu F, Liu T, Fu Y. An improved SoC estimation algorithm based on artificial neural network. 2015 8th International Symposium on Computational Intelligence and Design (ISCID). Piscataway: IEEE; 2015. [
Link]
10. Sarvi M, Safari M. Fuzzy, ANFIS and ICA trained neural network modeling of Ni-Cd batteries using experimental data. World Applied Programming. 2013;3(3):93-100. [
Link]
11. Jiani D, Zhitao L, Youyi W, Changyun W. A fuzzy logic-based model for Li-ion battery with SOC and temperature effect. 11th IEEE International Conference on Control & Automation (ICCA). Piscataway: IEEE; 2014. 19- Anand I, Mathur BL. State of charge estimation of lead acid batteries using neural network. 2013 International Conference on Circuits, Power and Computing Technologies (ICCPCT). Piscataway: IEEE; 2013. 23- Nori Khajavi M, Bavir MR, Farrokhi E. A new method in determining, rotor crack depth by using multi-scale permutation Entropy and ANFIS network. Modares Mechanical Engineering. 2015;15(7):31-39. [Persian] [
Link]
12. Shahriari M, Farrokhi M. State-of-charge estimation of VRLA batteries using neural networks and extended kalman filter. IFAC Proceedings Volumes. 2010;43(22):52-56. [
Link] [
DOI:10.3182/20100929-3-RO-4017.00010]
13. Tong S, Lacap JH, Park JW. Battery state of charge estimation using a load-classifying neural network. Journal of Energy Storage. 2016;7:236-243. [
Link] [
DOI:10.1016/j.est.2016.07.002]
14. Hussein AA. Capacity fade estimation in electric vehicle Li-ion batteries using artificial neural networks. IEEE Transactions on Industry Applications. 2015;51(3):2321-2330. [
Link] [
DOI:10.1109/TIA.2014.2365152]
15. Li IH, Wang WY, Su SF, Lee YS. A merged fuzzy neural network and its applications in battery state-of-charge estimation. IEEE Transactions on Energy Conversion. 2007;22(3):697-708. [
Link] [
DOI:10.1109/TEC.2007.895457]
16. Gholizade-Narm H, Charkhgard M. Lithium-ion battery state of charge estimation based on square-root unscented Kalman filter. IET Power Electronics. 2013;6(9):1833-1841. [
Link] [
DOI:10.1049/iet-pel.2012.0706]
17. Charkhgard M, Farroukhi M. State-of-charge estimation for Lithium-ion batteries using neural networks and EKF. IEEE Transactions on Industrial Electronics. 2010;57(12):4178-4187. [
Link] [
DOI:10.1109/TIE.2010.2043035]
18. He Z, Chen D, Pan C, Chen L, Wang S. State of charge estimation of power Li-ion batteries using a hybrid estimation algorithm based on UKF. Electrochimica Acta. 2016;211:101-109. [
Link] [
DOI:10.1016/j.electacta.2016.06.042]
19. Anand I, Mathur BL. State of charge estimation of lead acid batteries using neural network. 2013 International Conference on Circuits, Power and Computing Technologies (ICCPCT). Piscataway: IEEE; 2013. 23- Nori Khajavi M, Bavir MR, Farrokhi E. A new method in determining, rotor crack depth by using multi-scale permutation Entropy and ANFIS network. Modares Mechanical Engineering. 2015;15(7):31-39. [Persian] [
Link]
20. Chen M, Rincon-Mora GA. Accurate electrical battery model capable of predicting runtime and I-V performance. IEEE Transactions on Energy Conversion. 2006;21(2):504-511. [
Link] [
DOI:10.1109/TEC.2006.874229]
21. Marquardt DW. An algorithm for least-squares estimation of nonlinear parameters. Journal of the Society for Industrial and Applied Mathematics. 1963;11(2):431-441. [
Link] [
DOI:10.1137/0111030]
22. Xing Y, Ma EWM, Tsui KL, Pecht M. Battery management systems in electric and hybrid vehicles. Energies. 2011;4(11):1840-1857. [
Link] [
DOI:10.3390/en4111840]
23. Awadallah MA, Venkatesh B. Accuracy improvement of SOC estimation in lithium-ion batteries. Journal of Energy Storage. 2016;6:95-104. [
Link] [
DOI:10.1016/j.est.2016.03.003]
24. Wu T, Wang M, Xiao Q, Wang X. The SOC estimation of power Li-Ion battery based on ANFIS model. Smart Grid and Renewable Energy. 2012;3:51-55. [
Link] [
DOI:10.4236/sgre.2012.31007]
25. Wu T, Wang M, Xiao Q, Wang X. The SOC estimation of power Li-Ion battery based on ANFIS model. Smart Grid and Renewable Energy. 2012;3:51-55. [
Link] [
DOI:10.4236/sgre.2012.31007]