Reinforcement learning based adaptive power sharing of battery/supercapacitors hybrid storage in electric vehicles

References

• Abdelhedi, R., A. C. Ammari, A. Lahyani, A. Sari, and P. Venet. 2016. Optimal power sharing between batteries and supercapacitors in electric vehicles. 7th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT2016), pp. 97–103, Hammamet, Tunisia, December. 18–20.
   Google Scholar
• Abdelhedi, R., A. C. Ammari, A. Lahyani, A. Sari, and P. Venet. 2017a. An improved frequency sharing strategy between battery and supercapacitors in electric vehicle. ELECTRIMACS International Conference, Toulouse, France, July 4–6.
   Google Scholar
• Abdelhedi, R., A. Lahyani, A. C. Ammari, A. Sari, and P. Venet. December, 2017b. Frequency power sharing for battery/supercapacitors hybrid energy storage system in electric vehicles. International Review on Modelling and Simulations (I.RE.MO.S.). 10 (6):399–409. Praise Worthy Prize. doi:10.15866/iremos.v10i6.12364.
   Google Scholar
• Abdelhedi, R., A. Lahyani, A. C. Ammari, A. Sari, and P. Venet. 2018. Reinforcement learning-based power sharing between batteries and supercapacitors in electric vehicles. IEEE International Conference on Industrial Technology (ICIT). Lyon. 2072–77.
   Google Scholar
• Ammari, A. C. 2020. Adaptive battery aware power management of a computer with self power-managed components. Microprocessors and Microsystems 72:1–12. doi:10.1016/j.micpro.2019.102947.
   Web of Science ®Google Scholar
• ARBIN BT 2000 - Battery Test Equipment. http://slategrey.pt/wp-content/uploads/2016/06/BT-2000-Multi-Channel-Battery-Testing-System-da-ARBIN.pdf (July 2020).
   Google Scholar
• Baghdadi, I., O. Briat, A. Eddahech, J. M. Vinassa, I. Baghdadi, and P. Gyan. 2015. Electro-thermal model of lithium-ion batteries for electrified vehicles applications. IEEE 24th International Symposium on Industrial Electronics (ISIE), pp. 1248–52, Buzios, Brazil June, 3–5.
   Google Scholar
• Bai, W., T. Li, and S. Tong. 2020. NN reinforcement learning adaptive control for a class of nonstrict-feedback discrete-time systems. IEEE Transactions on Cybernetics 50 (11):4573–84. doi:10.1109/TCYB.2020.2963849.
   PubMed Web of Science ®Google Scholar
• Camara, M. B., B. Dakyo, and H. Gualous. March 2012. Polynomial control method of dc/dc converters for dc-bus voltage and currents management—battery and supercapacitors. IEEE Transactions on Power Electronics. 27(3):1455–67. doi: 10.1109/TPEL.2011.2164581.
   Web of Science ®Google Scholar
• Capasso, C., D. Lauria, and O. Veneri. 2017. Optimal control strategy of ultra capacitors in hybrid energy storage system for electric vehicles. Energy Procedia 142:1914–19. doi:10.1016/j.egypro.2017.12.390.
   Google Scholar
• Castaings, A., W. Lhomme, R. Trigui, and A. Bouscayrol. 2016. Comparison of energy management strategies of a battery/supercapacitors system for electric vehicle under real-time constraints. Applied Energy 163:190–200. doi:10.1016/j.apenergy.2015.11.020.
   Web of Science ®Google Scholar
• [Castaings, A., W. Lhomme, R. Trigui, A. Bouscayrol, and E. Redondo-Iglesias. 2015. Adaptive optimisation-based strategy of a battery/supercapacitor system for EV. IEEE Vehicle Power and Propulsion Conference (VPPC), pp. 1–6, Montréal, Canada, October 19–22.
   Google Scholar
• Duan, J., Z. Yi, D. Shi, C. Lin, X. Lu, and Z. Wang. September 2019. Reinforcement-learning-based optimal control of hybrid energy storage systems in hybrid ac–dc microgrids. IEEE Transactions on Industrial Informatics. 15(9):5355–64. doi: 10.1109/TII.2019.2896618.
   Web of Science ®Google Scholar
• German, R., S. Shili, A. Sari, P. Venet, and A. Bouscayrol. 2017. Characterization method for electrothermal model of li-ion large cells. IEEE Vehicle Power and Propulsion Conference (VPPC 2017). Belfort, France. December.
   Google Scholar
• Hu, B., J. Li, J. Yang, H. Bai, S. Li, Y. Sun, and X. Yang. 2019. Reinforcement learning approach to design practical adaptive control for a small-scale intelligent vehicle. Symmetry 11 (9):1139–56. doi:10.3390/sym11091139.
   Google Scholar
• Jiang, H., L. Xu, J. Li, Z. Hu, and M. Ouyang. 2019. Energy management and component sizing for a fuel cell/battery/supercapacitor hybrid powertrain based on two-dimensional optimization algorithms. Energy 177:386–96. doi:10.1016/j.energy.2019.04.110.
   Web of Science ®Google Scholar
• Khaldi, H. S., and A. C. Ammari. 2015. Fractional-order control of three level boost DC/DC converter used in hybrid energy storage system for electric vehicles. Proceedings of The Sixth International Renewable Energy Congress (IREC), 1–7, Sousse, Tunisia,March.
   Google Scholar
• Kouchachvili, L., W. Yaïci, and E. Entchev. 2018. Hybrid battery/supercapacitor energy storage system for the electric vehicles. Journal of Power Sources 374:237–48. doi:10.1016/j.jpowsour.2017.11.040.
   Web of Science ®Google Scholar
• Kumaravel, S., and S. Ashok. 2015. Optimal power management controller for a stand-alone solar PV/wind/battery hybrid energy system. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 37 (4):407–15. doi:10.1080/15567036.2011.576414.
   Web of Science ®Google Scholar
• Lahyani, A., P. Venet, A. Guermazi, and A. Troudi. April 2013. Battery/supercapacitors combination in uninterruptible power supply (UPS). IEEE Transactions on Power Electronics. 28(4):1509‑1522. doi: 10.1109/TPEL.2012.2210736.
   Web of Science ®Google Scholar
• Liu, C., Y. Wang, L. Wang, and Z. Chen. 2019. Load-adaptive real-time energy management strategy for battery/ultracapacitor hybrid energy storage system using dynamic programming optimization. Journal of Power Sources 438: doi: 10.1016/j.jpowsour.2019.227024.
   Web of Science ®Google Scholar
• Liu, T., X. Hu, S. E. Li, and D. Cao. August 2017. Reinforcement learning optimized look-ahead energy management of a parallel hybrid electric vehicle. IEEE ASME Transactions on Mechatronics. 22(4):1497–507. doi: 10.1109/TMECH.2017.2707338.
   Web of Science ®Google Scholar
• Mesbahi, T., N. Rizoug, P. Bartholomeüs, R. Sadoun, F. Khenfri, and P. Le Moigne. June 2017. Optimal energy management for a li-ion battery/supercapacitor hybrid energy storage system based on a particle swarm optimization incorporating Nelder–Mead simplex approach. IEEE Transactions on Intelligent Vehicles. 2(2):99–110.
   Google Scholar
• [Oukkacha, I., M. B. Camara, and B. Dakyo. 2018. Energy management in electric vehicle based on frequency sharing approach, using fuel cells, lithium batteries and Supercapacitors. 7th International Conference on Renewable Energy Research and Applications (ICRERA), Paris, 986–92.
   Google Scholar
• Pam, A., A. Bouscayrol, P. Fiani, and F. Noth. 2017. Rule-based energy management strategy for a parallel hybrid electric vehicle deduced from dynamic programming. IEEE Vehicle Power and Propulsion Conference (VPPC). 1–6. Belfort.
   Google Scholar
• Qi, X., G. Wu, K. Boriboonsomsin, and M. J. Barth. 2015. A novel blended real-time energy management strategy for plug-in hybrid electric vehicle commute trips. IEEE 18th International Conference on Intelligent Transportation Systems, 1002–1007, Las Palmas, Spain, September, 15–18.
   Google Scholar
• Qi, X., G. Wu, K. Boriboonsomsin, and M. J. Barth. August 2017. Development and evaluation of an evolutionary algorithm-based online energy management system for plug-in hybrid electric vehicles. IEEE Transactions on Intelligent Transportation Systems. 18(8):2181–91. doi: 10.1109/TITS.2016.2633542.
   Web of Science ®Google Scholar
• Rafik, F., H. Gualous, R. Gallay, A. Crausaz, and A. Berthon. 2006, March. Frequency, thermal and voltage supercapacitor characterization and modeling. Iba – Hbc. 165(2): 2007, 928–34.
   Google Scholar
• Rewatkar, M. R., and D. Z. Shende. 2020. Experimental investigation on cenosphere-based paper battery and electrochemical battery. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 42 (16):2018–33. doi:10.1080/15567036.2019.1607924.
   Web of Science ®Google Scholar
• Shengzhe, Z., W. Kai, and X. Wen. 2017. Fuzzy logic-based control strategy for a battery/supercapacitor hybrid energy storage system in electric vehicles. 5598–601. Jinan: Chinese Automation Congress (CAC).
   Google Scholar
• Stocker, R., A. Mumtaz, P. M. Braglia, and N. Lophitis. 2020. Universal li-ion cell electro-thermal model. IEEE Transactions on Transportation Electrification 1–1. doi:10.1109/TTE.2020.2986606.
   Google Scholar
• Sutton, R. S., and A. G. Barto. 2018. Reinforcement learning: an introduction, 97–113. 2nd ed. MA, USA: The MIT Press: Cambridge.
   Google Scholar
• Tremblay, O., and L. A. Dessaint. 2009. Experimental validation of a battery dynamic model for EV applications. World Electric Vehicle Journal 3 (2):289–98. doi:10.3390/wevj3020289.
   Google Scholar
• Triki, M., A. C. Ammari, Y. Wang, and M. Pedram. 2013. Reinforcement learning-based dynamic power management of a battery-powered system supplying multiple active modes. European Modelling Symposium. 437–442, Manchester, UK, November, 20–22.
   Google Scholar
• Triki, M., Y. Wang, A. C. Ammari, and M. Pedram. 2015. Hierarchical power management of a system with autonomously power-managed components using reinforcement learning. In Integration, the VLSI Journal 48:10–20. doi:10.1016/j.vlsi.2014.06.001.
   Web of Science ®Google Scholar
• Wang, Y., F. Li, H. Yu, Y. Wang, C. Qi, J. Yang, and F. Song. 2020. Optimal operation of microgrid with multi-energy complementary based on moth flame optimization algorithm. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 42 (7):785–806. doi:10.1080/15567036.2019.1587067.
   Web of Science ®Google Scholar
• Wang, Y., Z. Sun, and Z. Chen. 2019. Development of energy management system based on a rule-based power distribution strategy for hybrid power sources. Energy 175:1055–66. doi:10.1016/j.energy.2019.03.155.
   Web of Science ®Google Scholar
• Wongdet, P., and B. Marungsri. 2018. Hybrid energy storage system in standalone dc microgrid with ramp rate limitation for extending the lifespan of battery. International Electrical Engineering Congress (iEECON). 1–4. Krabi, Thailand.
   Google Scholar
• Xia, G. T., C. Li, K. Wang, and L. W. Li. 2019. Structural design and electrochemical performance of PANI/CNTs and MnO 2/CNTs Supercapacitor. Science of Advanced Materials 11 (8):1079–86. doi:10.1166/sam.2019.3487.
   Web of Science ®Google Scholar
• Zhou, S., and Z. Chen. 2019. Model prediction and rule based energy management strategy for hybrid energy storage system. IEEE 3rd International Electrical and Energy Conference (CIEEC). 427–32. Beijing, China.
   Google Scholar
• Zhou, Y., Y. Huang, J. Pang, and K. Wang. 2019. Remaining useful life prediction for supercapacitor based on long short-term memory neural network. Journal of Power Sources 440:227149. doi:10.1016/j.jpowsour.2019.227149.
   Web of Science ®Google Scholar
• Zhou, Y., A. Ravey, and M. Péra. 2019. A survey on driving prediction techniques for predictive energy management of plug-in hybrid electric vehicles. Journal of Power Sources 412:480–95. doi:10.1016/j.jpowsour.2018.11.085.
   Web of Science ®Google Scholar
• Zhou, Y., Y. Wang, K. Wang, L. Kang, F. Peng, L. Wang, and J. Pang. 2020. Hybrid genetic algorithm method for efficient and robust evaluation of remaining useful life of supercapacitors. Applied Energy 260: doi: 10.1016/j.apenergy.2019.114169.
   Web of Science ®Google Scholar