Capacity estimation and energy allocation model of new energy vehicle battery management system based on optimization algorithm

[1]Hannan, M. A., Hoque, M. M., Hussain, A., Yusof, Y., & Ker, P. J. (2018). State-of-the-art and energy management system of lithium-ion batteries in electric vehicle applications: Issues and recommendations. Ieee Access, 6, 19362-19378. HannanM. A., HoqueM. M., HussainA., YusofY. & KerP. J. (2018). State-of-the-art and energy management system of lithium-ion batteries in electric vehicle applications: Issues and recommendations. Ieee Access, 6, 19362-19378.Search in Google Scholar

[2]Wang, Y., Tian, J., Sun, Z., Wang, L., Xu, R., Li, M., & Chen, Z. (2020). A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems. Renewable and Sustainable Energy Reviews, 131, 110015. WangY., TianJ., SunZ., WangL., XuR., LiM. & ChenZ. (2020). A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems. Renewable and Sustainable Energy Reviews, 131, 110015.Search in Google Scholar

[3]He, H., Sun, F., Wang, Z., Lin, C., Zhang, C., Xiong, R., ... & Zhai, L. (2022). China’s battery electric vehicles lead the world: achievements in technology system architecture and technological breakthroughs. Green Energy and Intelligent Transportation, 1(1), 100020. HeH., SunF., WangZ., LinC., ZhangC., XiongR. ... & ZhaiL. (2022). China’s battery electric vehicles lead the world: achievements in technology system architecture and technological breakthroughs. Green Energy and Intelligent Transportation, 1(1), 100020.Search in Google Scholar

[4]Dai, H., Jiang, B., Hu, X., Lin, X., Wei, X., & Pecht, M. (2021). Advanced battery management strategies for a sustainable energy future: Multilayer design concepts and research trends. Renewable and Sustainable Energy Reviews, 138, 110480. DaiH., JiangB., HuX., LinX., WeiX. & PechtM. (2021). Advanced battery management strategies for a sustainable energy future: Multilayer design concepts and research trends. Renewable and Sustainable Energy Reviews, 138, 110480.Search in Google Scholar

[5]Hasib, S. A., Islam, S., Chakrabortty, R. K., Ryan, M. J., Saha, D. K., Ahamed, M. H., ... & Badal, F. R. (2021). A comprehensive review of available battery datasets, RUL prediction approaches, and advanced battery management. Ieee Access, 9, 86166-86193. HasibS. A., IslamS., ChakraborttyR. K., RyanM. J., SahaD. K., AhamedM. H. ... & BadalF. R. (2021). A comprehensive review of available battery datasets, RUL prediction approaches, and advanced battery management. Ieee Access, 9, 86166-86193.Search in Google Scholar

[6]Lipu, M. H., Hannan, M. A., Karim, T. F., Hussain, A., Saad, M. H. M., Ayob, A., ... & Mahlia, T. I. (2021). Intelligent algorithms and control strategies for battery management system in electric vehicles: Progress, challenges and future outlook. Journal of Cleaner Production, 292, 126044. LipuM. H., HannanM. A., KarimT. F., HussainA., SaadM. H. M., AyobA. ... & MahliaT. I. (2021). Intelligent algorithms and control strategies for battery management system in electric vehicles: Progress, challenges and future outlook. Journal of Cleaner Production, 292, 126044.Search in Google Scholar

[7]Sulaiman, N., Hannan, M. A., Mohamed, A., Ker, P. J., Majlan, E. H., & Daud, W. W. (2018). Optimization of energy management system for fuel-cell hybrid electric vehicles: Issues and recommendations. Applied energy, 228, 2061-2079. SulaimanN., HannanM. A., MohamedA., KerP. J., MajlanE. H. & DaudW. W. (2018). Optimization of energy management system for fuel-cell hybrid electric vehicles: Issues and recommendations. Applied energy, 228, 2061-2079.Search in Google Scholar

[8]Cai, W., Wu, X., Zhou, M., Liang, Y., & Wang, Y. (2021). Review and development of electric motor systems and electric powertrains for new energy vehicles. Automotive Innovation, 4, 3-22. CaiW., WuX., ZhouM., LiangY. & WangY. (2021). Review and development of electric motor systems and electric powertrains for new energy vehicles. Automotive Innovation, 4, 3-22.Search in Google Scholar

[9]Hu, X., Feng, F., Liu, K., Zhang, L., Xie, J., & Liu, B. (2019). State estimation for advanced battery management: Key challenges and future trends. Renewable and Sustainable Energy Reviews, 114, 109334. HuX., FengF., LiuK., ZhangL., XieJ. & LiuB. (2019). State estimation for advanced battery management: Key challenges and future trends. Renewable and Sustainable Energy Reviews, 114, 109334.Search in Google Scholar

[10]Tran, M. K., Panchal, S., Khang, T. D., Panchal, K., Fraser, R., & Fowler, M. (2022). Concept review of a cloud-based smart battery management system for lithium-ion batteries: Feasibility, logistics, and functionality. Batteries, 8(2), 19. TranM. K., PanchalS., KhangT. D., PanchalK., FraserR. & FowlerM. (2022). Concept review of a cloud-based smart battery management system for lithium-ion batteries: Feasibility, logistics, and functionality. Batteries, 8(2), 19.Search in Google Scholar

[11]Wu, B., Widanage, W. D., Yang, S., & Liu, X. (2020). Battery digital twins: Perspectives on the fusion of models, data and artificial intelligence for smart battery management systems. Energy and AI, 1, 100016. WuB., WidanageW. D., YangS. & LiuX. (2020). Battery digital twins: Perspectives on the fusion of models, data and artificial intelligence for smart battery management systems. Energy and AI, 1, 100016.Search in Google Scholar

[12]Li, W., Cao, D., Jöst, D., Ringbeck, F., Kuipers, M., Frie, F., & Sauer, D. U. (2020). Parameter sensitivity analysis of electrochemical model-based battery management systems for lithium-ion batteries. Applied Energy, 269, 115104. LiW., CaoD., JöstD., RingbeckF., KuipersM., FrieF. & SauerD. U. (2020). Parameter sensitivity analysis of electrochemical model-based battery management systems for lithium-ion batteries. Applied Energy, 269, 115104.Search in Google Scholar

[13]Lelie, M., Braun, T., Knips, M., Nordmann, H., Ringbeck, F., Zappen, H., & Sauer, D. U. (2018). Battery management system hardware concepts: An overview. Applied Sciences, 8(4), 534. LelieM., BraunT., KnipsM., NordmannH., RingbeckF., ZappenH. & SauerD. U. (2018). Battery management system hardware concepts: An overview. Applied Sciences, 8(4), 534.Search in Google Scholar

[14]Gabbar, H. A., Othman, A. M., & Abdussami, M. R. (2021). Review of battery management systems (BMS) development and industrial standards. Technologies, 9(2), 28. GabbarH. A., OthmanA. M. & AbdussamiM. R. (2021). Review of battery management systems (BMS) development and industrial standards. Technologies, 9(2), 28.Search in Google Scholar

[15]Slama, S. B. (2021). Design and implementation of home energy management system using vehicle to home (H2V) approach. Journal of Cleaner Production, 312, 127792. SlamaS. B. (2021). Design and implementation of home energy management system using vehicle to home (H2V) approach. Journal of Cleaner Production, 312, 127792.Search in Google Scholar

[16]Lin, Q., Wang, J., Xiong, R., Shen, W., & He, H. (2019). Towards a smarter battery management system: A critical review on optimal charging methods of lithium ion batteries. Energy, 183, 220-234. LinQ., WangJ., XiongR., ShenW. & HeH. (2019). Towards a smarter battery management system: A critical review on optimal charging methods of lithium ion batteries. Energy, 183, 220-234.Search in Google Scholar

[17]Li, X., & Wang, S. (2019). Energy management and operational control methods for grid battery energy storage systems. CSEE Journal of Power and Energy Systems, 7(5), 1026-1040. LiX. & WangS. (2019). Energy management and operational control methods for grid battery energy storage systems. CSEE Journal of Power and Energy Systems, 7(5), 1026-1040.Search in Google Scholar

[18]Lin, J., Liu, X., Li, S., Zhang, C., & Yang, S. (2021). A review on recent progress, challenges and perspective of battery thermal management system. International Journal of Heat and Mass Transfer, 167, 120834. LinJ., LiuX., LiS., ZhangC. & YangS. (2021). A review on recent progress, challenges and perspective of battery thermal management system. International Journal of Heat and Mass Transfer, 167, 120834.Search in Google Scholar

[19]Xiong, R., Li, L., & Tian, J. (2018). Towards a smarter battery management system: A critical review on battery state of health monitoring methods. Journal of Power Sources, 405, 18-29. XiongR., LiL. & TianJ. (2018). Towards a smarter battery management system: A critical review on battery state of health monitoring methods. Journal of Power Sources, 405, 18-29.Search in Google Scholar

[20]Liu, K., Li, K., Peng, Q., & Zhang, C. (2019). A brief review on key technologies in the battery management system of electric vehicles. Frontiers of mechanical engineering, 14, 47-64. LiuK., LiK., PengQ. & ZhangC. (2019). A brief review on key technologies in the battery management system of electric vehicles. Frontiers of mechanical engineering, 14, 47-64.Search in Google Scholar

[21]Shen, M., & Gao, Q. (2019). A review on battery management system from the modeling efforts to its multiapplication and integration. International Journal of Energy Research, 43(10), 5042-5075. ShenM. & GaoQ. (2019). A review on battery management system from the modeling efforts to its multiapplication and integration. International Journal of Energy Research, 43(10), 5042-5075.Search in Google Scholar

[22]Chauhan Brajlata, Tabassum Rashida, Tomar Sanjiv & Pal Amrindra. (2023). Analysis for the prediction of solar and wind generation in India using ARIMA, linear regression and random forest algorithms. Wind Engineering(2), 251-265. BrajlataChauhan, RashidaTabassum, SanjivTomar & AmrindraPal. (2023). Analysis for the prediction of solar and wind generation in India using ARIMA, linear regression and random forest algorithms. Wind Engineering(2), 251-265.Search in Google Scholar

[23]Cui Qi & Liu Feng. (2023). A new technique for influence maximization on social networks using a moth-flame optimization algorithm. Heliyon(11), e22191-e22191. QiCui & FengLiu. (2023). A new technique for influence maximization on social networks using a moth-flame optimization algorithm. Heliyon(11), e22191-e22191.Search in Google Scholar

[24]Juncai Song, Jing Wu, Xiaoqing Wang, Zhangling Duan, Xiaoxian Wang & Siliang Lu. (2024). Accurate classification of power quality disturbance based on 3D visualized spiral curve and hybrid ER-MVCNN model. Measurement 114654-. SongJuncai, WuJing, WangXiaoqing, DuanZhangling, WangXiaoxian & LuSiliang. (2024). Accurate classification of power quality disturbance based on 3D visualized spiral curve and hybrid ER-MVCNN model. Measurement 114654-.Search in Google Scholar

[25]Yangmei Zhang, Yang Bi & Junfang Li. (2024). Underwater image processing and target detection from particle swarm optimization algorithm. Signal, Image and Video Processing(1), 132-132. ZhangYangmei, BiYang & LiJunfang. (2024). Underwater image processing and target detection from particle swarm optimization algorithm. Signal, Image and Video Processing(1), 132-132.Search in Google Scholar