Model Development for State-of-Power Estimation of Large-Capacity Nickel-Manganese-Cobalt Oxide-Based Lithium-Ion Cell Validated Using a Real-Life Profile
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- Manoj Mathew & Stefan Janhunen & Mahir Rashid & Frank Long & Michael Fowler, 2018. "Comparative Analysis of Lithium-Ion Battery Resistance Estimation Techniques for Battery Management Systems," Energies, MDPI, vol. 11(6), pages 1-15, June.
- Esfandyari, M.J. & Esfahanian, V. & Hairi Yazdi, M.R. & Nehzati, H. & Shekoofa, O., 2019. "A new approach to consider the influence of aging state on Lithium-ion battery state of power estimation for hybrid electric vehicle," Energy, Elsevier, vol. 176(C), pages 505-520.
- Shun Xiang & Guangdi Hu & Ruisen Huang & Feng Guo & Pengkai Zhou, 2018. "Lithium-Ion Battery Online Rapid State-of-Power Estimation under Multiple Constraints," Energies, MDPI, vol. 11(2), pages 1-20, January.
- Yang, Jufeng & Xia, Bing & Huang, Wenxin & Fu, Yuhong & Mi, Chris, 2018. "Online state-of-health estimation for lithium-ion batteries using constant-voltage charging current analysis," Applied Energy, Elsevier, vol. 212(C), pages 1589-1600.
- Theodoros Kalogiannis & Md Sazzad Hosen & Mohsen Akbarzadeh Sokkeh & Shovon Goutam & Joris Jaguemont & Lu Jin & Geng Qiao & Maitane Berecibar & Joeri Van Mierlo, 2019. "Comparative Study on Parameter Identification Methods for Dual-Polarization Lithium-Ion Equivalent Circuit Model," Energies, MDPI, vol. 12(21), pages 1-35, October.
- Vikström, Hanna & Davidsson, Simon & Höök, Mikael, 2013. "Lithium availability and future production outlooks," Applied Energy, Elsevier, vol. 110(C), pages 252-266.
- Bo Jiang & Haifeng Dai & Xuezhe Wei & Letao Zhu & Zechang Sun, 2017. "Online Reliable Peak Charge/Discharge Power Estimation of Series-Connected Lithium-Ion Battery Packs," Energies, MDPI, vol. 10(3), pages 1-22, March.
- Dong, Guangzhong & Zhang, Xu & Zhang, Chenbin & Chen, Zonghai, 2015. "A method for state of energy estimation of lithium-ion batteries based on neural network model," Energy, Elsevier, vol. 90(P1), pages 879-888.
- Zuchang Gao & Cheng Siong Chin & Wai Lok Woo & Junbo Jia, 2017. "Integrated Equivalent Circuit and Thermal Model for Simulation of Temperature-Dependent LiFePO 4 Battery in Actual Embedded Application," Energies, MDPI, vol. 10(1), pages 1-22, January.
- Xiong, Rui & Sun, Fengchun & He, Hongwen & Nguyen, Trong Duy, 2013. "A data-driven adaptive state of charge and power capability joint estimator of lithium-ion polymer battery used in electric vehicles," Energy, Elsevier, vol. 63(C), pages 295-308.
- Berecibar, Maitane & Garmendia, Maitane & Gandiaga, Iñigo & Crego, Jon & Villarreal, Igor, 2016. "State of health estimation algorithm of LiFePO4 battery packs based on differential voltage curves for battery management system application," Energy, Elsevier, vol. 103(C), pages 784-796.
- Alexandros Nikolian & Yousef Firouz & Rahul Gopalakrishnan & Jean-Marc Timmermans & Noshin Omar & Peter Van den Bossche & Joeri Van Mierlo, 2016. "Lithium Ion Batteries—Development of Advanced Electrical Equivalent Circuit Models for Nickel Manganese Cobalt Lithium-Ion," Energies, MDPI, vol. 9(5), pages 1-23, May.
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Keywords
state of power; state of charge; internal resistance increase; battery management system; aging; validation profiles;All these keywords.
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