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A comprehensive model for lithium-ion batteries: From the physical principles to an electrical model

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  • Berrueta, Alberto
  • Urtasun, Andoni
  • Ursúa, Alfredo
  • Sanchis, Pablo

Abstract

The growing interest in e-mobility and the increasing installation of renewable energy-based systems are leading to rapid improvements in lithium-ion batteries. In this context, battery manufacturers and engineers require advanced models in order to study battery performance accurately. A number of Li-ion battery models are based on the representation of physical phenomena by electrochemical equations. Although providing detailed physics-based information, these models cannot take into account all the phenomena for a whole battery, given the high complexity of the equations. Other models are based on equivalent circuits and are easier to design and use. However, they fail to relate these circuit parameters to physical properties. In order to take the best of both modeling techniques, we propose an equivalent circuit model which keeps a straight correlation between its parameters and the battery electrochemical principles. Consequently, this model has the required simplicity to be used in the simulation of a whole battery, while providing the depth of detail needed to identify physical phenomena. Moreover, due to its high accuracy, it can be used in a wide range of environments, as shown in the experimental validations carried out in the final section of this paper.

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  • Berrueta, Alberto & Urtasun, Andoni & Ursúa, Alfredo & Sanchis, Pablo, 2018. "A comprehensive model for lithium-ion batteries: From the physical principles to an electrical model," Energy, Elsevier, vol. 144(C), pages 286-300.
    • Handle: RePEc:eee:energy:v:144:y:2018:i:c:p:286-300
    DOI: 10.1016/j.energy.2017.11.154
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    1. Stadler, Michael & Cardoso, Gonçalo & Mashayekh, Salman & Forget, Thibault & DeForest, Nicholas & Agarwal, Ankit & Schönbein, Anna, 2016. "Value streams in microgrids: A literature review," Applied Energy, Elsevier, vol. 162(C), pages 980-989.
    2. Wang, Yujie & Liu, Chang & Pan, Rui & Chen, Zonghai, 2017. "Modeling and state-of-charge prediction of lithium-ion battery and ultracapacitor hybrids with a co-estimator," Energy, Elsevier, vol. 121(C), pages 739-750.
    3. Li, Yanwen & Wang, Chao & Gong, Jinfeng, 2016. "A combination Kalman filter approach for State of Charge estimation of lithium-ion battery considering model uncertainty," Energy, Elsevier, vol. 109(C), pages 933-946.
    4. Jiang, Jiuchun & Liu, Sijia & Ma, Zeyu & Wang, Le Yi & Wu, Ke, 2016. "Butler-Volmer equation-based model and its implementation on state of power prediction of high-power lithium titanate batteries considering temperature effects," Energy, Elsevier, vol. 117(P1), pages 58-72.
    5. Soshinskaya, Mariya & Crijns-Graus, Wina H.J. & van der Meer, Jos & Guerrero, Josep M., 2014. "Application of a microgrid with renewables for a water treatment plant," Applied Energy, Elsevier, vol. 134(C), pages 20-34.
    6. Diouf, Boucar & Pode, Ramchandra, 2015. "Potential of lithium-ion batteries in renewable energy," Renewable Energy, Elsevier, vol. 76(C), pages 375-380.
    7. Zhang, Cheng & Allafi, Walid & Dinh, Quang & Ascencio, Pedro & Marco, James, 2018. "Online estimation of battery equivalent circuit model parameters and state of charge using decoupled least squares technique," Energy, Elsevier, vol. 142(C), pages 678-688.
    8. 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.
    9. Wang, Qian-Kun & He, Yi-Jun & Shen, Jia-Ni & Ma, Zi-Feng & Zhong, Guo-Bin, 2017. "A unified modeling framework for lithium-ion batteries: An artificial neural network based thermal coupled equivalent circuit model approach," Energy, Elsevier, vol. 138(C), pages 118-132.
    10. Pascual, Julio & Barricarte, Javier & Sanchis, Pablo & Marroyo, Luis, 2015. "Energy management strategy for a renewable-based residential microgrid with generation and demand forecasting," Applied Energy, Elsevier, vol. 158(C), pages 12-25.
    11. Zheng, Linfeng & Zhang, Lei & Zhu, Jianguo & Wang, Guoxiu & Jiang, Jiuchun, 2016. "Co-estimation of state-of-charge, capacity and resistance for lithium-ion batteries based on a high-fidelity electrochemical model," Applied Energy, Elsevier, vol. 180(C), pages 424-434.
    12. Li, Xianglin & Huang, Jing & Faghri, Amir, 2015. "Modeling study of a Li–O2 battery with an active cathode," Energy, Elsevier, vol. 81(C), pages 489-500.
    13. Deng, Zhongwei & Deng, Hao & Yang, Lin & Cai, Yishan & Zhao, Xiaowei, 2017. "Implementation of reduced-order physics-based model and multi-parameters identification strategy for lithium-ion battery," Energy, Elsevier, vol. 138(C), pages 509-519.
    14. Umeozor, Evar Chinedu & Trifkovic, Milana, 2016. "Operational scheduling of microgrids via parametric programming," Applied Energy, Elsevier, vol. 180(C), pages 672-681.
    15. Velik, Rosemarie & Nicolay, Pascal, 2014. "Grid-price-dependent energy management in microgrids using a modified simulated annealing triple-optimizer," Applied Energy, Elsevier, vol. 130(C), pages 384-395.
    16. Zhang, Xu & Wang, Yujie & Yang, Duo & Chen, Zonghai, 2016. "An on-line estimation of battery pack parameters and state-of-charge using dual filters based on pack model," Energy, Elsevier, vol. 115(P1), pages 219-229.
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