Parameter identification and state-of-charge prediction of decommissioned lithium batteries

Qian Shi; Junkai Li; Qi Qin; Chijian Zhang

doi:10.1051/e3sconf/202126701017

All issues

Volume 267 (2021)

E3S Web Conf., 267 (2021) 01017

References

Open Access

Issue		E3S Web Conf. Volume 267, 2021 7^th International Conference on Energy Science and Chemical Engineering (ICESCE 2021)


Article Number		01017
Number of page(s)		5
Section		Energy Development and Utilization and Energy-Saving Technology Application
DOI		https://doi.org/10.1051/e3sconf/202126701017
Published online		04 June 2021

I. U. Khalil, M. Ahsan, I. Ullah, A. Adnan, N. Khan and S. Nawaz, “SOC prediction of Lithium-Ion Battery using Extended Kalman Filter,” 2018 International Symposium on Recent Advances in Electrical Engineering (RAEE), Islamabad, Pakistan, 2018, pp. 1-5. [Google Scholar]
X. Liu et al., “Online identification of power battery parameters for electric vehicles using a decoupling multiple forgetting factors recursive least squares method,” in CSEE Journal of Power and Energy Systems, vol. 6, no. 3, pp. 735-742, Sept. 2020. [Google Scholar]
Li P, Wang H, Xing Z, et al. Joint estimation of SOC and SOH for lithium-ion batteries based on EKF multiple time scales[J]. 2020. [Google Scholar]
D. A. Pola et al., “Particle-Filtering-Based Discharge Time Prognosis for Lithium-Ion Batteries With a Statistical Characterization of Use Profiles,” in IEEE Transactions on Reliability, vol. 64, no. 2, pp. 710720, June 2015. [Google Scholar]
Xu Y, Hu M, Fu C, et al. State of Charge Estimation for Lithium-Ion Batteries Based on Temperature-Dependent Second-Order RC Model[J]. Electronics, 2019, 8(9):1012-. [Google Scholar]
Tian Y, Lai R, Li X, et al. A combined method for state-of-charge estimation for lithium-ion batteries using a long short-term memory network and an adaptive cubature Kalman filter[J]. Applied Energy, 2020, 265. [Google Scholar]
Wang Houlian, Zhou Gongbo. State of charge prediction of supercapacitors via combination of Kalman filtering and backpropagation neural network[J]. IET Electric Power Applications, 2018, 12(4). [Google Scholar]
Aung H, Low K S, Soon J J. State-of-charge estimation using particle swarm optimization with inverse barrier constraint in a nanosatellite[C]// Industrial Electronics & Applications. IEEE, 2015. [Google Scholar]
Chandra Shekar A, Anwar S. Real-Time State-of-Charge Estimation via Particle Swarm Optimization on a Lithium-Ion Electrochemical Cell Model[J]. Batteries, 2019, 5(1). [Google Scholar]
Mao X, Song S, Ding F, et al. SOC Estimation of Lithium Battery Based on IPSO-BP Neural Network[J]. Journal of Physics: Conference Series, 2020, 1684(1):012152 (6pp). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.

[1] I. U. Khalil, M. Ahsan, I. Ullah, A. Adnan, N. Khan and S. Nawaz, “SOC prediction of Lithium-Ion Battery using Extended Kalman Filter,” 2018 International Symposium on Recent Advances in Electrical Engineering (RAEE), Islamabad, Pakistan, 2018, pp. 1-5. [Google Scholar]

[2] X. Liu et al., “Online identification of power battery parameters for electric vehicles using a decoupling multiple forgetting factors recursive least squares method,” in CSEE Journal of Power and Energy Systems, vol. 6, no. 3, pp. 735-742, Sept. 2020. [Google Scholar]

[3] Li P, Wang H, Xing Z, et al. Joint estimation of SOC and SOH for lithium-ion batteries based on EKF multiple time scales[J]. 2020. [Google Scholar]

[4] D. A. Pola et al., “Particle-Filtering-Based Discharge Time Prognosis for Lithium-Ion Batteries With a Statistical Characterization of Use Profiles,” in IEEE Transactions on Reliability, vol. 64, no. 2, pp. 710720, June 2015. [Google Scholar]

[5] Xu Y, Hu M, Fu C, et al. State of Charge Estimation for Lithium-Ion Batteries Based on Temperature-Dependent Second-Order RC Model[J]. Electronics, 2019, 8(9):1012-. [Google Scholar]

[6] Tian Y, Lai R, Li X, et al. A combined method for state-of-charge estimation for lithium-ion batteries using a long short-term memory network and an adaptive cubature Kalman filter[J]. Applied Energy, 2020, 265. [Google Scholar]

[7] Wang Houlian, Zhou Gongbo. State of charge prediction of supercapacitors via combination of Kalman filtering and backpropagation neural network[J]. IET Electric Power Applications, 2018, 12(4). [Google Scholar]

[8] Aung H, Low K S, Soon J J. State-of-charge estimation using particle swarm optimization with inverse barrier constraint in a nanosatellite[C]// Industrial Electronics & Applications. IEEE, 2015. [Google Scholar]

[9] Chandra Shekar A, Anwar S. Real-Time State-of-Charge Estimation via Particle Swarm Optimization on a Lithium-Ion Electrochemical Cell Model[J]. Batteries, 2019, 5(1). [Google Scholar]

[10] Mao X, Song S, Ding F, et al. SOC Estimation of Lithium Battery Based on IPSO-BP Neural Network[J]. Journal of Physics: Conference Series, 2020, 1684(1):012152 (6pp). [Google Scholar]