Computer Modeling and Parameter Estimation of Power Battery Performance for New Energy Vehicles under Hot Working Conditions

Authors

DOI:

https://doi.org/10.4108/ew.7209

Keywords:

Kalman filter, SOE, New Energy Vehicle, Power Battery, Parameter Estimation

Abstract

With the aggravation of environmental pollution problems and the reduction of non-renewable energy sources such as oil, new energy vehicles have gradually become the focus of attention, and the application of their power batteries has become more and more widespread. The state of energy (SOE) of the power battery is an important basis for energy scheduling. Therefore, the study used computer technology to develop an analogous model of the power battery and evaluated its properties at various temperatures in order to precisely analyze the performance of the battery under thermal conditions. At the same time, to address the limitations in parameter estimation, the study uses the improved Kalman filter (KF) algorithm to optimize it. The results revealed that the estimation errors of the improved cubature Kalman filter (CKF) algorithm were reduced by 0.52%, 2.91% and 3.10% compared with the traditional CKF algorithm, EKF algorithm and UKF algorithm, respectively. In summary, the research on computer modeling and parameter estimation of the performance of new energy vehicle power batteries under hot working conditions provides important support and reference for the efficient operation and safety of new energy power batteries under hot working conditions.

Downloads

Download data is not yet available.

References

[1] A. Beaucamp, M. Muddasar, I. S. Amiinu, M. M. Leite, M. Culebras, K. Latha, and M. N. Collins, "Lignin for energy applications–state of the art, life cycle, technoeconomic analysis and future trends," Green Chemistry, vol. 24, no. 21, pp. 8193-8226, 2022. doi: 10.1039/D2GC02724K.

[2] K. Szulecki and I. Overland, "Russian nuclear energy diplomacy and its implications for energy security in the context of the war in Ukraine," Nature Energy, vol. 8, no. 4, pp. 413-421, 2023. doi: 10.1038/s41560-023-01228-5.

[3] J. Xu, X. Cai, S. Cai, Y. Shao, C. Hu, S. Lu, and S. Ding, "High-energy lithium-ion batteries: recent progress and a promising future in applications," Energy & Environmental Materials, vol. 6, no. 5, pp. 12450-12459, 2023. doi: 10.1002/eem2.12450.

[4] C. D. Quilty, D. Wu, W. Li, D. C. Bock, L. Wang, L. M. Housel, and E. S. Takeuchi, "Electron and ion transport in lithium and lithium-ion battery negative and positive composite electrodes," Chemical Reviews, vol. 123, no. 4, pp. 1327-1363, 2023. doi: 10.1021/acs.chemrev.2c00214.

[5] M. Zhang, Y. Liu, D. Li, X. Cui, L. Wang, L. Li, and K. Wang, "Electrochemical impedance spectroscopy: A new chapter in the fast and accurate estimation of the state of health for lithium-ion batteries," Energies, vol. 16, no. 4, pp. 1599-1606, 2023. doi: 10.3390/en16041599.

[6] J. Xu, J. Zhang, T. P. Pollard, Q. Li, S. Tan, S. Hou, and C. Wang, "Electrolyte design for Li-ion batteries under extreme operating conditions," Nature, vol. 614, no. 7949, pp. 694-700, 2023. doi: 10.1038/s41586-022-05627-8.

[7] Y. Song, L. Wang, L. Sheng, D. Ren, H. Liang, Y. Li, and X. He, "The significance of mitigating crosstalk in lithium-ion batteries: a review," Energy & Environmental Science, vol. 16, no. 5, pp. 1943-1963, 2023. doi: 10.1039/D3EE00441D.

[8] Z. Huang, Z. Deng, Y. Zhong, M. Xu, S. Li, X. Liu, and Y. Huang, "Progress and challenges of prelithiation technology for lithium-ion battery," Carbon Energy, vol. 4, no. 6, pp. 1107-1132, 2022. doi: 10.1002/cey2.256.

[9] R. I. Alfian, A. Ma'arif, and S. Sunardi, "Noise reduction in the accelerometer and gyroscope sensor with the Kalman filter algorithm," Journal of Robotics and Control (JRC), vol. 2, no. 3, pp. 180-189, 2021. doi: 10.18196/jrc.2375.

[10] Y. Luo, P. Qi, Y. Kan, J. Huang, H. Huang, J. Luo, and S. Zhao, "State of charge estimation method based on the extended Kalman filter algorithm with consideration of time-varying battery parameters," International Journal of Energy Research, vol. 44, no. 13, pp. 10538-10550, 2020. doi: 10.1002/er.5687.

[11] W. Xu, S. Wang, C. Jiang, C. Fernandez, C. Yu, Y. Fan, and W. Cao, "A novel adaptive dual extended Kalman filtering algorithm for the Li-ion battery state of charge and state of health co-estimation," International Journal of Energy Research, vol. 45, no. 10, pp. 14592-14602, 2021. doi: 10.1002/er.6719.

[12] D. Feng, C. Wang, C. He, Y. Zhuang, and X. G. **a, "Kalman-filter-based integration of IMU and UWB for high-accuracy indoor positioning and navigation," IEEE Internet of Things Journal, vol. 7, no. 4, pp. 3133-3146, 2020. doi: 10.1109/JIOT.2020.2965115.

[13] J. Lv, B. Jiang, and X. Wang, "Estimation of the state of charge of lithium batteries based on adaptive unscented Kalman filter algorithm," Electronics, vol. 9, no. 9, pp. 1425-1431, 2020. doi: 10.3390/electronics9091425.

[14] H. Li, H. Qi, H. Cao, and L. Yuan, "Industrial policy and technological innovation of new energy vehicle industry in China," Energies, vol. 15, no. 24, pp. 9264-9269, 2022. doi: 10.3390/en15249264.

[15] I. S. Sorlei, N. Bizon, P. Thounthong, M. Varlam, E. Carcadea, M. Culcer, and M. Raceanu, "Fuel cell electric vehicles—A brief review of current topologies and energy management strategies," Energies, vol. 14, no. 1, pp. 252-258, 2021. doi: 10.3390/en14010252.

[16] A. König, L. Nicoletti, D. Schröder, S. Wolff, A. Waclaw, and M. Lienkamp, "An overview of parameter and cost for battery electric vehicles," World Electric Vehicle Journal, vol. 12, no. 1, pp. 21-26, 2021. doi: 10.3390/wevj12010021.

[17] Y. Zhu, X. Li, Q. Liu, S. Li, and Y. Xu, "A comprehensive review of energy management strategies for hybrid electric vehicles," Mechanical Sciences, vol. 13, no. 1, pp. 147-188, 2022. doi: 10.5194/ms-13-147-2022.

[18] H. Farhadi Gharibeh and M. Farrokhifar, "Online multi-level energy management strategy based on rule-based and optimization-based approaches for fuel cell hybrid electric vehicles," Applied Sciences, vol. 11, no. 9, pp. 3849-3856, 2021. doi: 10.3390/app11093849.

[19] T. Deng, P. Tang, Z. Su, and Y. Luo, "Systematic design and optimization method of multimode hybrid electric vehicles based on equivalent tree graph," IEEE Transactions on Power Electronics, vol. 35, no. 12, pp. 13465-13474, 2020. doi: 10.1109/TPEL.2020.2990209.

[20] G. G. Farivar, W. Manalastas, H. D. Tafti, S. Ceballos, A. Sanchez-Ruiz, E. C. Lovell, and J. Pou, "Grid-connected energy storage systems: State-of-the-art and emerging technologies," Proceedings of the IEEE, vol. 111, no. 4, pp. 397-420, 2022. doi: 10.1109/JPROC.2022.3183289.

Downloads

Published

06-09-2024

How to Cite

1.
Zhang H. Computer Modeling and Parameter Estimation of Power Battery Performance for New Energy Vehicles under Hot Working Conditions. EAI Endorsed Trans Energy Web [Internet]. 2024 Sep. 6 [cited 2024 Oct. 10];11. Available from: https://publications.eai.eu/index.php/ew/article/view/7209