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Electric vehicles and battery energy storage are effective technical paths to achieve carbon neutrality, and lithium-ion batteries (LiBs) are very critical energy storage devices, which is of great significance to the goal. However, the battery’s characteristics of instant degradation seriously affect its long life and high safety applications. The aging mechanisms of LiBs are complex and multifaceted, strongly influenced by numerous interacting factors. Currently, the degree of capacity fading is commonly used to describe the aging of the battery, and the ratio of the maximum available capacity to the rated capacity of the battery is defined as the state of health (SOH). However, the aging or health of the battery should be multifaceted. To realize the multi-dimensional comprehensive evaluation of battery health status, a novel SOH estimation method driven by multidimensional aging characteristics is proposed through the improved single-particle model. The parameter identification and sensitivity analysis of the model were carried out during the whole cycle of life in a wide temperature environment. Nine aging characteristic parameters were obtained to describe the SOH. Combined with aging mechanisms, the current health status was evaluated from four aspects: capacity level, lithium-ion diffusion, electrochemical reaction, and power capacity. The proposed method can more comprehensively evaluate the aging characteristics of batteries, and the SOH estimation error is within 2%.
Xiong, R., Sun, Y., Wang, C., Tian, J., Chen, X., Li, H., Zhang, Q. (2023). A data-driven method for extracting aging features to accurately predict the battery health. Energy Storage Materials, 57: 460–470.
Zhou, X., Huang, J. (2020). Impedance-based diagnosis of lithium ion batteries: Identification of physical parameters using multi-output relevance vector regression. Journal of Energy Storage, 31: 101629.
Xiong, R., Tian, J., Shen, W., Lu, J., Sun, F. (2023). Semi-supervised estimation of capacity degradation for lithium ion batteries with electrochemical impedance spectroscopy. Journal of Energy Chemistry, 76: 404–413.
Wang, W., Wang, J., Tian, J., Lu, J., Xiong, R. (2021). Application of digital twin in smart battery management systems. Chinese Journal of Mechanical Engineering, 34: 57.
Wei, Z., Zhao, J., He, H., Ding, G., Cui, H., Liu, L. (2021). Future smart battery and management: Advanced sensing from external to embedded multi-dimensional measurement. Journal of Power Sources, 489: 229462.
Yu, Q., Dai, L., Xiong, R., Chen, Z., Zhang, X., Shen, W. (2022). Current sensor fault diagnosis method based on an improved equivalent circuit battery model. Applied Energy, 310: 118588.
Pang, B., Chen, L., Dong, Z. (2022). Data-driven degradation modeling and SOH prediction of Li-ion batteries. Energies, 15: 5580.
Sun, Y., Xiong, R., Meng, X., Deng, X., Li, H., Sun, F. (2024). Battery degradation evaluation based on impedance spectra using a limited number of voltage-capacity curves. eTransportation, 22: 100347.
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.
Fu, Y., Xu, J., Shi, M., Mei, X. (2022). A fast impedance calculation-based battery state-of-health estimation method. IEEE Transactions on Industrial Electronics, 69: 7019–7028.
Chen, X., Hu, Y., Li, S., Wang, Y., Li, D., Luo, C., Xue, X., Xu, F., Zhang, Z., Gong, Z., et al. (2021). State of health (SoH) estimation and degradation modes analysis of pouch NMC532/graphite Li-ion battery. Journal of Power Sources, 498: 229884.
Hu, X., Che, Y., Lin, X., Onori, S. (2021). Battery health prediction using fusion-based feature selection and machine learning. IEEE Transactions on Transportation Electrification, 7: 382–398.
Khaleghi Rahimian, S., Tang, Y. (2023). A practical data-driven battery state-of-health estimation for electric vehicles. IEEE Transactions on Industrial Electronics, 70: 1973–1982.
Wen, J., Zou, Q., Chen, C., Wei, Y. (2022). Linear correlation between state-of-health and incremental state-of-charge in Li-ion batteries and its application to SoH evaluation. Electrochimica Acta, 434: 141300.
Su, X., Sun, B., Wang, J., Zhang, W., Ma, S., He, X., Ruan, H. (2022). Fast capacity estimation for lithium-ion battery based on online identification of low-frequency electrochemical impedance spectroscopy and Gaussian process regression. Applied Energy, 322: 119516.
Feng, X., Weng, C., He, X., Han, X., Lu, L., Ren, D., Ouyang, M. (2019). Online state-of-health estimation for Li-ion battery using partial charging segment based on support vector machine. IEEE Transactions on Vehicular Technology, 68: 8583–8592.
Lee, G., Kwon, D., Lee, C. (2023). A convolutional neural network model for SOH estimation of Li-ion batteries with physical interpretability. Mechanical Systems and Signal Processing, 188: 110004.
Chen, C., Xiong, R., Shen, W. (2018). A lithium-ion battery-in-the-loop approach to test and validate multiscale dual H infinity filters for state-of-charge and capacity estimation. IEEE Transactions on Power Electronics, 33: 332–342.
Bi, Y., Yin, Y., Choe, S. Y. (2020). Online state of health and aging parameter estimation using a physics-based life model with a particle filter. Journal of Power Sources, 476: 228655.
Cen, Z., Kubiak, P. (2020). Lithium-ion battery SOC/SOH adaptive estimation via simplified single particle model. International Journal of Energy Research, 44: 12444–12459.
Yu, Q., Liu, Y., Long, S., Jin, X., Li, J., Shen, W. (2022). A branch current estimation and correction method for a parallel connected battery system based on dual BP neural networks. Green Energy and Intelligent Transportation, 1: 100029.
Fang, D., Wu, W., Li, J., Yuan, W., Liu, T., Dai, C., Wang, Z., Zhao, M. (2023). Performance simulation method and state of health estimation for lithium-ion batteries based on aging-effect coupling model. Green Energy and Intelligent Transportation, 2: 100082.
Feng, X., Zhang, Y., Xiong, R., Tang, A. (2023). Estimating battery state of health with 10-Min relaxation voltage across various charging states of charge. iEnergy, 2: 308–313.
Li, J., Wang, D., Deng, L., Cui, Z., Lyu, C., Wang, L., Pecht, M. (2020). Aging modes analysis and physical parameter identification based on a simplified electrochemical model for lithium-ion batteries. Journal of Energy Storage, 31: 101538.
Tian, J., Wang, Y., Chen, Z. (2021). An improved single particle model for lithium-ion batteries based on main stress factor compensation. Journal of Cleaner Production, 278: 123456.
Luerssen, C., Verbois, H., Gandhi, O., Reindl, T., Sekhar, C., Cheong, D. (2021). Global sensitivity and uncertainty analysis of the levelised cost of storage (LCOS) for solar-PV-powered cooling. Applied Energy, 286: 116533.
Xiong, R., Li, L., Li, Z., Yu, Q., Mu, H. (2018). An electrochemical model based degradation state identification method of Lithium-ion battery for all-climate electric vehicles application. Applied Energy, 219: 264–275.
Ge, C., Zheng, Y., Yu, Y. (2022). State of charge estimation of lithium-ion battery based on improved forgetting factor recursive least squares-extended Kalman filter joint algorithm. Journal of Energy Storage, 55: 105474.
Lin, M., Wu, D., Meng, J., Wang, W., Wu, J. (2023). Health prognosis for lithium-ion battery with multi-feature optimization. Energy, 264: 126307.
Li, S., Zhang, C., Du, J., Cong, X., Zhang, L., Jiang, Y., Wang, L. (2022). Fault diagnosis for lithium-ion batteries in electric vehicles based on signal decomposition and two-dimensional feature clustering. Green Energy and Intelligent Transportation, 1: 100009.
Leng, F., Wei, Z., Tan, C. M., Yazami, R. (2017). Hierarchical degradation processes in lithium-ion batteries during ageing. Electrochimica Acta, 256: 52–62.
Ramadesigan, V., Chen, K., Burns, N. A., Boovaragavan, V., Braatz, R. D., Subramanian, V. R. (2011). Parameter estimation and capacity fade analysis of lithium-ion batteries using reformulated models. Journal of the electrochemical society, 158: A1048.
Zhou, X., Huang, J., Pan, Z., Ouyang, M. (2019). Impedance characterization of lithium-ion batteries aging under high-temperature cycling: Importance of electrolyte-phase diffusion. Journal of Power Sources, 426: 216–222.
Gaberšček, M. (2021). Understanding Li-based battery materials via electrochemical impedance spectroscopy. Nature Communications, 12: 6513.
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