[1] Jafari, M., Gauchia, A., Zhao, S., Zhang, K., Gauchia, L., "
Electric vehicle battery cycle aging evaluation in real-world daily driving and vehicle-to-grid services", IEEE Transactions on Transportation Electrification, Vol. 4, No. 1, pp. 122–134, 2018.
https://doi.org/10.1109/TTE.2017.2764320.
[2] Babaeiyazdi, I., Rezaei-Zare, A., Shokrzadeh, S., "
Transfer learning with deep neural network for capacity prediction of Li-Ion batteries using EIS measurement", IEEE Transactions on Transportation Electrification, Vol. 9, No. 1, pp. 886–895, 2023.
https://doi.org/10.1109/TTE.2022.3170230.
[3] Li, J., Deng, Z., Che, Y., Xie, Y., Hu, X., Teodorescu, R., "
Degradation pattern recognition and features extrapolation for battery capacity trajectory prediction", IEEE Transactions on Transportation Electrification, pp. 1–1, 2024.
https://doi.org/10.1109/TTE.2023.3336618.
[4] Wu, Y., Xue, Q., Shen, J., Lei, Z., Chen, Z., Liu, Y., "
State of health estimation for lithium-ion batteries based on healthy features and long short-term memory", IEEE Access, Vol. 8, pp. 28533–28547, 2020.
https://doi.org/10.1109/ACCESS.2020.2972344.
[5] Naguib, M., Kollmeyer, P., Emadi, A., "
Application of deep neural networks for lithium-ion battery surface temperature estimation under driving and fast charge conditions", IEEE Transactions on Transportation Electrification, Vol. 9, No. 1, pp. 1153-1165, 2023.
https://doi.org/10.1109/TTE.2022.3200225.
[6] Dai, H., Zhao, G., Lin, M., Wu, J., Zheng, G., "
A novel estimation method for the state of health of lithium-ion battery using prior knowledge-based neural network and Markov Chain", IEEE Transactions on Industrial Electronics, Vol. 66, No. 10, pp. 7706-7716, 2019.
https://doi.org/10.1109/TIE.2018.2880703.
[7] Gou, B., Xu, Y., Feng, X., "
An ensemble learning-based data-driven method for online state-of-health estimation of lithium-ion batteries", IEEE Transactions on Transportation Electrification, Vol. 7, No. 2, pp. 422-436, 2021.
https://doi.org/10.1109/TTE.2020.3029295.
[8] Shu, X., Chen, Z., Shen, J., Shen, S., Guo, F., Zhang, Y., Liu, Y., "
Ensemble learning and voltage reconstruction based state of health estimation for lithium-ion batteries with twenty random samplings", IEEE Transactions on Power Electronics, Vol. 38, No. 4, pp. 5538-5548, 2023.
https://doi.org/10.1109/TPEL.2023.3235872.
[9] Zhang, S., Yuan, W., Wang, Y., Cheng, S., Wang, J., "
Capacity estimation for lithium-ion batteries based on impedance spectral dynamics and deep gaussian process", IEEE Transactions on Power Electronics, Vol. 39, No. 8, pp. 10287-10298, 2024.
https://doi.org/10.1109/TPEL.2024.3395513.
[10] Liu, K., Li, Y., Hu, X., Lucu, M., Widanage, W.D., "
Gaussian process regression with automatic relevance determination kernel for calendar aging prediction of lithium-ion batteries", IEEE Transactions on Industrial Informatics, Vol. 16, No. 6, pp. 3767-3777, 2020.
https://doi.org/10.1109/TII.2019.2941747.
[11] Wei, Y., Wu, D., "
State of health and remaining useful life prediction of lithium-ion batteries with conditional graph convolutional network", Expert Systems with Applications, Vol. 238: 122041, 2024.
https://doi.org/10.1016/j.eswa.2023.122041.
[12] Wei, Y., Wu, D., "
Prediction of state of health and remaining useful life of lithium-ion battery using graph convolutional network with dual attention mechanisms", Reliability Engineering & System Safety, Vol. 230:108947, 2023.
https://doi.org/10.1016/j.ress.2022.108947.
[13] Bole, B., Kulkarni, C.S., Daigle, M., "
Adaptation of an electrochemistry-based li-ion battery model to account for deterioration observed under randomized use", Annual Conference of the PHM Society, Vol. 6, No. 1, 2014.
https://doi.org/10.36001/phmconf.2014.v6i1.2490.
[14] Palacín, M.R., "
Understanding ageing in li-ion batteries: a chemical issue", Chemical Society Reviews, Vol. 47, No. 13, pp. 4924–4933, 2018.
https://doi.org/10.1039/C7CS00889A.
[15] Danzer, M.A., Liebau, V., Maglia, F., "Aging of lithium-ion batteries for electric vehicles", Advances in Battery Technologies for Electric Vehicles, pp. 359–387, 2015. https://doi.org/10.1016/B978-1-78242-377-5.00014-5.
[16] Ma, S., Jiang, M., Tao, P., Song, C., Wu, J., Wang, J., Deng, T., Shang, W., "
Temperature effect and thermal impact in lithium-ion batteries: A review", Progress in Natural Science: Materials International, Vol. 28, No. 6, pp. 653–666, 2018.
https://doi.org/10.1016/j.pnsc.2018.11.002.
[17] Ren, D., Smith, K., Guo, D., Han, X., Feng, X., Lu, L., Ouyang, M., Li, J., "
Investigation of lithium plating-stripping process in li-ion batteries at low temperature using an electrochemical model", Journal of The Electrochemical Society, Vol. 165, No. 10, pp. A2167–A2178, 2018.
https://doi.org/10.1149/2.0661810jes.
[18] Ouyang, D., Weng, J., Chen, M., Liu, J., Wang, J., "
Experimental analysis on the degradation behavior of overdischarged lithium‐ion battery combined with the effect of high‐temperature environment", International Journal of Energy Research, Vol. 44, No. 1, pp. 229–241, 2019.
https://doi.org/10.1002/er.4898.
[19] Hu, X., Che, Y., Lin, X., Onori, S., "
Battery health prediction using fusion-based feature selection and machine learning", IEEE Transactions on Transportation Electrification, Vol. 7, No. 2, pp. 382–398, 2021.
https://doi.org/10.1109/TTE.2020.3017090.
[20] Liu, K., Hu, X., Wei, Z., Li, Y., Jiang, Y., "
Modified gaussian process regression models for cyclic capacity prediction of lithium-ion batteries", IEEE Transactions on Transportation Electrification, Vol. 5, No. 4., pp. 1225–1236, 2019.
https://doi.org/10.1109/TTE.2019.2944802.
[21] Choi, Y., Yun, J., Jang, P., "
A deep learning approach for state of health estimation of lithium-ion batteries based on differential thermal voltammetry", IEEE Access, Vol. 12, pp. 89921–89932, 2024.
https://doi.org/10.1109/ACCESS.2024.3419837.
[22] Mao, L., Wen, J. , Zhao, J., Qu, K., "
Online state-of-health estimation of lithium-ion batteries based on a novel equal voltage range sampling count number health indicator", IEEE Transactions on Transportation Electrification, Vol. 10, No. 1, pp. 2277–2292, 2024.
https://doi.org/10.1109/TTE.2023.3283572.
[23] Lin, C., Xu, J., Hou, J., Liang, Y., Mei, X., "
Ensemble method with heterogeneous models for battery state-of-health estimation", IEEE Transactions on Industrial Informatics, Vol. 19, No. 10, pp. 10160-10169, 2023.
https://doi.org/10.1109/TII.2023.3240920.
[24] Bamati, S., Chaoui, H., "
Developing an online data-driven state of health estimation of lithium-ion batteries under random sensor measurement unavailability", IEEE Transactions on Transportation Electrification, Vol. 9, No. 1, pp. 1128-1141, 2023.
https://doi.org/10.1109/TTE.2022.3199115.
[25] Ye, J., Xie, Q., Lin, M., Wu, J., "
A method for estimating the state of health of lithium-ion batteries based on physics-informed neural network", Energy, Vol. 294:130828, 2024.
https://doi.org/10.1016/j.energy.2024.130828.
[26] Cai, L., Lin, J., Liao, X., "
A data-driven method for state of health prediction of lithium-ion batteries in a unified framework", Journal of Energy Storage, Vol. 51: 104371, 2022.
https://doi.org/10.1016/j.est.2022.104371.
)