With the continuous advancement of artificial intelligence technology, its application in lithium battery health prediction has been increasingly widespread. From the vantage point of the variations in the number of patent applications and focal points, and by leveraging the IPC classification, an analysis is carried out on the macro-distribution of patent applications and the burgeoning growth of relevant applications. Through the text data within the patents, the branches and technical trajectories of artificial intelligence based lithium battery health prediction technology are gradually becoming more distinct. This approach enables the prediction of technological development trends and challenges in this field, thereby assisting research institutions and enterprises in closely tracking the technological progress in a specific domain.

Xiaofen Fang, Qin Fang, Qichang Zhuo, Tao Li. Analysis of Li-ion Batteries Health Estimation Technology Route Based on Patent Big Data. Academic Journal of Energy (2025), Vol. 4, Issue 1: 1-10. https://doi.org/10.38007/RE.2025.040101.

[1] Wang, Yuxing, et al. "Lithium and lithium-ion batteries for applications in microelectronic devices: A review." Journal of Power Sources 286 (2015): 330-345.

[2] Patraboy, Timothy J., Michael D. Farrington, and George J. Donaldson. "Reliability of lithium batteries in search and rescue beacons." Journal of power sources 65.1-2 (1997): 93-99.

[3] Zheng, Yuejiu, et al. "A capacity prediction framework for lithium-ion batteries using fusion prediction of empirical model and data-driven method." Energy 237 (2021): 121556.

[4] Sun, Fengchun, et al. "Model-based dynamic multi-parameter method for peak power estimation of lithium-ion batteries." Applied Energy 96 (2012): 378-386.

[5] Ma, Bo, Wei Guo, and Zhaojun Steven Li. "Prediction of lithium-ion battery capacity based on the arma method." 2020 Global Reliability and Prognostics and Health Management (PHM-Shanghai). IEEE, 2020.

[6] Xu, Huanwei, et al. "An improved CNN-LSTM model-based state-of-health estimation approach for lithium-ion batteries." Energy 276 (2023): 127585.

[7] Gu, Xinyu, et al. "A time-series Wasserstein GAN method for state-of-charge estimation of lithium-ion batteries." Journal of Power Sources 581 (2023): 233472.

[8] Fang X, Chen Z, Li J Q. Optimized multi-head self-attention mechanism for SOH prediction in lithium-ion battery energy storage systems within microgrids[J]. Electric Power Systems Research, 2025, 238: 111182.

[9] Xu, Liqianyun, et al. "Data-driven-aided strategies in battery lifecycle management: prediction, monitoring, and optimization." Energy Storage Materials 59 (2023): 102785.

[10] Aaldering, Lukas Jan, and Chie Hoon Song. "Tracing the technological development trajectory in post-lithium-ion battery technologies: A patent-based approach." Journal of Cleaner Production 241 (2019): 118343.

[11] Wang, Huilong, et al. "Understanding technological innovation and evolution of energy storage in China: Spatial differentiation of innovations in lithium-ion battery industry." Journal of Energy Storage 66 (2023): 107307.

[12] Xiong, Qian, and Tang, Wenzhe. "Text mining-based patent analysis on informatization for construction management of hydraulic engineering projects." Journal of Tsinghua University (Science and Technology) 63.2 (2023): 223-232.