A novel state-of-health estimation for the lithium-ion battery using a convolutional neural network and transformer model

Citations

Cited by:

1. Li, Ziyang & Zhang, Xiangwen & Gao, Wei, 2024. "State of health estimation of lithium-ion battery during fast charging process based on BiLSTM-Transformer," Energy, Elsevier, vol. 311(C).
2. Sulaiman, Mohd Herwan & Mustaffa, Zuriani & Zakaria, Nor Farizan & Saari, Mohd Mawardi, 2023. "Using the evolutionary mating algorithm for optimizing deep learning parameters for battery state of charge estimation of electric vehicle," Energy, Elsevier, vol. 279(C).
3. Tao, Junjie & Wang, Shunli & Cao, Wen & Cui, Yixiu & Fernandez, Carlos & Guerrero, Josep M., 2024. "Innovative multiscale fusion – Antinoise extended long short-term memory neural network modeling for high precision state of health estimation of lithium-ion batteries," Energy, Elsevier, vol. 312(C).
4. Li, Xiaopeng & Zhao, Minghang & Zhong, Shisheng & Li, Junfu & Fu, Song & Yan, Zhiqi, 2024. "BMSFormer: An efficient deep learning model for online state-of-health estimation of lithium-ion batteries under high-frequency early SOC data with strong correlated single health indicator," Energy, Elsevier, vol. 313(C).
5. Chianese, Giovanni & Iannucci, Luigi & Veneri, Ottorino & Capasso, Clemente, 2025. "Real-time estimation of battery SoC through neural networks trained with model-based datasets: Experimental implementation and performance comparison," Applied Energy, Elsevier, vol. 389(C).
6. Hou, Guolian & Ye, Lingling & Huang, Ting & Huang, Congzhi, 2024. "Intelligent modeling of combined heat and power unit under full operating conditions via improved crossformer and precise sparrow search algorithm," Energy, Elsevier, vol. 308(C).
7. John Guirguis & Ryan Ahmed, 2024. "Transformer-Based Deep Learning Models for State of Charge and State of Health Estimation of Li-Ion Batteries: A Survey Study," Energies, MDPI, vol. 17(14), pages 1-13, July.
8. You, Yuqiang & Lin, Mingqiang & Meng, Jinhao & Wu, Ji & Wang, Wei, 2024. "Multi-scenario surface temperature estimation in lithium-ion batteries with transfer learning and LGT augmentation," Energy, Elsevier, vol. 304(C).
9. Chen, Bingyang & Wang, Kai & Xu, Degang & Xia, Juan & Fan, Lulu & Zhou, Jiehan, 2024. "Global–local attention network and value-informed federated strategy for predicting power battery state of health," Energy, Elsevier, vol. 313(C).
10. Zhao, Jingyuan & Wang, Zhenghong & Wu, Yuyan & Burke, Andrew F., 2025. "Predictive pretrained transformer (PPT) for real-time battery health diagnostics," Applied Energy, Elsevier, vol. 377(PD).
11. Zhang, Hao & Gao, Jingyi & Kang, Le & Zhang, Yi & Wang, Licheng & Wang, Kai, 2023. "State of health estimation of lithium-ion batteries based on modified flower pollination algorithm-temporal convolutional network," Energy, Elsevier, vol. 283(C).
12. Xu, Huanwei & Wu, Lingfeng & Xiong, Shizhe & Li, Wei & Garg, Akhil & Gao, Liang, 2023. "An improved CNN-LSTM model-based state-of-health estimation approach for lithium-ion batteries," Energy, Elsevier, vol. 276(C).
13. Xue, Jingsong & Ma, Wentao & Feng, Xiaoyang & Guo, Peng & Guo, Yaosong & Hu, Xianzhi & Chen, Badong, 2023. "Stacking integrated learning model via ELM and GRU with mixture correntropy loss for robust state of health estimation of lithium-ion batteries," Energy, Elsevier, vol. 284(C).
14. Cai, Nian & Que, Xiaoping & Zhang, Xu & Feng, Weiguo & Zhou, Yinghong, 2024. "A deep learning framework for the joint prediction of the SOH and RUL of lithium-ion batteries based on bimodal images," Energy, Elsevier, vol. 302(C).
15. Deng, Shuhan & Chen, Zhuyun & Lan, Hao & Yue, Ke & Huang, Zhicong & Li, Weihua, 2024. "Remaining useful life prediction with spatio-temporal graph transform and weakly supervised adversarial network: An application in power components," Energy, Elsevier, vol. 313(C).
16. Zhao, Wanjie & Ding, Wei & Zhang, Shujing & Zhang, Zhen, 2024. "Enhancing lithium-ion battery lifespan early prediction using a multi-branch vision transformer model," Energy, Elsevier, vol. 302(C).
17. Chenyuan Liu & Heng Li & Kexin Li & Yue Wu & Baogang Lv, 2025. "Deep Learning for State of Health Estimation of Lithium-Ion Batteries in Electric Vehicles: A Systematic Review," Energies, MDPI, vol. 18(6), pages 1-20, March.
18. Han, Tengfei & Lu, Zhiqiang & Yu, Jianbo, 2025. "Dynamic weighted federated contrastive self-supervised learning for state-of-health estimation of Lithium-ion battery with insufficient labeled samples," Applied Energy, Elsevier, vol. 383(C).
19. Zhao, Xiaoyu & Wang, Zuolu & Miao, Haiyan & Yang, Wenxian & Gu, Fengshou & Ball, Andrew D., 2024. "A label-free battery state of health estimation method based on adversarial multi-domain adaptation network and relaxation voltage," Energy, Elsevier, vol. 308(C).
20. Dou, Bowen & Hou, Shujuan & Li, Hai & Zhao, Yanpeng & Fan, Yue & Sun, Lei & Chen, Hao-sen, 2025. "Cross-domain state of health estimation for lithium-ion battery based on latent space consistency using few-unlabeled data," Energy, Elsevier, vol. 320(C).
21. Soo, Yin-Yi & Wang, Yujie & Xiang, Haoxiang & Chen, Zonghai, 2024. "Machine learning based battery pack health prediction using real-world data," Energy, Elsevier, vol. 308(C).
22. Zhang, Ran & Ji, ChunHui & Zhou, Xing & Liu, Tianyu & Jin, Guang & Pan, Zhengqiang & Liu, Yajie, 2024. "Capacity estimation of lithium-ion batteries with uncertainty quantification based on temporal convolutional network and Gaussian process regression," Energy, Elsevier, vol. 297(C).
23. Xiong, Ran & Wang, Shunli & Huang, Qi & Yu, Chunmei & Fernandez, Carlos & Xiao, Wei & Jia, Jun & Guerrero, Josep M., 2024. "Improved cooperative competitive particle swarm optimization and nonlinear coefficient temperature decreasing simulated annealing-back propagation methods for state of health estimation of energy stor," Energy, Elsevier, vol. 292(C).
24. Jia, Chenyu & Tian, Yukai & Shi, Yuanhao & Jia, Jianfang & Wen, Jie & Zeng, Jianchao, 2023. "State of health prediction of lithium-ion batteries based on bidirectional gated recurrent unit and transformer," Energy, Elsevier, vol. 285(C).
25. Wang, Tong & Wu, Yan & Zhu, Keming & Cen, Jianmeng & Wang, Shaohong & Huang, Yuqi, 2025. "Deep learning and polarization equilibrium based state of health estimation for lithium-ion battery using partial charging data," Energy, Elsevier, vol. 317(C).
26. Sun, Wenjie & Wu, Chengke & Xie, Chengde & Wang, Xikang & Guo, Yuanjun & Tang, Yongbing & Zhang, Yanhui & Li, Kang & Du, Guanhao & Yang, Zhile & Yao, Wenjiao, 2025. "Fine-tuning enables state of health estimation for lithium-ion batteries via a time series foundation model," Energy, Elsevier, vol. 318(C).
27. Tang, Telu & Yang, Xiangguo & Li, Muheng & Li, Xin & Huang, Hai & Guan, Cong & Huang, Jiangfan & Wang, Yufan & Zhou, Chaobin, 2025. "Deep learning model-based real-time state-of-health estimation of lithium-ion batteries under dynamic operating conditions," Energy, Elsevier, vol. 317(C).
28. Chen, Kui & Luo, Yang & Long, Zhou & Li, Yang & Nie, Guangbo & Liu, Kai & Xin, Dongli & Gao, Guoqiang & Wu, Guangning, 2025. "Big data-driven prognostics and health management of lithium-ion batteries:A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 214(C).
29. Singh, S. & Budarapu, P.R., 2024. "Deep machine learning approaches for battery health monitoring," Energy, Elsevier, vol. 300(C).
30. Meng, Huixing & Geng, Mengyao & Han, Te, 2023. "Long short-term memory network with Bayesian optimization for health prognostics of lithium-ion batteries based on partial incremental capacity analysis," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
31. Wu, Rui & Tian, Jinpeng & Yao, Jiachi & Han, Te & Hu, Chunsheng, 2025. "Confidence-aware quantile Transformer for reliable degradation prediction of battery energy storage systems," Reliability Engineering and System Safety, Elsevier, vol. 260(C).
32. Chen, Bingyang & Zeng, Xingjie & Zhang, Weishan & Fan, Lulu & Cao, Shaohua & Zhou, Jiehan, 2023. "Knowledge sharing-based multi-block federated learning for few-shot oil layer identification," Energy, Elsevier, vol. 283(C).
33. Lai, Rucong & Wang, Jie & Tian, Yong & Tian, Jindong, 2024. "FedCBE: A federated-learning-based collaborative battery estimation system with non-IID data," Applied Energy, Elsevier, vol. 368(C).