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Prognostics of battery capacity based on charging data and data-driven methods for on-road vehicles

Citations

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  1. Sun, Shukai & Che, Liang & Zhao, Ruifeng & Chen, Yizhe & Li, Ming, 2025. "Multi-task learning and voltage reconstruction-based battery degradation prediction under variable operating conditions of energy storage applications," Energy, Elsevier, vol. 317(C).
  2. Yu, Jingmei & Cai, Yaoyang & Yang, Xinle & Li, Lei, 2025. "A parallel LTCN-PHA network for remaining useful life prediction of lithium-ion batteries," Energy, Elsevier, vol. 337(C).
  3. Ji, Shanling & Zhang, Zhisheng & Stein, Helge S. & Zhu, Jianxiong, 2025. "Flexible health prognosis of battery nonlinear aging using temporal transfer learning," Applied Energy, Elsevier, vol. 377(PD).
  4. Joselyn Stephane Menye & Mamadou-Baïlo Camara & Brayima Dakyo, 2025. "Lithium Battery Degradation and Failure Mechanisms: A State-of-the-Art Review," Energies, MDPI, vol. 18(2), pages 1-43, January.
  5. Jin, Haiyan & Ru, Rui & Cai, Lei & Meng, Jinhao & Wang, Bin & Peng, Jichang & Yang, Shengxiang, 2025. "A synthetic data generation method and evolutionary transformer model for degradation trajectory prediction in lithium-ion batteries," Applied Energy, Elsevier, vol. 377(PD).
  6. Zhang, Dayu & Wang, Zhenpo & Liu, Peng & She, Chengqi & Wang, Qiushi & Zhou, Litao & Qin, Zian, 2024. "A multi-step fast charging-based battery capacity estimation framework of real-world electric vehicles," Energy, Elsevier, vol. 294(C).
  7. Zhao, Xinwei & Liu, Yonggui & Xiao, Bin, 2025. "Enhanced prediction for battery aging capacity using an efficient temporal convolutional network," Energy, Elsevier, vol. 320(C).
  8. Xiong, Xin & Wang, Yujie & Jiang, Cong & Sun, Zhendong & Chen, Zonghai, 2025. "Multi-physics data and model feature fusion for lithium-ion battery capacity estimation by transformer-based deep learning," Energy, Elsevier, vol. 335(C).
  9. Chen, Xiang & Wang, Xingxing & Deng, Yelin, 2025. "Federated learning-based prediction of electric vehicle battery pack capacity using time-domain and frequency-domain feature extraction," Energy, Elsevier, vol. 319(C).
  10. Liu, Jia & Li, Chang & Liu, Hongao & Che, Yunhong & Li, Jinwen & Xie, Yang & Wu, Ranglei & Yang, Yalian & Hu, Xiaosong, 2025. "Rapid battery pack state of health estimation for electric vehicles considering polarization features in multi-stage charging," Energy, Elsevier, vol. 335(C).
  11. Chen, Hongxing & She, Chengqi & Yue, Wenhui & Bin, Guangfu & Tang, Jinjun & Zhang, Lei, 2025. "Battery SOH assessment for real-world EVs based on discharging process characteristic and ensemble learning approach," Energy, Elsevier, vol. 336(C).
  12. Zhang, Kui & Rayeem, Safwat Khair & Mai, Weijie & Tian, Jinpeng & Ma, Liang & Zhang, Tieling & Chung, C.Y., 2025. "Enhancing battery health estimation using incomplete charging curves and knowledge-guided deep learning," Reliability Engineering and System Safety, Elsevier, vol. 262(C).
  13. Xiong, Xin & Wang, Yujie & Jiang, Cong & Xiang, Haoxiang & Chen, Zonghai, 2025. "A control-oriented electro-thermal-mechanical modeling method for lithium-ion batteries considering aging effects," Applied Energy, Elsevier, vol. 400(C).
  14. Wang, Cong & Chen, Yunxia, 2024. "Unsupervised dynamic prognostics for abnormal degradation of lithium-ion battery," Applied Energy, Elsevier, vol. 365(C).
  15. Zhou, Shirun & Wang, Qiqi & Yang, Fangfang, 2025. "Early lifetime prediction of lithium-ion batteries based on classical image encoding methods," Energy, Elsevier, vol. 336(C).
  16. Fu, Shiyi & Fan, Hongtao & Jin, Zhaorui & Ji, Fan & Tao, Yulin & Dong, Yachao & Chen, Xunyuan & Shao, Minghao & Yuan, Shuyu & Wang, Yu & Sun, Yaojie, 2026. "Recent progress in state of health estimation for lithium-ion batteries: From laboratory to practical application," Renewable and Sustainable Energy Reviews, Elsevier, vol. 226(PB).
  17. Xiong, Xin & Wang, Yujie & Jiang, Cong & Zhang, Xingchen & Xiang, Haoxiang & Chen, Zonghai, 2024. "End-to-end deep learning powered battery state of health estimation considering multi-neighboring incomplete charging data," Energy, Elsevier, vol. 292(C).
  18. Wang, Zhen & Zhao, Li & Li, Yiding & Wang, Wenwei, 2025. "A data-efficient method for lithium-ion battery state-of-health estimation based on real-time frequent itemset image encoding," Applied Energy, Elsevier, vol. 398(C).
  19. Wu, Xiankui & Li, Penghua & Zhang, Yangming & Zhou, Jingjing & Xiang, Sheng & Hou, Jie & Wang, Guodong & Dustdar, Schahram, 2025. "Knowledge distillation-based lightweight deformable network for remaining useful life prognostics of vehicle power battery," Energy, Elsevier, vol. 337(C).
  20. Ning Chen & Yihang Xie & Yuanhao Cheng & Huaiqing Wang & Yu Zhou & Xu Zhao & Jiayao Chen & Chunhua Yang, 2025. "A Review of Cross-Scale State Estimation Techniques for Power Batteries in Electric Vehicles: Evolution from Single-State to Multi-State Cooperative Estimation," Energies, MDPI, vol. 18(19), pages 1-27, October.
  21. Wen, Jie & Jia, Chenyu & Xia, Guangshu, 2025. "State of health prediction of lithium-ion batteries for driving conditions based on full parameter domain sparrow search algorithm and dual-module bidirectional gated recurrent unit," Energy, Elsevier, vol. 335(C).
  22. Dwivedi, Shalini & Akula, Aparna & Pecht, Michael, 2024. "Predictive analytics for prolonging lithium-ion battery lifespan through informed storage conditions," Energy, Elsevier, vol. 308(C).
  23. He, Rui & Peng, Tian & Zhang, Xinyu & Chen, Zhigang & Yao, Junhao & Nazir, Muhammad Shahzad & Zhang, Chu, 2026. "A novel hybrid model for state of health prediction in lithium batteries based on non-stationary transformers optimized by tree-structured Parzen estimator considering health factors," Applied Energy, Elsevier, vol. 402(PC).
  24. Oyewole, Isaiah & Hassanieh, Wael & Chelbi, Meriam & Chehade, Abdallah, 2025. "Uncertainty-aware deep learning with physics-informed bayesian sampling for lithium-ion battery prognostics," Applied Energy, Elsevier, vol. 402(PA).
  25. Chen, Baoliang & Liu, Yonggui, 2025. "Data driven-based health prognostics and charge estimation for lithium-ion batteries under varying discharging patterns," Energy, Elsevier, vol. 335(C).
  26. Tao, Siyi & Zhu, Jiangong & Li, Yuan & Chen, Siyang & Wang, Xiuwu & Wang, Xueyuan & Jiang, Bo & Chang, Wei & Wei, Xuezhe & Dai, Haifeng, 2025. "State-of-health estimation for EV battery packs via incremental capacity curves and S-transform," Applied Energy, Elsevier, vol. 397(C).
  27. Catherine Rincón-Maya & Fernando Guevara-Carazas & Freddy Hernández-Barajas & Carmen Patino-Rodriguez & Olga Usuga-Manco, 2023. "Remaining Useful Life Prediction of Lithium-Ion Battery Using ICC-CNN-LSTM Methodology," Energies, MDPI, vol. 16(20), pages 1-20, October.
  28. Gao, Kai & Li, Qi & Hu, Lin & Huang, Jing & Li, Heng & Wu, Yue, 2026. "Physical informed neural network for SOH estimation of lithium-ion battery with electrochemical mechanism," Energy, Elsevier, vol. 342(C).
  29. Hongao Liu & Chang Li & Xiaosong Hu & Jinwen Li & Kai Zhang & Yang Xie & Ranglei Wu & Ziyou Song, 2025. "Multi-modal framework for battery state of health evaluation using open-source electric vehicle data," Nature Communications, Nature, vol. 16(1), pages 1-12, December.
  30. Yuan, Zhu & Deng, Zhongwei & He, Yvxin & Ning, Zhansheng & Liu, Jincheng, 2025. "Multi-step prediction of battery state of health based on self-supervised pre-training and transfer learning using the xPatch model," Energy, Elsevier, vol. 341(C).
  31. Li, Yan & He, Zhaoxia & Ye, Min & Wang, Qiao & Lian, Gaoqi & Sun, Yiding & Wei, Meng, 2025. "A semi-supervised learning strategy for lithium-ion battery capacity estimation with limited impedance data," Energy, Elsevier, vol. 319(C).
  32. Zhou, Yu & Liu, Shenyan & Kou, Gang & Kang, Fengming, 2025. "Degradation variation pattern mining based on BEAST time series decomposition integrated functional principal component analysis," Reliability Engineering and System Safety, Elsevier, vol. 259(C).
  33. Zhang, Zhaosheng & Sun, Shoukun & Wang, Zhenpo & Lin, Ni, 2025. "Battery retirement state prediction method based on real-world data and the TabNet model," Energy, Elsevier, vol. 334(C).
  34. Huakun Huang & Dingrong Dai & Longtao Guo & Sihui Xue & Huijun Wu, 2023. "AI and Big Data-Empowered Low-Carbon Buildings: Challenges and Prospects," Sustainability, MDPI, vol. 15(16), pages 1-21, August.
  35. Chen, Guanxu & Yang, Fangfang & Peng, Weiwen & Fan, Yuqian & Lyu, Ximin, 2024. "State-of-health estimation for lithium-ion batteries based on Kullback–Leibler divergence and a retentive network," Applied Energy, Elsevier, vol. 376(PB).
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