Multivariate stacked bidirectional long short term memory for lithium-ion battery health management

Citations

Cited by:

1. Liu, Mingyuan & He, Wei & Ma, Ning & Zhu, Hailong & Zhou, Guohui, 2025. "A new reliability health status assessment model for complex systems based on belief rule base," Reliability Engineering and System Safety, Elsevier, vol. 254(PA).
2. Li, Tingkai & Liu, Jinqiang & Thelen, Adam & Mishra, Ankush Kumar & Yang, Xiao-Guang & Wang, Zhaoyu & Hu, Chao, 2025. "Coupling a capacity fade model with machine learning for early prediction of the battery capacity trajectory," Applied Energy, Elsevier, vol. 389(C).
3. Meng, Jinhao & You, Yuqiang & Lin, Mingqiang & Wu, Ji & Song, Zhengxiang, 2024. "Multi-scenarios transferable learning framework with few-shot for early lithium-ion battery lifespan trajectory prediction," Energy, Elsevier, vol. 286(C).
4. Li, Xuanlin & Hu, Yawei & Wang, Hang & Liu, Yongbin & Liu, Xianzeng & Lu, Huitian, 2025. "A closed-form continuous-depth neural-based hybrid difference features re-representation network for RUL prediction," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
5. Li, Naipeng & Wang, Mingyang & Lei, Yaguo & Yang, Bin & Li, Xiang & Si, Xiaosheng, 2025. "Remaining useful life prediction of lithium-ion battery with nonparametric degradation modeling and incomplete data," Reliability Engineering and System Safety, Elsevier, vol. 256(C).
6. Zhang, Chaolong & Luo, Laijin & Yang, Zhong & Du, Bolun & Zhou, Ziheng & Wu, Ji & Chen, Liping, 2024. "Flexible method for estimating the state of health of lithium-ion batteries using partial charging segments," Energy, Elsevier, vol. 295(C).
7. Zhang, Jiusi & Tian, Jilun & Yan, Pengfei & Wu, Shimeng & Luo, Hao & Yin, Shen, 2024. "Multi-hop graph pooling adversarial network for cross-domain remaining useful life prediction: A distributed federated learning perspective," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
8. Wang, Shunli & Fan, Yongcun & Jin, Siyu & Takyi-Aninakwa, Paul & Fernandez, Carlos, 2023. "Improved anti-noise adaptive long short-term memory neural network modeling for the robust remaining useful life prediction of lithium-ion batteries," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
9. Saravanakumar Venkatesan & Yongyun Cho, 2024. "Multi-Timeframe Forecasting Using Deep Learning Models for Solar Energy Efficiency in Smart Agriculture," Energies, MDPI, vol. 17(17), pages 1-29, August.
10. Huang, Zhelin & Ma, Zhihua, 2024. "Remaining useful life prediction of lithium-ion batteries based on autoregression with exogenous variables model," Reliability Engineering and System Safety, Elsevier, vol. 252(C).
11. Wang, Run-Zi & Gu, Hang-Hang & Liu, Yu & Miura, Hideo & Zhang, Xian-Cheng & Tu, Shan-Tung, 2023. "Surrogate-modeling-assisted creep-fatigue reliability assessment in a low-pressure turbine disc considering multi-source uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 240(C).
12. Xie, Lin & Ustolin, Federico & Lundteigen, Mary Ann & Li, Tian & Liu, Yiliu, 2022. "Performance analysis of safety barriers against cascading failures in a battery pack," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
13. Song, Dengwei & Cheng, Yujie & Zhou, An & Lu, Chen & Chong, Jin & Ma, Jian, 2024. "Remaining useful life prediction and cycle life test optimization for multiple-formula battery: A method based on multi-source transfer learning," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
14. Zhou, Danhua & Wang, Bin & Zhu, Chao & Zhou, Fang & Wu, Hong, 2023. "A light-weight feature extractor for lithium-ion battery health prognosis," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
15. Wei, Yupeng & Wu, Dazhong, 2023. "Prediction of state of health and remaining useful life of lithium-ion battery using graph convolutional network with dual attention mechanisms," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
16. Li, Butong & Zhu, Junjie & Zhao, Xufeng, 2025. "A prior knowledge-guided predictive framework for LCF life and its implementation in shaft-like components under multiaxial loading," Reliability Engineering and System Safety, Elsevier, vol. 260(C).
17. Wang, Fujin & Zhao, Zhibin & Zhai, Zhi & Shang, Zuogang & Yan, Ruqiang & Chen, Xuefeng, 2023. "Explainability-driven model improvement for SOH estimation of lithium-ion battery," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
18. Xu, Xiaodong & Tang, Shengjin & Han, Xuebing & Lu, Languang & Wu, Yu & Yu, Chuanqiang & Sun, Xiaoyan & Xie, Jian & Feng, Xuning & Ouyang, Minggao, 2023. "Fast capacity prediction of lithium-ion batteries using aging mechanism-informed bidirectional long short-term memory network," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
19. Zhan, Yuling & Kong, Ziqian & Wang, Ziqi & Jin, Xiaohang & Xu, Zhengguo, 2024. "Remaining useful life prediction with uncertainty quantification based on multi-distribution fusion structure," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
20. Ma, Yan & Shan, Ce & Gao, Jinwu & Chen, Hong, 2023. "Multiple health indicators fusion-based health prognostic for lithium-ion battery using transfer learning and hybrid deep learning method," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
21. Yin, Xiuxian & He, Wei & Cao, You & Ma, Ning & Zhou, Guohui & Li, Hongyu, 2024. "A new health state assessment method based on interpretable belief rule base with bimetric balance," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
22. Wang, Shaochen & Tian, Wende & Li, Chuankun & Cui, Zhe & Liu, Bin, 2023. "Mechanism-based deep learning for tray efficiency soft-sensing in distillation process," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
23. 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).
24. Giovane Ronei Sylvestrin & Joylan Nunes Maciel & Marcio Luís Munhoz Amorim & João Paulo Carmo & José A. Afonso & Sérgio F. Lopes & Oswaldo Hideo Ando Junior, 2025. "State of the Art in Electric Batteries’ State-of-Health (SoH) Estimation with Machine Learning: A Review," Energies, MDPI, vol. 18(3), pages 1-77, February.
25. Lu, Cheng & Teng, Da & Chen, Jun-Yu & Fei, Cheng-Wei & Keshtegar, Behrooz, 2023. "Adaptive vectorial surrogate modeling framework for multi-objective reliability estimation," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
26. Shu, Xing & Shen, Jiangwei & Chen, Zheng & Zhang, Yuanjian & Liu, Yonggang & Lin, Yan, 2022. "Remaining capacity estimation for lithium-ion batteries via co-operation of multi-machine learning algorithms," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
27. Meng, Fanbing & Yang, Fangfang & Yang, Jun & Xie, Min, 2023. "A power model considering initial battery state for remaining useful life prediction of lithium-ion batteries," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
28. Wang, Fujin & Zhao, Zhibin & Zhai, Zhi & Guo, Yanjie & Xi, Huan & Wang, Shibin & Chen, Xuefeng, 2023. "Feature disentanglement and tendency retainment with domain adaptation for Lithium-ion battery capacity estimation," Reliability Engineering and System Safety, Elsevier, vol. 230(C).