Remaining Useful Life Prediction of Lithium-ion Batteries Based on Wiener Process Under Time-Varying Temperature Condition

Citations

Cited by:

1. Zhang, Yixing & Feng, Fei & Wang, Shunli & Meng, Jinhao & Xie, Jiale & Ling, Rui & Yin, Hongpeng & Zhang, Ke & Chai, Yi, 2023. "Joint nonlinear-drift-driven Wiener process-Markov chain degradation switching model for adaptive online predicting lithium-ion battery remaining useful life," Applied Energy, Elsevier, vol. 341(C).
2. Zhang, Shuyi & Zhai, Qingqing & Li, Yaqiu, 2023. "Degradation modeling and RUL prediction with Wiener process considering measurable and unobservable external impacts," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
3. Yu, Wennian & Shao, Yimin & Xu, Jin & Mechefske, Chris, 2022. "An adaptive and generalized Wiener process model with a recursive filtering algorithm for remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
4. Ma, Jie & Cai, Li & Liao, Guobo & Yin, Hongpeng & Si, Xiaosheng & Zhang, Peng, 2023. "A multi-phase Wiener process-based degradation model with imperfect maintenance activities," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
5. Zhang, Ao & Wang, Zhihua & Bao, Rui & Liu, Chengrui & Wu, Qiong & Cao, Shihao, 2023. "A novel failure time estimation method for degradation analysis based on general nonlinear Wiener processes," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
6. He, Xinxin & Wang, Zhijian & Li, Yanfeng & Khazhina, Svetlana & Du, Wenhua & Wang, Junyuan & Wang, Wenzhao, 2022. "Joint decision-making of parallel machine scheduling restricted in job-machine release time and preventive maintenance with remaining useful life constraints," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
7. 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).
8. Chen, Dan & Meng, Jinhao & Huang, Huanyang & Wu, Ji & Liu, Ping & Lu, Jiwu & Liu, Tianqi, 2022. "An Empirical-Data Hybrid Driven Approach for Remaining Useful Life prediction of lithium-ion batteries considering capacity diving," Energy, Elsevier, vol. 245(C).
9. Ardeshiri, Reza Rouhi & Liu, Ming & Ma, Chengbin, 2022. "Multivariate stacked bidirectional long short term memory for lithium-ion battery health management," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
10. 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).
11. Zhang, Jiusi & Jiang, Yuchen & Li, Xiang & Huo, Mingyi & Luo, Hao & Yin, Shen, 2022. "An adaptive remaining useful life prediction approach for single battery with unlabeled small sample data and parameter uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
12. Tian, Yuan & Han, Minghao & Kulkarni, Chetan & Fink, Olga, 2022. "A prescriptive Dirichlet power allocation policy with deep reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
13. Wang, Zhijie & Zhai, Qingqing & Chen, Piao, 2021. "Degradation modeling considering unit-to-unit heterogeneity-A general model and comparative study," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
14. Nagulapati, Vijay Mohan & Lee, Hyunjun & Jung, DaWoon & Brigljevic, Boris & Choi, Yunseok & Lim, Hankwon, 2021. "Capacity estimation of batteries: Influence of training dataset size and diversity on data driven prognostic models," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
15. Pedersen, Tom Ivar & Liu, Xingheng & Vatn, Jørn, 2023. "Maintenance optimization of a system subject to two-stage degradation, hard failure, and imperfect repair," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
16. 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).
17. 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).
18. Lin, Mingqiang & You, Yuqiang & Wang, Wei & Wu, Ji, 2023. "Battery health prognosis with gated recurrent unit neural networks and hidden Markov model considering uncertainty quantification," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
19. Chen, Xiaowu & Liu, Zhen, 2022. "A long short-term memory neural network based Wiener process model for remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
20. Gu, Hang-Hang & Wang, Run-Zi & Tang, Min-Jin & Zhang, Xian-Cheng & Tu, Shan-Tung, 2024. "Data-physics-model based fatigue reliability assessment methodology for high-temperature components and its application in steam turbine rotor," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
21. Cao, Mengda & Zhang, Tao & Liu, Yajie & Zhang, Yajun & Wang, Yu & Li, Kaiwen, 2022. "An ensemble learning prognostic method for capacity estimation of lithium-ion batteries based on the V-IOWGA operator," Energy, Elsevier, vol. 257(C).
22. Hu, Changhua & Xing, Yuanxing & Du, Dangbo & Si, Xiaosheng & Zhang, Jianxun, 2023. "Remaining useful life estimation for two-phase nonlinear degradation processes," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
23. Thelen, Adam & Li, Meng & Hu, Chao & Bekyarova, Elena & Kalinin, Sergey & Sanghadasa, Mohan, 2022. "Augmented model-based framework for battery remaining useful life prediction," Applied Energy, Elsevier, vol. 324(C).
24. Pang, Zhenan & Li, Tianmei & Pei, Hong & Si, Xiaosheng, 2023. "A condition-based prognostic approach for age- and state-dependent partially observable nonlinear degrading system," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
25. Liu, Xingheng & Matias, José & Jäschke, Johannes & Vatn, Jørn, 2022. "Gibbs sampler for noisy Transformed Gamma process: Inference and remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
26. Qin, Shuidan & Wang, Bing Xing & Tsai, Tzong-Ru & Wang, Xiaofei, 2023. "The prediction of remaining useful lifetime for the Weibull k-out-of-n load-sharing system," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
27. Qin, Shuidan & Wang, Bing Xing & Wu, Wenhui & Ma, Chao, 2022. "The prediction intervals of remaining useful life based on constant stress accelerated life test data," European Journal of Operational Research, Elsevier, vol. 301(2), pages 747-755.
28. 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).
29. Zhou, Hang & Farsi, Maryam & Harrison, Andrew & Parlikad, Ajith Kumar & Brintrup, Alexandra, 2023. "Civil aircraft engine operation life resilient monitoring via usage trajectory mapping on the reliability contour," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
30. 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).
31. 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).
32. He, Jiabei & Tian, Yi & Wu, Lifeng, 2022. "A hybrid data-driven method for rapid prediction of lithium-ion battery capacity," Reliability Engineering and System Safety, Elsevier, vol. 226(C).