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Data-driven prediction of battery cycle life before capacity degradation

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Cited by:

  1. Shi, Haotian & Wang, Shunli & Huang, Qi & Fernandez, Carlos & Liang, Jianhong & Zhang, Mengyun & Qi, Chuangshi & Wang, Liping, 2024. "Improved electric-thermal-aging multi-physics domain coupling modeling and identification decoupling of complex kinetic processes based on timescale quantification in lithium-ion batteries," Applied Energy, Elsevier, vol. 353(PB).
  2. Zhang, Shuxin & Liu, Zhitao & Su, Hongye, 2023. "State of health estimation for lithium-ion batteries on few-shot learning," Energy, Elsevier, vol. 268(C).
  3. Ospina Agudelo, Brian & Zamboni, Walter & Postiglione, Fabio & Monmasson, Eric, 2023. "Battery State-of-Health estimation based on multiple charge and discharge features," Energy, Elsevier, vol. 263(PA).
  4. Zhao, Bo & Zhang, Weige & Zhang, Yanru & Zhang, Caiping & Zhang, Chi & Zhang, Junwei, 2024. "Research on the remaining useful life prediction method for lithium-ion batteries by fusion of feature engineering and deep learning," Applied Energy, Elsevier, vol. 358(C).
  5. Chen, Junxiong & Hu, Yuanjiang & Zhu, Qiao & Rashid, Haroon & Li, Hongkun, 2023. "A novel battery health indicator and PSO-LSSVR for LiFePO4 battery SOH estimation during constant current charging," Energy, Elsevier, vol. 282(C).
  6. Li, Naipeng & Wang, Mingyang & Lei, Yaguo & Si, Xiaosheng & Yang, Bin & Li, Xiang, 2024. "A nonparametric degradation modeling method for remaining useful life prediction with fragment data," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
  7. Zhao, Li & Ke, Hanchen & Li, Yuqi & Chen, Yong, 2023. "Research on personalized charging strategy of electric bus under time-varying constraints," Energy, Elsevier, vol. 276(C).
  8. Ni, Yulong & Xu, Jianing & Zhu, Chunbo & Pei, Lei, 2022. "Accurate residual capacity estimation of retired LiFePO4 batteries based on mechanism and data-driven model," Applied Energy, Elsevier, vol. 305(C).
  9. Deng, Zhihua & Chan, Siew Hwa & Chen, Qihong & Liu, Hao & Zhang, Liyan & Zhou, Keliang & Tong, Sirui & Fu, Zhichao, 2023. "Efficient degradation prediction of PEMFCs using ELM-AE based on fuzzy extension broad learning system," Applied Energy, Elsevier, vol. 331(C).
  10. Fujin Wang & Zhi Zhai & Zhibin Zhao & Yi Di & Xuefeng Chen, 2024. "Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
  11. Wang, Fu-Kwun & Amogne, Zemenu Endalamaw & Chou, Jia-Hong & Tseng, Cheng, 2022. "Online remaining useful life prediction of lithium-ion batteries using bidirectional long short-term memory with attention mechanism," Energy, Elsevier, vol. 254(PB).
  12. Oyewole, Isaiah & Chehade, Abdallah & Kim, Youngki, 2022. "A controllable deep transfer learning network with multiple domain adaptation for battery state-of-charge estimation," Applied Energy, Elsevier, vol. 312(C).
  13. Chen, Dinghong & Zhang, Weige & Zhang, Caiping & Sun, Bingxiang & Cong, XinWei & Wei, Shaoyuan & Jiang, Jiuchun, 2022. "A novel deep learning-based life prediction method for lithium-ion batteries with strong generalization capability under multiple cycle profiles," Applied Energy, Elsevier, vol. 327(C).
  14. Wan, Shuaibin & Liang, Xiongwei & Jiang, Haoran & Sun, Jing & Djilali, Ned & Zhao, Tianshou, 2021. "A coupled machine learning and genetic algorithm approach to the design of porous electrodes for redox flow batteries," Applied Energy, Elsevier, vol. 298(C).
  15. Wang, Hao & He, Hongwen & Bai, Yunfei & Yue, Hongwei, 2022. "Parameterized deep Q-network based energy management with balanced energy economy and battery life for hybrid electric vehicles," Applied Energy, Elsevier, vol. 320(C).
  16. Zhang, Wencan & Ouyang, Nan & Yin, Xiuxing & Li, Xingyao & Wu, Weixiong & Huang, Liansheng, 2022. "Data-driven early warning strategy for thermal runaway propagation in Lithium-ion battery modules with variable state of charge," Applied Energy, Elsevier, vol. 323(C).
  17. Li, Guanzheng & Li, Bin & Li, Chao & Wang, Shuai, 2023. "State-of-health rapid estimation for lithium-ion battery based on an interpretable stacking ensemble model with short-term voltage profiles," Energy, Elsevier, vol. 263(PE).
  18. Si, Xiao-Sheng & Li, Tianmei & Zhang, Jianxun & Lei, Yaguo, 2022. "Nonlinear degradation modeling and prognostics: A Box-Cox transformation perspective," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
  19. Li, Renzheng & Hong, Jichao & Zhang, Huaqin & Chen, Xinbo, 2022. "Data-driven battery state of health estimation based on interval capacity for real-world electric vehicles," Energy, Elsevier, vol. 257(C).
  20. Zhao, Chunyang & Andersen, Peter Bach & Træholt, Chresten & Hashemi, Seyedmostafa, 2023. "Grid-connected battery energy storage system: a review on application and integration," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
  21. Karina Masalkovaitė & Paul Gasper & Donal P. Finegan, 2024. "Predicting the heat release variability of Li-ion cells under thermal runaway with few or no calorimetry data," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
  22. Das, Kaushik & Kumar, Roushan & Krishna, Anurup, 2024. "Analyzing electric vehicle battery health performance using supervised machine learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
  23. Gu, Pingwei & Zhang, Ying & Duan, Bin & Zhang, Chenghui & Kang, Yongzhe, 2024. "Rapid and flexible lithium-ion battery performance evaluation using random charging curve based on deep learning," Energy, Elsevier, vol. 293(C).
  24. Shi, Haotian & Wang, Shunli & Fernandez, Carlos & Yu, Chunmei & Xu, Wenhua & Dablu, Bobobee Etse & Wang, Liping, 2022. "Improved multi-time scale lumped thermoelectric coupling modeling and parameter dispersion evaluation of lithium-ion batteries," Applied Energy, Elsevier, vol. 324(C).
  25. Li, Yi & Liu, Kailong & Foley, Aoife M. & Zülke, Alana & Berecibar, Maitane & Nanini-Maury, Elise & Van Mierlo, Joeri & Hoster, Harry E., 2019. "Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
  26. da Silva, Samuel Filgueira & Eckert, Jony Javorski & Corrêa, Fernanda Cristina & Silva, Fabrício Leonardo & Silva, Ludmila C.A. & Dedini, Franco Giuseppe, 2022. "Dual HESS electric vehicle powertrain design and fuzzy control based on multi-objective optimization to increase driving range and battery life cycle," Applied Energy, Elsevier, vol. 324(C).
  27. He, Ning & Wang, Qiqi & Lu, Zhenfeng & Chai, Yike & Yang, Fangfang, 2024. "Early prediction of battery lifetime based on graphical features and convolutional neural networks," Applied Energy, Elsevier, vol. 353(PA).
  28. Zhou, Yuekuan, 2024. "AI-driven battery ageing prediction with distributed renewable community and E-mobility energy sharing," Renewable Energy, Elsevier, vol. 225(C).
  29. Ibraheem, Rasheed & Wu, Yue & Lyons, Terry & dos Reis, Gonçalo, 2023. "Early prediction of Lithium-ion cell degradation trajectories using signatures of voltage curves up to 4-minute sub-sampling rates," Applied Energy, Elsevier, vol. 352(C).
  30. 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).
  31. Tang, Aihua & Jiang, Yihan & Nie, Yuwei & Yu, Quanqing & Shen, Weixiang & Pecht, Michael G., 2023. "Health and lifespan prediction considering degradation patterns of lithium-ion batteries based on transferable attention neural network," Energy, Elsevier, vol. 279(C).
  32. Gu, Xin & Li, Jinglun & Zhu, Yuhao & Wang, Yue & Mao, Ziheng & Shang, Yunlong, 2023. "A quick and intelligent screening method for large-scale retired batteries based on cloud-edge collaborative architecture," Energy, Elsevier, vol. 285(C).
  33. Zhou, Yuekuan, 2022. "A regression learner-based approach for battery cycling ageing prediction―advances in energy management strategy and techno-economic analysis," Energy, Elsevier, vol. 256(C).
  34. Guo, Wenchao & Yang, Lin & Deng, Zhongwei & Li, Jilin & Bian, Xiaolei, 2023. "Rapid online health estimation for lithium-ion batteries based on partial constant-voltage charging segment," Energy, Elsevier, vol. 281(C).
  35. Gomez, William & Wang, Fu-Kwun & Chou, Jia-Hong, 2024. "Li-ion battery capacity prediction using improved temporal fusion transformer model," Energy, Elsevier, vol. 296(C).
  36. 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).
  37. Gong, Dongliang & Gao, Ying & Kou, Yalin & Wang, Yurang, 2022. "State of health estimation for lithium-ion battery based on energy features," Energy, Elsevier, vol. 257(C).
  38. Gu, Xubo & Bai, Hanyu & Cui, Xiaofan & Zhu, Juner & Zhuang, Weichao & Li, Zhaojian & Hu, Xiaosong & Song, Ziyou, 2024. "Challenges and opportunities for second-life batteries: Key technologies and economy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
  39. Zou, Weitao & Li, Jianwei & Yang, Qingqing & Wan, Xinming & He, Yuntang & Lan, Hao, 2023. "A real-time energy management approach with fuel cell and battery competition-synergy control for the fuel cell vehicle," Applied Energy, Elsevier, vol. 334(C).
  40. Rogers, Daniel J. & Aslett, Louis J.M. & Troffaes, Matthias C.M., 2021. "Modelling of modular battery systems under cell capacity variation and degradation," Applied Energy, Elsevier, vol. 283(C).
  41. Englberger, Stefan & Abo Gamra, Kareem & Tepe, Benedikt & Schreiber, Michael & Jossen, Andreas & Hesse, Holger, 2021. "Electric vehicle multi-use: Optimizing multiple value streams using mobile storage systems in a vehicle-to-grid context," Applied Energy, Elsevier, vol. 304(C).
  42. Lu, Zhenfeng & Fei, Zicheng & Wang, Benfei & Yang, Fangfang, 2024. "A feature fusion-based convolutional neural network for battery state-of-health estimation with mining of partial voltage curve," Energy, Elsevier, vol. 288(C).
  43. Zhou, Yuekuan, 2023. "Sustainable energy sharing districts with electrochemical battery degradation in design, planning, operation and multi-objective optimisation," Renewable Energy, Elsevier, vol. 202(C), pages 1324-1341.
  44. Kong, Jin-zhen & Yang, Fangfang & Zhang, Xi & Pan, Ershun & Peng, Zhike & Wang, Dong, 2021. "Voltage-temperature health feature extraction to improve prognostics and health management of lithium-ion batteries," Energy, Elsevier, vol. 223(C).
  45. Rauf, Huzaifa & Khalid, Muhammad & Arshad, Naveed, 2022. "Machine learning in state of health and remaining useful life estimation: Theoretical and technological development in battery degradation modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
  46. Xue, Qiao & Li, Junqiu & Xu, Peipei, 2022. "Machine learning based swift online capacity prediction of lithium-ion battery through whole cycle life," Energy, Elsevier, vol. 261(PA).
  47. Jiahuan Lu & Rui Xiong & Jinpeng Tian & Chenxu Wang & Fengchun Sun, 2023. "Deep learning to estimate lithium-ion battery state of health without additional degradation experiments," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
  48. Wei, Meng & Ye, Min & Zhang, Chuanwei & Wang, Qiao & Lian, Gaoqi & Xia, Baozhou, 2024. "Integrating mechanism and machine learning based capacity estimation for LiFePO4 batteries under slight overcharge cycling," Energy, Elsevier, vol. 296(C).
  49. Kurucan, Mehmet & Özbaltan, Mete & Yetgin, Zeki & Alkaya, Alkan, 2024. "Applications of artificial neural network based battery management systems: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
  50. Wang, Qiao & Ye, Min & Wei, Meng & Lian, Gaoqi & Li, Yan, 2023. "Random health indicator and shallow neural network based robust capacity estimation for lithium-ion batteries with different fast charging protocols," Energy, Elsevier, vol. 271(C).
  51. 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).
  52. 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).
  53. Yan, Lisen & Peng, Jun & Gao, Dianzhu & Wu, Yue & Liu, Yongjie & Li, Heng & Liu, Weirong & Huang, Zhiwu, 2022. "A hybrid method with cascaded structure for early-stage remaining useful life prediction of lithium-ion battery," Energy, Elsevier, vol. 243(C).
  54. 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).
  55. Lyu, Guangzheng & Zhang, Heng & Miao, Qiang, 2023. "An interpretable state of health estimation method for lithium-ion batteries based on multi-category and multi-stage features," Energy, Elsevier, vol. 283(C).
  56. 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).
  57. Khaleghi, Sahar & Hosen, Md Sazzad & Van Mierlo, Joeri & Berecibar, Maitane, 2024. "Towards machine-learning driven prognostics and health management of Li-ion batteries. A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
  58. 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).
  59. Chengjian Xu & Paul Behrens & Paul Gasper & Kandler Smith & Mingming Hu & Arnold Tukker & Bernhard Steubing, 2023. "Electric vehicle batteries alone could satisfy short-term grid storage demand by as early as 2030," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
  60. Wu, Ji & Fang, Leichao & Dong, Guangzhong & Lin, Mingqiang, 2023. "State of health estimation of lithium-ion battery with improved radial basis function neural network," Energy, Elsevier, vol. 262(PB).
  61. Ma, Yan & Shan, Ce & Gao, Jinwu & Chen, Hong, 2022. "A novel method for state of health estimation of lithium-ion batteries based on improved LSTM and health indicators extraction," Energy, Elsevier, vol. 251(C).
  62. Wang, Zhe & Yang, Fangfang & Xu, Qiang & Wang, Yongjian & Yan, Hong & Xie, Min, 2023. "Capacity estimation of lithium-ion batteries based on data aggregation and feature fusion via graph neural network," Applied Energy, Elsevier, vol. 336(C).
  63. Wang, Yixiu & Zhu, Jiangong & Cao, Liang & Gopaluni, Bhushan & Cao, Yankai, 2023. "Long Short-Term Memory Network with Transfer Learning for Lithium-ion Battery Capacity Fade and Cycle Life Prediction," Applied Energy, Elsevier, vol. 350(C).
  64. Kim, Sung Wook & Oh, Ki-Yong & Lee, Seungchul, 2022. "Novel informed deep learning-based prognostics framework for on-board health monitoring of lithium-ion batteries," Applied Energy, Elsevier, vol. 315(C).
  65. Adrian Chmielewski & Piotr Piórkowski & Krzysztof Bogdziński & Jakub Możaryn, 2023. "Application of a Bidirectional DC/DC Converter to Control the Power Distribution in the Battery–Ultracapacitor System," Energies, MDPI, vol. 16(9), pages 1-40, April.
  66. Sohn, Suyeon & Byun, Ha-Eun & Lee, Jay H., 2022. "Two-stage deep learning for online prediction of knee-point in Li-ion battery capacity degradation," Applied Energy, Elsevier, vol. 328(C).
  67. Che, Yunhong & Zheng, Yusheng & Forest, Florent Evariste & Sui, Xin & Hu, Xiaosong & Teodorescu, Remus, 2024. "Predictive health assessment for lithium-ion batteries with probabilistic degradation prediction and accelerating aging detection," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
  68. Ruixue Liu & Guannan He & Xizhe Wang & Dharik Mallapragada & Hongbo Zhao & Yang Shao-Horn & Benben Jiang, 2024. "A cross-scale framework for evaluating flexibility values of battery and fuel cell electric vehicles," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
  69. Tang, Aihua & Xu, Yuchen & Hu, Yuanzhi & Tian, Jinpeng & Nie, Yuwei & Yan, Fuwu & Tan, Yong & Yu, Quanqing, 2024. "Battery state of health estimation under dynamic operations with physics-driven deep learning," Applied Energy, Elsevier, vol. 370(C).
  70. Wang, Qiao & Ye, Min & Cai, Xue & Sauer, Dirk Uwe & Li, Weihan, 2023. "Transferable data-driven capacity estimation for lithium-ion batteries with deep learning: A case study from laboratory to field applications," Applied Energy, Elsevier, vol. 350(C).
  71. Che, Yunhong & Deng, Zhongwei & Li, Penghua & Tang, Xiaolin & Khosravinia, Kavian & Lin, Xianke & Hu, Xiaosong, 2022. "State of health prognostics for series battery packs: A universal deep learning method," Energy, Elsevier, vol. 238(PB).
  72. Li, Alan G. & West, Alan C. & Preindl, Matthias, 2022. "Towards unified machine learning characterization of lithium-ion battery degradation across multiple levels: A critical review," Applied Energy, Elsevier, vol. 316(C).
  73. Li, Renzheng & Wang, Hui & Dai, Haifeng & Hong, Jichao & Tong, Guangyao & Chen, Xinbo, 2022. "Accurate state of charge prediction for real-world battery systems using a novel dual-dropout-based neural network," Energy, Elsevier, vol. 250(C).
  74. Hsu, Chia-Wei & Xiong, Rui & Chen, Nan-Yow & Li, Ju & Tsou, Nien-Ti, 2022. "Deep neural network battery life and voltage prediction by using data of one cycle only," Applied Energy, Elsevier, vol. 306(PB).
  75. Fan, Guodong & Zhang, Xi, 2023. "Battery capacity estimation using 10-second relaxation voltage and a convolutional neural network," Applied Energy, Elsevier, vol. 330(PA).
  76. Wei, Meng & Ye, Min & Zhang, Chuanwei & Li, Yan & Zhang, Jiale & Wang, Qiao, 2023. "A multi-scale learning approach for remaining useful life prediction of lithium-ion batteries based on variational mode decomposition and Monte Carlo sampling," Energy, Elsevier, vol. 283(C).
  77. Wang, Jinyu & Zhang, Caiping & Zhang, Linjing & Su, Xiaojia & Zhang, Weige & Li, Xu & Du, Jingcai, 2023. "A novel aging characteristics-based feature engineering for battery state of health estimation," Energy, Elsevier, vol. 273(C).
  78. Pepe, Simona & Ciucci, Francesco, 2023. "Long-range battery state-of-health and end-of-life prediction with neural networks and feature engineering," Applied Energy, Elsevier, vol. 350(C).
  79. 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).
  80. S, Vignesh & Che, Hang Seng & Selvaraj, Jeyraj & Tey, Kok Soon & Lee, Jia Woon & Shareef, Hussain & Errouissi, Rachid, 2024. "State of Health (SoH) estimation methods for second life lithium-ion battery—Review and challenges," Applied Energy, Elsevier, vol. 369(C).
  81. Zhang, Jianyu & Lu, Wei, 2022. "Sparse data machine learning for battery health estimation and optimal design incorporating material characteristics," Applied Energy, Elsevier, vol. 307(C).
  82. Liu, Donglei & Wang, Shunli & Fan, Yongcun & Fernandez, Carlos & Blaabjerg, Frede, 2024. "An optimized multi-segment long short-term memory network strategy for power lithium-ion battery state of charge estimation adaptive wide temperatures," Energy, Elsevier, vol. 304(C).
  83. Chen, Zhang & Chen, Liqun & Ma, Zhengwei & Xu, Kangkang & Zhou, Yu & Shen, Wenjing, 2023. "Joint modeling for early predictions of Li-ion battery cycle life and degradation trajectory," Energy, Elsevier, vol. 277(C).
  84. Ye, Jinhua & Xie, Quan & Lin, Mingqiang & Wu, Ji, 2024. "A method for estimating the state of health of lithium-ion batteries based on physics-informed neural network," Energy, Elsevier, vol. 294(C).
  85. Wang, Tianyu & Ma, Zhongjing & Zou, Suli & Chen, Zhan & Wang, Peng, 2024. "Lithium-ion battery state-of-health estimation: A self-supervised framework incorporating weak labels," Applied Energy, Elsevier, vol. 355(C).
  86. Gao, Qinxiang & Lei, Tao & Yao, Wenli & Zhang, Xingyu & Zhang, Xiaobin, 2023. "A health-aware energy management strategy for fuel cell hybrid electric UAVs based on safe reinforcement learning," Energy, Elsevier, vol. 283(C).
  87. Lyu, Guangzheng & Zhang, Heng & Miao, Qiang, 2024. "An adaptive and interpretable SOH estimation method for lithium-ion batteries based-on relaxation voltage cross-scale features and multi-LSTM-RFR2," Energy, Elsevier, vol. 304(C).
  88. Wang, Cong & Chen, Yunxia, 2024. "Unsupervised dynamic prognostics for abnormal degradation of lithium-ion battery," Applied Energy, Elsevier, vol. 365(C).
  89. Zhang, Chen & Wang, Hongmin & Wu, Lifeng, 2023. "Life prediction model for lithium-ion battery considering fast-charging protocol," Energy, Elsevier, vol. 263(PE).
  90. Li, Fang & Min, Yongjun & Zhang, Ying & Zhang, Yong & Zuo, Hongfu & Bai, Fang, 2024. "State-of-health estimation method for fast-charging lithium-ion batteries based on stacking ensemble sparse Gaussian process regression," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
  91. Zhang, Haoxiang & Wang, Feng & Xu, Bing & Fiebig, Wieslaw, 2022. "Extending battery lifetime for electric wheel loaders with electric-hydraulic hybrid powertrain," Energy, Elsevier, vol. 261(PB).
  92. Zhao, Guangcai & Kang, Yongzhe & Huang, Peng & Duan, Bin & Zhang, Chenghui, 2023. "Battery health prognostic using efficient and robust aging trajectory matching with ensemble deep transfer learning," Energy, Elsevier, vol. 282(C).
  93. Jiangong Zhu & Yixiu Wang & Yuan Huang & R. Bhushan Gopaluni & Yankai Cao & Michael Heere & Martin J. Mühlbauer & Liuda Mereacre & Haifeng Dai & Xinhua Liu & Anatoliy Senyshyn & Xuezhe Wei & Michael K, 2022. "Data-driven capacity estimation of commercial lithium-ion batteries from voltage relaxation," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
  94. Zhang, Qisong & Yang, Lin & Guo, Wenchao & Qiang, Jiaxi & Peng, Cheng & Li, Qinyi & Deng, Zhongwei, 2022. "A deep learning method for lithium-ion battery remaining useful life prediction based on sparse segment data via cloud computing system," Energy, Elsevier, vol. 241(C).
  95. Son, Seho & Jeong, Siheon & Kwak, Eunji & Kim, Jun-hyeong & Oh, Ki-Yong, 2022. "Integrated framework for SOH estimation of lithium-ion batteries using multiphysics features," Energy, Elsevier, vol. 238(PA).
  96. Lv, Haichao & Kang, Lixia & Liu, Yongzhong, 2023. "Analysis of strategies to maximize the cycle life of lithium-ion batteries based on aging trajectory prediction," Energy, Elsevier, vol. 275(C).
  97. Ospina Agudelo, Brian & Zamboni, Walter & Monmasson, Eric, 2021. "Application domain extension of incremental capacity-based battery SoH indicators," Energy, Elsevier, vol. 234(C).
  98. Ruan, Haokai & Wei, Zhongbao & Shang, Wentao & Wang, Xuechao & He, Hongwen, 2023. "Artificial Intelligence-based health diagnostic of Lithium-ion battery leveraging transient stage of constant current and constant voltage charging," Applied Energy, Elsevier, vol. 336(C).
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  100. Lam, Dylon Hao Cheng & Lim, Yun Seng & Wong, Jianhui & Allahham, Adib & Patsios, Charalampos, 2023. "A novel characteristic-based degradation model of Li-ion batteries for maximum financial benefits of energy storage system during peak demand reductions," Applied Energy, Elsevier, vol. 343(C).
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  103. Feng, Xinhong & Zhang, Yongzhi & Xiong, Rui & Wang, Chun, 2024. "Comprehensive performance comparison among different types of features in data-driven battery state of health estimation," Applied Energy, Elsevier, vol. 369(C).
  104. Thebelt, Alexander & Tsay, Calvin & Lee, Robert M. & Sudermann-Merx, Nathan & Walz, David & Tranter, Tom & Misener, Ruth, 2022. "Multi-objective constrained optimization for energy applications via tree ensembles," Applied Energy, Elsevier, vol. 306(PB).
  105. Zhao, Jingyuan & Feng, Xuning & Wang, Junbin & Lian, Yubo & Ouyang, Minggao & Burke, Andrew F., 2023. "Battery fault diagnosis and failure prognosis for electric vehicles using spatio-temporal transformer networks," Applied Energy, Elsevier, vol. 352(C).
  106. Peng, Qiao & Li, Wei & Fowler, Michael & Chen, Tao & Jiang, Wei & Liu, Kailong, 2024. "Battery calendar degradation trajectory prediction: Data-driven implementation and knowledge inspiration," Energy, Elsevier, vol. 294(C).
  107. Liu, Yunpeng & Hou, Bo & Ahmed, Moin & Mao, Zhiyu & Feng, Jiangtao & Chen, Zhongwei, 2024. "A hybrid deep learning approach for remaining useful life prediction of lithium-ion batteries based on discharging fragments," Applied Energy, Elsevier, vol. 358(C).
  108. Hong, Jichao & Zhang, Huaqin & Zhang, Xinyang & Yang, Haixu & Chen, Yingjie & Wang, Facheng & Huang, Zhongguo & Wang, Wei, 2024. "Online accurate voltage prediction with sparse data for the whole life cycle of Lithium-ion batteries in electric vehicles," Applied Energy, Elsevier, vol. 369(C).
  109. Huang, Zhelin & Xu, Fan & Yang, Fangfang, 2023. "State of health prediction of lithium-ion batteries based on autoregression with exogenous variables model," Energy, Elsevier, vol. 262(PB).
  110. Zhu, Yunlong & Dong, Zhe & Cheng, Zhonghua & Huang, Xiaojin & Dong, Yujie & Zhang, Zuoyi, 2023. "Neural network extended state-observer for energy system monitoring," Energy, Elsevier, vol. 263(PA).
  111. Chen, Zhang & Shen, Wenjing & Chen, Liqun & Wang, Shuqiang, 2022. "Adaptive online capacity prediction based on transfer learning for fast charging lithium-ion batteries," Energy, Elsevier, vol. 248(C).
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