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Remaining useful life prediction of lithium-ion batteries based on false nearest neighbors and a hybrid neural network
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- Nickolay I. Shchurov & Sergey I. Dedov & Boris V. Malozyomov & Alexander A. Shtang & Nikita V. Martyushev & Roman V. Klyuev & Sergey N. Andriashin, 2021. "Degradation of Lithium-Ion Batteries in an Electric Transport Complex," Energies, MDPI, vol. 14(23), pages 1-33, December.
- Zhang, Meng & Hu, Tao & Wu, Lifeng & Kang, Guoqing & Guan, Yong, 2021. "A method for capacity estimation of lithium-ion batteries based on adaptive time-shifting broad learning system," Energy, Elsevier, vol. 231(C).
- 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).
- Yang, Bo & Qian, Yucun & Li, Qiang & Chen, Qian & Wu, Jiyang & Luo, Enbo & Xie, Rui & Zheng, Ruyi & Yan, Yunfeng & Su, Shi & Wang, Jingbo, 2024. "Critical summary and perspectives on state-of-health of lithium-ion battery," Renewable and Sustainable Energy Reviews, Elsevier, vol. 190(PA).
- Shu, Xing & Li, Guang & Shen, Jiangwei & Lei, Zhenzhen & Chen, Zheng & Liu, Yonggang, 2020. "A uniform estimation framework for state of health of lithium-ion batteries considering feature extraction and parameters optimization," Energy, Elsevier, vol. 204(C).
- Ma, Qiuhui & Zheng, Ying & Yang, Weidong & Zhang, Yong & Zhang, Hong, 2021. "Remaining useful life prediction of lithium battery based on capacity regeneration point detection," Energy, Elsevier, vol. 234(C).
- Mathews, Ian & Xu, Bolun & He, Wei & Barreto, Vanessa & Buonassisi, Tonio & Peters, Ian Marius, 2020. "Technoeconomic model of second-life batteries for utility-scale solar considering calendar and cycle aging," Applied Energy, Elsevier, vol. 269(C).
- Lidang Jiang & Qingsong Huang & Ge He, 2024. "Predicting the Remaining Useful Life of Lithium-Ion Batteries Using 10 Random Data Points and a Flexible Parallel Neural Network," Energies, MDPI, vol. 17(7), pages 1-20, April.
- Leung, Eunice & Ma, King F. & Xie, Nan, 2023. "Nonlinear modeling of sparkling drink bubbles using a physics informed long short term memory network," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).
- 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).
- Semeraro, Concetta & Caggiano, Mariateresa & Olabi, Abdul-Ghani & Dassisti, Michele, 2022. "Battery monitoring and prognostics optimization techniques: Challenges and opportunities," Energy, Elsevier, vol. 255(C).
- Felipe Salinas & Julia Kowal, 2020. "Classifying Aged Li-Ion Cells from Notebook Batteries," Sustainability, MDPI, vol. 12(9), pages 1-17, April.
- Xu, Tingting & Peng, Zhen & Wu, Lifeng, 2021. "A novel data-driven method for predicting the circulating capacity of lithium-ion battery under random variable current," Energy, Elsevier, vol. 218(C).
- Bao, Xinyuan & Chen, Liping & Lopes, António M. & Li, Xin & Xie, Siqiang & Li, Penghua & Chen, YangQuan, 2023. "Hybrid deep neural network with dimension attention for state-of-health estimation of Lithium-ion Batteries," Energy, Elsevier, vol. 278(C).
- He, Guannan & Ciez, Rebecca & Moutis, Panayiotis & Kar, Soummya & Whitacre, Jay F., 2020. "The economic end of life of electrochemical energy storage," Applied Energy, Elsevier, vol. 273(C).
- Wang, Cong & Chen, Yunxia, 2024. "Unsupervised dynamic prognostics for abnormal degradation of lithium-ion battery," Applied Energy, Elsevier, vol. 365(C).
- Ouyang, Tiancheng & Xu, Peihang & Chen, Jingxian & Su, Zixiang & Huang, Guicong & Chen, Nan, 2021. "A novel state of charge estimation method for lithium-ion batteries based on bias compensation," Energy, Elsevier, vol. 226(C).
- Carlos Antônio Rufino Júnior & Eleonora Riva Sanseverino & Pierluigi Gallo & Murilo Machado Amaral & Daniel Koch & Yash Kotak & Sergej Diel & Gero Walter & Hans-Georg Schweiger & Hudson Zanin, 2024. "Unraveling the Degradation Mechanisms of Lithium-Ion Batteries," Energies, MDPI, vol. 17(14), pages 1-52, July.
- Ethelbert Ezemobi & Mario Silvagni & Ahmad Mozaffari & Andrea Tonoli & Amir Khajepour, 2022. "State of Health Estimation of Lithium-Ion Batteries in Electric Vehicles under Dynamic Load Conditions," Energies, MDPI, vol. 15(3), pages 1-20, February.
- Zhang, Ying & Li, Yan-Fu, 2022. "Prognostics and health management of Lithium-ion battery using deep learning methods: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
- Yongsheng Shi & Tailin Li & Leicheng Wang & Hongzhou Lu & Yujun Hu & Beichen He & Xinran Zhai, 2023. "A Method for Predicting the Life of Lithium-Ion Batteries Based on Successive Variational Mode Decomposition and Optimized Long Short-Term Memory," Energies, MDPI, vol. 16(16), pages 1-16, August.
- Balyogi Mohan Dash & Om Prakash & Arun Kumar Samantaray, 2023. "Failure prognosis of the components with unlike degradation trends: A data-driven approach," Journal of Risk and Reliability, , vol. 237(6), pages 1132-1149, December.
- Cheng, Gong & Wang, Xinzhi & He, Yurong, 2021. "Remaining useful life and state of health prediction for lithium batteries based on empirical mode decomposition and a long and short memory neural network," Energy, Elsevier, vol. 232(C).
- Fei, Zicheng & Yang, Fangfang & Tsui, Kwok-Leung & Li, Lishuai & Zhang, Zijun, 2021. "Early prediction of battery lifetime via a machine learning based framework," Energy, Elsevier, vol. 225(C).
- Li, Penghua & Zhang, Zijian & Grosu, Radu & Deng, Zhongwei & Hou, Jie & Rong, Yujun & Wu, Rui, 2022. "An end-to-end neural network framework for state-of-health estimation and remaining useful life prediction of electric vehicle lithium batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
- 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).
- Mehta, Rohit & Gupta, Amit, 2024. "Mathematical modelling of electrochemical, thermal and degradation processes in lithium-ion cells—A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
- 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).
- 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).
- Shaheer Ansari & Afida Ayob & Molla Shahadat Hossain Lipu & Aini Hussain & Mohamad Hanif Md Saad, 2021. "Multi-Channel Profile Based Artificial Neural Network Approach for Remaining Useful Life Prediction of Electric Vehicle Lithium-Ion Batteries," Energies, MDPI, vol. 14(22), pages 1-22, November.
- Wang, Shuai & Ma, Hongyan & Zhang, Yingda & Li, Shengyan & He, Wei, 2023. "Remaining useful life prediction method of lithium-ion batteries is based on variational modal decomposition and deep learning integrated approach," Energy, Elsevier, vol. 282(C).
- 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).
- Shaheer Ansari & Afida Ayob & Molla Shahadat Hossain Lipu & Aini Hussain & Mohamad Hanif Md Saad, 2021. "Data-Driven Remaining Useful Life Prediction for Lithium-Ion Batteries Using Multi-Charging Profile Framework: A Recurrent Neural Network Approach," Sustainability, MDPI, vol. 13(23), pages 1-25, December.
- Pang, Hui & Chen, Kaiqiang & Geng, Yuanfei & Wu, Longxing & Wang, Fengbin & Liu, Jiahao, 2024. "Accurate capacity and remaining useful life prediction of lithium-ion batteries based on improved particle swarm optimization and particle filter," Energy, Elsevier, vol. 293(C).
- Li, Chuan & Zhang, Huahua & Ding, Ping & Yang, Shuai & Bai, Yun, 2023. "Deep feature extraction in lifetime prognostics of lithium-ion batteries: Advances, challenges and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
- 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).
- Haipeng Pan & Chengte Chen & Minming Gu, 2021. "A State of Health Estimation Method for Lithium-Ion Batteries Based on Improved Particle Filter Considering Capacity Regeneration," Energies, MDPI, vol. 14(16), pages 1-12, August.
- Liyuan Shao & Yong Zhang & Xiujuan Zheng & Xin He & Yufeng Zheng & Zhiwei Liu, 2023. "A Review of Remaining Useful Life Prediction for Energy Storage Components Based on Stochastic Filtering Methods," Energies, MDPI, vol. 16(3), pages 1-22, February.
- Lee, Jaewook & Lee, Jay H., 2024. "Simultaneous extraction of intra- and inter-cycle features for predicting lithium-ion battery's knees using convolutional and recurrent neural networks," Applied Energy, Elsevier, vol. 356(C).
- Hong, Joonki & Lee, Dongheon & Jeong, Eui-Rim & Yi, Yung, 2020. "Towards the swift prediction of the remaining useful life of lithium-ion batteries with end-to-end deep learning," Applied Energy, Elsevier, vol. 278(C).
- 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).
- 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).
- Zhang, Yong & Xin, Yuqi & Liu, Zhi-wei & Chi, Ming & Ma, Guijun, 2022. "Health status assessment and remaining useful life prediction of aero-engine based on BiGRU and MMoE," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
- Zhang, Yong & Tu, Lei & Xue, Zhiwei & Li, Sai & Tian, Lulu & Zheng, Xiujuan, 2022. "Weight optimized unscented Kalman filter for degradation trend prediction of lithium-ion battery with error compensation strategy," Energy, Elsevier, vol. 251(C).
- 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).
- Jun Peng & Zhiyong Zheng & Xiaoyong Zhang & Kunyuan Deng & Kai Gao & Heng Li & Bin Chen & Yingze Yang & Zhiwu Huang, 2020. "A Data-Driven Method with Feature Enhancement and Adaptive Optimization for Lithium-Ion Battery Remaining Useful Life Prediction," Energies, MDPI, vol. 13(3), pages 1-20, February.
- Sui, Xin & He, Shan & Vilsen, Søren B. & Meng, Jinhao & Teodorescu, Remus & Stroe, Daniel-Ioan, 2021. "A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery," Applied Energy, Elsevier, vol. 300(C).
- Yang, Yixin, 2021. "A machine-learning prediction method of lithium-ion battery life based on charge process for different applications," Applied Energy, Elsevier, vol. 292(C).