This document has not been cited yet.

1. Abbaszade, M. ; Salari, V. ; Mousavi, S.S. ; Zomorodi, M. ; Zhou, X. Application of quantum natural language processing for language translation. 2021 IEEE Access. 9 130434-130448
   Paper not yet in RePEc: Add citation now
   
2. Abdar, M. ; Pourpanah, F. ; Hussain, S. ; Rezazadegan, D. ; Liu, L. ; Ghavamzadeh, M. A review of uncertainty quantification in deep learning: techniques, applications and challenges. 2021 Inf Fusion. 76 243-297
   Paper not yet in RePEc: Add citation now
   

3. Bian, C. ; He, H. ; Yang, S. Stacked bidirectional long short-term memory networks for state-of-charge estimation of lithium-ion batteries. 2020 Energy. 191 -

4. Che, Y. ; Deng, Z. ; Lin, X. ; Hu, L. ; Hu, X. Predictive battery health management with transfer learning and online model correction. 2021 IEEE Trans Veh Technol. 70 1269-1277
   Paper not yet in RePEc: Add citation now
   
5. Chen, K. ; Laghrouche, S. ; Djerdir, A. Fuel cell health prognosis using unscented kalman filter: postal fuel cell electric vehicles case study. 2019 Int J Hydrogen Energy. 44 1930-1939
   Paper not yet in RePEc: Add citation now
   
6. Chen, S.Y.C. ; Yoo, S. ; Fang, Y.L.L. Quantum long short-term memory. 2022 :
   Paper not yet in RePEc: Add citation now
   

7. Cheng, G. ; Wang, X. ; He, Y. Remaining useful life and state of health prediction for lithium batteries based on empirical mode decomposition and a long and short memory neural network. 2021 Energy. 232 -

8. Emmanoulopoulos D, Dimoska S. Quantum machine learning in finance: time series forecasting 2022;1–20 arXiv:2202.00599vol. 1..
   Paper not yet in RePEc: Add citation now
   

9. Fan, L. ; Tu, Z. ; Chan, S.H. Technological and engineering design of a megawatt proton exchange membrane fuel cell system. 2022 Energy. 257 -

10. Fasahat, M. ; Manthouri, M. State of charge estimation of lithium-ion batteries using hybrid autoencoder and long short term memory neural networks. 2020 J Power Sources. 469 -
    Paper not yet in RePEc: Add citation now
    
11. Fermín-Cueto, P. ; McTurk, E. ; Allerhand, M. ; Medina-Lopez, E. ; Anjos, M.F. ; Sylvester, J. Identification and machine learning prediction of knee-point and knee-onset in capacity degradation curves of lithium-ion cells. 2020 Energy & AI. 1 -
    Paper not yet in RePEc: Add citation now
    
12. Gourivaeu, R. ; Hilairet, M. ; Hissel, D. ; Jemeï, S. ; Jouin, M. ; Lechartier, E. . 2014 IEEE PHM 2014 data challenge: outline, experiments, scoring of results, winners. PHM. 1-12
    Paper not yet in RePEc: Add citation now
    
13. Guo, Y. ; Wang, Y. ; Ding, P. ; Huang, K. Future degradation trajectory prediction of lithium-ion battery based on a three-step similarity evaluation criterion for battery selection and transfer learning. 2023 J Energy Storage. 72 -
    Paper not yet in RePEc: Add citation now
    
14. Hochreiter, S. ; Schmidhuber, J. Long short-term memory. 1997 Neural Comput. 9 1735-1780
    Paper not yet in RePEc: Add citation now
    
15. Kandidayeni, M. ; Trovão, J.P. ; Soleymani, M. ; Boulon, L. Towards health-aware energy management strategies in fuel cell hybrid electric vehicles: a review. 2022 Int J Hydrogen Energy. 47 10021-10043
    Paper not yet in RePEc: Add citation now
    
16. Kebede, G.A. ; Lo, S.-C. ; Wang, F.-K. ; Chou, J.-H. Transfer learning-based deep learning models for proton exchange membrane fuel remaining useful life prediction. 2024 Fuel. 367 -
    Paper not yet in RePEc: Add citation now
    
17. Kim, S. ; Choi, Y.Y. ; Kim, K.J. ; Choi, J.-I. Forecasting state-of-health of lithium-ion batteries using variational long short-term memory with transfer learning. 2021 J Energy Storage. 41 -
    Paper not yet in RePEc: Add citation now
    
18. Li, P. ; Zhang, Z. ; Xiong, Q. ; Ding, B. ; Hou, J. ; Luo, D. State-of-health estimation and remaining useful life prediction for the lithium-ion battery based on a variant long short term memory neural network. 2020 J Power Sources. 459 -
    Paper not yet in RePEc: Add citation now
    
19. Li, S. ; Luan, W. ; Wang, C. ; Chen, Y. ; Zhuang, Z. Degradation prediction of proton exchange membrane fuel cell based on Bi-LSTM-GRU and ESN fusion prognostic framework. 2022 Int J Hydrogen Energy. 47 33466-33478
    Paper not yet in RePEc: Add citation now
    
20. Li, X. ; Yu, D. ; Søren Byg, V. ; Daniel, Ioan S. The development of machine learning-based remaining useful life prediction for lithium-ion batteries. 2023 J Energy Chem. 82 103-121
    Paper not yet in RePEc: Add citation now
    

21. Liu, H. ; Chen, J. ; Hissel, D. ; Lu, J. ; Hou, M. ; Shao, Z. Prognostics methods and degradation indexes of proton exchange membrane fuel cells: a review. 2020 Renew Sustain Energy Rev. 123 -

22. Liu, J. ; Li, Q. ; Chen, W. ; Yan, Y. ; Qiu, Y. ; Cao, T. Remaining useful life prediction of PEMFC based on long short-term memory recurrent neural networks. 2019 Int J Hydrogen Energy. 44 5470-5480
    Paper not yet in RePEc: Add citation now
    
23. Liu, J. ; Li, Q. ; Yang, H. ; Han, Y. ; Jiang, S. ; Chen, W. Sequence fault diagnosis for PEMFC water management subsystem using deep learning with t-SNE. 2019 IEEE Access. 7 92009-92019
    Paper not yet in RePEc: Add citation now
    
24. Liu, Y. ; He, Y. ; Bian, H. ; Guo, W. ; Zhang, X. A review of lithium-ion battery state of charge estimation based on deep learning: directions for improvement and future trends. 2022 J Energy Storage. 52 -
    Paper not yet in RePEc: Add citation now
    

25. Liu, Z. ; Xu, S. ; Zhao, H. ; Wang, Y. Durability estimation and short-term voltage degradation forecasting of vehicle PEMFC system: development and evaluation of machine learning models. 2022 Appl Energy. 326 -

26. Ma, J. ; Liu, X. ; Zou, X. ; Yue, M. ; Shang, P. ; Kang, L. Degradation prognosis for proton exchange membrane fuel cell based on hybrid transfer learning and intercell differences. 2021 ISA Trans. 113 149-165
    Paper not yet in RePEc: Add citation now
    

27. Ma, R. ; Yang, T. ; Breaz, E. ; Li, Z. ; Briois, P. ; Gao, F. Data-driven proton exchange membrane fuel cell degradation predication through deep learning method. 2018 Appl Energy. 231 102-115

28. Ma, T. ; Xu, J. ; Li, R. ; Yao, N. ; Yang, Y. Online short-term remaining useful life prediction of fuel cell vehicles based on cloud system. 2021 Energies. 14 -

29. Ming, W. ; Sun, P. ; Zhang, Z. ; Qiu, W. ; Du, J. ; Li, X. A systematic review of machine learning methods applied to fuel cells in performance evaluation, durability prediction, and application monitoring. 2023 Int J Hydrogen Energy. 48 5197-5228
    Paper not yet in RePEc: Add citation now
    

30. Oyewole, I. ; Chehade, A. ; Kim, Y. A controllable deep transfer learning network with multiple domain adaptation for battery state-of-charge estimation. 2022 Appl Energy. 312 -

31. Padha, A. ; Sahoo, A. QCLR: quantum-LSTM contrastive learning framework for continuous mental health monitoring. 2024 Expert Syst Appl. 238 -
    Paper not yet in RePEc: Add citation now
    

32. Ren, X. ; Liu, S. ; Yu, X. ; Dong, X. A method for state-of-charge estimation of lithium-ion batteries based on PSO-LSTM. 2021 Energy. 234 -

33. Sahajpal, K. ; Rana, K.P.S. ; Kumar, V. Accurate long-term prognostics of proton exchange membrane fuel cells using recurrent and convolutional neural networks. 2023 Int J Hydrogen Energy. -
    Paper not yet in RePEc: Add citation now
    
34. Shahvandi, M.K. ; Gou, J. ; Soja, B. Deep quantum learning with long short-term memory for geodetic time series prediction: application to length of day prediction. 2021 Earth Sp Sci Open Arch. 1 -
    Paper not yet in RePEc: Add citation now
    
35. Sun, B. ; Liu, X. ; Wang, J. ; Wei, X. ; Yuan, H. ; Dai, H. Short-term performance degradation prediction of a commercial vehicle fuel cell system based on CNN and LSTM hybrid neural network. 2023 Int J Hydrogen Energy. 48 8613-8628
    Paper not yet in RePEc: Add citation now
    
36. Sun, X. ; Xie, M. ; Fu, J. ; Zhou, F. ; Liu, J. An improved neural network model for predicting the remaining useful life of proton exchange membrane fuel cells. 2023 Int J Hydrogen Energy. 48 25499-25511
    Paper not yet in RePEc: Add citation now
    
37. Tang, X. ; Qin, X. ; Wei, K. ; Xu, S. A novel online degradation model for proton exchange membrane fuel cell based on online transfer learning. 2023 Int J Hydrogen Energy. 48 13617-13632
    Paper not yet in RePEc: Add citation now
    
38. Tian, J. ; Chen, C. ; Shen, W. ; Sun, F. ; Xiong, R. Deep learning framework for lithium-ion battery state of charge estimation: recent advances and future perspectives. 2023 Energy Storage Mater. 61 -
    Paper not yet in RePEc: Add citation now
    
39. Vichard, L. ; Steiner, N.Y. ; Zerhouni, N. ; Hissel, D. Hybrid fuel cell system degradation modeling methods: a comprehensive review. 2021 J Power Sources. 506 -
    Paper not yet in RePEc: Add citation now
    

40. Wang, F.-K. ; Amogne, Z.E. ; Chou, J.-H. ; Tseng, C. Online remaining useful life prediction of lithium-ion batteries using bidirectional long short-term memory with attention mechanism. 2022 Energy. 254 -

41. Wang, S. ; Takyi-Aninakwa, P. ; Jin, S. ; Yu, C. ; Fernandez, C. ; Stroe, D.-I. An improved feedforward-long short-term memory modeling method for the whole-life-cycle state of charge prediction of lithium-ion batteries considering current-voltage-temperature variation. 2022 Energy. 254 -

42. Wang, Y. ; Pang, Y. ; Xu, H. ; Martinez, A. ; Chen, K.S. PEM fuel cell and electrolysis cell technologies and hydrogen infrastructure development – a review. 2022 Energy Environ Sci. 15 2288-2328
    Paper not yet in RePEc: Add citation now
    

43. Xie, Q. ; Liu, R. ; Huang, J. ; Su, J. Residual life prediction of lithium-ion batteries based on data preprocessing and a priori knowledge-assisted CNN-LSTM. 2023 Energy. 281 -

44. Xu, Z. ; Yu, W. ; Zhang, C. ; Chen, Y. Quantum convolutional long short-term memory based on variational quantum algorithms in the era of NISQ. 2024 Inf. 15 175-
    Paper not yet in RePEc: Add citation now
    
45. Yue, M. ; Jemei, S. ; Gouriveau, R. ; Zerhouni, N. Review on health-conscious energy management strategies for fuel cell hybrid electric vehicles: degradation models and strategies. 2019 Int J Hydrogen Energy. 44 6844-6861
    Paper not yet in RePEc: Add citation now
    
46. Zhao, J. ; Tu, Z. ; Chan, S.H. Carbon corrosion mechanism and mitigation strategies in a proton exchange membrane fuel cell (PEMFC): a review. 2021 J Power Sources. 488 -
    Paper not yet in RePEc: Add citation now
    
47. Zhao, S. ; Zhang, C. ; Wang, Y. Lithium-ion battery capacity and remaining useful life prediction using board learning system and long short-term memory neural network. 2022 J Energy Storage. 52 -
    Paper not yet in RePEc: Add citation now
    
48. Zuo, B. ; Cheng, J. ; Zhang, Z. Degradation prediction model for proton exchange membrane fuel cells based on long short-term memory neural network and Savitzky-Golay filter. 2021 Int J Hydrogen Energy. 46 15928-15937
    Paper not yet in RePEc: Add citation now
    

49. Zuo, J. ; Lv, H. ; Zhou, D. ; Xue, Q. ; Jin, L. ; Zhou, W. Deep learning based prognostic framework towards proton exchange membrane fuel cell for automotive application. 2021 Appl Energy. 281 -

1. SOC prediction for electric buses based on interpretable transformer model: Impact of traffic conditions and feature importance. (2025). Tang, Jinjun ; Hu, Lipeng ; Liang, Xiao ; Xu, Fuqiao.
   In: Energy.
   RePEc:eee:energy:v:324:y:2025:i:c:s0360544225015774.

   Full description at Econpapers || Download paper

2. Generating comprehensive lithium battery charging data with generative AI. (2025). Jiang, Lidang ; Hu, Changyan ; Zhao, Hang ; Chen, Junxiong.
   In: Applied Energy.
   RePEc:eee:appene:v:377:y:2025:i:pc:s0306261924019871.

   Full description at Econpapers || Download paper

3. An embedding layer-based quantum long short-term memory model with transfer learning for proton exchange membrane fuel stack remaining useful life prediction. (2024). Woldegiorgis, Bereket Haile ; Lo, Shih-Che ; Kebede, Getnet Awoke ; Wang, Fu-Kwun.
   In: Energy.
   RePEc:eee:energy:v:308:y:2024:i:c:s0360544224028299.

   Full description at Econpapers || Download paper

4. A novel battery SOC estimation method based on random search optimized LSTM neural network. (2024). Chai, Xuqing ; Li, Shihao ; Liang, Fengwei.
   In: Energy.
   RePEc:eee:energy:v:306:y:2024:i:c:s0360544224023570.

   Full description at Econpapers || Download paper

5. Exploring large language model for generic and robust state-of-charge estimation of Li-ion batteries: A mixed prompt learning method. (2024). Duan, Zhiyu ; Hao, Yaqian ; Yang, Shunkun ; Feng, Junlan ; Bian, Chong.
   In: Energy.
   RePEc:eee:energy:v:302:y:2024:i:c:s0360544224016293.

   Full description at Econpapers || Download paper

6. State-of-charge estimation hybrid method for lithium-ion batteries using BiGRU and AM co-modified Seq2Seq network and H-infinity filter. (2024). Xu, Xiaobin ; Kuang, Pan ; Li, Kangqun ; Zhou, Fei.
   In: Energy.
   RePEc:eee:energy:v:300:y:2024:i:c:s0360544224013756.

   Full description at Econpapers || Download paper

7. Battery state of health estimation across electrochemistry and working conditions based on domain adaptation. (2024). Deng, Zhongwei ; Zhang, Xiaohong ; Bao, Huanhuan ; Cheng, Duanqian ; Liu, Chenghao.
   In: Energy.
   RePEc:eee:energy:v:297:y:2024:i:c:s0360544224010673.

   Full description at Econpapers || Download paper

8. State of charge estimation for Li-ion battery during dynamic driving process based on dual-channel deep learning methods and conditional judgement. (2024). Liu, Xiaori ; Zhang, Qiang ; Wan, Guangwei.
   In: Energy.
   RePEc:eee:energy:v:294:y:2024:i:c:s0360544224007205.

   Full description at Econpapers || Download paper

9. A hybrid deep learning model for lithium-ion batteries state of charge estimation based on quantile regression and attention. (2024). Zeng, Ziqing ; Long, Xinlin ; Fu, Lijun ; Liu, Lang.
   In: Energy.
   RePEc:eee:energy:v:294:y:2024:i:c:s0360544224006066.

   Full description at Econpapers || Download paper

10. A novel integrated SOC–SOH estimation framework for whole-life-cycle lithium-ion batteries. (2024). Yang, Shunkun ; Wu, Mengdan ; Huang, Haichi ; Bian, Chong.
    In: Energy.
    RePEc:eee:energy:v:288:y:2024:i:c:s036054422303195x.

    Full description at Econpapers || Download paper

11. FedCBE: A federated-learning-based collaborative battery estimation system with non-IID data. (2024). Tian, Yong ; Lai, Rucong ; Wang, Jie.
    In: Applied Energy.
    RePEc:eee:appene:v:368:y:2024:i:c:s0306261924009176.

    Full description at Econpapers || Download paper

12. Data-Driven Methods for the State of Charge Estimation of Lithium-Ion Batteries: An Overview. (2023). Giazitzis, Spyridon ; Leva, Sonia ; Eleftheriadis, Panagiotis ; Ogliari, Emanuele.
    In: Forecasting.
    RePEc:gam:jforec:v:5:y:2023:i:3:p:32-599:d:1239485.

    Full description at Econpapers || Download paper

13. Temperature-Based State-of-Charge Estimation Using Neural Networks, Gradient Boosting Machine and a Jetson Nano Device for Batteries. (2023). Kim, Min Chan ; Lee, Jonghyun ; Hwang, Jihwan.
    In: Energies.
    RePEc:gam:jeners:v:16:y:2023:i:6:p:2639-:d:1094121.

    Full description at Econpapers || Download paper

14. A Systematic and Comparative Study of Distinct Recurrent Neural Networks for Lithium-Ion Battery State-of-Charge Estimation in Electric Vehicles. (2023). Wei, Xuezhe ; Tao, Siyi ; Dai, Haifeng ; Jiang, BO.
    In: Energies.
    RePEc:gam:jeners:v:16:y:2023:i:4:p:2008-:d:1072216.

    Full description at Econpapers || Download paper

15. Cloud-Based Artificial Intelligence Framework for Battery Management System. (2023). Wang, Zhenghong ; Lian, Yubo ; Zhao, Jingyuan ; Shi, Dapai ; Eze, Chika ; Burke, Andrew F.
    In: Energies.
    RePEc:gam:jeners:v:16:y:2023:i:11:p:4403-:d:1159268.

    Full description at Econpapers || Download paper

16. Bioinspired spiking spatiotemporal attention framework for lithium-ion batteries state-of-health estimation. (2023). Zhang, Ying ; Li, Yan-Fu ; Wang, Huan.
    In: Renewable and Sustainable Energy Reviews.
    RePEc:eee:rensus:v:188:y:2023:i:c:s1364032123005853.

    Full description at Econpapers || Download paper

17. Deep feature extraction in lifetime prognostics of lithium-ion batteries: Advances, challenges and perspectives. (2023). Bai, Yun ; Yang, Shuai ; Ding, Ping ; Zhang, Huahua ; Li, Chuan.
    In: Renewable and Sustainable Energy Reviews.
    RePEc:eee:rensus:v:184:y:2023:i:c:s1364032123004331.

    Full description at Econpapers || Download paper

18. Robust state-of-charge estimation method for lithium-ion batteries based on the fusion of time series relevance vector machine and filter algorithm. (2023). Zhang, Xin ; Fan, Xingming ; Yun, Xiang ; Meng, Xiangfei ; Wang, Chao.
    In: Energy.
    RePEc:eee:energy:v:285:y:2023:i:c:s0360544223028608.

    Full description at Econpapers || Download paper

19. Attention-based encoder-decoder networks for state of charge estimation of lithium-ion battery. (2023). Zhang, YU ; Wu, Lifeng.
    In: Energy.
    RePEc:eee:energy:v:268:y:2023:i:c:s0360544223000592.

    Full description at Econpapers || Download paper

20. Multi-step ahead voltage prediction and voltage fault diagnosis based on gated recurrent unit neural network and incremental training. (2023). Chen, Zheng ; Zhang, Yuanjian ; Liu, Yonggang ; Shu, Xing ; Shen, Jiangwei ; Zhao, Hongqian.
    In: Energy.
    RePEc:eee:energy:v:266:y:2023:i:c:s0360544222033825.

    Full description at Econpapers || Download paper

21. State of Charge Estimation for Electric Vehicle Battery Management Systems Using the Hybrid Recurrent Learning Approach with Explainable Artificial Intelligence. (2022). Shahriar, Saleh Mohammed ; Ahsan, Mominul ; Nahiduzzaman, MD ; Haider, Julfikar ; Bhuiyan, Erphan A.
    In: Energies.
    RePEc:gam:jeners:v:15:y:2022:i:21:p:8003-:d:955612.

    Full description at Econpapers || Download paper

22. Lithium Battery State-of-Charge Estimation Based on a Bayesian Optimization Bidirectional Long Short-Term Memory Neural Network. (2022). Zhan, Yuedong ; Wang, Yinshuang ; Yang, Biao.
    In: Energies.
    RePEc:gam:jeners:v:15:y:2022:i:13:p:4670-:d:847988.

    Full description at Econpapers || Download paper

23. Prognostics and health management of Lithium-ion battery using deep learning methods: A review. (2022). Zhang, Ying ; Li, Yan-Fu.
    In: Renewable and Sustainable Energy Reviews.
    RePEc:eee:rensus:v:161:y:2022:i:c:s1364032122002015.

    Full description at Econpapers || Download paper

24. An optimized relevant long short-term memory-squared gain extended Kalman filter for the state of charge estimation of lithium-ion batteries. (2022). Xu, Wenhua ; Wang, Shunli ; Takyi-Aninakwa, Paul ; Li, Huan ; Zhang, Hongying ; Fernandez, Carlos.
    In: Energy.
    RePEc:eee:energy:v:260:y:2022:i:c:s0360544222019880.

    Full description at Econpapers || Download paper

25. Towards efficient state of charge estimation of lithium-ion batteries using canonical correlation analysis. (2022). Ni, Zichuan ; Yang, Ying ; Xiu, Xianchao.
    In: Energy.
    RePEc:eee:energy:v:254:y:2022:i:pc:s0360544222013184.

    Full description at Econpapers || Download paper

26. A deep learning approach to state of charge estimation of lithium-ion batteries based on dual-stage attention mechanism. (2022). Zhang, Zhen ; Tang, Yugui ; Yang, Kuo.
    In: Energy.
    RePEc:eee:energy:v:244:y:2022:i:pb:s0360544222001360.

    Full description at Econpapers || Download paper

27. Lithium-ion battery state-of-charge estimation for small target sample sets using the improved GRU-based transfer learning. (2022). Chen, Zhenhang ; Wang, Ya-Xiong ; Zhang, Wei.
    In: Energy.
    RePEc:eee:energy:v:244:y:2022:i:pb:s0360544222000810.

    Full description at Econpapers || Download paper

28. Effect analysis on SOC values of the power lithium manganate battery during discharging process and its intelligent estimation. (2022). Huang, Zhonghua ; Wei, Kexiang ; Tan, Jiqiu ; Zhu, Hong ; Zuo, Hongyan ; Zhang, Bin.
    In: Energy.
    RePEc:eee:energy:v:238:y:2022:i:pb:s0360544221021022.

    Full description at Econpapers || Download paper

29. Effects analysis on active equalization control of lithium-ion batteries based on intelligent estimation of the state-of-charge. (2022). Wen, Ming ; Wei, Kexiang ; Chen, Jingwei ; Zhu, Hao ; Zhang, Bin ; Huang, Zhonghua ; Deng, Yuanwang ; Zeng, Yan.
    In: Energy.
    RePEc:eee:energy:v:238:y:2022:i:pb:s0360544221020703.

    Full description at Econpapers || Download paper

30. An optimized long short-term memory-weighted fading extended Kalman filtering model with wide temperature adaptation for the state of charge estimation of lithium-ion batteries. (2022). Yang, Xiaoyong ; Wang, Shunli ; Takyi-Aninakwa, Paul ; Zhang, Hongying ; Fernandez, Carlos.
    In: Applied Energy.
    RePEc:eee:appene:v:326:y:2022:i:c:s0306261922013009.

    Full description at Econpapers || Download paper

31. A controllable deep transfer learning network with multiple domain adaptation for battery state-of-charge estimation. (2022). Kim, Youngki ; Oyewole, Isaiah ; Chehade, Abdallah.
    In: Applied Energy.
    RePEc:eee:appene:v:312:y:2022:i:c:s0306261922001842.

    Full description at Econpapers || Download paper

32. State-of-charge estimation of lithium-ion battery based on clockwork recurrent neural network. (2021). Zhang, Zhongwei ; Feng, Xiong ; Miao, Shuwen ; Zhu, Qiao ; Chen, Junxiong.
    In: Energy.
    RePEc:eee:energy:v:236:y:2021:i:c:s036054422101608x.

    Full description at Econpapers || Download paper

33. A method for state-of-charge estimation of lithium-ion batteries based on PSO-LSTM. (2021). Ren, Xiaoqing ; Yu, Xiaodong ; Dong, Xia ; Liu, Shulin.
    In: Energy.
    RePEc:eee:energy:v:234:y:2021:i:c:s0360544221014845.

    Full description at Econpapers || Download paper

34. Remaining useful life prediction of lithium battery based on capacity regeneration point detection. (2021). Zhang, Yong ; Zheng, Ying ; Yang, Weidong ; Ma, Qiuhui.
    In: Energy.
    RePEc:eee:energy:v:234:y:2021:i:c:s036054422101481x.

    Full description at Econpapers || Download paper

35. Synthetic state of charge estimation for lithium-ion batteries based on long short-term memory network modeling and adaptive H-Infinity filter. (2021). Chen, Zheng ; Zhang, Yuanjian ; Liu, Yonggang ; Shu, Xing ; Shen, Jiangwei ; Zhao, Hongqian.
    In: Energy.
    RePEc:eee:energy:v:228:y:2021:i:c:s0360544221008793.

    Full description at Econpapers || Download paper

36. State of charge estimation of lithium-ion battery using denoising autoencoder and gated recurrent unit recurrent neural network. (2021). Feng, Xiong ; Zhu, Qiao ; Jiang, Lin ; Chen, Junxiong.
    In: Energy.
    RePEc:eee:energy:v:227:y:2021:i:c:s0360544221007003.

    Full description at Econpapers || Download paper

37. Prognostics of battery cycle life in the early-cycle stage based on hybrid model. (2021). Peng, Zhen ; Zhang, YU ; Guan, Yong ; Wu, Lifeng.
    In: Energy.
    RePEc:eee:energy:v:221:y:2021:i:c:s036054422100150x.

    Full description at Econpapers || Download paper

38. State of charge estimation for lithium-ion battery based on an Intelligent Adaptive Extended Kalman Filter with improved noise estimator. (2021). Huang, Rui ; Bhagat, Rohit ; Zhou, Quan ; Sun, Daoming ; Wang, Chongming ; Zhang, Cheng ; Yu, Xiaoli ; Amietszajew, Taz.
    In: Energy.
    RePEc:eee:energy:v:214:y:2021:i:c:s0360544220321320.

    Full description at Econpapers || Download paper

39. An Improved Gated Recurrent Unit Network Model for State-of-Charge Estimation of Lithium-Ion Battery. (2020). Shao, Yulong ; Duan, Wenxian ; Peng, Silun ; Song, Shixin ; Xiao, Feng.
    In: Energies.
    RePEc:gam:jeners:v:13:y:2020:i:23:p:6366-:d:455329.

    Full description at Econpapers || Download paper

Report date: 2025-10-04 08:03:10 || Missing content? Let us know