Remaining useful life prediction for lithium-ion batteries based on a hybrid model combining the long short-term memory and Elman neural networks
This paper presents a novel hybrid Elman-LSTM method for battery remaining useful life prediction by combining the empirical model decomposition algorithm and long short-term memory and Elman neural networks. The empirical model decomposition algorithm is employed to decompose the recorded battery c...
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| Published in | Journal of energy storage Vol. 21; pp. 510 - 518 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.02.2019
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| Subjects | |
| Online Access | Get full text |
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| Abstract | This paper presents a novel hybrid Elman-LSTM method for battery remaining useful life prediction by combining the empirical model decomposition algorithm and long short-term memory and Elman neural networks. The empirical model decomposition algorithm is employed to decompose the recorded battery capacity verse cycle number data into several sub-layers. The recurrent long short-term memory and Elman neural networks are then established to predict high- and low-frequency sub-layers, respectively. Comprehensive battery test datasets have been collected and used for model parameterization and performance evaluation. The comparison results indicate that the proposed hybrid Elman-LSTM model yields superior performance relative to the other counterparts and can predict the battery remaining useful life with high accuracy. The relative prediction errors are 3.3% and 3.21% based on two unseen datasets, respectively. |
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| AbstractList | This paper presents a novel hybrid Elman-LSTM method for battery remaining useful life prediction by combining the empirical model decomposition algorithm and long short-term memory and Elman neural networks. The empirical model decomposition algorithm is employed to decompose the recorded battery capacity verse cycle number data into several sub-layers. The recurrent long short-term memory and Elman neural networks are then established to predict high- and low-frequency sub-layers, respectively. Comprehensive battery test datasets have been collected and used for model parameterization and performance evaluation. The comparison results indicate that the proposed hybrid Elman-LSTM model yields superior performance relative to the other counterparts and can predict the battery remaining useful life with high accuracy. The relative prediction errors are 3.3% and 3.21% based on two unseen datasets, respectively. |
| Author | Dong, Peng Wang, Zhenpo Zhang, Lei Li, Xiaoyu |
| Author_xml | – sequence: 1 givenname: Xiaoyu surname: Li fullname: Li, Xiaoyu organization: National Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, Beijing, 100081, China – sequence: 2 givenname: Lei orcidid: 0000-0002-2095-1976 surname: Zhang fullname: Zhang, Lei email: lei_zhang@bit.edu.cn organization: National Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, Beijing, 100081, China – sequence: 3 givenname: Zhenpo surname: Wang fullname: Wang, Zhenpo email: wangzhenpo@bit.edu.cn organization: National Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, Beijing, 100081, China – sequence: 4 givenname: Peng surname: Dong fullname: Dong, Peng organization: National Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, Beijing, 100081, China |
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| Cites_doi | 10.1016/j.rser.2016.08.039 10.1207/s15516709cog1402_1 10.1016/j.jpowsour.2017.05.004 10.1109/TR.2018.2803798 10.1016/j.energy.2016.12.004 10.1016/j.jpowsour.2018.04.012 10.1016/j.microrel.2012.12.004 10.1016/j.microrel.2015.06.133 10.1016/j.jpowsour.2014.07.176 10.4271/2011-01-2247 10.1016/j.measurement.2014.11.031 10.1016/j.apenergy.2015.08.119 10.1109/ACCESS.2017.2759094 10.1109/ACCESS.2018.2824838 10.1109/ICPHM.2017.7998298 10.1016/j.ress.2010.08.009 10.1098/rspa.1998.0193 10.1016/j.jpowsour.2018.10.069 10.1016/j.energy.2017.08.017 10.36001/phmconf.2010.v2i1.1896 10.1016/j.neucom.2004.10.077 10.1016/j.ijepes.2013.10.020 10.1016/j.apenergy.2013.12.020 10.1109/TIE.2016.2623260 10.1016/j.microrel.2017.06.045 10.1016/j.apenergy.2014.03.086 10.20964/2018.05.84 10.1149/2.0251701jes 10.1016/j.jclepro.2018.06.182 10.1109/TVT.2017.2762368 10.1016/j.ymssp.2017.02.027 10.1016/j.microrel.2017.02.012 |
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| References | Zhang, Xiong, He, Pecht (bib0040) 2018 Richardson, Osborne, Howey (bib0025) 2017; 357 Richardson, Berkl, Osborne, Howey (bib0060) 2018 Wang, Zhang, Liu, Pan, Chen (bib0035) 2018; 389 Voronov, Frisk, Krysander (bib0045) 2018; 67 Hu, Jain, Tamirisa, Gorka (bib0100) 2014; 126 Liu, Zhou, Pan, Peng, Peng (bib0110) 2015; 63 7–13. Qin, Zeng, Guo (bib0135) 2015; 55 Zio, Peloni (bib0070) 2011; 96 J. Liu, A. Saxena, G. Kai, B. Saha, W. Wang. An adaptive recurrent neural network for remaining useful life prediction of lithium-ion batteries. Dong, Jin, Lou, Wang (bib0125) 2014; 271 Zhang, Hu, Wang, Sun, Deng, Dorrell (bib0020) 2018; 67 Elman (bib0170) 1990; 14 G. Zhao, G. Zhang, Y. Liu, B. Zhang, C. Hu. Lithium-ion battery remaining useful life prediction with deep belief network and relevance vector machine. Wang, Ma, Zhang (bib0050) 2017; 5 Ng, Xing, Tsui (bib0075) 2014; 118 Zhang, Zhang, Sun, Wang (bib0015) 2018; 6 Wang, Zhao, Yang, Tsui (bib0055) 2017; 93 Yang, Peng, Wang, Yuan, Wu (bib0065) 2018; 13 Chen, Zheng, Jiang, Zhang, Jiang, Chen (bib0085) 2017; 138 Lyu, Lai, Ge, Yu, Wang, Ma (bib0090) 2017; 120 Huang (bib0160) 1998; 454 Li, Wang, Zhang, Zou, Dorrell (bib0030) 2019; 410-411 . Zhang, Xiong, He, Liu (bib0155) 2017 Miao, Xie, Cui, Liang, Pecht (bib0080) 2013; 53 Khelif, Chebel-Morello, Malinowski, Laajili, Fnaiech, Zerhouni (bib0105) 2017; 64 Rezvani, Lee, Lee (bib0150) 2011 Song, Liu, Yang, Peng (bib0130) 2017; 75 Liang, Bressler, Desimone, Fries (bib0165) 2005; 65 Liu, Song, Li, Liao, Peng (bib0115) 2018; 199 Peters, Baumann, Zimmermann, Braun, Weil (bib0005) 2017; 67 Zhang, Miao, Liu (bib0095) 2017; 75 Patil (bib0120) 2015; 159 Li, Li, Xiong, Chai, Zhang (bib0175) 2014; 55 Blomgren (bib0010) 2017; 164 Zhang (10.1016/j.est.2018.12.011_bib0095) 2017; 75 10.1016/j.est.2018.12.011_bib0140 Dong (10.1016/j.est.2018.12.011_bib0125) 2014; 271 Li (10.1016/j.est.2018.12.011_bib0175) 2014; 55 Wang (10.1016/j.est.2018.12.011_bib0050) 2017; 5 Zhang (10.1016/j.est.2018.12.011_bib0020) 2018; 67 Patil (10.1016/j.est.2018.12.011_bib0120) 2015; 159 Richardson (10.1016/j.est.2018.12.011_bib0025) 2017; 357 Chen (10.1016/j.est.2018.12.011_bib0085) 2017; 138 Zio (10.1016/j.est.2018.12.011_bib0070) 2011; 96 Qin (10.1016/j.est.2018.12.011_bib0135) 2015; 55 Richardson (10.1016/j.est.2018.12.011_bib0060) 2018 Peters (10.1016/j.est.2018.12.011_bib0005) 2017; 67 Zhang (10.1016/j.est.2018.12.011_bib0015) 2018; 6 Miao (10.1016/j.est.2018.12.011_bib0080) 2013; 53 Voronov (10.1016/j.est.2018.12.011_bib0045) 2018; 67 Liu (10.1016/j.est.2018.12.011_bib0110) 2015; 63 Liang (10.1016/j.est.2018.12.011_bib0165) 2005; 65 Liu (10.1016/j.est.2018.12.011_bib0115) 2018; 199 Rezvani (10.1016/j.est.2018.12.011_bib0150) 2011 Li (10.1016/j.est.2018.12.011_bib0030) 2019; 410-411 Lyu (10.1016/j.est.2018.12.011_bib0090) 2017; 120 Khelif (10.1016/j.est.2018.12.011_bib0105) 2017; 64 Elman (10.1016/j.est.2018.12.011_bib0170) 1990; 14 Hu (10.1016/j.est.2018.12.011_bib0100) 2014; 126 Song (10.1016/j.est.2018.12.011_bib0130) 2017; 75 Yang (10.1016/j.est.2018.12.011_bib0065) 2018; 13 Wang (10.1016/j.est.2018.12.011_bib0035) 2018; 389 Zhang (10.1016/j.est.2018.12.011_bib0155) 2017 Wang (10.1016/j.est.2018.12.011_bib0055) 2017; 93 10.1016/j.est.2018.12.011_bib0145 Huang (10.1016/j.est.2018.12.011_bib0160) 1998; 454 Ng (10.1016/j.est.2018.12.011_bib0075) 2014; 118 Zhang (10.1016/j.est.2018.12.011_bib0040) 2018 Blomgren (10.1016/j.est.2018.12.011_bib0010) 2017; 164 |
| References_xml | – volume: 13 start-page: 4991 year: 2018 end-page: 5004 ident: bib0065 article-title: The remaining useful life estimation of lithium-ion battery based on improved extreme learning machine algorithm publication-title: Int. J. Electrochem. Sci. – reference: G. Zhao, G. Zhang, Y. Liu, B. Zhang, C. Hu. Lithium-ion battery remaining useful life prediction with deep belief network and relevance vector machine. – year: 2018 ident: bib0060 article-title: Gaussian process regression for in-situ capacity estimation of lithium-ion batteries publication-title: IEEE Trans. Ind. Informat. – volume: 75 start-page: 288 year: 2017 end-page: 295 ident: bib0095 article-title: Remaining useful life prediction of lithium-ion battery using an improved UPF method based on MCMC publication-title: Microelectron. Reliab. – volume: 55 start-page: 749 year: 2014 end-page: 759 ident: bib0175 article-title: Application of a hybrid quantized Elman neural network in short-term load forecasting publication-title: Int. J. Electric. Power Energy Syst. – reference: , 7–13. – volume: 389 start-page: 93 year: 2018 end-page: 105 ident: bib0035 article-title: Multi-timescale power and energy assessment of lithium-ion battery and supercapacitor hybrid system using extended Kalman filter publication-title: J. Power Sources – volume: 199 start-page: 1050 year: 2018 end-page: 1065 ident: bib0115 article-title: On-line life cycle health assessment for lithium-ion battery in electric vehicles publication-title: J. Cleaner Prod. – volume: 454 start-page: 903 year: 1998 end-page: 995 ident: bib0160 article-title: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis publication-title: Proc. R. Soc. Lond. A: Math. Phys. Eng. Sci. – volume: 96 start-page: 403 year: 2011 end-page: 409 ident: bib0070 article-title: Particle filtering prognostic estimation of the remaining useful life of nonlinear components publication-title: Reliab. Eng. Syst. Saf. – volume: 55 start-page: 1280 year: 2015 end-page: 1284 ident: bib0135 article-title: Robust prognostics for state of health estimation of lithium-ion batteries based on an improved PSO–SVR model publication-title: Microelectron. Reliab. – volume: 164 start-page: A5019 year: 2017 end-page: A5025 ident: bib0010 article-title: The development and future of lithium ion batteries publication-title: J. Electrochem. Soc. – volume: 120 start-page: 975 year: 2017 end-page: 984 ident: bib0090 article-title: A lead-acid battery’s remaining useful life prediction by using electrochemical model in the particle filtering framework publication-title: Energy – volume: 64 start-page: 2276 year: 2017 end-page: 2285 ident: bib0105 article-title: Direct remaining useful life estimation based on support vector regression publication-title: IEEE Trans. Ind. Electron. – volume: 410-411 start-page: 106 year: 2019 end-page: 114 ident: bib0030 article-title: State-of-health estimation for Li-ion batteries by combing the incremental capacity analysis method with grey relational analysis publication-title: J. Power Sources – volume: 75 start-page: 142 year: 2017 end-page: 153 ident: bib0130 article-title: Data-driven hybrid remaining useful life estimation approach for spacecraft lithium-ion battery publication-title: Microelectron. Reliab. – volume: 67 start-page: 491 year: 2017 end-page: 506 ident: bib0005 article-title: The environmental impact of Li-ion batteries and the role of key parameters–a review publication-title: Renew. Sustain. Energy Rev. – volume: 118 start-page: 114 year: 2014 end-page: 123 ident: bib0075 article-title: A naive Bayes model for robust remaining useful life prediction of lithium-ion battery publication-title: Appl. Energy – volume: 93 start-page: 531 year: 2017 end-page: 544 ident: bib0055 article-title: Nonlinear-drifted Brownian motion with multiple hidden states for remaining useful life prediction of rechargeable batteries publication-title: Mech. Syst. Sig. Process. – volume: 126 start-page: 182 year: 2014 end-page: 189 ident: bib0100 article-title: Method for estimating capacity and predicting remaining useful life of lithium-ion battery publication-title: Appl. Energy – start-page: 1 year: 2017 end-page: 4 ident: bib0155 article-title: A LSTM-RNN method for the lithuim-ion battery remaining useful life prediction publication-title: 2017 Prognostics and System Health Management Conference (PHM-Harbin) – volume: 67 start-page: 623 year: 2018 end-page: 639 ident: bib0045 article-title: Data-driven battery lifetime prediction and confidence estimation for heavy-duty trucks publication-title: IEEE Trans. Reliab. – volume: 63 start-page: 143 year: 2015 end-page: 151 ident: bib0110 article-title: Lithium-ion battery remaining useful life estimation with an optimized relevance vector machine algorithm with incremental learning publication-title: Measurement – volume: 271 start-page: 114 year: 2014 end-page: 123 ident: bib0125 article-title: Lithium-ion battery state of health monitoring and remaining useful life prediction based on support vector regression-particle filter publication-title: J. Power Sources – volume: 14 start-page: 179 year: 1990 end-page: 211 ident: bib0170 article-title: Finding structure in time publication-title: Cogn. Sci. – volume: 6 start-page: 23848 year: 2018 end-page: 23863 ident: bib0015 article-title: An overview on thermal safety issues of lithium-ion batteries for electric vehicle application publication-title: IEEE Access – volume: 5 start-page: 21286 year: 2017 end-page: 21295 ident: bib0050 article-title: State-of-health estimation for lithium-ion batteries based on the multi-island genetic algorithm and the gaussian process regression publication-title: IEEE Access – volume: 53 start-page: 805 year: 2013 end-page: 810 ident: bib0080 article-title: Remaining useful life prediction of lithium-ion battery with unscented particle filter technique publication-title: Microelectron. Reliab. – reference: . – year: 2011 ident: bib0150 article-title: A comparative analysis of techniques for electric vehicle battery prognostics and health management (PHM) publication-title: SAE Technical Paper – volume: 159 start-page: 285 year: 2015 end-page: 297 ident: bib0120 article-title: A novel multistage support vector machine based approach for Li ion battery remaining useful life estimation publication-title: Appl. Energy – volume: 67 start-page: 1027 year: 2018 end-page: 1035 ident: bib0020 article-title: Multiobjective optimal sizing of hybrid energy storage system for electric vehicles publication-title: IEEE Trans. Veh. Technol. – volume: 138 start-page: 1199 year: 2017 end-page: 1208 ident: bib0085 article-title: Practical failure recognition model of lithium-ion batteries based on partial charging process publication-title: Energy – reference: J. Liu, A. Saxena, G. Kai, B. Saha, W. Wang. An adaptive recurrent neural network for remaining useful life prediction of lithium-ion batteries. – volume: 357 start-page: 209 year: 2017 end-page: 219 ident: bib0025 article-title: Gaussian process regression for forecasting battery state of health publication-title: J. Power Sources – volume: 65 start-page: 801 year: 2005 end-page: 807 ident: bib0165 article-title: Empirical mode decomposition: a method for analyzing neural data publication-title: Neurocomputing – year: 2018 ident: bib0040 article-title: Long short-term memory recurrent neural network for remaining useful life prediction of lithium-ion batteries publication-title: IEEE Trans. Veh. Technol. – volume: 67 start-page: 491 year: 2017 ident: 10.1016/j.est.2018.12.011_bib0005 article-title: The environmental impact of Li-ion batteries and the role of key parameters–a review publication-title: Renew. Sustain. Energy Rev. doi: 10.1016/j.rser.2016.08.039 – volume: 14 start-page: 179 issue: 2 year: 1990 ident: 10.1016/j.est.2018.12.011_bib0170 article-title: Finding structure in time publication-title: Cogn. Sci. doi: 10.1207/s15516709cog1402_1 – volume: 357 start-page: 209 year: 2017 ident: 10.1016/j.est.2018.12.011_bib0025 article-title: Gaussian process regression for forecasting battery state of health publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2017.05.004 – volume: 67 start-page: 623 issue: 2 year: 2018 ident: 10.1016/j.est.2018.12.011_bib0045 article-title: Data-driven battery lifetime prediction and confidence estimation for heavy-duty trucks publication-title: IEEE Trans. Reliab. doi: 10.1109/TR.2018.2803798 – volume: 120 start-page: 975 year: 2017 ident: 10.1016/j.est.2018.12.011_bib0090 article-title: A lead-acid battery’s remaining useful life prediction by using electrochemical model in the particle filtering framework publication-title: Energy doi: 10.1016/j.energy.2016.12.004 – volume: 389 start-page: 93 year: 2018 ident: 10.1016/j.est.2018.12.011_bib0035 article-title: Multi-timescale power and energy assessment of lithium-ion battery and supercapacitor hybrid system using extended Kalman filter publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2018.04.012 – volume: 53 start-page: 805 issue: 6 year: 2013 ident: 10.1016/j.est.2018.12.011_bib0080 article-title: Remaining useful life prediction of lithium-ion battery with unscented particle filter technique publication-title: Microelectron. Reliab. doi: 10.1016/j.microrel.2012.12.004 – volume: 55 start-page: 1280 issue: 9 year: 2015 ident: 10.1016/j.est.2018.12.011_bib0135 article-title: Robust prognostics for state of health estimation of lithium-ion batteries based on an improved PSO–SVR model publication-title: Microelectron. Reliab. doi: 10.1016/j.microrel.2015.06.133 – volume: 271 start-page: 114 year: 2014 ident: 10.1016/j.est.2018.12.011_bib0125 article-title: Lithium-ion battery state of health monitoring and remaining useful life prediction based on support vector regression-particle filter publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2014.07.176 – year: 2011 ident: 10.1016/j.est.2018.12.011_bib0150 article-title: A comparative analysis of techniques for electric vehicle battery prognostics and health management (PHM) doi: 10.4271/2011-01-2247 – volume: 63 start-page: 143 year: 2015 ident: 10.1016/j.est.2018.12.011_bib0110 article-title: Lithium-ion battery remaining useful life estimation with an optimized relevance vector machine algorithm with incremental learning publication-title: Measurement doi: 10.1016/j.measurement.2014.11.031 – volume: 159 start-page: 285 year: 2015 ident: 10.1016/j.est.2018.12.011_bib0120 article-title: A novel multistage support vector machine based approach for Li ion battery remaining useful life estimation publication-title: Appl. Energy doi: 10.1016/j.apenergy.2015.08.119 – volume: 5 start-page: 21286 year: 2017 ident: 10.1016/j.est.2018.12.011_bib0050 article-title: State-of-health estimation for lithium-ion batteries based on the multi-island genetic algorithm and the gaussian process regression publication-title: IEEE Access doi: 10.1109/ACCESS.2017.2759094 – volume: 6 start-page: 23848 year: 2018 ident: 10.1016/j.est.2018.12.011_bib0015 article-title: An overview on thermal safety issues of lithium-ion batteries for electric vehicle application publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2824838 – ident: 10.1016/j.est.2018.12.011_bib0140 doi: 10.1109/ICPHM.2017.7998298 – volume: 96 start-page: 403 issue: 3 year: 2011 ident: 10.1016/j.est.2018.12.011_bib0070 article-title: Particle filtering prognostic estimation of the remaining useful life of nonlinear components publication-title: Reliab. Eng. Syst. Saf. doi: 10.1016/j.ress.2010.08.009 – volume: 454 start-page: 903 issue: 1971 year: 1998 ident: 10.1016/j.est.2018.12.011_bib0160 article-title: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis publication-title: Proc. R. Soc. Lond. A: Math. Phys. Eng. Sci. doi: 10.1098/rspa.1998.0193 – volume: 410-411 start-page: 106 year: 2019 ident: 10.1016/j.est.2018.12.011_bib0030 article-title: State-of-health estimation for Li-ion batteries by combing the incremental capacity analysis method with grey relational analysis publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2018.10.069 – volume: 138 start-page: 1199 year: 2017 ident: 10.1016/j.est.2018.12.011_bib0085 article-title: Practical failure recognition model of lithium-ion batteries based on partial charging process publication-title: Energy doi: 10.1016/j.energy.2017.08.017 – ident: 10.1016/j.est.2018.12.011_bib0145 doi: 10.36001/phmconf.2010.v2i1.1896 – start-page: 1 year: 2017 ident: 10.1016/j.est.2018.12.011_bib0155 article-title: A LSTM-RNN method for the lithuim-ion battery remaining useful life prediction – volume: 65 start-page: 801 year: 2005 ident: 10.1016/j.est.2018.12.011_bib0165 article-title: Empirical mode decomposition: a method for analyzing neural data publication-title: Neurocomputing doi: 10.1016/j.neucom.2004.10.077 – volume: 55 start-page: 749 year: 2014 ident: 10.1016/j.est.2018.12.011_bib0175 article-title: Application of a hybrid quantized Elman neural network in short-term load forecasting publication-title: Int. J. Electric. Power Energy Syst. doi: 10.1016/j.ijepes.2013.10.020 – volume: 118 start-page: 114 year: 2014 ident: 10.1016/j.est.2018.12.011_bib0075 article-title: A naive Bayes model for robust remaining useful life prediction of lithium-ion battery publication-title: Appl. Energy doi: 10.1016/j.apenergy.2013.12.020 – volume: 64 start-page: 2276 issue: 3 year: 2017 ident: 10.1016/j.est.2018.12.011_bib0105 article-title: Direct remaining useful life estimation based on support vector regression publication-title: IEEE Trans. Ind. Electron. doi: 10.1109/TIE.2016.2623260 – volume: 75 start-page: 142 year: 2017 ident: 10.1016/j.est.2018.12.011_bib0130 article-title: Data-driven hybrid remaining useful life estimation approach for spacecraft lithium-ion battery publication-title: Microelectron. Reliab. doi: 10.1016/j.microrel.2017.06.045 – volume: 126 start-page: 182 year: 2014 ident: 10.1016/j.est.2018.12.011_bib0100 article-title: Method for estimating capacity and predicting remaining useful life of lithium-ion battery publication-title: Appl. Energy doi: 10.1016/j.apenergy.2014.03.086 – volume: 13 start-page: 4991 year: 2018 ident: 10.1016/j.est.2018.12.011_bib0065 article-title: The remaining useful life estimation of lithium-ion battery based on improved extreme learning machine algorithm publication-title: Int. J. Electrochem. Sci. doi: 10.20964/2018.05.84 – volume: 164 start-page: A5019 issue: 1 year: 2017 ident: 10.1016/j.est.2018.12.011_bib0010 article-title: The development and future of lithium ion batteries publication-title: J. Electrochem. Soc. doi: 10.1149/2.0251701jes – year: 2018 ident: 10.1016/j.est.2018.12.011_bib0040 article-title: Long short-term memory recurrent neural network for remaining useful life prediction of lithium-ion batteries publication-title: IEEE Trans. Veh. Technol. – volume: 199 start-page: 1050 year: 2018 ident: 10.1016/j.est.2018.12.011_bib0115 article-title: On-line life cycle health assessment for lithium-ion battery in electric vehicles publication-title: J. Cleaner Prod. doi: 10.1016/j.jclepro.2018.06.182 – volume: 67 start-page: 1027 issue: 2 year: 2018 ident: 10.1016/j.est.2018.12.011_bib0020 article-title: Multiobjective optimal sizing of hybrid energy storage system for electric vehicles publication-title: IEEE Trans. Veh. Technol. doi: 10.1109/TVT.2017.2762368 – volume: 93 start-page: 531 year: 2017 ident: 10.1016/j.est.2018.12.011_bib0055 article-title: Nonlinear-drifted Brownian motion with multiple hidden states for remaining useful life prediction of rechargeable batteries publication-title: Mech. Syst. Sig. Process. doi: 10.1016/j.ymssp.2017.02.027 – year: 2018 ident: 10.1016/j.est.2018.12.011_bib0060 article-title: Gaussian process regression for in-situ capacity estimation of lithium-ion batteries publication-title: IEEE Trans. Ind. Informat. – volume: 75 start-page: 288 year: 2017 ident: 10.1016/j.est.2018.12.011_bib0095 article-title: Remaining useful life prediction of lithium-ion battery using an improved UPF method based on MCMC publication-title: Microelectron. Reliab. doi: 10.1016/j.microrel.2017.02.012 |
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| SubjectTerms | Electric vehicles Elman neural network Lithium-ion batteries Long short-term memory Remaining useful life |
| Title | Remaining useful life prediction for lithium-ion batteries based on a hybrid model combining the long short-term memory and Elman neural networks |
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