The performance of lithium-ion batteries will decline dramatically with the increase in usage time, which will cause anxiety in using lithium-ion batteries. Some data-driven models have been employed to predict the remaining useful life (RUL) model of lithium-ion batteries. However, there are limitations to the accuracy and applicability of traditional machine learning models or just a single deep learning model. This paper presents a fusion model based on convolutional neural network (CNN) and long short-term memory network (LSTM), named CNN-LSTM, to measure the RUL of lithium-ion batteries. Firstly, this model uses the grey relational analysis to extract the main features affecting the RUL as the health index (HI) of the battery. In addition, the fusion model can capture the non-linear characteristics and time-space relationships well, which helps find the capacity decay and failure threshold of lithium-ion batteries. The experimental results show that: 1) Traditional machine learning is less effective than LSTM. 2) The CNN-LSTM fusion model is superior to the single LSTM model in predicting performance. 3) The proposed model is superior to other comparable models in error indexes, which could reach 0.36% and 0.38e-4 in mean absolute percentage error (MAPE) and mean square error (MSE), respectively. 4) The proposed model can accurately find the failure threshold and the decay fluctuation for the lithium-ion battery.
Citation: Dewang Chen, Xiaoyu Zheng, Ciyang Chen, Wendi Zhao. Remaining useful life prediction of the lithium-ion battery based on CNN-LSTM fusion model and grey relational analysis[J]. Electronic Research Archive, 2023, 31(2): 633-655. doi: 10.3934/era.2023031
The performance of lithium-ion batteries will decline dramatically with the increase in usage time, which will cause anxiety in using lithium-ion batteries. Some data-driven models have been employed to predict the remaining useful life (RUL) model of lithium-ion batteries. However, there are limitations to the accuracy and applicability of traditional machine learning models or just a single deep learning model. This paper presents a fusion model based on convolutional neural network (CNN) and long short-term memory network (LSTM), named CNN-LSTM, to measure the RUL of lithium-ion batteries. Firstly, this model uses the grey relational analysis to extract the main features affecting the RUL as the health index (HI) of the battery. In addition, the fusion model can capture the non-linear characteristics and time-space relationships well, which helps find the capacity decay and failure threshold of lithium-ion batteries. The experimental results show that: 1) Traditional machine learning is less effective than LSTM. 2) The CNN-LSTM fusion model is superior to the single LSTM model in predicting performance. 3) The proposed model is superior to other comparable models in error indexes, which could reach 0.36% and 0.38e-4 in mean absolute percentage error (MAPE) and mean square error (MSE), respectively. 4) The proposed model can accurately find the failure threshold and the decay fluctuation for the lithium-ion battery.
| [1] |
C. Depcik, T. Cassady, B. Collicott, S. P. Burugupally, J. Hobeck, Comparison of lithium-ion ion batteries, hydrogen fueled combustion Engines, and a hydrogen fuel cell in powering a small Unmanned Aerial Vehicle, Energy Convers. Manage., 207 (2020), 112514. https://doi.org/10.1016/j.enconman.2020.112514 doi: 10.1016/j.enconman.2020.112514
|
| [2] |
M. Chen, G. Rincon-Mora, Accurate electrical battery model capable of predicting runtime and Ⅰ–Ⅴ performance, IEEE Trans. Power Syst., 21 (2006), 504–511. https://doi.org/10.1109/TEC.2006.874229 doi: 10.1109/TEC.2006.874229
|
| [3] |
J. B. Goodenough, K. S. Park, The li-ion rechargeable battery: A perspective, J. Am. Chem. Soc., 135 (2013), 1167–1176. https://doi.org/10.1021/ja3091438 doi: 10.1021/ja3091438
|
| [4] |
Z. Liu, B. He, Z. Zhang, W. Deng, D. Dong, S. Xia, et al., Lithium/graphene composite anode with 3D structural LiF protection layer for high-performance lithium metal batteries, ACS Appl. Mater. Interfaces., 14 (2022), 2871–2880. https://doi.org/10.1021/acsami.1c21263 doi: 10.1021/acsami.1c21263
|
| [5] |
A. Attanayaka, J. Karunadasa, K. Hemapala, Estimation of state of charge for lithium-ion batteries-A review, AIMS Energy, 7 (2019), 186–210. https://doi.org/10.3934/energy.2019.2.186 doi: 10.3934/energy.2019.2.186
|
| [6] |
A. Basia, Z. Simeu-Abazi, E. Gascard, P. Zwolinski, Review on state of health estimation methodologies for lithium-ion batteries in the context of circular economy, CIRP J. Manuf. Sci. Technol., 32 (2021), 517–528. https://doi.org/10.1016/j.cirpj.2021.02.004 doi: 10.1016/j.cirpj.2021.02.004
|
| [7] |
C. Julien, A. Mauger, A. Abdel-Ghany, A. Hashem, K. Zaghib, Smart materials for energy storage in Li-ion batteries, AIMS Mater. Sci., 3 (2016), 137–148. https://doi.org/10.3934/matersci.2016.1.137 doi: 10.3934/matersci.2016.1.137
|
| [8] |
M. Ge, Y. Liu, X. Jiang, J. Liu, A review on state of health estimations and remaining useful life prognostics of lithium-ion batteries, Measurement, 174 (2021), 109057. https://doi.org/10.1016/j.measurement.2021.109057 doi: 10.1016/j.measurement.2021.109057
|
| [9] |
C. Hu, B. Youn, P. Wang, J. K. Yoon, Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life, Reliab. Eng. Syst. Saf., 103 (2012), 120–135. https://doi.org/10.1016/j.ress.2012.03.008 doi: 10.1016/j.ress.2012.03.008
|
| [10] |
S. Jarid, M. Das, An electro-thermal model based fast optimal charging strategy for lithium-ion batteries, AIMS Energy, 9 (2021), 915–933. https://doi.org/10.3934/energy.2021043 doi: 10.3934/energy.2021043
|
| [11] |
G. Ma, Y. Zhang, C. Cheng, B. Zhou, P. Hu, Y. Yuan, Remaining useful life prediction of lithium-ion batteries based on false nearest neighbors and a hybrid neural network, Appl. Energy, 253 (2019), 113626. https://doi.org/10.1016/j.apenergy.2019.113626 doi: 10.1016/j.apenergy.2019.113626
|
| [12] |
L. Wu, X. Fu, Y. Guan, Review of the remaining useful life prognostics of vehicle lithium-ion batteries using data-driven methodologies, Appl. Sci., 6 (2016), 166. https://doi.org/10.3390/app6060166 doi: 10.3390/app6060166
|
| [13] |
A. Nuhic, T. Terzimehic, T. Soczka-Guth, M. Buchholz, K. Dietmayer, Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methods, J. Power Sources, 239 (2013), 680–688. https://doi.org/10.1016/j.jpowsour.2012.11.146 doi: 10.1016/j.jpowsour.2012.11.146
|
| [14] |
S. Wang, S. Jin, D. Bai, Y. Fan, H. Shi, C. Fernandez, A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries, Energy Rep., 7 (2021), 5562–5574. https://doi.org/10.1016/j.egyr.2021.08.182 doi: 10.1016/j.egyr.2021.08.182
|
| [15] |
N. Khare, P. Singh, J. K. Vassiliou, A novel magnetic field prob-ing technique for determining state of health of sealed lead-acid batteries, J. Power Sources, 218 (2012), 462–473. https://doi.org/10.1016/j.jpowsour.2012.06.085 doi: 10.1016/j.jpowsour.2012.06.085
|
| [16] |
A. Mevawalla, Y. Shabeer, M. K. Tran, S. Panchal, M. Fowler, R. Fraser, Thermal modelling utilizing multiple experimentally measurable parameters, Batteries, 8 (2022), 147. https://doi.org/10.3390/batteries8100147 doi: 10.3390/batteries8100147
|
| [17] |
Y. Wang, D. Dan, Y. Zhang, Y. Qian, S. Panchal, M. Fowler, et al., A novel heat dissipation structure based on flat heat pipe for battery thermal management system, Int. J. Energy Res., 46 (2022), 15961–15980. https://doi.org/10.1002/er.8294 doi: 10.1002/er.8294
|
| [18] |
Y. Xie, W. Li, X. Hu, M. K. Tran, S. Panchal, M. Fowler, et al., Co-estimation of SOC and three-dimensional SOT for lithium-ion batteries based on distributed spatial-temporal online correction, IEEE Trans. Ind. Electron., 2022 (2022), 1–10. https://doi.org/10.1109/TIE.2022.3199905 doi: 10.1109/TIE.2022.3199905
|
| [19] | Y. Xing, N. Williard, K. L. Tsui, M. Pecht, A comparative review of prognostics-based reliability methods for Lithium batteries, in 2011 Prognostics and System Health Managment Confernece, 2011. https://doi.org/10.1109/PHM.2011.5939585 |
| [20] |
D. Wang, F. Yang, K. L. Tsui, Q. Zhou, B. S. Bae, Remaining useful life prediction of lithium-ion batteries based on spherical cubature particle filter, IEEE Trans. Instrum. Meas., 65 (2016), 1282–1291. https://doi.org/10.1109/TIM.2016.2534258 doi: 10.1109/TIM.2016.2534258
|
| [21] |
M. K. Tran, A. DaCosta, A. Mevawalla, S. Panchal, M. Fowler, Comparative study of equivalent circuit models performance in four common lithium-ion batteries: LFP, NMC, LMO, NCA, Batteries, 7 (2021), 51. https://doi.org/10.3390/batteries7030051 doi: 10.3390/batteries7030051
|
| [22] |
Z. Lyu, R. Gao, L. Chen, Li-ion battery state of health estimation and remaining useful life prediction through a model-data-fusion method, IEEE Trans. Power Electron., 36 (2021), 6228–6240. https://doi.org/10.1109/TPEL.2020.3033297 doi: 10.1109/TPEL.2020.3033297
|
| [23] |
S. Wang, P. Ren, P. Takyi-Aninakwa, S. Jin, C. Fernandez, A critical review of improved deep convolutional neural network for multi-timescale state prediction of lithium-ion batteries, Energies, 15 (2022), 5053. https://doi.org/10.3390/en15145053 doi: 10.3390/en15145053
|
| [24] |
S. Jin, X. Sui, X. Huang, S. Wang, R. Teodorescu, D. I. Stroe, Overview of machine learning methods for lithium-ion battery remaining useful lifetime prediction, Electronics, 10 (2021), 3126. https://doi.org/10.3390/electronics10243126 doi: 10.3390/electronics10243126
|
| [25] |
P. Khumprom, N. Yodo, A data-driven predictive prognostic model for lithium-ion batteries based on a deep learning algorithm, Energies, 12 (2019), 660. https://doi.org/10.3390/en12040660 doi: 10.3390/en12040660
|
| [26] |
L. Cai, J. Meng, D. I. Stroe, J. Peng, R. Teodorescu, Multi-objective optimization of data-driven model for lithium-ion battery SOH estimation with short-term feature, IEEE Trans. Power Electron., 35 (2020), 11855–11864. https://doi.org/10.1109/TPEL.2020.2987383 doi: 10.1109/TPEL.2020.2987383
|
| [27] |
T. Qin, S. Zeng, J. Guo, Robust prognostics for state of health estimation of lithium-ion batteries based on an improved PSO-SVR model, Microelectron. Reliab., 55 (2015), 1280–1284. https://doi.org/10.1016/j.microrel.2015.06.133 doi: 10.1016/j.microrel.2015.06.133
|
| [28] | Y. Cai, Y. Lin, Z. Deng, X. Zhao, D. Hao, Prediction of lithium-ion battery remaining useful life based on hybrid data-driven method with optimized parameter, in 2017 2nd International Conference on Power and Renewable Energy (ICPRE), 2017. https://doi.org/10.1109/ICPRE.2017.8390489 |
| [29] |
B. Gou, Y. Xu, X. Feng, State-of-health estimation and remaining useful life prediction for lithium-ion battery using a hybrid data-driven method, IEEE Trans. Veh. Technol., 69 (2020), 10854–10867. https://doi.org/10.1109/TVT.2020.3014932 doi: 10.1109/TVT.2020.3014932
|
| [30] |
G. Ma, Y. Zhang, C. Cheng, B. Zhou, Y. Yuan, Remaining useful life prediction of lithium-ion batteries based on false nearest neighbors and a hybrid neural network, Appl. Energy, 253 (2019), 113626. https://doi.org/10.1016/j.apenergy.2019.113626 doi: 10.1016/j.apenergy.2019.113626
|
| [31] |
Y. Zhang, R. Xiong, H. He, M. Pecht, Long short-term memory recurrent neural network for remaining useful life prediction of lithium-ion batteries, IEEE Trans. Veh. Technol., 67 (2018), 5695–5705. https://doi.org/10.1109/TVT.2018.2805189 doi: 10.1109/TVT.2018.2805189
|
| [32] |
S. Yalçın, S. Panchal, M. S. Herdem, A CNN-ABC model for estimation and optimization of heat generation rate and voltage distributions of lithium-ion batteries for electric vehicles, Int. J. Heat Mass. Tran., 199 (2022), 123486. https://doi.org/10.1016/j.ijheatmasstransfer.2022.123486 doi: 10.1016/j.ijheatmasstransfer.2022.123486
|
| [33] |
F. Wang, Z. Zhao, J. Ren, Z. Zhai, S. Wang, X. Chen, A transferable lithium-ion battery remaining useful life prediction method from cycle-consistency of degradation trend, J. Power Sources, 521 (2022), 230975. https://doi.org/10.1016/j.jpowsour.2022.230975 doi: 10.1016/j.jpowsour.2022.230975
|
| [34] |
S. Wang, P. Takyi-Aninakwa, S. Jin, C. Yu, C. Fernandez, D. I. Stroe, 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, Energy, 254 (2022), 124224. https://doi.org/10.1016/j.energy.2022.124224 doi: 10.1016/j.energy.2022.124224
|
| [35] |
M. Xia, X. Zheng, M. Imran, M. Shoaib, Data-driven prognosis method using hybrid deep recurrent neural network, Appl. Soft Comput., 93 (2020), 106351. https://doi.org/10.1016/j.asoc.2020.106351 doi: 10.1016/j.asoc.2020.106351
|
| [36] |
A. Kara, A data-driven approach based on deep neural networks for lithium-ion battery prognostics, Neural Comput. Appl., 33 (2021), 13525–13538. https://doi.org/10.1007/s00521-021-05976-x doi: 10.1007/s00521-021-05976-x
|
| [37] |
C. Wang, N. Lu, S. Wang, Y. Cheng, B. Jiang, Dynamic long short-term memory neural-network-based indirect remaining-useful-life prognosis for satellite lithium-ion battery, Appl. Sci., 8 (2018), 2078. https://doi.org/10.3390/app8112078 doi: 10.3390/app8112078
|
| [38] |
P. Li, Z. Zhang, Q. Xiong, B. Ding, S. Li, State-of-health estimation and remaining useful life prediction for the lithium-ion battery based on a variant long short term memory neural network, J. Power Sources, 459 (2020), 228069. https://doi.org/10.1016/j.jpowsour.2020.228069 doi: 10.1016/j.jpowsour.2020.228069
|
| [39] |
M. Geraldi, E. Ghisi, Data-driven framework towards realistic bottom-up energy benchmarking using an Artificial Neural Network, Appl. Energy, 306 (2022), 117960. https://doi.org/10.1016/j.apenergy.2021.117960 doi: 10.1016/j.apenergy.2021.117960
|
| [40] |
R. Lei, Z. Li, H. Sheng, S. Zhao, W. Hao, Z. Lin, Remaining useful life prediction for lithium-ion battery: A deep learning approach, IEEE Access, 6 (2018), 50587–50598. https://doi.org/10.1109/ACCESS.2018.2858856 doi: 10.1109/ACCESS.2018.2858856
|
| [41] |
N. Harting, R. Schenkendorf, N. Wolff, U. Krewer, State-of-health identification of lithium-ion batteries based on nonlinear frequency response analysis: First steps with machine learning, Appl. Sci., 8 (2018), 821. https://doi.org/10.3390/app8050821 doi: 10.3390/app8050821
|
| [42] |
B. Zraibi, C. Okar, H. Chaoui, M. Mansouri, Remaining useful life assessment for lithium-ion batteries using CNN-LSTM-DNN hybrid method, IEEE Trans. Veh. Technol., 70 (2021), 4252–4261. https://doi.org/10.1109/TVT.2021.3071622 doi: 10.1109/TVT.2021.3071622
|
| [43] |
Y. Anagun, S. Isik, E. Seke, SRLibrary: Comparing different loss functions for super-resolution over various convolutional architectures, J. Visual Commun. Image Represent., 61 (2019), 178–187. https://doi.org/10.1016/j.jvcir.2019.03.027 doi: 10.1016/j.jvcir.2019.03.027
|
| [44] |
Y. Zhou, M. Huang, Lithium-ion batteries remaining useful life prediction based on a mixture of empirical mode decomposition and ARIMA model, Microelectron. Reliab., 65 (2016), 265–273. https://doi.org/10.1016/j.microrel.2016.07.151 doi: 10.1016/j.microrel.2016.07.151
|
| [45] |
R. Sekhar, P. Shah, S. Panchal, M. Fowler, R. Fraser, Distance to empty soft sensor for ford escape electric vehicle, Results Control Optim., 9 (2022), 100168, https://doi.org/10.1016/j.rico.2022.100168 doi: 10.1016/j.rico.2022.100168
|
| [46] |
M. Li, J. Lu, Z. Chen, K. Amine, 30 years of lithium-ion batteries, Adv. Mater., 30 (2018), 1800561. https://doi.org/10.1002/adma.201800561 doi: 10.1002/adma.201800561
|
| [47] |
M. M. Kabir, D. E. Demirocak, Degradation mechanisms in Li-ion batteries: a state-of-the-art review, Int. J. Energy Res., 41 (2017), 1963–1986. https://doi.org/10.1002/er.3762 doi: 10.1002/er.3762
|
| [48] |
J. Vetter, P. Novák, M. R. Wagner, C. Veit, K. C. Möller, J. O. Besenhard, et al., Ageing mechanisms in lithium-ion batterie, J. Power Sources, 147 (2005), 269–281. https://doi.org/10.1016/j.jpowsour.2005.01.006 doi: 10.1016/j.jpowsour.2005.01.006
|
| [49] | B. Saha, K. Goebel, Battery data set, in NASA Ames Prognostics Data Repository, 2007. Available from: http://ti.arc.nasa.gov/project/prognostic-data-repository. |
| [50] |
L. Alzubaidi, J. Zhang, A. J. Humaidi, A. Al-Dujaili, Y. Duan, O. Al-Shamma, et al., Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions, J. Big Data, 8 (2021), 1–74. https://doi.org/10.1186/s40537-021-00444-8 doi: 10.1186/s40537-021-00444-8
|
| [51] |
D. Yao, B. Li, H. Liu, J. Yang, L. Jia, Remaining useful life prediction of roller bearings based on improved 1D-CNN and simple recurrent unit, Measurement, 175 (2021), 109166. https://doi.org/10.1016/j.measurement.2021.109166 doi: 10.1016/j.measurement.2021.109166
|
| [52] |
A. Sherstinsky, Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network, Physica D, 404 (2020), 132306. https://doi.org/10.1016/j.physd.2019.132306 doi: 10.1016/j.physd.2019.132306
|
| [53] |
Z. Shi, A. Chehade, A dual-LSTM framework combining change point detection and remaining useful life prediction, Reliab. Eng. Syst. Saf., 205 (2021), 107257. https://doi.org/10.1016/j.ress.2020.107257 doi: 10.1016/j.ress.2020.107257
|
| [54] |
Y. Choi, S. Ryu, K. Park, H. Kim, Machine learning-based lithium-ion battery capacity estimation exploiting multi-channel charging profiles, IEEE Access, 7 (2019), 75143–75152. https://doi.org/10.1109/ACCESS.2019.2920932 doi: 10.1109/ACCESS.2019.2920932
|
| [55] |
X. Hu, J. Jiang, D. Cao, B. Egardt, Battery health prognosis for electric vehicles using sample entropy and sparse bayesian predictive modeling, IEEE Trans. Ind. Electron., 63 (2016), 2645–2656. https://doi.org/10.1109/TIE.2015.2461523 doi: 10.1109/TIE.2015.2461523
|
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Dewang Chen, Xiaoyu Zheng, Ciyang Chen, Wendi Zhao. Remaining useful life prediction of the lithium-ion battery based on CNN-LSTM fusion model and grey relational analysis[J]. Electronic Research Archive, 2023, 31(2): 633-655. doi: 10.3934/era.2023031
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