Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Predicting the state of charge and health of batteries using data-driven machine learning

An Author Correction to this article was published on 12 June 2020

Abstract

Machine learning is a specific application of artificial intelligence that allows computers to learn and improve from data and experience via sets of algorithms, without the need for reprogramming. In the field of energy storage, machine learning has recently emerged as a promising modelling approach to determine the state of charge, state of health and remaining useful life of batteries. First, we review the two most studied types of battery models in the literature for battery state prediction: the equivalent circuit and physics-based models. Based on the current limitations of these models, we showcase the promise of various machine learning techniques for fast and accurate battery state prediction. Finally, we highlight the major challenges involved, especially in accurate modelling over length and time, performing in situ calculations and high-throughput data generation. Overall, this work provides insights into real-time, explainable machine learning for battery production, management and optimization in the future.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: A machine learning approach for SOC, SOH and RUL predictions of Li-ion batteries.
Fig. 2: Accuracy versus CPU time for ECM, SPM and P2D model.
Fig. 3: High-throughput battery fabrication and testing.

References

  1. 1.

    Whittingham, M. S. Ultimate limits to intercalation reactions for lithium batteries. Chem. Rev. 114, 11414–11443 (2014).

    Article  Google Scholar 

  2. 2.

    Li, Y. et al. Data-driven health estimation and lifetime prediction of lithium-ion batteries: a review. Renew. Sust. Energy Rev. 113, 109254 (2019).

    Article  Google Scholar 

  3. 3.

    Severson, K. A. et al. Data-driven prediction of battery cycle life before capacity degradation. Nat. Energy 4, 383–391 (2019). This work presented a simple data-driven linear model for accurate prediction of RUL of lithium-ion batteries (>90% accuracy) using only early cycle data with no prior knowledge of degradation mechanisms.

    Article  Google Scholar 

  4. 4.

    Nuhic, A., Terzimehic, T., Soczka-Guth, T., Buchholz, M. & Dietmayer, K. Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methods. J. Power Sources 239, 680–688 (2013). This work presented a new data-driven approach using support-vector machine for embedding diagnosis and prognostics of battery health for automotive applications, and is able to take into account the effects of environmental, ambient and load conditions as well as the operation history.

    Article  Google Scholar 

  5. 5.

    Cuma, M. C. & Koroglu, T. A. A comprehensive review on estimation strategies used in hybrid and battery electric vehicles. Renew. Sust. Energy Rev. 42, 517–531 (2015).

    Article  Google Scholar 

  6. 6.

    Waag, W., Fleischer, C. & Sauer, D. U. Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles. J. Power Sources 258, 321–339 (2014).

    Article  Google Scholar 

  7. 7.

    Hannan, M. A., Lipu, M. S. H., Hussain, A. & Mohamed, A. A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: challenges and recommendations. Renew. Sust. Energy Rev. 78, 834–854 (2017).

    Article  Google Scholar 

  8. 8.

    Zheng, Y., Ouyang, M., Han, X., Lu, L. & Li, J. Investigating the error sources of the online state of charge estimation methods for lithium-ion batteries in electric vehicles. J. Power Sources 377, 161–188 (2018).

    Article  Google Scholar 

  9. 9.

    Xiong, R., Cao, J., Yu, Q., He, H. & Sun, F. Critical review on the battery state of charge estimation methods for electric vehicles. IEEE Access 6, 1832–1843 (2017).

    Article  Google Scholar 

  10. 10.

    Xiong, R., Li, L. & Tian, J. Towards a smarter battery management system: a critical review on battery state of health monitoring methods. J. Power Sources 405, 18–29 (2018).

    Article  Google Scholar 

  11. 11.

    Zou, Y., Hu, X., Ma, H. & Li, S. E. Combined state of charge and state of health estimation over lithium-ion battery cell cycle lifespan for electric vehicles. J. Power Sources 273, 793–803 (2015).

    Article  Google Scholar 

  12. 12.

    Zhang, Y., Song, W., Lin, S., Lv, J. & Feng, Z. A critical review on state of charge of batteries. J. Renew. Sustain. Energy 5, 021403 (2013).

    Article  Google Scholar 

  13. 13.

    Chang, W. Y. The state of charge estimating methods for battery: a review. Int. Schol. Res. Not. Appl. Math. 2013, 953792 (2013).

    MATH  Google Scholar 

  14. 14.

    Lu, L., Han, X., Li, J., Hua, J. & Ouyang, M. A review on the key issues for lithium-ion battery management in electric vehicles. J. Power Sources 226, 272–288 (2013).

    Article  Google Scholar 

  15. 15.

    Nejad, S., Gladwin, D. T. & Stone, D. A. A systematic review of lumped-parameter equivalent circuit models for real-time estimation of lithium-ion battery states. J. Power Sources 316, 183–196 (2016).

    Article  Google Scholar 

  16. 16.

    Johnson, V. H. Battery performance models in ADVISOR. J. Power Sources 110, 321–329 (2002).

    Article  Google Scholar 

  17. 17.

    Huria, T., Ludovici, G. & Lutzemberger, G. State of charge estimation of high power lithium iron phosphate cells. J. Power Sources 249, 92–102 (2014).

    Article  Google Scholar 

  18. 18.

    Plett, G. L. Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 2. Modeling and identification. J. Power Sources 134, 262–276 (2004).

    Article  Google Scholar 

  19. 19.

    Fairweather, A. J., Foster, M. P. & Stone, D. A. Modelling of VRLA batteries over operational temperature range using pseudo random binary sequences. J. Power Sources 207, 56–59 (2012).

    Article  Google Scholar 

  20. 20.

    Shahriari, M. & Farrokhi, M. Online state-of-health estimation of VRLA batteries using state of charge. IEEE Trans. Ind. Electron. 60, 191–202 (2013).

    Article  Google Scholar 

  21. 21.

    Bhangu, B. S., Bentley, P., Stone, D. A. & Bingham, C. M. Observer techniques for estimating the state-of-charge and state-of-health of VRLABs for hybrid electric vehicles. In IEEE Vehicle Power and Propulsion Conf. 10, 780–789 (IEEE, 2005).

  22. 22.

    Gould, C. R., Bingham, C. M., Stone, D. A. & Bentley, P. New battery model and state-of-health determination through subspace parameter estimation and state-observer techniques. IEEE Trans. Veh. Technol. 58, 3905–3916 (2009).

    Article  Google Scholar 

  23. 23.

    Kim, T. & Qiao, W. A hybrid battery model capable of capturing dynamic circuit characteristics and nonlinear capacity effects. IEEE Trans. Energy Conver. 26, 1172–1180 (2011).

    Article  Google Scholar 

  24. 24.

    Sitterly, M., Wang, L. Y., Yin, G. G. & Wang, C. Enhanced identification of battery models for real-time battery management. IEEE Trans. Sustain. Energy 2, 300–308 (2011).

    Article  Google Scholar 

  25. 25.

    Hu, X., Li, S. & Peng, H. A comparative study of equivalent circuit models for Li-ion batteries. J. Power Sources 198, 359–367 (2012).

    Article  Google Scholar 

  26. 26.

    Doyle, M., Fuller, T. F. & Newman, J. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. J. Electrochem. Soc. 140, 1526–1533 (1993). This work presented a full cell battery model for lithium anode, solid polymer electrolyte and insertion composite cathode based on concentrated solution theory, setting the foundation for the well-known physics-based battery model: the P2D model.

    Article  Google Scholar 

  27. 27.

    Fuller, T. F., Doyle, M. & Newman, J. Simulation and optimization of the dual lithium ion insertion cell. J. Electrochem. Soc. 141, 1–10 (1994). This work presented a model for dual lithium ion insertion (rocking-chair) cell, setting the foundation for the well-known physics-based battery model: the P2D model.

    Article  Google Scholar 

  28. 28.

    Jokar, A., Rajabloo, B., Désilets, M. & Lacroix, M. Review of simplified pseudo-two dimensional models of lithium-ion batteries. J. Power Sources 327, 44–55 (2016).

    Article  Google Scholar 

  29. 29.

    Santhanagopalan, S., Guo, Q., Ramadass, P. & White, R. E. Review of models for predicting the cycling performance of lithium ion batteries. J. Power Sources 156, 620–628 (2006).

    Article  Google Scholar 

  30. 30.

    Guo, M., Sikha, G. & White, R. E. Single-particle model for a lithium-ion cell: thermal behavior. J. Electrochem. Soc. 158, A122–A132 (2011).

    Article  Google Scholar 

  31. 31.

    Zhang, D., Popov, B. N. & White, R. E. Modeling lithium intercalation of a single spinel particle under potentiodynamic control. J. Electrochem. Soc. 147, 831–838 (2000).

    Article  Google Scholar 

  32. 32.

    Ramadesigan, V. et al. Modeling and simulation of lithium-ion batteries from a systems engineering perspective. J. Electrochem. Soc. 159, R31–R45 (2012). This work reviewed efforts in the modelling and simulation of Li-ion batteries and their use in the design of better batteries, and suggested the multiscale, robust reduced-order and reformulation models to be the future directions for battery model development.

    Article  Google Scholar 

  33. 33.

    Rahimian, S. K., Rayman, S. & White, R. E. Extension of physics-based single particle model for higher charge–discharge rates. J. Power Sources 224, 180–194 (2013).

    Article  Google Scholar 

  34. 34.

    Luo, W., Lyu, C., Wang, L. & Zhang, L. A new extension of physics-based single particle model for higher charge–discharge rates. J. Power Sources 241, 295–310 (2013).

    Article  Google Scholar 

  35. 35.

    Han, X., Ouyang, M., Lu, L. & Li, J. Simplification of physics-based electrochemical model for lithium ion battery on electric vehicle. Part II: pseudo-two-dimensional model simplification and state of charge estimation. J. Power Sources 278, 814–825 (2015).

    Article  Google Scholar 

  36. 36.

    Li, J., Adewuyi, K., Lotfi, N., Landers, R. G. & Park, J. A single particle model with chemical/mechanical degradation physics for lithium ion battery state of health (SOH) estimation. Appl. Energy 212, 1178–1190 (2018).

    Article  Google Scholar 

  37. 37.

    Northrop, P. W. C. et al. Efficient simulation and reformulation of lithium-ion battery models for enabling electric transportation. J. Electrochem. Soc. 161, E3149–E3157 (2014).

    Article  Google Scholar 

  38. 38.

    Subramanian, V. R., Ritter, J. A. & White, R. E. Approximate solutions for galvanostatic discharge of spherical particles I. Constant diffusion coefficient. J. Electrochem. Soc. 148, E444–E449 (2001).

    Article  Google Scholar 

  39. 39.

    Subramanian, V. R., Diwakar, V. D. & Tapriyal, D. Efficient macro-micro scale coupled modeling of batteries. J. Electrochem. Soc. 152, A2002–A2008 (2005).

    Article  Google Scholar 

  40. 40.

    Cai, L. & White, R. E. Reduction of model order based on proper orthogonal decomposition for lithium-ion battery simulations. J. Electrochem. Soc. 156, A154–A161 (2009).

    Article  Google Scholar 

  41. 41.

    Smith, K. A., Rahn, C. D. & Wang, C.-Y. Model order reduction of 1D diffusion systems via residue grouping. ASME J. Dyn. Syst. Meas. Control 130, 011012 (2008).

    Article  Google Scholar 

  42. 42.

    Forman, J. C., Bashash, S., Stein, J. L. & Fathy, H. K. Reduction of an electrochemistry based Li-ion battery model via quasi-linearization and padé approximation. J. Electrochem. Soc. 158, A93–A101 (2011).

    Article  Google Scholar 

  43. 43.

    Wang, C. Y., Gu, W. B. & Liaw, B. Y. Micro-macroscopic coupled modeling of batteries and fuel cells I. Model development. J. Electrochem. Soc. 145, 3407–3417 (1998).

    Article  Google Scholar 

  44. 44.

    Guo, J., Li, Z. & Pecht, M. A bayesian approach for Li-Ion battery capacity fade modeling and cycles to failure prognostics. J. Power Sources 281, 173–184 (2015).

    Article  Google Scholar 

  45. 45.

    Wu, B., Han, S., Shin, K. G. & Lu, W. Application of artificial neural networks in design of lithium-ion batteries. J. Power Sources 395, 128–136 (2018).

    Article  Google Scholar 

  46. 46.

    Zahid, T., Xu, K., Li, W., Li, C. & Li, H. State of charge estimation for electric vehicle power battery using advanced machine learning algorithm under diversified drive cycles. Energy 162, 871–882 (2018). This work proposed a subtractive clustering-based adaptive neural fuzzy interface system model to estimate the SOC of a battery, which is apposite for all EV batteries including nickel–metal hydride, lead–acid and Li-ion.

    Article  Google Scholar 

  47. 47.

    Chemali, E., Kollmeyer, P. J., Preindl, M. & Emadi, A. State-of-charge estimation of Li-ion batteries using deep neural networks: a machine learning approach. J. Power Sources 400, 242–255 (2018).

    Article  Google Scholar 

  48. 48.

    Jiménez-Bermejo, D., Fraile-Ardanuy, J., Castaño-Solis, S., Merino, J. & Álvaro-Hermana, R. Using dynamic neural networks for battery state of charge estimation in electric vehicles. Procedia Comput. Sci. 130, 533–540 (2018).

    Article  Google Scholar 

  49. 49.

    Mansouri, S. S., Karvelis, P., Georgoulas, G. & Nikolakopoulos, G. Remaining useful battery life prediction for UAVs based on machine learning. IFAC-PapersOnLine 50, 4727–4732 (2017).

    Article  Google Scholar 

  50. 50.

    Donato, T. H. R. & Quiles, M. G. Machine learning systems based on xgBoost and MLP neural network applied in satellite lithium-ion battery sets impedance estimation. Adv. Comput. Intell. 5, 1–20 (2018).

    Google Scholar 

  51. 51.

    Huang, C. et al. Robustness evaluation of extended and unscented Kalman filter for battery state of charge estimation. IEEE Access 6, 27617–27628 (2018).

    Article  Google Scholar 

  52. 52.

    Ren, L. et al. Remaining useful life prediction for lithium-ion battery: a deep learning approach. IEEE Access 6, 50587–50598 (2018).

    Article  Google Scholar 

  53. 53.

    Khumprom, P. & Yodo, N. A data-driven predictive prognostic model for lithium-ion batteries based on a deep learning algorithm. Energies 12, 660 (2019).

    Article  Google Scholar 

  54. 54.

    Sahinoglu, G. et al. Battery state-of-charge estimation based on regular/recurrent Gaussian process regression. IEEE Trans. Ind. Electron. 65, 4311–4321 (2017).

    Article  Google Scholar 

  55. 55.

    Álvarez Antón, J. C. et al. Battery state-of-charge estimator using the SVM technique. Appl. Math. Model. 37, 6244–6253 (2013).

    Article  Google Scholar 

  56. 56.

    Tong, S., Lacap, J. H. & Park, J. W. Battery state of charge estimation using a load-classifying neural network. J. Energy Storage 7, 236–243 (2016).

    Article  Google Scholar 

  57. 57.

    Kang, L., Zhao, X. & Ma, J. A new neural network model for the state-of-charge estimation in the battery degradation process. Appl. Energy 121, 20–27 (2014).

    Article  Google Scholar 

  58. 58.

    Hu, X., Li, S. E. & Yang, Y. Advanced machine learning approach for lithium-ion battery state estimation in electric vehicles. IEEE Trans. Transport. Electrific. 2, 140–149 (2016).

    Article  Google Scholar 

  59. 59.

    Wu, T., Wang, M., Xiao, Q. & Wang, X. The SOC estimation of power Li-ion battery based on ANFIS model. Smart Grid Renew. Energy 3, 51–55 (2012).

    Article  Google Scholar 

  60. 60.

    Wu, J., Wang, Y., Zhang, X. & Chen, Z. A novel state of health estimation method of Li-ion battery using group method of data handling. J. Power Sources 327, 457–464 (2016).

    Article  Google Scholar 

  61. 61.

    Hu, C., Jain, G., Schmidt, C., Strief, C. & Sullivan, M. Online estimation of lithium-ion battery capacity using sparse bayesian learning. J. Power Sources 289, 105–113 (2015).

    Article  Google Scholar 

  62. 62.

    Berecibar, M. et al. Online state of health estimation on NMC cells based on predictive analytics. J. Power Sources 320, 239–250 (2016).

    Article  Google Scholar 

  63. 63.

    Richardson, R. R., Osborne, M. A. & Howey, D. A. Gaussian process regression for forecasting battery state of health. J. Power Sources 357, 209–219 (2017).

    Article  Google Scholar 

  64. 64.

    Zhang, Y., Xiong, R., He, H. & Liu, Z. A LSTM-RNN method for the lithium-ion battery remaining useful life prediction. In Prognostics and System Health Management Conf. 1–4 (IEEE, 2017).

  65. 65.

    Hu, J. N. et al. State-of-charge estimation for battery management system using optimized support vector machine for regression. J. Power Sources 269, 682–693 (2014).

    Article  Google Scholar 

  66. 66.

    Tseng, K.-H., Liang, J.-W., Chang, W. & Huang, S.-C. Regression models using fully discharged voltage and internal resistance for state of health estimation of lithium-ion batteries. Energies 8, 2889–2907 (2015).

    Article  Google Scholar 

  67. 67.

    Hussein, A. A. Kalman filters versus neural networks in battery state-of-charge estimation: a comparative study. Int. J. Mod. Nonlinear Theor. Appl. 3, 199–209 (2014).

    Article  Google Scholar 

  68. 68.

    Yang, D., Wang, Y., Pan, R., Chen, R. & Chen, Z. A neural network based state-of-health estimation of lithium-ion battery in electric vehicles. Energy Procedia 105, 2059–2064 (2017).

    Article  Google Scholar 

  69. 69.

    Dawson-Elli, N., Lee, S. B., Pathak, M., Mitra, K. & Subramanian, V. R. Data science approaches for electrochemical engineers: an introduction through surrogate model development for lithium-ion batteries. J. Electrochem. Soc. 165, A1–A15 (2018).

    Article  Google Scholar 

  70. 70.

    Li, X., Wang, H., Gu, B. & Ling, C. X. Data sparseness in linear SVM. In Proc. Twenty-Fourth Int. Joint Conf. Artificial Intelligence 3628–3634 (IJCAI, 2015).

  71. 71.

    Rendle, S. Factorization Machines. In Proc. 2010 IEEE Int. Conf. Data Mining 995–1000 (IEEE, 2010).

  72. 72.

    Girard, A. & Murray-Smith, R. Gaussian processes: prediction at a noisy input and application to iterative multiple-step ahead forecasting of time-series. In Proc. Hamilton Summer School on Switching and Learning in Feedback Systems (eds Murray-Smith, R. & Shorten, R.) 158–184 (Springer, 2005).

  73. 73.

    Dawson-Elli, N., Kolluri, S., Mitra, K. & Subramanian, V. R. On the creation of a chess-AI inspired problem-specific optimizer for the pseudo two-dimensional battery model using neural networks. J. Electrochem. Soc. 166, A886–A896 (2019).

    Article  Google Scholar 

  74. 74.

    Wang, A., Kadam, S., Li, H., Shi, S. & Qi, Y. Review on modeling of the anode solid electrolyte interphase (SEI) for lithium-ion batteries. npj Comput. Mater. 4, 15 (2018).

    Article  Google Scholar 

  75. 75.

    Kumar, H., Detsi, E., Abraham, D. P. & Shenoy, V. B. Fundamental mechanisms of solvent decomposition involved in solid-electrolyte interphase formation in sodium ion batteries. Chem. Mater. 28, 8930–8941 (2016).

    Article  Google Scholar 

  76. 76.

    Hong, Z. & Viswanathan, V. Prospect of thermal shock induced healing of lithium dendrite. ACS Energy Lett. 4, 1012–1019 (2019).

    Article  Google Scholar 

  77. 77.

    Liang, L. & Chen, L.-Q. Nonlinear phase field model for electrodeposition in electrochemical systems. Appl. Phys. Lett. 105, 263903 (2014).

    Article  Google Scholar 

  78. 78.

    Takaki, T. Phase-field modelling and simulations of dendrite growth. ISIJ Int. 54, 437–444 (2014).

    Article  Google Scholar 

  79. 79.

    Bai, P., Cogswell, D. A. & Bazant, M. Z. Suppression of phase separation in LiFePO4 nanoparticles during battery discharge. Nano Lett. 11, 4890–4896 (2011).

    Article  Google Scholar 

  80. 80.

    Cogswell, D. A. & Bazant, M. Z. Theory of coherent nucleation in phase-separating nanoparticles. Nano Lett. 13, 3036–3041 (2013).

    Article  Google Scholar 

  81. 81.

    Cogswell, D. A. & Bazant, M. Z. Coherency strain and the kinetics of phase separation in LiFePO4 nanoparticles. ACS Nano 6, 2215–2225 (2012).

    Article  Google Scholar 

  82. 82.

    Ahmad, Z., Xie, T., Maheshwari, C., Grossman, J. C. & Viswanathan, V. Machine learning enabled computational screening of inorganic solid electrolytes for suppression of dendrite formation in lithium metal anodes. ACS Cent. Sci. 4, 996–1006 (2018).

    Article  Google Scholar 

  83. 83.

    Joshi, R. P. et al. Machine learning the voltage of electrode materials in metal-ion batteries. ACS Appl. Mater. Interfaces 11, 18494–18503 (2019).

    Article  Google Scholar 

  84. 84.

    Aspuru-Guzik, A. & Persson, K. Materials acceleration platform: accelerating advanced energy materials discovery by integrating high-throughput methods and artificial intelligence. Mission Innov. 6, 1–100 (2018).

    Google Scholar 

  85. 85.

    Correa-Baena, J.-P. et al. Accelerating materials development via automation, machine learning, and high-performance computing. Joule 2, 1410–1420 (2018).

    Article  Google Scholar 

  86. 86.

    Tabor, D. P. et al. Accelerating the discovery of materials for clean energy in the era of smart automation. Nat. Rev. Mater. 3, 5–20 (2018).

    Article  Google Scholar 

  87. 87.

    Reuter, J. A., Spacek, D. V. & Snyder, M. P. High-throughput sequencing technologies. Mol. Cell 58, 586–597 (2015).

    Article  Google Scholar 

  88. 88.

    Ley, S. V., Fitzpatrick, D. E., Ingham, R. J. & Myers, R. M. Organic synthesis: march of the machines. Angew. Chem. Int. Ed. Engl. 54, 3449–3464 (2015).

    Article  Google Scholar 

  89. 89.

    Mannodi-Kanakkithodi, A., Pilania, G., Huan, T. D., Lookman, T. & Ramprasad, R. Machine learning strategy for accelerated design of polymer dielectrics. Sci. Rep. 6, 20952 (2016).

    Article  Google Scholar 

  90. 90.

    Shevlin, M. Practical high-throughput experimentation for chemists. ACS Med. Chem. Lett. 8, 601–607 (2017).

    Article  Google Scholar 

  91. 91.

    Jain, A. et al. Commentary: the materials project: a materials genome approach to accelerating materials innovation. APL Mater. 1, 011002 (2013). This work presented the core programme of the Materials Genome Initiative that uses high-throughput computing to discover the properties of all known inorganic materials.

    Article  Google Scholar 

  92. 92.

    Saal, J. E., Kirklin, S., Aykol, M., Meredig, B. & Wolverton, C. Materials design and discovery with high-throughput density functional theory: the open quantum materials database (OQMD). JOM 65, 1501–1509 (2013).

    Article  Google Scholar 

  93. 93.

    Jain, A. et al. A high-throughput infrastructure for density functional theory calculations. Comput. Mater. Sci. 50, 2295–2310 (2011).

    Article  Google Scholar 

  94. 94.

    Xiao, R. J., Li, H. & Chen, L. Q. Development of new lithium battery materials by material genome initiative. Acta Phys. Sin. 67, 128801 (2018).

    Google Scholar 

  95. 95.

    Shandiz, M. A. & Gauvin, R. Application of machine learning methods for the prediction of crystal system of cathode materials in lithium-ion batteries. Comput. Mater. Sci. 117, 270–278 (2016).

    Article  Google Scholar 

  96. 96.

    Takagishi, Y., Yamanaka, T. & Yamaue, T. Machine learning approaches for designing mesoscale structure of Li-ion battery electrodes. Batteries 5, 54 (2019).

    Article  Google Scholar 

  97. 97.

    Okamoto, Y. Applying Bayesian approach to combinatorial problem in chemistry. J. Phys. Chem. A 121, 3299–3304 (2017).

    Article  Google Scholar 

  98. 98.

    Allam, O., Cho, B. W., Kim, K. C. & Jang, S. S. Application of DFT-based machine learning for developing molecular electrode materials in Li-ion batteries. RSC Adv. 8, 39414 (2018).

    Article  Google Scholar 

  99. 99.

    Gu, G. H., Noh, J., Kim, I. & Jung, Y. Machine learning for renewable energy materials. J. Mater. Chem. A 7, 17096 (2019).

    Article  Google Scholar 

  100. 100.

    Cheng, L. et al. Accelerating electrolyte discovery for energy storage with high-throughput screening. J. Phys. Chem. Lett. 6, 283–291 (2015).

    Article  Google Scholar 

  101. 101.

    Khetan, A., Luntz, A. & Viswanathan, V. Trade-offs in capacity and rechargeability in nonaqueous Li–O2 batteries: solution-driven growth versus nucleophilic stability. J. Phys. Chem. Lett. 6, 1254–1259 (2015).

    Article  Google Scholar 

  102. 102.

    Schütter, C. et al. Rational design of new electrolyte materials for electrochemical double layer capacitors. J. Power Sources 326, 541–548 (2016).

    Article  Google Scholar 

  103. 103.

    Okamoto, Y. & Kubo, Y. Ab initio calculations of the redox potentials of additives for lithium-ion batteries and their prediction through machine learning. ACS Omega 3, 7868–7874 (2018).

    Article  Google Scholar 

  104. 104.

    Curtarolo, S. et al. The high-throughput highway to computational materials design. Nat. Mater. 12, 191–201 (2013).

    Article  Google Scholar 

  105. 105.

    Qu, X. et al. The electrolyte genome project: a big data approach in battery materials discovery. Comput. Mater. Sci. 103, 56–67 (2015).

    Article  Google Scholar 

  106. 106.

    Cubuk, E. D., Sendek, A. D. & Reed, E. J. Screening billions of candidates for solid lithium-ion conductors: a transfer learning approach for small data. J. Chem. Phys. 150, 214701 (2019).

    Article  Google Scholar 

  107. 107.

    Jalem, R. et al. Bayesian-driven first-principles calculations for accelerating exploration of fast ion conductors for rechargeable battery application. Sci. Rep. 8, 5845 (2018).

    Article  Google Scholar 

  108. 108.

    Sendek, A. D. et al. Machine learning-assisted discovery of solid Li-ion conducting materials. Chem. Mater. 31, 342–352 (2019).

    Article  Google Scholar 

  109. 109.

    Liu, P. et al. High throughput materials research and development for lithium ion batteries. J. Materiomics 3, 202–208 (2017).

    Article  Google Scholar 

  110. 110.

    Lyu, Y., Liu, Y., Cheng, T. & Guo, B. High-throughput characterization methods for lithium batteries. J. Materiomics 3, 221–229 (2017).

    Article  Google Scholar 

  111. 111.

    Grey, C. P. & Tarascon, J. M. Sustainability and in situ monitoring in battery development. Nat. Mater. 16, 45–56 (2016).

    Article  Google Scholar 

  112. 112.

    Wang, X., Xiao, R., Li, H. & Chen, L. Discovery and design of lithium battery materials via high-throughput modeling. Chinese Phys. B. 27, 128801 (2018).

    Article  Google Scholar 

  113. 113.

    Schiele, A. et al. High-throughput in situ pressure analysis of lithium-ion batteries. Anal. Chem. 89, 8122–8128 (2017).

    Article  Google Scholar 

  114. 114.

    Roberts, M. & Owen, J. High-throughput method to study the effect of precursors and temperature, applied to the synthesis of LiNi1/3Co1/3Mn1/3O2 for lithium batteries. ACS Comb. Sci. 13, 126–134 (2011).

    Article  Google Scholar 

  115. 115.

    Maruyama, S., Kubokawa, O., Nanbu, K., Fujimoto, K. & Matsumoto, Y. Combinatorial synthesis of epitaxial LiCoO2 thin films on SrTiO3(001) via on-substrate sintering of Li2CO3 and CoO by pulsed laser deposition. ACS Comb. Sci. 18, 343–348 (2016).

    Article  Google Scholar 

  116. 116.

    Vogt, S. et al. Composition characterization of combinatorial materials by scanning X-ray fluorescence microscopy using microfocused synchrotron X-ray beam. Appl. Surf. Sci. 223, 214–219 (2004).

    Article  Google Scholar 

  117. 117.

    Orikasa, Y. et al. Direct observation of a metastable crystal phase of LixFePO4 under electrochemical phase transition. J. Am. Chem. Soc. 135, 5497–5500 (2013).

    Article  Google Scholar 

  118. 118.

    Kwade, A. et al. Current status and challenges for automotive battery production technologies. Nat. Energy 3, 290–300 (2018). This work presented a summary of the state-of-the-art production technologies for automotive Li-ion batteries, discussing the key relationships between process, quality and performance, as well as the impact of materials and processes on scale and cost.

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the Singapore National Research Foundation (NRF-NRFF2017-04).

Author information

Affiliations

Authors

Contributions

This work was written through the contributions of all authors.

Corresponding authors

Correspondence to Qingyu Yan or Gareth J. Conduit or Zhi Wei Seh.

Ethics declarations

Competing interests

G.J.C. declares a potential financial conflict of interest as chief technology officer of Intellegens, UK.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ng, MF., Zhao, J., Yan, Q. et al. Predicting the state of charge and health of batteries using data-driven machine learning. Nat Mach Intell 2, 161–170 (2020). https://doi.org/10.1038/s42256-020-0156-7

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing