Recurrent Neural Networks for Estimating the State of Health of Lithium-Ion Batteries
Source abstract
Abstract
Rapid technological changes and disruptive innovations have resulted in a significant shift in people’s behavior and requirements. Electronic gadgets, including smartphones, notebooks, and other devices, are indispensable to everyday routines. Consequently, the demand for high-capacity batteries has surged, which has enabled extended device autonomy. An alternative approach to address this demand is battery swapping, which can potentially extend the battery life of electronic devices. Although battery sharing in electric vehicles has been well studied, smartphone applications still need to be explored. Crucially, assessing the batteries’ state of health (SoH) presents a challenge, necessitating consensus on the best estimation methods to develop effective battery swap strategies. This paper proposes a model for estimating the SoH curve of lithium-ion batteries using the state of charge curve. The model was designed for smartphone battery swap applications utilizing Gated Recurrent Unit (GRU) neural networks. To validate the model, a system was developed to conduct destructive tests on batteries and study their behavior over their lifetimes. The results demonstrated the high precision of the model in estimating the SoH of batteries under various charge and discharge parameters. The proposed approach exhibits low computational complexity, low cost, and easily measurable input parameters, making it an attractive solution for smartphone battery swap applications.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Battery Technologies Research · Advancements in Battery Materials · Fault Detection and Control Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Postgraduate Programme in Metrology, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro 22451-900, RJ, Brazil
Funding
Coordination for the Improvement of Higher Education Personnel · National Council for Scientific and Technological Development (CNPq) · Development and Research Program of ANEEL in partnership with ENEL Rio Enterprise
References (71)
Show source-provided references
1. Brondani, M.F. (2015). Mathematical Modeling of the Lifetime of Lithium-Ion Polymer Batteries Using Genetic Algorithms. [Master’s Thesis, Universidade Regional do Noroeste do Estado do Rio Grande do Sul (Unijuí)].
2. Koch, S., Birke, K.P., and Kuhn, R. (2018). Fast Thermal runaway detection for lithium-ion cells in large scale traction batteries. Batteries, 4.10.3390/batteries4020016 ↗
3. Reserve lithium-ion batteries: Deciphering in situ lithiation of lithium-ion free vanadium pentoxide cathode with graphitic anode10.1016/j.carbon.2022.11.085 ↗
4. Infante, W.F., Ma, J., and Chi, Y. (October, January 28). Operational Strategy and Load Profile Sensitivity Analysis for an Electric Vehicle Battery Swapping Station. Proceedings of the 2016 IEEE International Conference on Power System Technology (POWERCON), Wollongong, NSW, Australia.10.1109/POWERCON.2016.7753877 ↗
5. Zhong, L., and Pei, M. (2020). Optimal design for a shared swap charging system considering the electric vehicle battery charging rate. Energies, 13.10.3390/en13051213 ↗
6. An extended differential flatness approach for the health-conscious nonlinear model predictive control of Lithium-Ion batteries10.1109/TCST.2016.2624143 ↗
7. State-of-charge estimation for onboard LiFePO4 batteries with adaptive state update in specific open-circuit-voltage ranges10.1016/j.apenergy.2023.121581 ↗
8. Optimization of battery swapping stations with heterogeneity, charging degradation and PV-option10.1016/j.est.2023.109509 ↗
9. Smart system for children’s chronic illness monitoring10.1016/j.inffus.2017.06.002 ↗
10. Monteiro, L.E.C., Repolho, H.M.V., Calili, R.F., Louzada, D.R., Teixeira, R.S.D., and Vieira, R.S. (2022). Optimization of a mobile energy storage network. Energies, 15.10.3390/en15010186 ↗
11. MIT Electric Vehicle Team (2024, January 10). A Guide to Understanding Battery Specifications. Available online: http://web.mit.edu/evt/summary_battery_specifications.pdf.
12. Yao, L., Xu, S., Tang, A., Zhou, F., Hou, J., Xiao, Y., and Fu, Z. (2021). A review of lithium-ion battery state of health estimation and prediction methods. World Electr. Veh. J., 12.10.3390/wevj12030113 ↗
13. State-of-charge estimation and state-of-health prediction of a Li-Ion degraded battery based on an EKF combined with a per-unit system10.1109/TVT.2011.2168987 ↗
14. A deep learning approach for state-of-health estimation of lithium-ion batteries based on differential thermal voltametry and attention mechanism10.3389/fenrg.2023.1178151 ↗
15. A physics-informed dynamic deep autoencoder for accurate state-of-health prediction of lithium-ion battery10.1007/s00521-022-07291-5 ↗
16. Physics-informed neural network approach for heat generation rate estimation of lithium-ion battery under various driving conditions10.1016/j.jechem.2022.11.036 ↗
17. Gaussian process regression for forecasting battery state of health10.1016/j.jpowsour.2017.05.004 ↗
18. Development of a voltage curve prediction model for lithium-ion battery based on destructive tests10.1088/1742-6596/1826/1/012091 ↗
19. Baterias Automotivas: Panorama da Indústria No Brasil
20. The Lithium-ion battery: State of the art and future perspectives10.1016/j.rser.2018.03.002 ↗
21. Smith, K., Shi, Y., and Santhanagopalan, S. (2015, January 1–3). Degradation Mechanisms and Lifetime Prediction for Lithium-Ion Batteries—A Control Perspective. Proceedings of the 2015 American Control Conference (ACC), Chicago, IL, USA.10.1109/ACC.2015.7170820 ↗
22. Lithium ion, Lithium metal, and alternative rechargeable battery technologies: The odyssey for high energy density10.1007/s10008-017-3610-7 ↗
23. Online state of health prediction method for Lithium-ion batteries, based on gated recurrent unit neural networks10.1002/er.5413 ↗
24. Pajovic, M., Orlik, P.V., Wada, T., and Takegami, T. (2019, January 22–25). A Data-Driven Method for Predicting Capacity Degradation of Rechargeable Batteries. Proceedings of the 2019 IEEE 17th International Conference on Industrial Informatics (INDIN), Helsinki, Finland.10.1109/INDIN41052.2019.8972129 ↗
25. State-of-health monitoring and prediction of lithium-ion battery using probabilistic indication and state-space model10.1109/TIM.2015.2444237 ↗
26. A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations10.1016/j.rser.2017.05.001 ↗
27. Novel state-of-charge estimation method for lithium polymer batteries using electrochemical impedance spectroscopy10.6113/JPE.2011.11.2.237 ↗
28. Online state of health estimation on NMC cells based on predictive analytics10.1016/j.jpowsour.2016.04.109 ↗
29. Online state-of-health estimation algorithm for lithium-ion batteries in electric vehicles based on compression ratio of open circuit voltage10.1016/j.est.2022.106258 ↗
30. Theuerkauf, D., and Swan, L. (2022). Characteristics of open circuit voltage relaxation in lithium-ion batteries for the purpose of state of charge and state of health analysis. Batteries, 8.10.3390/batteries8080077 ↗
31. Liu, X., Li, Y., Gu, P., Zhang, Y., Duan, B., and Zhang, C. (2022, January 25–27). An Accurate State of Health Estimation for Retired Lithium-ion Batteries Based on Electrochemical Impedance Spectroscopy. Proceedings of the 2022 41st Chinese Control Conference (CCC), Hefei, China.10.23919/CCC55666.2022.9901759 ↗
32. Unterrieder, C., Lunglmayr, M., Marsili, S., and Huemer, M. (2014, January 1–5). Battery state-of-charge estimation prototype using EMF voltage prediction. Proceedings of the 2014 IEEE International Symposium on Circuits and Systems (ISCAS), Melbourne, VIC, Australia.10.1109/ISCAS.2014.6865212 ↗
33. A new state-of-health estimation method for lithium-ion batteries through the intrinsic relationship between ohmic internal resistance and capacity10.1016/j.measurement.2017.11.016 ↗
34. State of health prediction model based on internal resistance10.1002/er.5383 ↗
35. Koc, Y., Doğru, U.E., and Bilir, R.A. (2022, January 14–17). Evaluation of Internal Resistance Methods for Tracking Battery State of Health. Proceedings of the 2022 4th Global Power, Energy and Communication Conference (GPECOM), Nevsehir, Turkey.10.1109/GPECOM55404.2022.9815564 ↗
36. Electrochemical impedance spectroscopy with practical rest-times for battery management applications10.1109/ACCESS.2021.3077211 ↗
37. A method for state of charge and state of health estimation of lithium-ion battery based on adaptive unscented Kalman filter
38. Electrochemical impedance spectroscopy based state-of-health estimation for lithium-ion battery considering temperature and state-of-charge effect10.1109/TTE.2022.3160021 ↗
39. Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries10.1016/j.apenergy.2008.11.021 ↗
40. Enhanced Coulomb counting method for SoC and SoH estimation based on Coulombic efficiency10.1109/ACCESS.2023.3244801 ↗
41. Nonlinear observers for predicting state-of-charge and state-of-health of lead-acid batteries for hybrid-electric vehicles10.1109/TVT.2004.842461 ↗
42. The co-estimation of state of charge, state of health, and state of function for lithium-ion batteries in electric vehicles10.1109/TVT.2017.2751613 ↗
43. State-of-charge and state-of-health estimation for lithium-ion batteries based on dual fractional-order extended Kalman filter and online parameter identification10.1109/ACCESS.2021.3068813 ↗
44. Co-estimation of state of charge and state of health for lithium-ion batteries based on fractional-order model with multi-innovations unscented Kalman filter method10.1016/j.est.2022.104904 ↗
45. Real-time state of health estimation for solid oxide fuel cells based on unscented Kalman filter10.3390/en15072534 ↗
46. Heiries, V., and Montaru, M. (2021, January 27–29). State-of-charge and state-of-health online estimation of Li-ion battery for the more electrical aircraft based on semi-empirical ageing model and Sigma-Point Kalman Filtering. Proceedings of the 2021 Smart Systems Integration (SSI), Grenoble, France.
47. Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 2: Simultaneous state and parameter estimation10.1016/j.jpowsour.2006.06.004 ↗
48. Remaining useful life prediction and state of health diagnosis of lithium-ion battery based on second-order central difference particle filter10.1109/ACCESS.2020.2974401 ↗
49. On-line estimation of state-of-charge of Li-ion batteries in electric vehicle using the resampling particle filter10.1016/j.trd.2014.07.013 ↗
50. Putra, W.S., Kuswanto, J., Ashari, W.M., and Koprawi, M. (2021, January 30–31). Comparative Study of Kalman Filter and H infinity Filter for Current Sensorless Battery Health Analysis. Proceedings of the 4th International Conference on Information and Communications Technology (ICOIACT), Yogyakarta, Indonesia.10.1109/ICOIACT53268.2021.9563993 ↗
51. HFCM-LSTM: A novel hybrid framework for state-of-health estimation of lithium-ion battery10.1016/j.egyr.2023.01.109 ↗
52. A convolutional neural network model for SOH estimation of Li-ion batteries with physical interpretability10.1016/j.ymssp.2022.110004 ↗
53. Zenati, A., Desprez, P., Razik, H., and Rael, S. (2012, January 4–8). A Methodology to Assess the State of Health of Lithium-Ion Batteries based on the Battery’s Parameters and a Fuzzy Logic System. Proceedings of the IEEE International Electric Vehicle Conference, Greenville, SC, USA.10.1109/IEVC.2012.6183268 ↗
54. Ananto, P., Syabani, F., Indra, W.D., Wahyunggoro, O., and Cahyadi, A.I. (2013, January 26–28). The State of Health of Li-Po Batteries Based on the Battery’s Parameters and a Fuzzy Logic System. Proceedings of the 2013 Joint International Conference on Rural Information & Communication Technology and Electric-Vehicle Technology (rICT & ICeV-T), Bandung, Indonesia.10.1109/rICT-ICeVT.2013.6741508 ↗
55. Intelligent fuzzy control strategy for battery energy storage system considering frequency support, SoC management, and C-rate protection10.1016/j.est.2022.104851 ↗
56. Online state-of-health estimation for second-use lithium-ion batteries based on weighted least squares support vector machine10.1109/ACCESS.2020.3026552 ↗
57. Soh prediction for lithium-ion battery based on improved support vector regression10.1080/15435075.2022.2027773 ↗
58. Synchronous state of health estimation and remaining useful lifetime prediction of Li-ion battery through optimized relevance vector machine framework10.1016/j.energy.2022.123852 ↗
59. An indirect State-of-Health estimation method based on improved genetic and back propagation for online lithium-ion battery used in electric vehicles10.1109/TVT.2022.3196225 ↗
60. Estimating state of health of lithium-ion batteries based on generalized regression neural network and quantum genetic algorithm10.1016/j.asoc.2022.109688 ↗
61. A multi-timescale estimator for lithium-ion battery state of charge and state of energy estimation using dual H infinity filter10.1109/ACCESS.2019.2959396 ↗
62. Remaining useful life prediction based on denoising technique and deep neural network for lithium-ion capacitors10.1016/j.etran.2020.100078 ↗
63. Ezemobi, E., Silvagni, M., Mozaffari, A., Tonoli, A., and Khajepour, A. (2022). State of health estimation of lithium-ion batteries in electric vehicles under dynamic load conditions. Energies, 15.10.3390/en15031234 ↗
64. State of health estimation for lithium-ion batteries based on temperature prediction and gated recurrent unit neural network10.1016/j.jpowsour.2021.230892 ↗
65. Semanjski, I., and Gautama, S. (2016). Forecasting the state of health of electric vehicle batteries to evaluate the viability of car sharing practices. Energies, 9.10.3390/en9121025 ↗
66. Chen, Z., Song, X., Xiao, R., Shen, J., and Xia, X. (2018, January 21–25). State of health estimation for lithium-ion battery based on long short-term memory networks. Proceedings of the 2018 Joint International Conference on Energy, Ecology and Environment (ICEEE) and International Conference on Electric and Intelligent Vehicles (ICEIV), Melbourne, Australia.10.12783/dteees/iceee2018/27855 ↗
67. Martins, F., Almeida, M.F., Calili, R., and Oliveira, A. (2020). Design thinking applied to smart home projects: A user-centric and sustainable perspective. Sustainability, 12.10.3390/su122310031 ↗
68. Simon, H. (1998). Neural Networks: A Comprehensive Foundation, Prentice Hall PTR.
69. Batteries state of health estimation via efficient neural networks with multiple channel charging profiles10.1109/ACCESS.2020.3047732 ↗
70. Representation learning: A review and new perspectives10.1109/TPAMI.2013.50 ↗
71. State-of-health estimation of lithium-ion batteries based on semi-supervised transfer component analysis10.1016/j.apenergy.2020.115504 ↗