Accurate state of health (SOH) estimation of lithium-ion batteries is of great importance for achieving efficient energy management in the overall battery energy storage system. Traditional data-driven methods for the SOH estimation of lithium-ion batteries usually require an enormous amount of data from the whole charging phase, which leads to poor performance in both computational efficiency and computational cost. To address this issue, this paper proposes the SOH estimation methods for lithium-ion batteries based on the limited data from a selected charging voltage interval. First, this study uses incremental capacity curves and Pearson correlation analysis to select an optimal and limited charging voltage interval that is the most relevant to lithium-ion battery degradation. Then, the SOH estimation based on two typical data-driven methods, including random forest regression (RFR) and support vector regression (SVR), would be implemented with the selected charging voltage interval. Results show that both the RFR and the SVR methods can achieve excellent accuracy, while each has its own irreplaceable advantages. However, compared with other voltage intervals using the two data-driven methods, the corresponding SOH estimation with the selected charging voltage interval shows the best performance. Hence, the data-driven methods based on the selected charging voltage interval have significant potential and advantages in the field of lithium-ion battery SOH estimation.
Citation: Junguang Sun, Xiaodong Zhang, Wenrui Cao, Lili Bo, Changhai Liu, Bin Wang. State of health estimation of lithium-ion batteries based on data-driven methods with a selected charging voltage interval[J]. AIMS Energy, 2025, 13(2): 290-308. doi: 10.3934/energy.2025012
| [1] | Christophe Chalons . Theoretical and numerical aspects of the interfacial coupling: The scalar Riemann problem and an application to multiphase flows. Networks and Heterogeneous Media, 2010, 5(3): 507-524. doi: 10.3934/nhm.2010.5.507 |
| [2] | Paola Goatin, Chiara Daini, Maria Laura Delle Monache, Antonella Ferrara . Interacting moving bottlenecks in traffic flow. Networks and Heterogeneous Media, 2023, 18(2): 930-945. doi: 10.3934/nhm.2023040 |
| [3] | Mauro Garavello, Roberto Natalini, Benedetto Piccoli, Andrea Terracina . Conservation laws with discontinuous flux. Networks and Heterogeneous Media, 2007, 2(1): 159-179. doi: 10.3934/nhm.2007.2.159 |
| [4] | Raimund Bürger, Christophe Chalons, Rafael Ordoñez, Luis Miguel Villada . A multiclass Lighthill-Whitham-Richards traffic model with a discontinuous velocity function. Networks and Heterogeneous Media, 2021, 16(2): 187-219. doi: 10.3934/nhm.2021004 |
| [5] | Clément Cancès . On the effects of discontinuous capillarities for immiscible two-phase flows in porous media made of several rock-types. Networks and Heterogeneous Media, 2010, 5(3): 635-647. doi: 10.3934/nhm.2010.5.635 |
| [6] | Michael Herty, Niklas Kolbe, Siegfried Müller . Central schemes for networked scalar conservation laws. Networks and Heterogeneous Media, 2023, 18(1): 310-340. doi: 10.3934/nhm.2023012 |
| [7] | Adriano Festa, Simone Göttlich, Marion Pfirsching . A model for a network of conveyor belts with discontinuous speed and capacity. Networks and Heterogeneous Media, 2019, 14(2): 389-410. doi: 10.3934/nhm.2019016 |
| [8] | Alexander Kurganov, Anthony Polizzi . Non-oscillatory central schemes for traffic flow models with Arrhenius look-ahead dynamics. Networks and Heterogeneous Media, 2009, 4(3): 431-451. doi: 10.3934/nhm.2009.4.431 |
| [9] | Raimund Bürger, Stefan Diehl, M. Carmen Martí, Yolanda Vásquez . A difference scheme for a triangular system of conservation laws with discontinuous flux modeling three-phase flows. Networks and Heterogeneous Media, 2023, 18(1): 140-190. doi: 10.3934/nhm.2023006 |
| [10] | Raimund Bürger, Stefan Diehl, María Carmen Martí . A conservation law with multiply discontinuous flux modelling a flotation column. Networks and Heterogeneous Media, 2018, 13(2): 339-371. doi: 10.3934/nhm.2018015 |
Accurate state of health (SOH) estimation of lithium-ion batteries is of great importance for achieving efficient energy management in the overall battery energy storage system. Traditional data-driven methods for the SOH estimation of lithium-ion batteries usually require an enormous amount of data from the whole charging phase, which leads to poor performance in both computational efficiency and computational cost. To address this issue, this paper proposes the SOH estimation methods for lithium-ion batteries based on the limited data from a selected charging voltage interval. First, this study uses incremental capacity curves and Pearson correlation analysis to select an optimal and limited charging voltage interval that is the most relevant to lithium-ion battery degradation. Then, the SOH estimation based on two typical data-driven methods, including random forest regression (RFR) and support vector regression (SVR), would be implemented with the selected charging voltage interval. Results show that both the RFR and the SVR methods can achieve excellent accuracy, while each has its own irreplaceable advantages. However, compared with other voltage intervals using the two data-driven methods, the corresponding SOH estimation with the selected charging voltage interval shows the best performance. Hence, the data-driven methods based on the selected charging voltage interval have significant potential and advantages in the field of lithium-ion battery SOH estimation.
| [1] |
Yu WH, Guo Y, Xu SM, et al. (2023) Comprehensive recycling of lithium-ion batteries: Fundamentals, pretreatment, and perspectives. Energy Storage Mater 54: 172–220. http://doi.org/10.1016/j.ensm.2022.10.033 doi: 10.1016/j.ensm.2022.10.033
|
| [2] |
Zhu AH, Bian XY, Han WJ, et al. (2023) The application of deep eutectic solvents in lithium-ion battery recycling: A comprehensive review. Resour Conserv Recy 188: 106690. http://doi.org/10.1016/j.resconrec.2022.106690 doi: 10.1016/j.resconrec.2022.106690
|
| [3] |
Naseri F, Gil S, Barbu C, et al. (2023) Digital twin of electric vehicle battery systems: Comprehensive review of the use cases, requirements, and platforms. Renewable Sustainable Energy Rev 179: 113280. http://doi.org/10.1016/j.rser.2023.113280 doi: 10.1016/j.rser.2023.113280
|
| [4] |
Che YH, Hu XS, Lin XK, et al. (2023) Health prognostics for lithium-ion batteries: Mechanisms, methods, and prospects. Energy Environ Sci 16: 338–371. http://doi.org/10.1039/d2ee03019e doi: 10.1039/d2ee03019e
|
| [5] |
Kong JZ, Yang FF, Zhang X, et al. (2021) Voltage-temperature health feature extraction to improve prognostics and health management of lithium-ion batteries. Energy 223: 120114. http://doi.org/10.1016/j.energy.2021.120114 doi: 10.1016/j.energy.2021.120114
|
| [6] |
Deng ZW, Xu L, Liu HA, et al. (2023) Prognostics of battery capacity based on charging data and data-driven methods for on-road vehicles. Appl Energy 339: 120954. http://doi.org/10.1016/j.apenergy.2023.120954 doi: 10.1016/j.apenergy.2023.120954
|
| [7] |
Wang JW, Deng ZW, Yu T, et al. (2022) State of health estimation based on modified Gaussian process regression for lithium-ion batteries. J Energy Storage 51: 104512. http://doi.org/10.1016/j.est.2022.104512 doi: 10.1016/j.est.2022.104512
|
| [8] |
Geng CM, Zhang TH, Chen B, et al. (2023) Battery state of health estimation using GA-BP neural network on data feature mining. Ieice Electron Expr 20: 20230370. http://doi.org/10.1587/elex.20.20230370 doi: 10.1587/elex.20.20230370
|
| [9] |
Hu XS, Deng ZW, Lin XK, et al. (2021) Research directions for next-generation battery management solutions in automotive applications. Renewable Sustainable Energy Rev 152: 111695. http://doi.org/10.1016/j.rser.2021.111695 doi: 10.1016/j.rser.2021.111695
|
| [10] |
Zhang Y, Li YF (2022) Prognostics and health management of Lithium-ion battery using deep learning methods: A review. Renewable Sustainable Energy Rev 161: 112282. http://doi.org/10.1016/j.rser.2022.112282 doi: 10.1016/j.rser.2022.112282
|
| [11] |
He W, Williard N, Osterman M, et al. (2011) Prognostics of lithium-ion batteries based on Dempster-Shafer theory and the Bayesian Monte Carlo method. J Power Sources 196: 10314–10321. http://doi.org/10.1016/j.jpowsour.2011.08.040 doi: 10.1016/j.jpowsour.2011.08.040
|
| [12] |
Johnannes S, Stefan K, Madeleine E, et al. (2014) A holistic aging model for Li(NiMnCo)O2 based 18650 lithium-ion batteries. J Power Sources 257: 325–334. http://doi.org/10.1016/j.jpowsour.2014.02.012 doi: 10.1016/j.jpowsour.2014.02.012
|
| [13] |
Zhu MY, Qian KF, Liu XT (2024) A three-time-scale dual extended Kalman filtering for parameter and state estimation of Li-ion battery. Proc Inst Mech Eng D J Automob Eng 238: 1352–1367. http://doi.org/10.1177/09544070231153440 doi: 10.1177/09544070231153440
|
| [14] |
Sun JG, Du R, Chen LX, et al. (2024) State of charge estimation for power batteries in new energy vehicles considering temperature fluctuations. J Xi'an Jiaotong Univ 58: 39–51. http://doi.org/10.7652/xjtuxb202411004 doi: 10.7652/xjtuxb202411004
|
| [15] |
Ge B, Luo Y, Li LX, et al. (2023) Real-time state estimation of lithium batteries used for energy storage in electric vehicle charging stations with wind-solar complementary power system. J Xi'an Jiaotong Univ 57: 55–65.http://doi.org/10.7652/xjtuxb202301006 doi: 10.7652/xjtuxb202301006
|
| [16] |
Ji SL, Zhang ZS, Stein HS, et al. (2025) Flexible health prognosis of battery nonlinear aging using temporal transfer learning. Appl Energy 377: 124766. http://doi.org/10.1016/j.apenergy.2024.124766 doi: 10.1016/j.apenergy.2024.124766
|
| [17] |
Zheng K, Meng JH, Yang ZP, et al. (2024) Refined lithium-ion battery state of health estimation with charging segment adjustment. Appl Energy 375: 124077. http://doi.org/10.1016/j.apenergy.2024.124077 doi: 10.1016/j.apenergy.2024.124077
|
| [18] |
Tang AH, Xu YC, Hu YZ, et al. (2024) Battery state of health estimation under dynamic operations with physics-driven deep learning. Appl Energy 370: 123632. http://doi.org/10.1016/j.apenergy.2024.123632 doi: 10.1016/j.apenergy.2024.123632
|
| [19] |
Wen JP, Chen X, Li XH, et al. (2022) SOH prediction of lithium battery based on IC curve feature and BP network. Energy 261: 125234. https://doi.org/10.1016/j.energy.2022.125234 doi: 10.1016/j.energy.2022.125234
|
| [20] |
Ren Y, Tang T, Jiang FS, et al. (2025) A novel state of health estimation method for lithium-ion battery pack based on cross generative adversarial networks. Appl Energy 377: 124385. https://doi.org/10.1016/j.apenergy.2024.124385 doi: 10.1016/j.apenergy.2024.124385
|
| [21] |
Li XB, Fan DQ, Liu XT, et al. (2024) State of health estimation for lithium-ion batteries based on improved bat algorithm optimation kernel extreme learning machine. J Energy Storage 101: 113756. https://doi.org/10.1016/j.est.2024.113756 doi: 10.1016/j.est.2024.113756
|
| [22] |
Liu JZ, Liu XT (2023) An improved method of state of health prediction for lithium batteries considering different temperature. J Energy Storage 63: 107028. https://doi.org/10.1016/j.est.2023.107028 doi: 10.1016/j.est.2023.107028
|
| [23] |
Ren Y, Tang T, Xia Q, et al. (2024) A data and physical model joint driven method for lithium-ion battery remaining useful life prediction under complex dynamic conditions. J Energy Storage 79: 110065. https://doi.org/10.1016/j.est.2023.110065 doi: 10.1016/j.est.2023.110065
|
| [24] |
Ruan HK, He HW, Wei ZB, et al. (2023) State of health estimation of lithium-ion battery based on constant-voltage charging reconstruction. IEEE J Em Sel Top Power Electron 11: 4393–4402. http://doi.org/10.1109/jestpe.2021.3098836 doi: 10.1109/jestpe.2021.3098836
|
| [25] |
Edge JS, O'Kane S, Prosser R, et al. (2021) Lithium ion battery degradation: what you need to know. Phys Chem Chem Phys 23: 8200–8221. http://doi.org/10.1039/d1cp00359c doi: 10.1039/d1cp00359c
|
| [26] |
Lin CP, Xu J, Jiang DL, et al. (2024) A comparative study of data-driven battery capacity estimation based on partial charging curves. J Energy Chem 88: 409–420. http://doi.org/10.1016/j.jechem.2023.09.025 doi: 10.1016/j.jechem.2023.09.025
|
| [27] |
Xiong R, Sun Y, Wang CX, et al. (2023) A data-driven method for extracting aging features to accurately predict the battery health. Energy Storage Mater 57: 460–470. http://doi.org/10.1016/j.ensm.2023.02.034 doi: 10.1016/j.ensm.2023.02.034
|
| [28] |
Li KQ, Wang YJ, Chen ZH (2022) A comparative study of battery state-of-health estimation based on empirical mode decomposition and neural network. J Energy Storage 54: 105333. http://doi.org/10.1016/j.est.2022.105333 doi: 10.1016/j.est.2022.105333
|
| [29] |
Li Y, Abdel-Monem M, Gopalakrishnan R, et al. (2018) A quick on-line state of health estimation method for Li-ion battery with incremental capacity curves processed by Gaussian filter. J Power Sources 373: 40–53. http://doi.org/10.1016/j.jpowsour.2017.10.092 doi: 10.1016/j.jpowsour.2017.10.092
|
| [30] |
Lin CP, Xu J, Shi MJ, et al. (2022) Constant current charging time based fast state-of-health estimation for lithium-ion batteries. Energy 247: 123556. http://doi.org/10.1016/j.energy.2022.123556 doi: 10.1016/j.energy.2022.123556
|
| [31] |
Bian XL, Wei ZG, Li WH, et al. (2022) State-of-Health estimation of lithium-ion batteries by fusing an open circuit voltage model and incremental capacity analysis. IEEE Trans Power Electr 37: 2226–2236. http://doi.org/10.1109/tpel.2021.3104723 doi: 10.1109/tpel.2021.3104723
|
| [32] |
Li XY, Wang ZP, Zhang L, et al. (2019) State-of-health estimation for Li-ion batteries by combing the incremental capacity analysis method with grey relational analysis. J Power Sources 410: 106–114. http://doi.org/10.1016/j.jpowsour.2018.10.069 doi: 10.1016/j.jpowsour.2018.10.069
|
| [33] |
Lin MQ, Yan CH, Wang W, et al. (2023) A data-driven approach for estimating state-of-health of lithium-ion batteries considering internal resistance. Energy 277: 127675. http://doi.org/10.1016/j.energy.2023.127675 doi: 10.1016/j.energy.2023.127675
|
| [34] |
Yan YZ, Wang B, Wang CH, et al. (2024) Adaptive maximum available energy evaluation for lithium battery in hydrogen-electirc hybrid unmanned aerial vehicle applications considering dynamic ambient temperature and aging level. Energy Convers Manage 314: 118685. https://doi.org/10.1016/j.enconman.2024.118685 doi: 10.1016/j.enconman.2024.118685
|
| [35] |
Peng KL, Deng ZW, Bao ZB, et al. (2023) Data-driven battery capacity estimation based on partial discharging capacity curve for lithium-ion batteries. J Energy Storage 67: 107549. http://doi.org/10.1016/j.est.2023.107549 doi: 10.1016/j.est.2023.107549
|
| [36] |
Pan WJ, Luo XS, Zhu MT, et al. (2021) A health indicator extraction and optimization for capacity estimation of Li-ion battery using incremental capacity curves. J Energy Storage 42: 103072. http://doi.org/10.1016/j.est.2021.103072 doi: 10.1016/j.est.2021.103072
|
| [37] |
Deng ZW, Xu L, Liu HA, et al. (2024) Rapid health estimation of in-service battery packs based on limited labels and domain adaptation. J Energy Chem 89: 345–354. https://doi.org/10.1016/j.jechem.2023.10.056 doi: 10.1016/j.jechem.2023.10.056
|
| [38] |
Liu KL, Li Y, Hu XS, et al. (2020) Gaussian process regression with automatic relevance determination kernel for calendar aging prediction of lithium-ion batteries. IEEE Trans Ind Inform 16: 3767–3777. http://doi.org/10.1109/tii.2019.2941747 doi: 10.1109/tii.2019.2941747
|
| [39] |
Lin C, Xu J, Hou J, et al. (2023) A fast data-driven battery capacity estimation method under non-constant current charging and variable temperature. Energy Storage Mater 63: 102967. http://doi.org/10.1016/j.ensm.2023.102967 doi: 10.1016/j.ensm.2023.102967
|
| [40] |
Sattianadan D, Sharma RK, Fernandez SG, et al. (2024) Estimation of state of charge and state of health of batteries using hybrid method and recurrent neural network. AIP Conf Proc 3037: 020016–020028. http://doi.org/10.1063/5.0196476 doi: 10.1063/5.0196476
|
| [41] |
Wang FJ, Zhai Z, Liu BC, et al. (2024) Open access dataset, code library and benchmarking deep learning approaches for state-of-health estimation of lithium-ion batteries. J Energy Storage 77: 109884. http://doi.org/10.1016/j.est.2023.109884 doi: 10.1016/j.est.2023.109884
|
| [42] |
Zhang SX, Liu ZT, Su HY (2023) State of health estimation for lithium-ion batteries on few-shot learning. Energy 268: 126726. http://doi.org/10.1016/j.energy.2023.126726 doi: 10.1016/j.energy.2023.126726
|
| 1. | MOUHAMADOU SAMSIDY GOUDIABY, GUNILLA KREISS, A RIEMANN PROBLEM AT A JUNCTION OF OPEN CANALS, 2013, 10, 0219-8916, 431, 10.1142/S021989161350015X | |
| 2. | Jean-Marc Herard, 2012, The Coupling of Multi-phase Flow Models, 978-1-60086-933-4, 10.2514/6.2012-3357 | |
| 3. | Benjamin Boutin, Frédéric Coquel, Philippe G. LeFloch, Coupling techniques for nonlinear hyperbolic equations. Ⅱ. resonant interfaces with internal structure, 2021, 16, 1556-181X, 283, 10.3934/nhm.2021007 |
Junguang Sun, Xiaodong Zhang, Wenrui Cao, Lili Bo, Changhai Liu, Bin Wang. State of health estimation of lithium-ion batteries based on data-driven methods with a selected charging voltage interval[J]. AIMS Energy, 2025, 13(2): 290-308. doi: 10.3934/energy.2025012
DownLoad: