A Method for Battery Health Estimation Based on Charging Time Segment
Abstract
:1. Introduction
2. The Estimation Method for Lithium-Ion Batteries
2.1. Construction of Health Indicators
2.2. Principles of Particle Filter
2.3. Improvements to Particle Filter
- (1)
- Each cuckoo lays an egg, stacked in a randomly selected nest;
- (2)
- The best high-quality egg will be transferred to the next generation;
- (3)
- The number of nests is fixed, and the probability of cuckoo eggs being found is P(a).
- (1)
- The fixed discovery probability is replaced by dynamic discovery probability.
- (2)
- The relationship between search precision and search speed is balanced by introducing the changing trend of the function value into the step update formula.
- The algorithm uses a large step size in the early stages to improve the global search speed in battery information;
- The algorithm also uses a small step size in the later stages to improve the accuracy of the algorithm.
2.4. Combination of the Algorithms
- (1)
- Initialize the particle, set the value of t to 0. Sampling the prior probability distribution achieves particle group . obeys the density function .
- (2)
- Set the weight of every single particle as , the random direction obeys uniform distribution while the random step size is obtained from a distribution described in Equation (11).
- (3)
- Replace the resampling step by searching for particles globally using the optimized cuckoo search particle filter idea.
- Initialize the ICS algorithm. Initialize the algorithm parameters: step size , discovery probability, and the optimized initial particles .
- Introduce the sample particles into the ICS algorithm. The operation should follow steps 2–5 in Section 2.4.
- Identify the fitness value: use a suitable fitness function to calculate the fitness of each particle and identify the particle with highest fitness values. The fitness function of is defined as in the equation below referring to the definition formula of the particle weight in the particle filter [38,42]:
- Output the particle after the resample process.
- (4)
- Calculate of the importance weight using Equation (17).
- (5)
- Normalization is performed using Equation (18).
- (6)
- Output state estimation: the resampled particles are fed into the state equation. The observed particles can be directly obtained in the next round of filtering. The output particle status is given by Equation (19).
3. Experiment
3.1. Lithium-Ion Aging Cycle Experiment
3.2. Results and Discussion
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Zhang, J.; Gao, A.-T.; Han, Y.-S.; Zuo, X.-W.; Zhang, F. A health prognostic algorithm for Li-ion battery based on particle filter. Chin. J. Power Sources 2015, 39, 1377–1380. [Google Scholar] [CrossRef]
- Rezvanizaniani, S.M.; Liu, Z.; Chen, Y.; Lee, J. Review and recent advances in battery health monitoring and prognostics technologies for electric vehicle (EV) safety and mobility. J. Power Sources 2014, 256, 110–124. [Google Scholar] [CrossRef]
- Ma, Z.; Yang, R.; Wang, Z. A novel data-model fusion state-of-health estimation approach for lithium-ion batteries. Appl. Energy 2019, 237, 836–847. [Google Scholar] [CrossRef]
- Weng, C.; Feng, X.; Sun, J.; Peng, H. State-of-health monitoring of lithium-ion battery modules and packs via incremental capacity peak tracking. Appl. Energy 2016, 180, 360–368. [Google Scholar] [CrossRef] [Green Version]
- Shen, D.; Wu, L.; Kang, G.; Guan, Y.; Peng, Z. A novel online method for predicting the remaining useful life of lithium-ion batteries considering random variable discharge current. Energy 2021, 218, 119490. [Google Scholar] [CrossRef]
- Kim, B.; Chang, I.S.; Dinsdale, R.M.; Guwy, A.J. Accurate measurement of internal resistance in microbial fuel cells by improved scanning electrochemical impedance spectroscopy. Electrochim. Acta 2021, 366, 137388. [Google Scholar] [CrossRef]
- Okamoto, E.; Nakamura, M.; Akasaka, Y.; Inoue, Y.; Abe, Y.; Chinzei, T.; Saito, I.; Isoyama, T.; Mochizuki, S.; Imachi, K.; et al. Analysis of heat generation of lithium ion rechargeable batteries used in implantable battery systems for driving undulation pump ventricular assist device. Artif. Organs 2007, 31, 538–541. [Google Scholar] [CrossRef]
- Ng, S.S.Y.; Xing, Y.; Tsui, K.L. A naive Bayes model for robust remaining useful life prediction of lithium-ion battery. Appl. Energy 2014, 118, 114–123. [Google Scholar] [CrossRef]
- Uno, M.; Tanaka, K. Accelerated charge–discharge cycling test and cycle life prediction model for supercapacitors in alternative battery applications. IEEE Trans. Ind. Electron. 2012, 59, 4704–4712. [Google Scholar] [CrossRef]
- Zhou, J.; Liu, D.; Peng, Y.; Peng, X. Dynamic battery remaining useful life estimation: An on-line data-driven approach. In Proceedings of the 2012 IEEE International Instrumentation and Measurement Technology Conference Proceedings, Graz, Austria, 13–16 May 2012; pp. 2196–2199. [Google Scholar] [CrossRef]
- Shen, W.X.; Chan, C.C.; Lo, E.W.C.; Chau, K.T. Estimation of battery available capacity under variable discharge currents. J. Power Sources 2002, 103, 180–187. [Google Scholar] [CrossRef]
- Liu, X.; Yang, Y.; He, Y.; Zhang, J.; Zheng, X.; Ma, M.; Zeng, G. A new dynamic SOH estimation of lead-acid battery for substation application. Int. J. Energy Res. 2016, 41, 579–592. [Google Scholar] [CrossRef]
- Smith, K.A.; Rahn, C.D.; Wang, C.-Y. Control oriented 1D electrochemical model of lithium ion battery. Energy Convers. Manag. 2007, 48, 2565–2578. [Google Scholar] [CrossRef]
- Smagin, D.I.; Trofimov, A.A.; Napreenko, K.S.; Neveshkina, A.R. Mathematical model of lithium-ion battery cell and battery (Lib) on its basis. IOP Conf. Ser. Mater. Sci. Eng. 2020, 714, 012027. [Google Scholar] [CrossRef]
- He, H.; Xiong, R.; Fan, J. Evaluation of lithium-ion battery equivalent circuit models for state of charge estimation by an experimental approach. Energies 2011, 4, 582–598. [Google Scholar] [CrossRef]
- Chen, G.-J.; Liu, Y.-H.; Wang, S.-C.; Luo, Y.-F.; Yang, Z.-Z. Searching for the optimal current pattern based on grey wolf optimizer and equivalent circuit model of Li-ion batteries. J. Energy Storage 2021, 33, 101933. [Google Scholar] [CrossRef]
- Zhang, J.; Lv, D.; Simeone, A. Artificial neural network–based multisensor monitoring system for collision damage assessment of lithium-ion battery cells. Energy Technol. 2020, 8, 2000031. [Google Scholar] [CrossRef]
- Seaman, A.; Dao, T.S.; Mcphee, J. A survey of mathematics-based equivalent-circuit and electrochemical battery models for hybrid and electric vehicle simulation. J. Power Sources 2014, 256, 410–423. [Google Scholar] [CrossRef] [Green Version]
- Dauga, S.; Aldaouab, I. Artificial neural network approach to improve the performance of battery and thermal storage. Proc. SPIE 2020, 11382, 113820G. [Google Scholar] [CrossRef]
- Bi, J.; Zhang, T.; Yu, H.; Kang, Y. State-of-health estimation of lithium-ion battery packs in electric vehicles based on genetic resampling particle filter. Appl. Energy 2016, 182, 558–568. [Google Scholar] [CrossRef] [Green Version]
- Guo, G.F.; Shui, L.; Wu, X.L.; Cao, B.G. SOC estimation for Li-ion battery using SVM based on particle swarm optimization. Adv. Mat. Res. 2014, 1051, 1004–1008. [Google Scholar] [CrossRef]
- Singh, P.; Kaneria, S.; Broadhead, J.; Wang, X.; Burdick, J. Fuzzy logic estimation of SOH of 125Ah VRLA batteries. In Proceedings of the 2004 10th International Workshop on Computational Electronics (IEEE Cat. No.04EX915), Chicago, IL, USA, 19–23 September 2004; pp. 524–531. [Google Scholar] [CrossRef]
- Wang, X.; Fan, W.; Li, S.; Li, X.; Wang, L. SOH estimation of lithium-ion battery pack based on integrated state information from cells. Appl. Sci. 2020, 10, 6637. [Google Scholar] [CrossRef]
- Wei, J.; Dong, G.; Chen, Z. Remaining useful life prediction and state of health diagnosis for lithium-ion batteries using particle filter and support vector regression. IEEE Trans. Ind. Electron. 2018, 65, 5634–5643. [Google Scholar] [CrossRef]
- Lim, J. Performance degradation due to particle impoverishment in particle filtering. J. Electr. Eng. Technol. 2014, 9, 2107–2113. [Google Scholar] [CrossRef] [Green Version]
- Chang, Y.; Fang, H. A hybrid prognostic method for system degradation based on particle filter and relevance vector machine. Reliab. Eng. Syst. Saf. 2019, 186, 51–63. [Google Scholar] [CrossRef]
- Nicely, M.A.; Wells, B.E. Improved parallel resampling methods for particle filtering. IEEE Access 2019, 7, 47593–47604. [Google Scholar] [CrossRef]
- Cheng, F.; Qu, L.; Qiao, W.; Hao, L. Enhanced particle filtering for bearing remaining useful life prediction of wind turbine drivetrain gearboxes. IEEE Trans. Ind. Electron. 2019, 66, 4738–4748. [Google Scholar] [CrossRef]
- Gao, B.; Hu, X.; Peng, Z.; Song, Y. Application of intelligent water drop algorithm in process planning optimization. Int. J. Adv. Manuf. Technol. 2020, 106, 5199–5211. [Google Scholar] [CrossRef]
- Feng, W.; Rao, Z.; Wang, Z. Research on the application of ant colony algorithm in underwater path planning. In Proceedings of the 2016 International Symposium on Advances in Electrical, Electronics and Computer Engineering, Guangzhou, China, 12–13 March 2016; Atlantis Press: Paris, France, 2016; pp. 46–50. [Google Scholar] [CrossRef] [Green Version]
- Yildiz, A.R. Cuckoo search algorithm for the selection of optimal machining parameters in milling operations. Int. J. Adv. Manuf. Technol. 2013, 64, 55–61. [Google Scholar] [CrossRef]
- Ahmed, B.S.; Abdulsamad, T.S.; Potrus, M.Y. Achievement of minimized combinatorial test suite for configuration-aware software functional testing using the Cuckoo Search algorithm. Inf. Softw. Technol. 2015, 66, 13–29. [Google Scholar] [CrossRef] [Green Version]
- Gui-Xia, F.; Ming-Liang, G.; Guo-Feng, Z.; Wen-Can, L.; Li-Na, L. An improved particle filter based on cuckoo search for visual tracking. In Proceedings of the 2018 Chinese Control and Decision Conference (CCDC), Shenyang, China, 9–11 June 2018. [Google Scholar]
- Han, W.; Xu, J.; Zhou, M.; Tian, G.; Wang, P.; Shen, X.; Hou, E. Cuckoo Search and Particle Filter-Based Inversing Approach to Estimating Defects via Magnetic Flux Leakage Signals. IEEE Trans. Magn. 2016, 52, 1–11. [Google Scholar] [CrossRef]
- Meng, J.; Cai, L.; Stroe, D.-I.; Luo, G.; Sui, X.; Teodorescu, R. Lithium-ion battery state-of-health estimation in electric vehicle using optimized partial charging voltage profiles. Energy 2019. [Google Scholar] [CrossRef]
- Schaltz, E.; Stroe, D.-I.; Norregaard, K.; Johnsen, B.; Christensen, A. Partial Charging Method for Lithium-Ion Battery State-of-Health Estimation. In Proceedings of the 2019 Fourteenth International Conference on Ecological Vehicles and Renewable Energies (EVER), Monte-Carlo, Monaco, 8–10 May 2019. [Google Scholar] [CrossRef]
- Dong, G.; Wei, J.; Zhang, C.; Chen, Z. Online state of charge estimation and open circuit voltage hysteresis modeling of LiFePO4 battery using invariant imbedding method. Appl. Energy 2016, 162, 163–171. [Google Scholar] [CrossRef]
- Xing, Y.; Ma, E.W.M.; Tsui, K.-L.; Pecht, M. An ensemble model for predicting the remaining useful performance of lithium-ion batteries. Microelectron. Reliab. 2013, 53, 811–820. [Google Scholar] [CrossRef]
- Israeli, O. A Shapley-based decomposition of the R-square of a linear regression. J. Econ. Inequal. 2007, 5, 199–212. [Google Scholar] [CrossRef]
- Boland, J.; Howlett, P.; Piantadosi, J. Matching the grade correlation coefficient using a copula with maximum disorder. J. Ind. Manag. Optim. 2007, 3, 305–312. [Google Scholar]
- Gao, M.-L.; Li, L.-L.; Sun, X.-M.; Yin, L.-J.; Li, H.-T.; Luo, D.-S. Firefly algorithm (FA) based particle filter method for visual tracking. Optik Int. J. Light Electron Opt. 2015. [Google Scholar] [CrossRef]
- Yang, X.S.; Deb, S. Cuckoo Search via Lévy flights. In Proceedings of the World Congress on Nature & Biologically Inspired Computing, Coimbatore, India, 9–11 December 2010. [Google Scholar] [CrossRef]
- Qiu, X.; Wu, W.; Wang, S. Remaining useful life prediction of lithium-ion battery based on improved cuckoo search particle filter and a novel state of charge estimation method. J. Power Sources 2020, 450, 227700. [Google Scholar] [CrossRef]
Correlation Coefficient | CS2#35 | CS2#36 | CS2#37 |
---|---|---|---|
−0.973 | −0.987 | −0.995 | |
−0.983 | −0.991 | −0.994 |
Batteries | Capacity | Weight | Size |
---|---|---|---|
CX2 | 1100 mAh | 21.1 g | 5.4 × 33.6 × 50.6 mm |
CS2 | 1350 mAh | 28 g | 6.6 × 33.8 × 50 mm |
Battery | Estimated Result | The Value of the Error | March Result | ||
---|---|---|---|---|---|
EOP Cycles | SOH | Mean | Maximum | RMSE | |
CS2#37 | +5 | 79.50% | 0.001 | −0.018 | 0.00143 |
CS2#38 | −8 | 80.96% | 0.005 | 0.019 | 0.00122 |
CX2#37 | +3 | 79.90% | 0.002 | −0.016 | 0.00175 |
CX2#38 | −10 | 81.14% | 0.004 | 0.017 | 0.00184 |
Battery | RMSE | ||
---|---|---|---|
CS-PF | ICS-PF | PF | |
CS2#37 | 0.00251 | 0.00143 | 0.00307 |
CS2#38 | 0.00275 | 0.00122 | 0.00268 |
CX2#37 | 0.00211 | 0.00175 | 0.00301 |
CX2#38 | 0.00213 | 0.00184 | 0.00329 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Liu, S.-X.; Zhou, Y.-F.; Liu, Y.-L.; Lian, J.; Huang, L.-J. A Method for Battery Health Estimation Based on Charging Time Segment. Energies 2021, 14, 2612. https://doi.org/10.3390/en14092612
Liu S-X, Zhou Y-F, Liu Y-L, Lian J, Huang L-J. A Method for Battery Health Estimation Based on Charging Time Segment. Energies. 2021; 14(9):2612. https://doi.org/10.3390/en14092612
Chicago/Turabian StyleLiu, Shao-Xun, Ya-Fu Zhou, Yan-Liang Liu, Jing Lian, and Li-Jian Huang. 2021. "A Method for Battery Health Estimation Based on Charging Time Segment" Energies 14, no. 9: 2612. https://doi.org/10.3390/en14092612