RESEARCH PAPER
Useful energy prediction model of a Lithium-ion cell operating on various duty cycles
| 1 | Poznan University of Technology, Faculty of Control, Robotics and Electrical Engineering, ul. Piotrowo 3a, 60-965 Poznan, Poland |
Publication date: 2022-06-30
Eksploatacja i Niezawodność – Maintenance and Reliability 2022;24(2):317–329
HIGHLIGHTS
- A new non-parametric useful energy model for long-term prediction was developed.
- Developed model takes into account the lifetime degradation of the cell.
- Identification of three types of RUEc evolution over exploitation period of the cells.
- XAI techniques were used to quantify effect of model parameters on RUEc.
- The proposed methodology can be applied to electrochemical cells of other types.
KEYWORDS
ABSTRACT
The paper deals with the subject of the prediction of useful energy during the cycling of a lithium-ion cell (LIC), using machine learning-based techniques. It was demonstrated that depending on the combination of cycling parameters, the useful energy (RUEc) that can be transferred during a full cycle is variable, and also three different types of evolution of changes in RUEc were identified. The paper presents a new non-parametric RUEc prediction model based on Gaussian process regression. It was proven that the proposed methodology enables the RUEc prediction for LICs discharged, above the depth of discharge, at a level of 70% with an acceptable error, which is confirmed for new load profiles. Furthermore, techniques associated with explainable artificial intelligence were applied to determine the significance of model input parameters – the variable importance method – and to determine the quantitative effect of individual model parameters (their reciprocal interaction) on RUEc – the accumulated local effects model of the first and second order.
REFERENCES (69)
1.
Apley D W, Zhu J. Visualizing the effects of predictor variables in black box supervised learning models. Journal of the Royal Statistical Society. Series B: Statistical Methodology 2020; 82(4): 1059-1086, https://doi.org/10.1111/rssb.1....
2.
Belt J, Utgikar V, Bloom I. Calendar and PHEV cycle life aging of high-energy, lithium-ion cells containing blended spinel and layered-oxide cathodes. Journal of Power Sources 2011; 196(23): 10213-10221, https://doi.org/10.1016/j.jpow....
3.
Bloom I, Cole B W, Sohn J J et al. An accelerated calendar and cycle life study of Li-ion cells. Journal of Power Sources 2001; 101(2): 238-247, https://doi.org/10.1016/S0378-....
4.
Broussely M, Biensan P, Bonhomme F et al. Main aging mechanisms in Li ion batteries. Journal of Power Sources 2005; 146(1-2): 90-96, https://doi.org/10.1016/j.jpow....
5.
Burzyński D, Kasprzyk L. A novel method for the modeling of the state of health of lithium-ion cells using machine learning for practical applications. Knowledge-Based Systems 2021; 219: 106900, https://doi.org/10.1016/j.knos....
6.
Burzyński D, Pietracho R, Kasprzyk L, Tomczewski A. Analysis and Modeling of the Wear-Out Process of a Lithium-Nickel-Manganese-Cobalt Cell during Cycling Operation under Constant Load Conditions. Energies 2019; 12(20): 3899, https://doi.org/10.3390/en1220....
7.
Chen Z, Ren Y, Jansen A N et al. New class of nonaqueous electrolytes for long-life and safe lithium-ion batteries. Nature Communications 2013; 4: 1-8, https://doi.org/10.1038/ncomms....
8.
Diao W, Saxena S, Pecht M. Accelerated cycle life testing and capacity degradation modeling of LiCoO2-graphite cells. Journal of Power Sources 2019; 435(June): 226830, https://doi.org/10.1016/j.jpow....
9.
Dong G, Zhang X, Zhang Ch, Chen Z. A method for state of energy estimation of lithium-ion batteries based on neural network model. Energy 2015; 90(1)1:879-888, https://doi.org/10.1016/j.ener....
10.
dos Reis G, Strange C, Yadav M, Li S. Lithium-ion battery data and where to find it. Energy and AI 2021, https://doi.org/10.1016/j.egya....
11.
Dudézert C, Reynier Y, Duffault J M, Franger S. Fatigue damage approach applied to Li-ion batteries ageing characterization. Materials Science and Engineering B: Solid-State Materials for Advanced Technology 2016; 213: 177-189, https://doi.org/10.1016/j.mseb....
12.
Ecker M, Gerschler J B, Vogel J et al. Development of a lifetime prediction model for lithium-ion batteries based on extended accelerated aging test data. Journal of Power Sources 2012; 215: 248-257, https://doi.org/10.1016/j.jpow....
13.
Edström K, Dominko R, Fichtner M et al. BATTERY 2030+ Roadmap. 2020: 83, https://doi.org/10.33063/diva2....
14.
El Mejdoubi A, Chaoui H, Gualous H et al. Lithium-ion batteries health prognosis considering aging conditions. IEEE Transactions on Power Electronics 2019; 34(7): 6834-6844, https://doi.org/10.1109/TPEL.2....
15.
Gao Y, Jiang J, Zhang C et al. Lithium-ion battery aging mechanisms and life model under different charging stresses. Journal of Power Sources 2017; 356: 103-114, https://doi.org/10.1016/j.jpow....
16.
Han X, Ouyang M, Lu L, Li J. A comparative study of commercial lithium ion battery cycle life in electric vehicle: Capacity loss estimation. Journal of Power Sources 2014; 268: 658-669, https://doi.org/10.1016/j.jpow....
17.
Hasib S A, Islam S, Chakrabortty R K et al. A Comprehensive Review of Available Battery Datasets, RUL Prediction Approaches, and Advanced Battery Management. IEEE Access 2021; 9: 86166-86193, https://doi.org/10.1109/ACCESS....
18.
Haykin S, Simon S. Neural Networks and learning machines. Prentice Hall; New York 2009.
19.
How D N T, Hannan M A, Lipu M S H et al. State-of-Charge Estimation of Li-Ion Battery in Electric Vehicles: A Deep Neural Network Approach. IEEE Transactions on Industry Applications 2020; 56(5): 5565-5574, https://doi.org/10.1109/TIA.20....
20.
Hu X, Jiang J, Cao D, Egardt B. Battery health prognosis for electric vehicles using sample entropy and sparse Bayesian predictive modeling. IEEE Transactions on Industrial Electronics 2016; 63(4): 2645-2656.
21.
Kasprzyk L. Modelling and analysis of dynamic states of the lead-acid batteries in electric vehicles. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2017; 19(2): 229-236, https://doi.org/10.17531/ein.2....
22.
Kasprzyk L, Domeracka A, Burzyński D. Modelowanie pracy i trwałości akumulatorów litowo-jonowych w pojazdach elektrycznych. Przegląd Elektrotechniczny 2018; 1(12): 160-163, https://doi.org/10.15199/48.20....
23.
Keil P, Schuster S F, Wilhelm J et al. Calendar Aging of Lithium-Ion Batteries. Journal of The Electrochemical Society 2016; 163(9): A1872- A1880, https://doi.org/10.1149/2.0411....
24.
Li I H, Wang W Y, Su S F, Lee Y S. A merged fuzzy neural network and its applications in battery state-of-charge estimation. IEEE Transactions on Energy Conversion 2007; 22(3): 697-708, https://doi.org/10.1109/TEC.20....
25.
Li K, Wei F, Tseng K J, Soong B H. A Practical Lithium-Ion Battery Model for State of Energy and Voltage Responses Prediction Incorporating Temperature and Ageing Effects. IEEE Transactions on Industrial Electronics 2018; 65(8): 6696-6708, https://doi.org/10.1109/TIE.20....
26.
Li X, Miao J, Ye J. Lithium-ion battery remaining useful life prediction based on grey support vector machines. Advances in Mechanical Engineering 2015; 7(12): 1-8, https://doi.org/10.1177/168781....
27.
Li X, Pan K, Fan G et al. A physics-based fractional order model and state of energy estimation for lithium ion batteries. Part II: Parameter identification and state of energy estimation for LiFePO4 battery. Journal of Power Sources 2017; 367: 202-213, https://doi.org/10.1016/j.jpow....
28.
Li Y, Liu K, Foley A M et al. Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review. Renewable and Sustainable Energy Reviews 2019. doi:10.1016/j.rser.2019.109254, https://doi.org/10.1016/j.rser....
29.
Li Y, Li K, Liu X, Zhang L. Fast battery capacity estimation using convolutional neural networks. Transactions of the Institute of Measurement and Control 2020, https://doi.org/10.1177/014233....
30.
Liu G, Ouyang M, Lu L et al. A highly accurate predictive-adaptive method for lithium-ion battery remaining discharge energy prediction in electric vehicle applications. Applied Energy 2015; 149: 297-314, https://doi.org/10.1016/j.apen....
31.
Liu K, Li Y, Hu X et al. Gaussian Process Regression with Automatic Relevance Determination Kernel for Calendar Aging Prediction of Lithium-Ion Batteries. IEEE Transactions on Industrial Informatics 2020; 16(6): 3767-3777, https://doi.org/10.1109/TII.20....
32.
Liu K, Shang Y, Ouyang Q, Widanage W D. A Data-Driven Approach with Uncertainty Quantification for Predicting Future Capacities and Remaining Useful Life of Lithium-ion Battery. IEEE Transactions on Industrial Electronics 2021; 68(4): 3170-3180, https://doi.org/10.1109/TIE.20....
33.
Liu T, Cheng L, Pan Z, Sun Q. Cycle life prediction of lithium-ion cells under complex temperature profiles. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2016; 18(1): 25-31, https://doi.org/10.17531/ein.2....
34.
Molnar C. Interpretable Machine Learning. A Guide for Making Black Box Models Explainable. Book 2019: 247.
35.
Motapon S N, Lachance E, Dessaint L-A, Al-Haddad K. A Generic Cycle Life Model for Lithium-Ion Batteries Based on Fatigue Theory and Equivalent Cycle Counting. IEEE Open Journal of the Industrial Electronics Society 2020; 1(August): 207-217, https://doi.org/10.1109/OJIES.....
36.
Naumann M, Spingler F, Jossen A. Analysis and modeling of cycle aging of a commercial LiFePO4/graphite cell. Journal of Power Sources 2020; 451(December 2019): 227666, https://doi.org/10.1016/j.jpow....
37.
Ning G, Popov B N. Cycle Life Modeling of Lithium-Ion Batteries. Journal of The Electrochemical Society 2004; 151(10): A1584, https://doi.org/10.1149/1.1787....
38.
Olmos J, Gandiaga I, Saez-de-Ibarra A et al. Modelling the cycling degradation of Li-ion batteries: Chemistry influenced stress factors. Journal of Energy Storage 2021; 40(May): 102765, https://doi.org/10.1016/j.est.....
39.
Pan R, Wang Y, Zhang X et al. Power capability prediction for lithium-ion batteries based on multiple constraints analysis. Electrochimica Acta 2017; 238: 120-133, https://doi.org/10.1016/j.elec....
40.
Pielecha I, Cieślik W, Szałek A. Operation of electric hybrid drive systems in varied driving conditions. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2018; 20(1): 16-23, https://doi.org/10.17531/ein.2....
41.
Pietracho R, Wenge C, Balischewski S et al. Potential of Using Medium Electric Vehicle Fleet in a Commercial Enterprise Transport in Germany on the Basis of Real-World GPS Data. Energies 2021; 14(17): 1-23, https://doi.org/10.3390/en1417....
42.
Rassmussen C E, Williams Ch K. Gaussian processes for machine learning. MIT press Cambridge; 2006:1, https://doi.org/10.7551/mitpre....
43.
Richardson R R, Birkl C R, Osborne M A, Howey D A. Gaussian Process Regression for in Situ Capacity Estimation of Lithium-Ion Batteries. IEEE Transactions on Industrial Informatics 2019; 15(1): 127-138, https://doi.org/10.1109/TII.20....
44.
Richardson R R, Osborne M A, Howey D A. Battery health prediction under generalized conditions using a Gaussian process transition model. Journal of Energy Storage 2019; 23(March): 320-328, https://doi.org/10.1016/j.est.....
45.
Safari M, Morcrette M, Teyssot A, Delacourt C. Multimodal Physics-Based Aging Model for Life Prediction of Li-Ion Batteries. Journal of The Electrochemical Society 2009; 156(3): A145, https://doi.org/10.1149/1.3043....
46.
Safari M, Morcrette M, Teyssot A, Delacourt C. Life Prediction Methods for Lithium-Ion Batteries Derived from a Fatigue Approach. Journal of The Electrochemical Society 2010; 157(7): A892, https://doi.org/10.1149/1.3432....
47.
Saha B, Goebel K, Poll S, Christophersen J. Prognostics methods for battery health monitoring using a Bayesian framework. IEEE Transactions on Instrumentation and Measurement 2009; 58(2): 291-296, https://doi.org/10.1109/TIM.20....
48.
Schmalstieg J, Käbitz S, Ecker M, Sauer D U. A holistic aging model for Li(NiMnCo)O2 based 18650 lithium-ion batteries. Journal of Power Sources 2014; 257: 325-334, https://doi.org/10.1016/j.jpow....
49.
Schulze M C, Neale N R. Half-Cell Cumulative Efficiency Forecasts Full-Cell Capacity Retention in Lithium-Ion Batteries. ACS Energy Letters 2021; 6(3): 1082-1086, https://doi.org/10.1021/acsene....
50.
Sikha G, Popov B N, White R E. Effect of Porosity on the Capacity Fade of a Lithium-Ion Battery. Journal of The Electrochemical Society 2004; 151(7): A1104, https://doi.org/10.1149/1.1759....
51.
Su C, Chen H, Wen Z. Prediction of remaining useful life for lithium-ion battery with multiple health indicators. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2021; 23(1): 176-183, https://doi.org/10.17531/ein.2....
52.
Tagade P, Hariharan K S, Ramachandran S et al. Deep Gaussian process regression for lithium-ion battery health prognosis and degradation mode diagnosis. Journal of Power Sources 2020; 445(October 2019): 227281, https://doi.org/10.1016/j.jpow....
53.
Tao L, Ma J, Cheng Y et al. A review of stochastic battery models and health management. Renewable and Sustainable Energy Reviews 2017; 80(February 2018): 716-732, https://doi.org/10.1016/j.rser....
54.
Vetter J, Novák P, Wagner M R et al. Ageing mechanisms in lithium-ion batteries. Journal of Power Sources 2005; 147(1-2): 269-281, https://doi.org/10.1016/j.jpow....
55.
Vidal C, Malysz P, Kollmeyer P, Emadi A. Machine Learning Applied to Electrified Vehicle Battery State of Charge and State of Health Estimation: State-of-the-Art. IEEE Access 2020; 8: 52796-52814, https://doi.org/10.1109/ACCESS....
56.
Wang A, Kadam S, Li H et al. Review on modeling of the anode solid electrolyte interphase (SEI) for lithium-ion batteries. npj Computational Materials 2018, https://doi.org/10.1038/s41524....
57.
Wang J, Liu P, Hicks-Garner J et al. Cycle-life model for graphite-LiFePO4 cells. Journal of Power Sources 2011; 196(8): 3942-3948, https://doi.org/10.1016/j.jpow....
58.
Wang J, Purewal J, Liu P et al. Degradation of lithium ion batteries employing graphite negatives and nickel e cobalt e manganese oxide þ spinel manganese oxide positives : Part 1 , aging mechanisms and life estimation. Journal of Power Sources 2014; 269: 937-948, https://doi.org/10.1016/j.jpow....
59.
Wang Y, Zhang C, Chen Z. A method for joint estimation of state-of-charge and available energy of LiFePO4 batteries. Applied Energy 2014; 135: 81-87, https://doi.org/10.1016/j.apen....
60.
Wang Y, Zhang C, Chen Z. Model-based state-of-energy estimation of lithium-ion batteries in electric vehicles. Energy Procedia 2016; 88: 998-1004, https://doi.org/10.1016/j.egyp....
61.
Wang Y, Zhang C, Chen Z. An adaptive remaining energy prediction approach for lithium-ion batteries in electric vehicles. Journal of Power Sources 2016; 305: 80-88, https://doi.org/10.1016/j.jpow....
62.
Wei P, Lu Z, Song J. Variable importance analysis: A comprehensive review. Reliability Engineering and System Safety 2015; 142: 399-432, https://doi.org/10.1016/j.ress....
63.
Weng C, Cui Y, Sun J, Peng H. On-board state of health monitoring of lithium-ion batteries using incremental capacity analysis with support vector regression. Journal of Power Sources 2013; 235: 36-44, https://doi.org/10.1016/j.jpow....
64.
Werner D, Paarmann S, Wetzel T. Calendar aging of li-ion cells-experimental investigation and empirical correlation. Batteries 2021, https://doi.org/10.3390/batter....
65.
Wik T, Fridholm B, Kuusisto H. Implementation and robustness of an analytically based battery state of power. Journal of Power Sources 2015; 287: 448-457, https://doi.org/10.1016/j.jpow....
66.
Xiong R, Zhang Y, He H et al. A double-scale, particle-filtering, energy state prediction algorithm for lithium-ion batteries. IEEE Transactions on Industrial Electronics 2017; 65(2): 1526-1538, https://doi.org/10.1109/TIE.20....
67.
Xiong R, Zhang Y, Wang J et al. Lithium-Ion Battery Health Prognosis Based on a Real Battery Management System Used in Electric Vehicles. IEEE Transactions on Vehicular Technology 2019; 68(5): 4110-4121, https://doi.org/10.1109/TVT.20....
68.
Zhang X, Wang Y, Liu C, Chen Z. A novel approach of remaining discharge energy prediction for large format lithium-ion battery pack. Journal of Power Sources 2017; 343: 216-225, https://doi.org/10.1016/j.jpow....
69.
Zhang Y, Tang Q, Zhang Y et al. Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning. Nature Communications 2020; 11(1): 6-11, https://doi.org/10.1038/s41467....
CITATIONS (3):
1.
Sensor data fusion in electrochemical applications: An overview and its application to electrochlorination monitoring
E.A. Ross, R.M. Wagterveld, J.D. Stigter, M.J.J. Mayer, K.J. Keesman
Computers & Chemical Engineering
E.A. Ross, R.M. Wagterveld, J.D. Stigter, M.J.J. Mayer, K.J. Keesman
Computers & Chemical Engineering
2.
A Review of the Applications of Explainable Machine Learning for Lithium–Ion Batteries: From Production to State and Performance Estimation
Niri Faraji, Koorosh Aslansefat, Sajedeh Haghi, Mojgan Hashemian, Rüdiger Daub, James Marco
Energies
Niri Faraji, Koorosh Aslansefat, Sajedeh Haghi, Mojgan Hashemian, Rüdiger Daub, James Marco
Energies
3.
Comprehensive Study on Reliability, Deterioration, and Ageing of Lithium-Ion Batteries in Electric-Powered Aircraft Assessed after a Storage Period Using Functional Data Analysis and Non-Parametric Back Up
David Vališ, Jiří Hlinka, Jan Koláček, Petr Procházka, Ondřej Pokora, Radoslav Cipín, Rostislav Koštial
Engineering Failure Analysis
David Vališ, Jiří Hlinka, Jan Koláček, Petr Procházka, Ondřej Pokora, Radoslav Cipín, Rostislav Koštial
Engineering Failure Analysis
Share
RELATED ARTICLE
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.