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RESEARCH PAPER
Useful energy prediction model of a Lithium-ion cell operating on various duty cycles
 
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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....
 
 
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