Elektrikli Araçlarda Otonom Batarya Yönetim Sistemi Literatür İncelemesi
Yıl 2024,
Cilt: 14 Sayı: 2, 7 - 22, 30.07.2024
Metin Yılmaz
,
Eyüp Çınar
,
Ahmet Yazıcı
Öz
Elektrikli araçlar hem dünya genelinde hem de ülkemizde giderek daha yaygın hale gelmektedir. Bu araçlarda, batarya en kritik bileşenlerdir. Akıllı bir batarya yönetim sistemi (Battery Management System - BMS) için doğru prognostik ve sağlık yönetimi (Prognostics and Health Management - PHM) büyük önem taşır. PHM ve BMS, elektrikli araçların güvenliği, verimliliği ve batarya ömrü açısından kritik bir rol oynamaktadır. Bu literatür incelemesi, elektrikli araçlar için PHM ve BMS konularının önemine vurgu yapmaktadır. Lityum-iyon (Li-ion) bataryaların hala en uygun seçeneklerden biridir, ancak batarya ömrü gibi bazı zorluklarla karşılaşılabilir. Bu nedenle, doğru batarya şarj durumu (State of Charge - SoC) ve bataryanın sağlık durumu (State of Health - SoH) tahminleriyle bir BMS, batarya ömrünü uzatmak ve güvenliği sağlamak için gereklidir. Bu çalışma, elektrikli araçlar için PHM ve BMS konularında gelecekteki araştırma gündemine yönelik analitik bir incelemedir. Batarya prognostiğinin önemine vurgu yapılarak, elektrikli araçların sağlıklı çalışması için daha fazla araştırmanın yapılması gerektiği vurgulanmaktadır.
Destekleyen Kurum
Türkiye Bilimsel ve Teknolojik Araştırma Kurumu’nun (TUBİTAK)
Teşekkür
Yazarlardan Metin Yılmaz 100/2000 YÖK Doktora bursu öğrencisidir.
Kaynakça
- [1] J. Deng, C. Bae, A. Denlinger, ve T. Miller, “Electric Vehicles Batteries: Requirements and Challenges”, Joule, c. 4, sy 3, ss. 511-515, Mar. 2020, doi: 10.1016/j.joule.2020.01.013.
- [2] A. Marongiu, F. G. W. Nußbaum, W. Waag, M. Garmendia, ve D. U. Sauer, “Comprehensive study of the influence of aging on the hysteresis behavior of a lithium iron phosphate cathode-based lithium ion battery – An experimental investigation of the hysteresis”, Applied Energy, c. 171, ss. 629-645, Haz. 2016, doi: 10.1016/j.apenergy.2016.02.086.
- [3] M. M. Hoque, M. A. Hannan, ve A. Mohamed, Model Development of Charge Equalization Controller for Lithium-Ion Battery. 2016.
- [4] U. Yayan, A. T. Arslan, ve H. Yucel, “A Novel Method for SoH Prediction of Batteries Based on Stacked LSTM with Quick Charge Data”, Applied Artificial Intelligence, c. 35, sy 6, ss. 421-439, May. 2021, doi: 10.1080/08839514.2021.1901033.
- [5] B. Yu, H. Qiu, L. Weng, K. Huo, S. Liu, ve H. Liu, “A Health Indicator for the Online Lifetime Estimation of an Electric Vehicle Power Li-Ion Battery”, World Electric Vehicle Journal, c. 11, sy 3, Art. sy 3, Eyl. 2020, doi: 10.3390/wevj11030059.
- [6] J. Wu, X. Cui, H. Zhang, ve M. Lin, “Health Prognosis With Optimized Feature Selection for Lithium-Ion Battery in Electric Vehicle Applications”, IEEE Transactions on Power Electronics, c. 36, sy 11, ss. 12646-12655, Kas. 2021, doi: 10.1109/TPEL.2021.3075558.
- [7] G. Dong, W. Han, ve Y. Wang, “Dynamic Bayesian Network-Based Lithium-Ion Battery Health Prognosis for Electric Vehicles”, IEEE Transactions on Industrial Electronics, c. 68, sy 11, ss. 10949-10958, Kas. 2021, doi: 10.1109/TIE.2020.3034855.
- [8] X. Hu, J. Jiang, D. Cao, ve B. Egardt, “Battery Health Prognosis for Electric Vehicles Using Sample Entropy and Sparse Bayesian Predictive Modeling”, IEEE Transactions on Industrial Electronics, c. 63, sy 4, ss. 2645-2656, Nis. 2016, doi: 10.1109/TIE.2015.2461523.
- [9] G. Dong, Z. Chen, J. Wei, ve Q. Ling, “Battery Health Prognosis Using Brownian Motion Modeling and Particle Filtering”, IEEE Transactions on Industrial Electronics, c. 65, sy 11, ss. 8646-8655, Kas. 2018, doi: 10.1109/TIE.2018.2813964.
- [10] M. A. Hannan, M. S. H. Lipu, A. Hussain, ve A. Mohamed, “A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations”, Renewable and Sustainable Energy Reviews, c. 78, ss. 834-854, Eki. 2017, doi: 10.1016/j.rser.2017.05.001.
- [11] S. M. Rezvanizaniani, Z. Liu, Y. Chen, ve J. Lee, “Review and recent advances in battery health monitoring and prognostics technologies for electric vehicle (EV) safety and mobility”, Journal of Power Sources, c. 256, ss. 110-124, Haz. 2014, doi: 10.1016/j.jpowsour.2014.01.085.
- [12] H. Wenzl vd., “Life prediction of batteries for selecting the technically most suitable and cost effective battery”, Journal of Power Sources, c. 144, sy 2, ss. 373-384, Haz. 2005, doi: 10.1016/j.jpowsour.2004.11.045.
- [13] C. Armenta-Deu ve T. Donaire, “Determination of an ageing factor for lead/acid batteries. 1. Kinetic aspects”, Journal of Power Sources, c. 58, sy 2, ss. 123-133, Şub. 1996, doi: 10.1016/S0378-7753(96)02371-3.
- [14] K. Qian, C. Zhou, M. Allan, ve Y. Yuan, “Modeling of Load Demand Due to EV Battery Charging in Distribution Systems”, IEEE Transactions on Power Systems, c. 26, sy 2, ss. 802-810, May. 2011, doi: 10.1109/TPWRS.2010.2057456.
- [15] M. Urbain, S. Rael, B. Davat, ve P. Desprez, “State Estimation of a Lithium-Ion Battery Through Kalman Filter”, içinde 2007 IEEE Power Electronics Specialists Conference, Haz. 2007, ss. 2804-2810. doi: 10.1109/PESC.2007.4342463.
- [16] R. C. Kroeze ve P. T. Krein, “Electrical battery model for use in dynamic electric vehicle simulations”, içinde 2008 IEEE Power Electronics Specialists Conference, Haz. 2008, ss. 1336-1342. doi: 10.1109/PESC.2008.4592119.
- [17] Z. B. Omariba, L. Zhang, ve D. Sun, “Review on Health Management System for Lithium-Ion Batteries of Electric Vehicles”, Electronics, c. 7, sy 5, Art. sy 5, May. 2018, doi: 10.3390/electronics7050072.
- [18] Y. Guo, X. Cai, S. Shen, G. Wang, ve J. Zhang, “Computational prediction and experimental evaluation of nitrate reduction to ammonia on rhodium”, Journal of Catalysis, c. 402, ss. 1-9, Eki. 2021, doi: 10.1016/j.jcat.2021.08.016.
- [19] A. M. Andwari, A. Pesiridis, S. Rajoo, R. Martinez-Botas, ve V. Esfahanian, “A review of Battery Electric Vehicle technology and readiness levels”, Renewable and Sustainable Energy Reviews, c. 78, ss. 414-430, Eki. 2017, doi: 10.1016/j.rser.2017.03.138.
- [20] K. Javed, R. Gouriveau, ve N. Zerhouni, “State of the art and taxonomy of prognostics approaches, trends of prognostics applications and open issues towards maturity at different technology readiness levels”, Mechanical Systems and Signal Processing, c. 94, ss. 214-236, Eyl. 2017, doi: 10.1016/j.ymssp.2017.01.050.
- [21] Z. Li, “Lithium-Ion Battery Management System for Electric Vehicles”, International Journal of Performability Engineering, c. 14, Ara. 2018, doi: 10.23940/ijpe.18.12.p28.31843194.
- [22] H. Rahimi-Eichi, U. Ojha, F. Baronti, ve M.-Y. Chow, “Battery Management System: An Overview of Its Application in the Smart Grid and Electric Vehicles”, EEE Ind. Electron. Mag., c. 7, sy 2, ss. 4-16, Haz. 2013, doi: 10.1109/MIE.2013.2250351.
- [23] D. Tingting, L. Jun, Z. Fuquan, Y. Yi, ve J. Qiqian, “Analysis on the influence of measurement error on state of charge estimation of LiFePO4 power Battery”, içinde 2011 International Conference on Materials for Renewable Energy & Environment, May. 2011, ss. 644-649. doi: 10.1109/ICMREE.2011.5930893.
- [24] Y. Xing, E. W. M. Ma, K. L. Tsui, ve M. Pecht, “Battery Management Systems in Electric and Hybrid Vehicles”, Energies, c. 4, sy 11, Art. sy 11, Kas. 2011, doi: 10.3390/en4111840.
- [25] J. P. Rivera-Barrera, N. Muñoz-Galeano, ve H. O. Sarmiento-Maldonado, “SoC Estimation for Lithium-ion Batteries: Review and Future Challenges”, Electronics, c. 6, sy 4, Art. sy 4, Ara. 2017, doi: 10.3390/electronics6040102.
- [26] J. Wu, C. Zhang, ve Z. Chen, “An online method for lithium-ion battery remaining useful life estimation using importance sampling and neural networks”, Applied Energy, c. 173, ss. 134-140, Tem. 2016, doi: 10.1016/j.apenergy.2016.04.057.
- [27] P. Ramadass, B. Haran, R. White, ve B. N. Popov, “Mathematical modeling of the capacity fade of Li-ion cells”, Journal of Power Sources, c. 123, sy 2, ss. 230-240, Eyl. 2003, doi: 10.1016/S0378-7753(03)00531-7.
- [28] S. B. Sarmah vd., “A Review of State of Health Estimation of Energy Storage Systems: Challenges and Possible Solutions for Futuristic Applications of Li-Ion Battery Packs in Electric Vehicles”, Journal of Electrochemical Energy Conversion and Storage, c. 16, sy 4, Mar. 2019, doi: 10.1115/1.4042987.
- [29] M. A. Roscher ve D. U. Sauer, “Dynamic electric behavior and open-circuit-voltage modeling of LiFePO4-based lithium ion secondary batteries”, Journal of Power Sources, c. 196, sy 1, ss. 331-336, Oca. 2011, doi: 10.1016/j.jpowsour.2010.06.098.
- [30] E. Raszmann, K. Baker, Y. Shi, ve D. Christensen, “Modeling stationary lithium-ion batteries for optimization and predictive control”, içinde 2017 IEEE Power and Energy Conference at Illinois (PECI), Şub. 2017, ss. 1-7. doi: 10.1109/PECI.2017.7935755.
- [31] J. Ordoñez, E. J. Gago, ve A. Girard, “Processes and technologies for the recycling and recovery of spent lithium-ion batteries”, Renewable and Sustainable Energy Reviews, c. 60, ss. 195-205, Tem. 2016, doi: 10.1016/j.rser.2015.12.363.
- [32] M. Dubarry, A. Devie, ve B. Y. Liaw, “The Value of Battery Diagnostics and Prognostics”, Journal of Energy and Power Sources, c. 1, ss. 242-249, Eyl. 2014.
- [33] M. Daowd, M. Antoine, N. Omar, P. Lataire, P. Van Den Bossche, ve J. Van Mierlo, “Battery Management System—Balancing Modularization Based on a Single Switched Capacitor and Bi-Directional DC/DC Converter with the Auxiliary Battery”, Energies, c. 7, sy 5, Art. sy 5, May. 2014, doi: 10.3390/en7052897.
- [34] A. Allam, S. Onori, S. Marelli, ve C. Taborelli, “Battery Health Management System for Automotive Applications: A retroactivity-based aging propagation study”, içinde 2015 American Control Conference (ACC), Tem. 2015, ss. 703-716. doi: 10.1109/ACC.2015.7170817.
- [35] G. L. Plett, “Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 1. Background”, Journal of Power Sources, c. 134, sy 2, ss. 252-261, Ağu. 2004, doi: 10.1016/j.jpowsour.2004.02.031.
- [36] X. Chen, W. Shen, M. Dai, Z. Cao, J. Jin, ve A. Kapoor, “Robust Adaptive Sliding-Mode Observer Using RBF Neural Network for Lithium-Ion Battery State of Charge Estimation in Electric Vehicles”, IEEE Transactions on Vehicular Technology, c. 65, sy 4, ss. 1936-1947, Nis. 2016, doi: 10.1109/TVT.2015.2427659.
- [37] Y.-J. He, J.-N. Shen, J.-F. Shen, ve Z.-F. Ma, “State of health estimation of lithium-ion batteries: A multiscale Gaussian process regression modeling approach”, AIChE Journal, c. 61, sy 5, ss. 1589-1600, 2015, doi: 10.1002/aic.14760.
- [38] S.-C. Huang, K.-H. Tseng, J.-W. Liang, C.-L. Chang, ve M. G. Pecht, “An Online SOC and SOH Estimation Model for Lithium-Ion Batteries”, Energies, c. 10, sy 4, Art. sy 4, Nis. 2017, doi: 10.3390/en10040512.
- [39] S. Jafari ve Y.-C. Byun, “Prediction of the Battery State Using the Digital Twin Framework Based on the Battery Management System”, IEEE Access, c. 10, ss. 124685-124696, 2022, doi: 10.1109/ACCESS.2022.3225093.
- [40] L. Wang, X. Zhao, Z. Deng, ve L. Yang, “Application of electrochemical impedance spectroscopy in battery management system: State of charge estimation for aging batteries”, Journal of Energy Storage, c. 57, s. 106275, Oca. 2023, doi: 10.1016/j.est.2022.106275.
- [41] G. Saldaña, J. I. S. Martín, I. Zamora, F. J. Asensio, O. Oñederra, ve M. González, “Empirical Electrical and Degradation Model for Electric Vehicle Batteries”, IEEE Access, c. 8, ss. 155576-155589, 2020, doi: 10.1109/ACCESS.2020.3019477.
- [42] M. Rezvani, M. AbuAli PhD, S. Lee, J. Lee, ve J. Ni PhD, “A Comparative Analysis of Techniques for Electric Vehicle Battery Prognostics and Health Management (PHM)”, program adı: Commercial Vehicle Engineering Congress, Eyl. 2011, ss. 2011-01-2247. doi: 10.4271/2011-01-2247.
- [43] J. Hemdani, M. Soltani, A. J. Telmoudi, ve A. Chaari, “Prediction of aging of battery for electric vehicles based on a modified version of neural networks”, içinde 2021 29th Mediterranean Conference on Control and Automation (MED), Haz. 2021, ss. 336-341. doi: 10.1109/MED51440.2021.9480161.
- [44] W. He, N. Williard, C. Chen, ve M. Pecht, “State of charge estimation for electric vehicle batteries under an adaptive filtering framework”, içinde Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing), May. 2012, ss. 1-5. doi: 10.1109/PHM.2012.6228849.
- [45] J. C. Álvarez Antón, P. J. García Nieto, F. J. de Cos Juez, F. Sánchez Lasheras, M. González Vega, ve M. N. Roqueñí Gutiérrez, “Battery state-of-charge estimator using the SVM technique”, Applied Mathematical Modelling, c. 37, sy 9, ss. 6244-6253, May. 2013, doi: 10.1016/j.apm.2013.01.024.
- [46] A. Mammone, M. Turchi, ve N. Cristianini, “Support vector machines”, içinde WIREs Computational Statistics, Kas. 2009, ss. 283-289. doi: 10.1002/wics.49.
- [47] S. Zhang, “A new method for lithium-ion battery’s SOH estimation and RUL prediction”, içinde 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA), May. 2018, ss. 2693-2697. doi: 10.1109/ICIEA.2018.8398166.
- [48] S. Song, C. Fei, ve H. Xia, “Lithium-Ion Battery SOH Estimation Based on XGBoost Algorithm with Accuracy Correction”, Energies, c. 13, sy 4, Art. sy 4, Oca. 2020, doi: 10.3390/en13040812.
- [49] J. Hong vd., “Thermal Runaway Prognosis of Battery Systems Using the Modified Multiscale Entropy in Real-World Electric Vehicles”, IEEE Transactions on Transportation Electrification, c. 7, sy 4, ss. 2269-2278, Ara. 2021, doi: 10.1109/TTE.2021.3079114.
- [50] A. Barr´, F. Suard, M. Gérard, ve D. Riu, “A Real-time Data-driven Method for Battery Health Prognostics in Electric Vehicle Use”, PHM Society European Conference, c. 2, sy 1, Art. sy 1, 2014, doi: 10.36001/phme.2014.v2i1.1514.
- [51] I. JORGE, A. SAMET, T. MESBAHI, ve R. BONÉ, “New ANN results on a major benchmark for the prediction of RUL of Lithium Ion batteries in electric vehicles”, içinde 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), Ara. 2020, ss. 1246-1253. doi: 10.1109/ICMLA51294.2020.00196.
- [52] D. Roman, S. Saxena, V. Robu, M. Pecht, ve D. Flynn, “Machine learning pipeline for battery state-of-health estimation”, Nat Mach Intell, c. 3, sy 5, Art. sy 5, May. 2021, doi: 10.1038/s42256-021-00312-3.
- [53] P. Jain, S. Saha, ve V. Sankaranarayanan, “Novel method to Estimate SoH of Lithium-Ion Batteries”, içinde 2021 Innovations in Energy Management and Renewable Resources(52042), Şub. 2021, ss. 1-5. doi: 10.1109/IEMRE52042.2021.9386881.
- [54] J. Zhang, X. Liu, A. Simeone, ve D. Lv, “A deformation-based approach to the SoH estimation of collided lithium-ion batteries”, IOP Conf. Ser.: Earth Environ. Sci., c. 463, sy 1, s. 012071, Mar. 2020, doi: 10.1088/1755-1315/463/1/012071.
- [55] X. Liu, J. Li, Z. Yao, Z. Wang, R. Si, ve Y. Diao, “Research on battery SOH estimation algorithm of energy storage frequency modulation system”, Energy Reports, c. 8, ss. 217-223, May. 2022, doi: 10.1016/j.egyr.2021.11.015.
- [56] A. Yang, Y. Wang, K. L. Tsui, ve Y. Zi, “Lithium-ion Battery SOH Estimation and Fault Diagnosis with Missing Data”, içinde 2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), May. 2019, ss. 1-6. doi: 10.1109/I2MTC.2019.8826888.
- [57] D.-I. Stroe ve E. Schaltz, “SOH Estimation of LMO/NMC-based Electric Vehicle Lithium-Ion Batteries Using the Incremental Capacity Analysis Technique”, içinde 2018 IEEE Energy Conversion Congress and Exposition (ECCE), Eyl. 2018, ss. 2720-2725. doi: 10.1109/ECCE.2018.8557998.
- [58] S. Chowdhury, M. N. Bin Shaheed, ve Y. Sozer, “An Integrated State of Health (SOH) Balancing Method for Lithium-Ion Battery Cells”, içinde 2019 IEEE Energy Conversion Congress and Exposition (ECCE), Eyl. 2019, ss. 5759-5763. doi: 10.1109/ECCE.2019.8912932.
- [59] S. Pang, J. Farrell, J. Du, ve M. Barth, “Battery state-of-charge estimation”, içinde Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148), Haz. 2001, ss. 1644-1649 c.2. doi: 10.1109/ACC.2001.945964.
- [60] Y. Zhou, H. Gu, T. Su, X. Han, L. Lu, ve Y. Zheng, “Remaining useful life prediction with probability distribution for lithium-ion batteries based on edge and cloud collaborative computation”, Journal of Energy Storage, c. 44, s. 103342, Ara. 2021, doi: 10.1016/j.est.2021.103342.
- [61] “Li-ion Battery Aging Datasets | NASA Open Data Portal”. Erişim: 27 Nisan 2023. [Çevrimiçi]. Erişim adresi: https://data.nasa.gov/dataset/Li-ion-Battery-Aging-Datasets/uj5r-zjdb
- [62] C. Xu, L. Li, Y. Xu, X. Han, ve Y. Zheng, “A vehicle-cloud collaborative method for multi-type fault diagnosis of lithium-ion batteries”, eTransportation, c. 12, s. 100172, May. 2022, doi: 10.1016/j.etran.2022.100172.
- [63] X. Yang vd., “Lithium-Ion Battery State of Health Estimation with Multi-Feature Collaborative Analysis and Deep Learning Method”, Batteries, c. 9, sy 2, Art. sy 2, Şub. 2023, doi: 10.3390/batteries9020120.
- [64] C. Birkl, “Oxford Battery Degradation Dataset 1”, 2017, Erişim: 27 Nisan 2023. [Çevrimiçi]. Erişim adresi: https://ora.ox.ac.uk/objects/uuid:03ba4b01-cfed-46d3-9b1a-7d4a7bdf6fac
- [65] T. Sun vd., “A cloud-edge collaborative strategy for capacity prognostic of lithium-ion batteries based on dynamic weight allocation and machine learning”, Energy, c. 239, s. 122185, Oca. 2022, doi: 10.1016/j.energy.2021.122185.
- [66] A. A. Chehade ve A. A. Hussein, “A Collaborative Gaussian Process Regression Model for Transfer Learning of Capacity Trends Between Li-Ion Battery Cells”, IEEE Transactions on Vehicular Technology, c. 69, sy 9, ss. 9542-9552, Eyl. 2020, doi: 10.1109/TVT.2020.3000970.
- [67] Y. Wang, R. Xu, C. Zhou, X. Kang, ve Z. Chen, “Digital twin and cloud-side-end collaboration for intelligent battery management system”, Journal of Manufacturing Systems, c. 62, ss. 124-134, Oca. 2022, doi: 10.1016/j.jmsy.2021.11.006.
- [68] T. Berghout, M. Benbouzid, Y. Amirat, ve G. Yao, “Lithium-ion Battery State of Health Prediction with a Robust Collaborative Augmented Hidden Layer Feedforward Neural Network Approach”, IEEE Transactions on Transportation Electrification, ss. 1-1, 2023, doi: 10.1109/TTE.2023.3237726.
- [69] S. Piller, M. Perrin, ve A. Jossen, “Methods for state-of-charge determination and their applications”, Journal of Power Sources, c. 96, sy 1, ss. 113-120, Haz. 2001, doi: 10.1016/S0378-7753(01)00560-2.
- [70] Y.-H. Chiang, W.-Y. Sean, ve J.-C. Ke, “Online estimation of internal resistance and open-circuit voltage of lithium-ion batteries in electric vehicles”, Journal of Power Sources, c. 196, sy 8, ss. 3921-3932, Nis. 2011, doi: 10.1016/j.jpowsour.2011.01.005.
- [71] N. Noura, L. Boulon, ve S. Jemeï, “A Review of Battery State of Health Estimation Methods: Hybrid Electric Vehicle Challenges”, World Electric Vehicle Journal, c. 11, sy 4, Art. sy 4, Ara. 2020, doi: 10.3390/wevj11040066.
- [72] C. Vidal, P. Malysz, P. Kollmeyer, ve A. Emadi, “Machine Learning Applied to Electrified Vehicle Battery State of Charge and State of Health Estimation: State-of-the-Art”, IEEE Access, c. 8, ss. 52796-52814, 2020, doi: 10.1109/ACCESS.2020.2980961.
- [73] R. Xiong, J. Cao, Q. Yu, H. He, ve F. Sun, “Critical Review on the Battery State of Charge Estimation Methods for Electric Vehicles”, IEEE Access, c. 6, ss. 1832-1843, 2018, doi: 10.1109/ACCESS.2017.2780258.
- [74] L. Yao vd., “A Review of Lithium-Ion Battery State of Health Estimation and Prediction Methods”, World Electric Vehicle Journal, c. 12, sy 3, Art. sy 3, Eyl. 2021, doi: 10.3390/wevj12030113.
- [75] A. S. Abdelaal, S. Mukhopadhyay, ve H. Rehman, “Battery Energy Management Techniques for an Electric Vehicle Traction System”, IEEE Access, c. 10, ss. 84015-84037, 2022, doi: 10.1109/ACCESS.2022.3195940.
[76] J. Hong vd., “Fault Prognosis and Isolation of Lithium-Ion Batteries in Electric Vehicles Considering Real-Scenario Thermal Runaway Risks”, IEEE Journal of Emerging and Selected Topics in Power Electronics, c. 11, sy 1, ss. 88-99, Şub. 2023, doi: 10.1109/JESTPE.2021.3097827.
- [77] D. Li, Z. Zhang, P. Liu, Z. Wang, ve L. Zhang, “Battery Fault Diagnosis for Electric Vehicles Based on Voltage Abnormality by Combining the Long Short-Term Memory Neural Network and the Equivalent Circuit Model”, IEEE Transactions on Power Electronics, c. 36, sy 2, ss. 1303-1315, Şub. 2021, doi: 10.1109/TPEL.2020.3008194.
- [78] B. Jiang, Z. Chen, ve F. Chen, “Influence of Sampling Delay on the Estimation of Lithium-Ion Battery Parameters and an Optimized Estimation Method”, Energies, c. 12, sy 10, Art. sy 10, Oca. 2019, doi: 10.3390/en12101878.
- [79] “Prognostics center of excellence - data repository”, NASA Ames Progn Res Center. Erişim: 29 Nisan 2023. [Çevrimiçi]. Erişim adresi: http://www.nasa.gov/intelligent-systems-division
- [80] “Battery Data | Center for Advanced Life Cycle Engineering”, CALCE battery research group. Erişim: 29 Nisan 2023. [Çevrimiçi]. Erişim adresi: https://calce.umd.edu/battery-data
- [81] “Experimental Data Platform”, Toyota Research Institute. Erişim: 29 Nisan 2023. [Çevrimiçi]. Erişim adresi: https://data.matr.io/1/
- [82] “Data for degradation of commercial lithium-ion cells as a function of chemistry and cycling conditions”, Sandia National Lab. Erişim: 29 Nisan 2023. [Çevrimiçi]. Erişim adresi: https://www.batteryarchive.org/snl_study.html
- [83] Y. Preger vd., “Degradation of Commercial Lithium-Ion Cells as a Function of Chemistry and Cycling Conditions”, J. Electrochem. Soc., c. 167, sy 12, s. 120532, Oca. 2020, doi: 10.1149/1945-7111/abae37.
- [84] D. Gun, H. Perez, ve S. Moura, “Berkeley: eCal fast charging test data”. Dryad, s. 293617695 bytes, 2015. doi: 10.6078/D1MS3X.
- [85] S. Zhang, “Data for: A data-driven coulomb counting method for state of charge calibration and estimation of lithium-ion battery - Mendeley Data”. Erişim: 29 Nisan 2023. [Çevrimiçi]. Erişim adresi: https://data.mendeley.com/datasets/c5dxwn6w92/1
- [86] M. Luzi, “Automotive Li-ion Cell Usage Data Set”. IEEE, 07 Eylül 2018. Erişim: 29 Nisan 2023. [Çevrimiçi]. Erişim adresi: https://ieee-dataport.org/documents/automotive-li-ion-cell-usage-data-set
- [87] “eVTOL Battery Dataset”. Carnegie Mellon University, 18 Mart 2021. doi: 10.1184/R1/14226830.v3.
- [88] “Data-driven prediction of battery cycle life before capacity degradation”, Cycle Life Prediction. Erişim: 29 Nisan 2023. [Çevrimiçi]. Erişim adresi: https://data.matr.io/1/projects/5c48dd2bc625d700019f3204
- [89] G. Pozzato, A. Allam, ve S. Onori, “Lithium-ion battery aging dataset based on electric vehicle real-driving profiles”, Data Brief, c. 41, s. 107995, Şub. 2022, doi: 10.1016/j.dib.2022.107995.
- [90] P. M. Attia vd., “Closed-loop optimization of fast-charging protocols for batteries with machine learning”, Nature, c. 578, sy 7795, ss. 397-402, Şub. 2020, doi: 10.1038/s41586-020-1994-5.
- [91] M. Dubarry, “Graphite//LFP synthetic training diagnosis dataset”, c. 1, May. 2020, doi: 10.17632/bs2j56pn7y.1.
- [92] P. Kollmeyer, “Panasonic 18650PF Li-ion Battery Data”, c. 1, Haz. 2018, doi: 10.17632/wykht8y7tg.1.
Autonomous Battery Management System in Electric Vehicles Literature Review
Yıl 2024,
Cilt: 14 Sayı: 2, 7 - 22, 30.07.2024
Metin Yılmaz
,
Eyüp Çınar
,
Ahmet Yazıcı
Öz
Electric vehicles are becoming increasingly prevalent worldwide, including in our country. In these vehicles, batteries are the most critical components. Accurate Prognostics and Health Management (PHM) are of great importance for an intelligent Battery Management System (BMS). PHM and BMS play a critical role in the safety, efficiency, and battery life of electric vehicles. This literature review emphasizes the significance of PHM and BMS in the context of electric vehicles. Lithium-ion (Li-ion) batteries remain one of the most suitable options, despite facing challenges such as battery life. Therefore, a BMS with accurate State of Charge (SoC) and State of Health (SoH) estimations are necessary to extend battery life and ensure safety. This study presents an analytical review of PHM and BMS for future research agendas in electric vehicles, highlighting the importance of battery prognostics and emphasizing the need for further research to ensure the healthy operation of electric vehicles.
Kaynakça
- [1] J. Deng, C. Bae, A. Denlinger, ve T. Miller, “Electric Vehicles Batteries: Requirements and Challenges”, Joule, c. 4, sy 3, ss. 511-515, Mar. 2020, doi: 10.1016/j.joule.2020.01.013.
- [2] A. Marongiu, F. G. W. Nußbaum, W. Waag, M. Garmendia, ve D. U. Sauer, “Comprehensive study of the influence of aging on the hysteresis behavior of a lithium iron phosphate cathode-based lithium ion battery – An experimental investigation of the hysteresis”, Applied Energy, c. 171, ss. 629-645, Haz. 2016, doi: 10.1016/j.apenergy.2016.02.086.
- [3] M. M. Hoque, M. A. Hannan, ve A. Mohamed, Model Development of Charge Equalization Controller for Lithium-Ion Battery. 2016.
- [4] U. Yayan, A. T. Arslan, ve H. Yucel, “A Novel Method for SoH Prediction of Batteries Based on Stacked LSTM with Quick Charge Data”, Applied Artificial Intelligence, c. 35, sy 6, ss. 421-439, May. 2021, doi: 10.1080/08839514.2021.1901033.
- [5] B. Yu, H. Qiu, L. Weng, K. Huo, S. Liu, ve H. Liu, “A Health Indicator for the Online Lifetime Estimation of an Electric Vehicle Power Li-Ion Battery”, World Electric Vehicle Journal, c. 11, sy 3, Art. sy 3, Eyl. 2020, doi: 10.3390/wevj11030059.
- [6] J. Wu, X. Cui, H. Zhang, ve M. Lin, “Health Prognosis With Optimized Feature Selection for Lithium-Ion Battery in Electric Vehicle Applications”, IEEE Transactions on Power Electronics, c. 36, sy 11, ss. 12646-12655, Kas. 2021, doi: 10.1109/TPEL.2021.3075558.
- [7] G. Dong, W. Han, ve Y. Wang, “Dynamic Bayesian Network-Based Lithium-Ion Battery Health Prognosis for Electric Vehicles”, IEEE Transactions on Industrial Electronics, c. 68, sy 11, ss. 10949-10958, Kas. 2021, doi: 10.1109/TIE.2020.3034855.
- [8] X. Hu, J. Jiang, D. Cao, ve B. Egardt, “Battery Health Prognosis for Electric Vehicles Using Sample Entropy and Sparse Bayesian Predictive Modeling”, IEEE Transactions on Industrial Electronics, c. 63, sy 4, ss. 2645-2656, Nis. 2016, doi: 10.1109/TIE.2015.2461523.
- [9] G. Dong, Z. Chen, J. Wei, ve Q. Ling, “Battery Health Prognosis Using Brownian Motion Modeling and Particle Filtering”, IEEE Transactions on Industrial Electronics, c. 65, sy 11, ss. 8646-8655, Kas. 2018, doi: 10.1109/TIE.2018.2813964.
- [10] M. A. Hannan, M. S. H. Lipu, A. Hussain, ve A. Mohamed, “A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations”, Renewable and Sustainable Energy Reviews, c. 78, ss. 834-854, Eki. 2017, doi: 10.1016/j.rser.2017.05.001.
- [11] S. M. Rezvanizaniani, Z. Liu, Y. Chen, ve J. Lee, “Review and recent advances in battery health monitoring and prognostics technologies for electric vehicle (EV) safety and mobility”, Journal of Power Sources, c. 256, ss. 110-124, Haz. 2014, doi: 10.1016/j.jpowsour.2014.01.085.
- [12] H. Wenzl vd., “Life prediction of batteries for selecting the technically most suitable and cost effective battery”, Journal of Power Sources, c. 144, sy 2, ss. 373-384, Haz. 2005, doi: 10.1016/j.jpowsour.2004.11.045.
- [13] C. Armenta-Deu ve T. Donaire, “Determination of an ageing factor for lead/acid batteries. 1. Kinetic aspects”, Journal of Power Sources, c. 58, sy 2, ss. 123-133, Şub. 1996, doi: 10.1016/S0378-7753(96)02371-3.
- [14] K. Qian, C. Zhou, M. Allan, ve Y. Yuan, “Modeling of Load Demand Due to EV Battery Charging in Distribution Systems”, IEEE Transactions on Power Systems, c. 26, sy 2, ss. 802-810, May. 2011, doi: 10.1109/TPWRS.2010.2057456.
- [15] M. Urbain, S. Rael, B. Davat, ve P. Desprez, “State Estimation of a Lithium-Ion Battery Through Kalman Filter”, içinde 2007 IEEE Power Electronics Specialists Conference, Haz. 2007, ss. 2804-2810. doi: 10.1109/PESC.2007.4342463.
- [16] R. C. Kroeze ve P. T. Krein, “Electrical battery model for use in dynamic electric vehicle simulations”, içinde 2008 IEEE Power Electronics Specialists Conference, Haz. 2008, ss. 1336-1342. doi: 10.1109/PESC.2008.4592119.
- [17] Z. B. Omariba, L. Zhang, ve D. Sun, “Review on Health Management System for Lithium-Ion Batteries of Electric Vehicles”, Electronics, c. 7, sy 5, Art. sy 5, May. 2018, doi: 10.3390/electronics7050072.
- [18] Y. Guo, X. Cai, S. Shen, G. Wang, ve J. Zhang, “Computational prediction and experimental evaluation of nitrate reduction to ammonia on rhodium”, Journal of Catalysis, c. 402, ss. 1-9, Eki. 2021, doi: 10.1016/j.jcat.2021.08.016.
- [19] A. M. Andwari, A. Pesiridis, S. Rajoo, R. Martinez-Botas, ve V. Esfahanian, “A review of Battery Electric Vehicle technology and readiness levels”, Renewable and Sustainable Energy Reviews, c. 78, ss. 414-430, Eki. 2017, doi: 10.1016/j.rser.2017.03.138.
- [20] K. Javed, R. Gouriveau, ve N. Zerhouni, “State of the art and taxonomy of prognostics approaches, trends of prognostics applications and open issues towards maturity at different technology readiness levels”, Mechanical Systems and Signal Processing, c. 94, ss. 214-236, Eyl. 2017, doi: 10.1016/j.ymssp.2017.01.050.
- [21] Z. Li, “Lithium-Ion Battery Management System for Electric Vehicles”, International Journal of Performability Engineering, c. 14, Ara. 2018, doi: 10.23940/ijpe.18.12.p28.31843194.
- [22] H. Rahimi-Eichi, U. Ojha, F. Baronti, ve M.-Y. Chow, “Battery Management System: An Overview of Its Application in the Smart Grid and Electric Vehicles”, EEE Ind. Electron. Mag., c. 7, sy 2, ss. 4-16, Haz. 2013, doi: 10.1109/MIE.2013.2250351.
- [23] D. Tingting, L. Jun, Z. Fuquan, Y. Yi, ve J. Qiqian, “Analysis on the influence of measurement error on state of charge estimation of LiFePO4 power Battery”, içinde 2011 International Conference on Materials for Renewable Energy & Environment, May. 2011, ss. 644-649. doi: 10.1109/ICMREE.2011.5930893.
- [24] Y. Xing, E. W. M. Ma, K. L. Tsui, ve M. Pecht, “Battery Management Systems in Electric and Hybrid Vehicles”, Energies, c. 4, sy 11, Art. sy 11, Kas. 2011, doi: 10.3390/en4111840.
- [25] J. P. Rivera-Barrera, N. Muñoz-Galeano, ve H. O. Sarmiento-Maldonado, “SoC Estimation for Lithium-ion Batteries: Review and Future Challenges”, Electronics, c. 6, sy 4, Art. sy 4, Ara. 2017, doi: 10.3390/electronics6040102.
- [26] J. Wu, C. Zhang, ve Z. Chen, “An online method for lithium-ion battery remaining useful life estimation using importance sampling and neural networks”, Applied Energy, c. 173, ss. 134-140, Tem. 2016, doi: 10.1016/j.apenergy.2016.04.057.
- [27] P. Ramadass, B. Haran, R. White, ve B. N. Popov, “Mathematical modeling of the capacity fade of Li-ion cells”, Journal of Power Sources, c. 123, sy 2, ss. 230-240, Eyl. 2003, doi: 10.1016/S0378-7753(03)00531-7.
- [28] S. B. Sarmah vd., “A Review of State of Health Estimation of Energy Storage Systems: Challenges and Possible Solutions for Futuristic Applications of Li-Ion Battery Packs in Electric Vehicles”, Journal of Electrochemical Energy Conversion and Storage, c. 16, sy 4, Mar. 2019, doi: 10.1115/1.4042987.
- [29] M. A. Roscher ve D. U. Sauer, “Dynamic electric behavior and open-circuit-voltage modeling of LiFePO4-based lithium ion secondary batteries”, Journal of Power Sources, c. 196, sy 1, ss. 331-336, Oca. 2011, doi: 10.1016/j.jpowsour.2010.06.098.
- [30] E. Raszmann, K. Baker, Y. Shi, ve D. Christensen, “Modeling stationary lithium-ion batteries for optimization and predictive control”, içinde 2017 IEEE Power and Energy Conference at Illinois (PECI), Şub. 2017, ss. 1-7. doi: 10.1109/PECI.2017.7935755.
- [31] J. Ordoñez, E. J. Gago, ve A. Girard, “Processes and technologies for the recycling and recovery of spent lithium-ion batteries”, Renewable and Sustainable Energy Reviews, c. 60, ss. 195-205, Tem. 2016, doi: 10.1016/j.rser.2015.12.363.
- [32] M. Dubarry, A. Devie, ve B. Y. Liaw, “The Value of Battery Diagnostics and Prognostics”, Journal of Energy and Power Sources, c. 1, ss. 242-249, Eyl. 2014.
- [33] M. Daowd, M. Antoine, N. Omar, P. Lataire, P. Van Den Bossche, ve J. Van Mierlo, “Battery Management System—Balancing Modularization Based on a Single Switched Capacitor and Bi-Directional DC/DC Converter with the Auxiliary Battery”, Energies, c. 7, sy 5, Art. sy 5, May. 2014, doi: 10.3390/en7052897.
- [34] A. Allam, S. Onori, S. Marelli, ve C. Taborelli, “Battery Health Management System for Automotive Applications: A retroactivity-based aging propagation study”, içinde 2015 American Control Conference (ACC), Tem. 2015, ss. 703-716. doi: 10.1109/ACC.2015.7170817.
- [35] G. L. Plett, “Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 1. Background”, Journal of Power Sources, c. 134, sy 2, ss. 252-261, Ağu. 2004, doi: 10.1016/j.jpowsour.2004.02.031.
- [36] X. Chen, W. Shen, M. Dai, Z. Cao, J. Jin, ve A. Kapoor, “Robust Adaptive Sliding-Mode Observer Using RBF Neural Network for Lithium-Ion Battery State of Charge Estimation in Electric Vehicles”, IEEE Transactions on Vehicular Technology, c. 65, sy 4, ss. 1936-1947, Nis. 2016, doi: 10.1109/TVT.2015.2427659.
- [37] Y.-J. He, J.-N. Shen, J.-F. Shen, ve Z.-F. Ma, “State of health estimation of lithium-ion batteries: A multiscale Gaussian process regression modeling approach”, AIChE Journal, c. 61, sy 5, ss. 1589-1600, 2015, doi: 10.1002/aic.14760.
- [38] S.-C. Huang, K.-H. Tseng, J.-W. Liang, C.-L. Chang, ve M. G. Pecht, “An Online SOC and SOH Estimation Model for Lithium-Ion Batteries”, Energies, c. 10, sy 4, Art. sy 4, Nis. 2017, doi: 10.3390/en10040512.
- [39] S. Jafari ve Y.-C. Byun, “Prediction of the Battery State Using the Digital Twin Framework Based on the Battery Management System”, IEEE Access, c. 10, ss. 124685-124696, 2022, doi: 10.1109/ACCESS.2022.3225093.
- [40] L. Wang, X. Zhao, Z. Deng, ve L. Yang, “Application of electrochemical impedance spectroscopy in battery management system: State of charge estimation for aging batteries”, Journal of Energy Storage, c. 57, s. 106275, Oca. 2023, doi: 10.1016/j.est.2022.106275.
- [41] G. Saldaña, J. I. S. Martín, I. Zamora, F. J. Asensio, O. Oñederra, ve M. González, “Empirical Electrical and Degradation Model for Electric Vehicle Batteries”, IEEE Access, c. 8, ss. 155576-155589, 2020, doi: 10.1109/ACCESS.2020.3019477.
- [42] M. Rezvani, M. AbuAli PhD, S. Lee, J. Lee, ve J. Ni PhD, “A Comparative Analysis of Techniques for Electric Vehicle Battery Prognostics and Health Management (PHM)”, program adı: Commercial Vehicle Engineering Congress, Eyl. 2011, ss. 2011-01-2247. doi: 10.4271/2011-01-2247.
- [43] J. Hemdani, M. Soltani, A. J. Telmoudi, ve A. Chaari, “Prediction of aging of battery for electric vehicles based on a modified version of neural networks”, içinde 2021 29th Mediterranean Conference on Control and Automation (MED), Haz. 2021, ss. 336-341. doi: 10.1109/MED51440.2021.9480161.
- [44] W. He, N. Williard, C. Chen, ve M. Pecht, “State of charge estimation for electric vehicle batteries under an adaptive filtering framework”, içinde Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing), May. 2012, ss. 1-5. doi: 10.1109/PHM.2012.6228849.
- [45] J. C. Álvarez Antón, P. J. García Nieto, F. J. de Cos Juez, F. Sánchez Lasheras, M. González Vega, ve M. N. Roqueñí Gutiérrez, “Battery state-of-charge estimator using the SVM technique”, Applied Mathematical Modelling, c. 37, sy 9, ss. 6244-6253, May. 2013, doi: 10.1016/j.apm.2013.01.024.
- [46] A. Mammone, M. Turchi, ve N. Cristianini, “Support vector machines”, içinde WIREs Computational Statistics, Kas. 2009, ss. 283-289. doi: 10.1002/wics.49.
- [47] S. Zhang, “A new method for lithium-ion battery’s SOH estimation and RUL prediction”, içinde 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA), May. 2018, ss. 2693-2697. doi: 10.1109/ICIEA.2018.8398166.
- [48] S. Song, C. Fei, ve H. Xia, “Lithium-Ion Battery SOH Estimation Based on XGBoost Algorithm with Accuracy Correction”, Energies, c. 13, sy 4, Art. sy 4, Oca. 2020, doi: 10.3390/en13040812.
- [49] J. Hong vd., “Thermal Runaway Prognosis of Battery Systems Using the Modified Multiscale Entropy in Real-World Electric Vehicles”, IEEE Transactions on Transportation Electrification, c. 7, sy 4, ss. 2269-2278, Ara. 2021, doi: 10.1109/TTE.2021.3079114.
- [50] A. Barr´, F. Suard, M. Gérard, ve D. Riu, “A Real-time Data-driven Method for Battery Health Prognostics in Electric Vehicle Use”, PHM Society European Conference, c. 2, sy 1, Art. sy 1, 2014, doi: 10.36001/phme.2014.v2i1.1514.
- [51] I. JORGE, A. SAMET, T. MESBAHI, ve R. BONÉ, “New ANN results on a major benchmark for the prediction of RUL of Lithium Ion batteries in electric vehicles”, içinde 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), Ara. 2020, ss. 1246-1253. doi: 10.1109/ICMLA51294.2020.00196.
- [52] D. Roman, S. Saxena, V. Robu, M. Pecht, ve D. Flynn, “Machine learning pipeline for battery state-of-health estimation”, Nat Mach Intell, c. 3, sy 5, Art. sy 5, May. 2021, doi: 10.1038/s42256-021-00312-3.
- [53] P. Jain, S. Saha, ve V. Sankaranarayanan, “Novel method to Estimate SoH of Lithium-Ion Batteries”, içinde 2021 Innovations in Energy Management and Renewable Resources(52042), Şub. 2021, ss. 1-5. doi: 10.1109/IEMRE52042.2021.9386881.
- [54] J. Zhang, X. Liu, A. Simeone, ve D. Lv, “A deformation-based approach to the SoH estimation of collided lithium-ion batteries”, IOP Conf. Ser.: Earth Environ. Sci., c. 463, sy 1, s. 012071, Mar. 2020, doi: 10.1088/1755-1315/463/1/012071.
- [55] X. Liu, J. Li, Z. Yao, Z. Wang, R. Si, ve Y. Diao, “Research on battery SOH estimation algorithm of energy storage frequency modulation system”, Energy Reports, c. 8, ss. 217-223, May. 2022, doi: 10.1016/j.egyr.2021.11.015.
- [56] A. Yang, Y. Wang, K. L. Tsui, ve Y. Zi, “Lithium-ion Battery SOH Estimation and Fault Diagnosis with Missing Data”, içinde 2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), May. 2019, ss. 1-6. doi: 10.1109/I2MTC.2019.8826888.
- [57] D.-I. Stroe ve E. Schaltz, “SOH Estimation of LMO/NMC-based Electric Vehicle Lithium-Ion Batteries Using the Incremental Capacity Analysis Technique”, içinde 2018 IEEE Energy Conversion Congress and Exposition (ECCE), Eyl. 2018, ss. 2720-2725. doi: 10.1109/ECCE.2018.8557998.
- [58] S. Chowdhury, M. N. Bin Shaheed, ve Y. Sozer, “An Integrated State of Health (SOH) Balancing Method for Lithium-Ion Battery Cells”, içinde 2019 IEEE Energy Conversion Congress and Exposition (ECCE), Eyl. 2019, ss. 5759-5763. doi: 10.1109/ECCE.2019.8912932.
- [59] S. Pang, J. Farrell, J. Du, ve M. Barth, “Battery state-of-charge estimation”, içinde Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148), Haz. 2001, ss. 1644-1649 c.2. doi: 10.1109/ACC.2001.945964.
- [60] Y. Zhou, H. Gu, T. Su, X. Han, L. Lu, ve Y. Zheng, “Remaining useful life prediction with probability distribution for lithium-ion batteries based on edge and cloud collaborative computation”, Journal of Energy Storage, c. 44, s. 103342, Ara. 2021, doi: 10.1016/j.est.2021.103342.
- [61] “Li-ion Battery Aging Datasets | NASA Open Data Portal”. Erişim: 27 Nisan 2023. [Çevrimiçi]. Erişim adresi: https://data.nasa.gov/dataset/Li-ion-Battery-Aging-Datasets/uj5r-zjdb
- [62] C. Xu, L. Li, Y. Xu, X. Han, ve Y. Zheng, “A vehicle-cloud collaborative method for multi-type fault diagnosis of lithium-ion batteries”, eTransportation, c. 12, s. 100172, May. 2022, doi: 10.1016/j.etran.2022.100172.
- [63] X. Yang vd., “Lithium-Ion Battery State of Health Estimation with Multi-Feature Collaborative Analysis and Deep Learning Method”, Batteries, c. 9, sy 2, Art. sy 2, Şub. 2023, doi: 10.3390/batteries9020120.
- [64] C. Birkl, “Oxford Battery Degradation Dataset 1”, 2017, Erişim: 27 Nisan 2023. [Çevrimiçi]. Erişim adresi: https://ora.ox.ac.uk/objects/uuid:03ba4b01-cfed-46d3-9b1a-7d4a7bdf6fac
- [65] T. Sun vd., “A cloud-edge collaborative strategy for capacity prognostic of lithium-ion batteries based on dynamic weight allocation and machine learning”, Energy, c. 239, s. 122185, Oca. 2022, doi: 10.1016/j.energy.2021.122185.
- [66] A. A. Chehade ve A. A. Hussein, “A Collaborative Gaussian Process Regression Model for Transfer Learning of Capacity Trends Between Li-Ion Battery Cells”, IEEE Transactions on Vehicular Technology, c. 69, sy 9, ss. 9542-9552, Eyl. 2020, doi: 10.1109/TVT.2020.3000970.
- [67] Y. Wang, R. Xu, C. Zhou, X. Kang, ve Z. Chen, “Digital twin and cloud-side-end collaboration for intelligent battery management system”, Journal of Manufacturing Systems, c. 62, ss. 124-134, Oca. 2022, doi: 10.1016/j.jmsy.2021.11.006.
- [68] T. Berghout, M. Benbouzid, Y. Amirat, ve G. Yao, “Lithium-ion Battery State of Health Prediction with a Robust Collaborative Augmented Hidden Layer Feedforward Neural Network Approach”, IEEE Transactions on Transportation Electrification, ss. 1-1, 2023, doi: 10.1109/TTE.2023.3237726.
- [69] S. Piller, M. Perrin, ve A. Jossen, “Methods for state-of-charge determination and their applications”, Journal of Power Sources, c. 96, sy 1, ss. 113-120, Haz. 2001, doi: 10.1016/S0378-7753(01)00560-2.
- [70] Y.-H. Chiang, W.-Y. Sean, ve J.-C. Ke, “Online estimation of internal resistance and open-circuit voltage of lithium-ion batteries in electric vehicles”, Journal of Power Sources, c. 196, sy 8, ss. 3921-3932, Nis. 2011, doi: 10.1016/j.jpowsour.2011.01.005.
- [71] N. Noura, L. Boulon, ve S. Jemeï, “A Review of Battery State of Health Estimation Methods: Hybrid Electric Vehicle Challenges”, World Electric Vehicle Journal, c. 11, sy 4, Art. sy 4, Ara. 2020, doi: 10.3390/wevj11040066.
- [72] C. Vidal, P. Malysz, P. Kollmeyer, ve A. Emadi, “Machine Learning Applied to Electrified Vehicle Battery State of Charge and State of Health Estimation: State-of-the-Art”, IEEE Access, c. 8, ss. 52796-52814, 2020, doi: 10.1109/ACCESS.2020.2980961.
- [73] R. Xiong, J. Cao, Q. Yu, H. He, ve F. Sun, “Critical Review on the Battery State of Charge Estimation Methods for Electric Vehicles”, IEEE Access, c. 6, ss. 1832-1843, 2018, doi: 10.1109/ACCESS.2017.2780258.
- [74] L. Yao vd., “A Review of Lithium-Ion Battery State of Health Estimation and Prediction Methods”, World Electric Vehicle Journal, c. 12, sy 3, Art. sy 3, Eyl. 2021, doi: 10.3390/wevj12030113.
- [75] A. S. Abdelaal, S. Mukhopadhyay, ve H. Rehman, “Battery Energy Management Techniques for an Electric Vehicle Traction System”, IEEE Access, c. 10, ss. 84015-84037, 2022, doi: 10.1109/ACCESS.2022.3195940.
[76] J. Hong vd., “Fault Prognosis and Isolation of Lithium-Ion Batteries in Electric Vehicles Considering Real-Scenario Thermal Runaway Risks”, IEEE Journal of Emerging and Selected Topics in Power Electronics, c. 11, sy 1, ss. 88-99, Şub. 2023, doi: 10.1109/JESTPE.2021.3097827.
- [77] D. Li, Z. Zhang, P. Liu, Z. Wang, ve L. Zhang, “Battery Fault Diagnosis for Electric Vehicles Based on Voltage Abnormality by Combining the Long Short-Term Memory Neural Network and the Equivalent Circuit Model”, IEEE Transactions on Power Electronics, c. 36, sy 2, ss. 1303-1315, Şub. 2021, doi: 10.1109/TPEL.2020.3008194.
- [78] B. Jiang, Z. Chen, ve F. Chen, “Influence of Sampling Delay on the Estimation of Lithium-Ion Battery Parameters and an Optimized Estimation Method”, Energies, c. 12, sy 10, Art. sy 10, Oca. 2019, doi: 10.3390/en12101878.
- [79] “Prognostics center of excellence - data repository”, NASA Ames Progn Res Center. Erişim: 29 Nisan 2023. [Çevrimiçi]. Erişim adresi: http://www.nasa.gov/intelligent-systems-division
- [80] “Battery Data | Center for Advanced Life Cycle Engineering”, CALCE battery research group. Erişim: 29 Nisan 2023. [Çevrimiçi]. Erişim adresi: https://calce.umd.edu/battery-data
- [81] “Experimental Data Platform”, Toyota Research Institute. Erişim: 29 Nisan 2023. [Çevrimiçi]. Erişim adresi: https://data.matr.io/1/
- [82] “Data for degradation of commercial lithium-ion cells as a function of chemistry and cycling conditions”, Sandia National Lab. Erişim: 29 Nisan 2023. [Çevrimiçi]. Erişim adresi: https://www.batteryarchive.org/snl_study.html
- [83] Y. Preger vd., “Degradation of Commercial Lithium-Ion Cells as a Function of Chemistry and Cycling Conditions”, J. Electrochem. Soc., c. 167, sy 12, s. 120532, Oca. 2020, doi: 10.1149/1945-7111/abae37.
- [84] D. Gun, H. Perez, ve S. Moura, “Berkeley: eCal fast charging test data”. Dryad, s. 293617695 bytes, 2015. doi: 10.6078/D1MS3X.
- [85] S. Zhang, “Data for: A data-driven coulomb counting method for state of charge calibration and estimation of lithium-ion battery - Mendeley Data”. Erişim: 29 Nisan 2023. [Çevrimiçi]. Erişim adresi: https://data.mendeley.com/datasets/c5dxwn6w92/1
- [86] M. Luzi, “Automotive Li-ion Cell Usage Data Set”. IEEE, 07 Eylül 2018. Erişim: 29 Nisan 2023. [Çevrimiçi]. Erişim adresi: https://ieee-dataport.org/documents/automotive-li-ion-cell-usage-data-set
- [87] “eVTOL Battery Dataset”. Carnegie Mellon University, 18 Mart 2021. doi: 10.1184/R1/14226830.v3.
- [88] “Data-driven prediction of battery cycle life before capacity degradation”, Cycle Life Prediction. Erişim: 29 Nisan 2023. [Çevrimiçi]. Erişim adresi: https://data.matr.io/1/projects/5c48dd2bc625d700019f3204
- [89] G. Pozzato, A. Allam, ve S. Onori, “Lithium-ion battery aging dataset based on electric vehicle real-driving profiles”, Data Brief, c. 41, s. 107995, Şub. 2022, doi: 10.1016/j.dib.2022.107995.
- [90] P. M. Attia vd., “Closed-loop optimization of fast-charging protocols for batteries with machine learning”, Nature, c. 578, sy 7795, ss. 397-402, Şub. 2020, doi: 10.1038/s41586-020-1994-5.
- [91] M. Dubarry, “Graphite//LFP synthetic training diagnosis dataset”, c. 1, May. 2020, doi: 10.17632/bs2j56pn7y.1.
- [92] P. Kollmeyer, “Panasonic 18650PF Li-ion Battery Data”, c. 1, Haz. 2018, doi: 10.17632/wykht8y7tg.1.