ESTIMATION OF ELECTRIC VEHICLE MULTIPLE PACKAGE LITHIUM-ION BATTERY STATE OF CHARGE USING AN EXTENDED MACHINE LEARNING

Öz

The estimation of battery state-of-charge (SOC) in electric or hybrid vehicle has vital importance in the designing process of battery management systems. The state-of-charge estimation is implemented using different modelling approaches, model-based estimators such as Kalman filtering and Luenberger observer and data-driven based modelling techniques like artificial neural network and machine learning methods. This study aimed to develop a battery state-of-charge estimation method and proposed a novel architecture for multiple battery back SOC estimation using an extended learning machine (ELM). The ELM approach is applied considering battery operating conditions using global vehicle driving profiles, New European Driving Cycle and Worldwide harmonized Light vehicles Test Procedure. The performance of the proposed SOC estimation method is evaluated by using statistical criteria (RMSE, R2, MAPE). Consequently, the obtained results show that a data-driven ELM approach with a less complex structure can obtain better performance compared with other advanced estimator methods under different operating conditions.

Anahtar Kelimeler

• Extended machine learning
• State of Charge
• Estimation
• Lithium-ion
• Battery

Elektrikli Araçların Çoklu Paket Li̇tyum-İyon Batarya Şarj Durumunu Geni̇şleti̇lmi̇ş Bi̇r Maki̇ne Öğrenmesi̇ Kullanarak Tahmi̇ni

Öz

Elektrikli veya hibrit araçlarda batarya şarj durumunun (SOC) tahmini, batarya yönetim sistemlerinin tasarım sürecinde hayati öneme sahiptir. Şarj durumu tahmini, farklı modelleme yaklaşımları, Kalman filtreleme ve Luenberger gözlemcisi gibi model tabanlı tahmin ediciler ve yapay sinir ağı ve makine öğrenimi yöntemleri gibi veri odaklı tabanlı modelleme teknikleri kullanılarak gerçekleştirilmektedir. Bu çalışma, bir batarya şarj durumu tahmin yöntemi geliştirmeyi amaçlamış ve genişletilmiş makine öğrenmesi (ELM) kullanarak çoklu batarya geri SOC tahmini için yeni bir mimari önermiştir. ELM yaklaşımı, küresel araç sürüş profilleri, Yeni Avrupa Sürüş Döngüsü ve Dünya Çapında Uyumlaştırılmış Hafif Araçlar Test Prosedürü kullanılarak ve akü çalışma koşulları dikkate alınarak uygulanmıştır. Önerilen SOC tahmin yönteminin performansı istatistiksel kriterler (RMSE, R2, MAPE) kullanılarak değerlendirilmiştir. Çalışma sonunda elde edilen sonuçlar, daha az karmaşık bir yapıya sahip veri güdümlü bir ELM yaklaşımının, farklı çalışma koşulları altında diğer gelişmiş tahmin yöntemlerine kıyasla daha iyi performans elde edebileceğini göstermektedir.

Anahtar Kelimeler

• Genişletilmiş makine öğrenmesi
• Şarj durumu
• Tahin
• Lityum-İyon
• Batarya

Kaynakça

1. Ahmed, R., El Sayed, M., Arasaratnam, I., Tjong, J., & Habibi, S. (2014). Reduced-Order Electrochemical Model Parameters Identification and SOC Estimation for Healthy and Aged Li-Ion Batteries Part I: Parameterization Model Development for Healthy Batteries. Ieee Journal Of Emergıng And Selected Topıcs In Power Electronıcs, 2(3), 659–677. https://doi.org/10.1109/JESTPE.2014.2331059 M4 - Citavi
2. Ahmed, R., Gazzarri, J., Onori, S., Habibi, S., Jackey, R., Rzemien, K., … LeSage, J. (2015). Model-Based Parameter Identification of Healthy and Aged Li-ion Batteries for Electric Vehicle Applications. SAE International Journal of Alternative Powertrains, 4(2), 233–247. https://doi.org/10.4271/2015-01-0252 S1 - 15 M4 - Citavi
3. Baba, A., & Adachi, S. (2014). SOC estimation of HEV/EV battery using series kalman filter. Electrical Engineering in Japan (English Translation of Denki Gakkai Ronbunshi), 187(2), 53–62. https://doi.org/10.1002/eej.22511
4. Baba, A., Itabashi, K., Teranishi, N., Edamoto, Y., Osamura, K., Maruta, I., & Adachi, S. (2016). Simultaneous Estimation of the SOC and Parameters of Batteries for HEV/EV. SAE Technical Papers, 2016-April(April). https://doi.org/10.4271/2016-01-1195
5. Belhani, A., M’Sirdi, N. K., & Naamane, A. (2013). Adaptive sliding mode observer for estimation of state of charge. Medıterranean Green Energy Forum 2013: Proceedıngs Of An Internatıonal Conference MGEF-13, 42, 377–386. https://doi.org/10.1016/j.egypro.2013.11.038 M4 - Citavi
6. Dang, X., Yan, L., Xu, K., Wu, X., Jiang, H., & Sun, H. (2016). Open-Circuit Voltage-Based State of Charge Estimation of Lithium-ion Battery Using Dual Neural Network Fusion Battery Model. Electrochimica Acta. https://doi.org/10.1016/j.electacta.2015.12.001
7. Farag, M., Fleckenstein, M., & Habibi, S. R. (2014). Li-ion battery SOC estimation using non-linear estimation strategies based on equivalent circuit models. SAE Technical Papers, 1. https://doi.org/10.4271/2014-01-1849
8. He, W., Williard, N., Chen, C., & Pecht, M. (2013). State of charge estimation for electric vehicle batteries using unscented kalman filtering. Microelectronics Reliability, 53(6), 840–847. https://doi.org/10.1016/j.microrel.2012.11.010

9. Hossain Lipu, M. S., Hannan, M. A., Hussain, A., & Saad, M. H. M. (2017). Optimal BP neural network algorithm for state of charge estimation of lithium-ion battery using PSO with PCA feature selection. Journal of Renewable and Sustainable Energy. https://doi.org/10.1063/1.5008491
10. Hu, X., Sun, F., & Zou, Y. (2010). Estimation of State of Charge of a Lithium-Ion Battery Pack for Electric Vehicles Using an Adaptive Luenberger Observer. Energies, 3(9), 1586–1603. https://doi.org/10.3390/en3091586
11. Huang, G. Bin, Chen, Y. Q., & Babri, H. A. (2000). Classification ability of single hidden layer feedforward neural networks. IEEE Transactions on Neural Networks. https://doi.org/10.1109/72.846750
12. Huang, G. Bin, Zhou, H., Ding, X., & Zhang, R. (2012). Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 42(2), 513–529. https://doi.org/10.1109/TSMCB.2011.2168604
13. Huang, G. Bin, Zhu, Q. Y., & Siew, C. K. (2006). Extreme learning machine: Theory and applications. Neurocomputing, 70(1–3), 489–501. https://doi.org/10.1016/j.neucom.2005.12.126
14. Jiang, C., Wang, S., Wu, B., Fernandez, C., Xiong, X., & Coffie-Ken, J. (2021a). A state-of-charge estimation method of the power lithium-ion battery in complex conditions based on adaptive square root extended Kalman filter. Energy, 219, 119603. https://doi.org/10.1016/j.energy.2020.119603
15. Jiang, C., Wang, S., Wu, B., Fernandez, C., Xiong, X., & Coffie-Ken, J. (2021b). A state-of-charge estimation method of the power lithium-ion battery in complex conditions based on adaptive square root extended Kalman filter. Energy, 219, 119603. https://doi.org/10.1016/j.energy.2020.119603
16. Liu, N., & Wang, H. (2010). Ensemble based extreme learning machine. IEEE Signal Processing Letters, 17(8), 754–757. https://doi.org/10.1109/LSP.2010.2053356
17. Ng, K. S., Moo, C.-S., Chen, Y.-P., & Hsieh, Y.-C. (2009). Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries. Applied Energy, 86(9), 1506–1511. https://doi.org/10.1016/j.apenergy.2008.11.021 M4 - Citavi
18. Plett, G. L. (2004). Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs. Journal of Power Sources, 134(2), 252–261. https://doi.org/10.1016/j.jpowsour.2004.02.031 M4 - Citavi
19. Shen, W. X., Chan, C. C., Lo, E. W. C., & Chau, K. T. (2002). A new battery available capacity indicator for electric vehicles using neural network. Energy Conversion and Management. https://doi.org/10.1016/S0196-8904(01)00078-4
20. Taborda, A. M., Varella, R. A., Farias, T. L., & Duarte, G. O. (2019). Evaluation of technological solutions for compliance of environmental legislation in light-duty passenger: A numerical and experimental approach. Transportation Research Part D: Transport and Environment, 70, 135–146. https://doi.org/10.1016/j.trd.2019.04.004
21. Tong, S., Lacap, J. H., & Park, J. W. (2016). Battery state of charge estimation using a load-classifying neural network. Journal of Energy Storage, 7, 236–243. https://doi.org/10.1016/j.est.2016.07.002
22. Wang, L., Huang, Y., Luo, X., Wang, Z., & Luo, S. (2011). Image deblurring with filters learned by extreme learning machine. Neurocomputing, 74(16), 2464–2474. https://doi.org/10.1016/j.neucom.2010.12.035
23. Wang, S., Fernandez, C., Shang, L., Li, Z., & Yuan, H. (2018). An integrated online adaptive state of charge estimation approach of high-power lithium-ion battery packs. Transactions of the Institute of Measurement and Control, 40(6), 1892–1910. https://doi.org/10.1177/0142331217694681
24. Wang, Y., Liu, C., Pan, R., & Chen, Z. (2017). Modeling and state-of-charge prediction of lithium-ion battery and ultracapacitor hybrids with a co-estimator. Energy, 121, 739–750. https://doi.org/10.1016/j.energy.2017.01.044
25. Zhang, Y., & Li, M. (2018). Robust Tolerance Optimization for Internal Combustion Engines under Parameter and Model Uncertainties Considering Metamodeling Uncertainty from Gaussian Processes. Journal of Computing and Information Science in Engineering, 18(4). https://doi.org/10.1115/1.4040608
26. Zheng, D., Wang, H., An, J., Chen, J., Pan, H., & Chen, L. (2018). Real-time estimation of battery state of charge with metabolic grey model and LabVIEW platform. IEEE Access, 6, 13170–13180. https://doi.org/10.1109/ACCESS.2018.2807805
27. Zhu, W., Miao, J., Hu, J., & Qing, L. (2014). Vehicle detection in driving simulation using extreme learning machine. Neurocomputing, 128, 160–165. https://doi.org/10.1016/j.neucom.2013.05.052