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A Battery Management System Design Including a SOC Estimation Approach for Lead-Acid Batteries

Yıl 2022, Cilt: 3 Sayı: 2, 300 - 313, 18.12.2022
https://doi.org/10.55546/jmm.1193510

Öz

Storage is one of the most important issues of the last decades. In particular, storage systems are needed in order to benefit more effectively from renewable energy systems where production cannot be controlled. One of the most important problems in storage is that as the amount of energy desired to be stored increases, the need for space also increases. Therefore, it is of great importance to manage energy effectively in such systems. In this study, a battery management system (BMS) that can be used for lead acid batteries has been designed. This BMS has a measurement and control system based on STM 32 microcontroller and is controlled via an interface prepared in the MATLAB Simulink environment and the test data is imported into the MATLAB Workspace environment. The designed system can also perform battery charge-discharge experiments in accordance with the battery characteristics. Charge-discharge experiments were carried out using the designed system, and a model was developed to determine the state of charge (SOC) of the battery using the data collected during these experiments. With the model developed based on Elman Neural Networks, the SOC of battery could be estimated at an error level of less than 1%.

Destekleyen Kurum

Afyon Kocatepe University Scientific Research Projects Coordination Unit

Proje Numarası

18.KARIYER.193

Teşekkür

This study was supported by Afyon Kocatepe University Scientific Research Projects Coordination Unit with Project number of 18. KARIYER.193.

Kaynakça

  • Akarslan E., Learning Vector Quantization based predictor model selection for hourly load demand forecasting, Applied Soft Computing 117, 108421, 2022. https://doi.org/10.1016/J.ASOC.2022.108421.
  • Ansari S., Ayob A., Hossain Lipu M. S., Hussain A., Md Saad M. H., Remaining useful life prediction for lithium-ion battery storage system: A comprehensive review of methods, key factors, issues and future outlook. Energy Reports 8, 12153-12185, 2022. https://doi.org/10.1016/j.egyr.2022.09.043.
  • Carkhuff B. G., Demirev P. A., Srinivasan R., Impedance-Based Battery Management System for Safety Monitoring of Lithium-Ion Batteries. IEEE Trans Ind Electron 65, 6497-6504, 2018. https://doi.org/10.1109/TIE.2017.2786199.
  • Cui Y., Lin K., Zhu J., Chen Y., Quantum-inspired degradation modeling and reliability evaluation of battery management system for electric vehicles. Journal of Energy Storage 52, 104840, 2022. https://doi.org/10.1016/J.EST.2022.104840.
  • Cui Z., Hu W., Zhang G., Zhang Z., Chen Z., An extended Kalman filter based SOC estimation method for Li-ion battery. Energy Reports 8(5), 81-87, 2022. https://doi.org/10.1016/J.EGYR.2022.02.116.
  • Elman J. L., Finding structure in time. Cognitive Science 14(2), 179-211, 1990. https://doi.org/10.1016/0364-0213(90)90002-E.
  • Hossain Lipu M. S., Hannan M. A., Karim T. F., Hussain A., Saad M. H. M., Ayob A., Miah M. S., Indra Mahlia T. M., Intelligent algorithms and control strategies for battery management system in electric vehicles: Progress, challenges and future outlook. Journal of Cleaner Production 292, 126044, 2021. https://doi.org/ 10.1016/j.jclepro.2021.126044.
  • Jin Y., Zhao W., Li Z., Liu B., Wang K., SOC estimation of lithium-ion battery considering the influence of discharge rate. Energy Reports 7(7), 1436-1446, 2021. https://doi.org/10.1016/J.EGYR.2021.09.099.
  • Kuchly J., Goussian A., Merveillaut M., Baghdadi I., Franger S., Nelson-Gruel D., Nouillant C., Chamaillard Y., Li-ion battery SOC estimation method using a Neural Network trained with data generated by a P2D model, IFAC-PapersOnLine 54(10), 336-343, 2021. https://doi.org/10.1016/J.IFACOL.2021.10.185.
  • Li Y., Chattopadhyay P., Xiong S., Ray A., Rahn C.D., Dynamic data-driven and model-based recursive analysis for estimation of battery state-of-charge. Applied Energy 184, 266-275, 2016. http://dx.doi.org/10.1016/j.apenergy.2016.10.025.
  • Li X., Zhang L., Wang Z., Dong P., Remaining useful life prediction for lithium-ion batteries based on a hybrid model combining the long short-term memory and Elman neural networks. Journal of Energy Storage 21, 510-518, 2018. https://doi.org/10.1016/j.est.2018.12.011.
  • Liu Q., Yu Q., The lithium battery SOC estimation on square root unscented Kalman filter, Energy Reports 8(7), 286–294, 2022. https://doi.org/10.1016/J.EGYR.2022.05.079.
  • Lv J., Wang X., Wang G., Song Y., Research on Control Strategy of Isolated DC Microgrid Based on SOC of Energy Storage System. Electronics 10(7), 834, 2021. https://doi.org/10.3390/ELECTRONICS10070834.
  • Okay K., Eray S., Eray A., Development of prototype battery management system for PV system. Renew Energy 181,1294-1304, 2022. https://doi.org/10.1016/J.RENENE.2021.09.118.
  • Ren H., Zhao Y., Chen S., Wang T., Design and implementation of a battery management system with active charge balance based on the SOC and SOH online estimation. Energy 166, 908-917, 2019. https://doi.org/10.1016/J.ENERGY.2018.10.133.
  • Shen Z, Rahn C. Model-based state-of-charge estimation for a valve-regulated lead-acid battery using matrix inequalities. Journal of Dynamic Systems, Measurement, and Control 135(4), 041015, 2018. https://doi.org/10.1115/1.4023766.
  • Singh K. V., Bansal H. O., Singh D., Hardware-in-the-loop Implementation of ANFIS based Adaptive SoC Estimation of Lithium-ion Battery for Hybrid Vehicle Applications, Journal of Energy Storage 27, 101124, 2020. https://doi.org/10.1016/J.EST.2019.101124.
  • Somasundaram P., Jegadheesan C., Pal Singh A., Vivekanandhan C., Suganth S., Vikaash R., Shankarguru E., Effect of ambient pressure on charging and discharging characteristics of lead acid battery. Materials Today Proceedings 64(1), 888-894 2022. https://doi.org/10.1016/J.MATPR.2022.05.401.
  • Tawalbeh M., Murtaza S. Z. M., Al-Othman A., Alami A. H., Singh K., Olabi A. G., Ammonia: A versatile candidate for the use in energy storage systems. Renewable Energy 194, 955-977, 2022. https://doi.org/10.1016/j.renene.2022.06.015.
  • Wang S., Fernandez C., Shang L., Li Z., Li J., Online state of charge estimation for the aerial lithium-ion battery packs based on the improved extended Kalman filter method. Journal of Energy Storage, 9, 69-83, 2017. http://dx.doi.org/10.1016/j.est.2016.09.008.
  • Wu Y., Zhao H., Wang Y., Li R., Zhou Y., Research on life cycle SOC estimation method of lithium-ion battery oriented to decoupling temperature. Energy Reports 8, 4182-4195, 2022. https://doi.org/10.1016/J.EGYR.2022.03.036.
  • Zhang Y., Yang G., Ma S., Non-intrusive load monitoring based on convolutional neural network with differential input. Procedia CIRP, 83, 670-674, 2019. https://doi.org/10.1016/j.procir.2019.04.110.
  • Zhao X., Xuan D., Zhao K., Li Z., Elman neural network using ant colony optimization algorithm for estimating of state of charge of lithium-ion battery. Journal of Energy Storage 32, 101789, 2020. https://doi.org/10.1016/j.est.2020.101789.

Kurşun Asit Bataryalar için SOC Tahmini Yaklaşımını İçeren Bir Batarya Yönetim Sistemi Tasarımı

Yıl 2022, Cilt: 3 Sayı: 2, 300 - 313, 18.12.2022
https://doi.org/10.55546/jmm.1193510

Öz

Depolama, son dönemin en önemli konularından biridir. Özellikle üretimin kontrol edilemediği yenilenebilir enerji sistemlerinden daha etkin yararlanabilmek için depolama sistemlerine ihtiyaç duyulmaktadır. Depolamada en önemli sorunlardan biri, depolanması istenen enerji miktarı arttıkça, alan ihtiyacının da artmasıdır. Bu nedenle bu tür sistemlerde enerjinin etkin bir şekilde yönetilmesi büyük önem taşımaktadır. Bu çalışmada kurşun asit bataryalar için kullanılabilecek bir batarya yönetim sistemi tasarlanmıştır. Bu pil yönetim sistemi, STM 32 mikro-denetleyici tabanlı bir ölçüm ve kontrol sistemine sahip olup, MATLAB Simulink ortamında hazırlanan bir arayüz üzerinden kontrol edilmekte ve test verileri MATLAB Workspace ortamına aktarılabilmektedir. Tasarlanan sistem ile, batarya özelliklerine uygun olarak batarya şarj-deşarj deneyleri de yapabilmektedir. Tasarlanan sistem kullanılarak şarj-deşarj deneyleri gerçekleştirilmiş ve bu deneyler sırasında toplanan veriler kullanılarak bataryanın şarj durumunu belirlemek için bir model geliştirilmiştir. Elman Sinir Ağları temel alınarak geliştirilen model ile batarya şarj durumu %1'den daha düşük bir hata seviyesinde tahmin edilebilmiştir.

Proje Numarası

18.KARIYER.193

Kaynakça

  • Akarslan E., Learning Vector Quantization based predictor model selection for hourly load demand forecasting, Applied Soft Computing 117, 108421, 2022. https://doi.org/10.1016/J.ASOC.2022.108421.
  • Ansari S., Ayob A., Hossain Lipu M. S., Hussain A., Md Saad M. H., Remaining useful life prediction for lithium-ion battery storage system: A comprehensive review of methods, key factors, issues and future outlook. Energy Reports 8, 12153-12185, 2022. https://doi.org/10.1016/j.egyr.2022.09.043.
  • Carkhuff B. G., Demirev P. A., Srinivasan R., Impedance-Based Battery Management System for Safety Monitoring of Lithium-Ion Batteries. IEEE Trans Ind Electron 65, 6497-6504, 2018. https://doi.org/10.1109/TIE.2017.2786199.
  • Cui Y., Lin K., Zhu J., Chen Y., Quantum-inspired degradation modeling and reliability evaluation of battery management system for electric vehicles. Journal of Energy Storage 52, 104840, 2022. https://doi.org/10.1016/J.EST.2022.104840.
  • Cui Z., Hu W., Zhang G., Zhang Z., Chen Z., An extended Kalman filter based SOC estimation method for Li-ion battery. Energy Reports 8(5), 81-87, 2022. https://doi.org/10.1016/J.EGYR.2022.02.116.
  • Elman J. L., Finding structure in time. Cognitive Science 14(2), 179-211, 1990. https://doi.org/10.1016/0364-0213(90)90002-E.
  • Hossain Lipu M. S., Hannan M. A., Karim T. F., Hussain A., Saad M. H. M., Ayob A., Miah M. S., Indra Mahlia T. M., Intelligent algorithms and control strategies for battery management system in electric vehicles: Progress, challenges and future outlook. Journal of Cleaner Production 292, 126044, 2021. https://doi.org/ 10.1016/j.jclepro.2021.126044.
  • Jin Y., Zhao W., Li Z., Liu B., Wang K., SOC estimation of lithium-ion battery considering the influence of discharge rate. Energy Reports 7(7), 1436-1446, 2021. https://doi.org/10.1016/J.EGYR.2021.09.099.
  • Kuchly J., Goussian A., Merveillaut M., Baghdadi I., Franger S., Nelson-Gruel D., Nouillant C., Chamaillard Y., Li-ion battery SOC estimation method using a Neural Network trained with data generated by a P2D model, IFAC-PapersOnLine 54(10), 336-343, 2021. https://doi.org/10.1016/J.IFACOL.2021.10.185.
  • Li Y., Chattopadhyay P., Xiong S., Ray A., Rahn C.D., Dynamic data-driven and model-based recursive analysis for estimation of battery state-of-charge. Applied Energy 184, 266-275, 2016. http://dx.doi.org/10.1016/j.apenergy.2016.10.025.
  • Li X., Zhang L., Wang Z., Dong P., Remaining useful life prediction for lithium-ion batteries based on a hybrid model combining the long short-term memory and Elman neural networks. Journal of Energy Storage 21, 510-518, 2018. https://doi.org/10.1016/j.est.2018.12.011.
  • Liu Q., Yu Q., The lithium battery SOC estimation on square root unscented Kalman filter, Energy Reports 8(7), 286–294, 2022. https://doi.org/10.1016/J.EGYR.2022.05.079.
  • Lv J., Wang X., Wang G., Song Y., Research on Control Strategy of Isolated DC Microgrid Based on SOC of Energy Storage System. Electronics 10(7), 834, 2021. https://doi.org/10.3390/ELECTRONICS10070834.
  • Okay K., Eray S., Eray A., Development of prototype battery management system for PV system. Renew Energy 181,1294-1304, 2022. https://doi.org/10.1016/J.RENENE.2021.09.118.
  • Ren H., Zhao Y., Chen S., Wang T., Design and implementation of a battery management system with active charge balance based on the SOC and SOH online estimation. Energy 166, 908-917, 2019. https://doi.org/10.1016/J.ENERGY.2018.10.133.
  • Shen Z, Rahn C. Model-based state-of-charge estimation for a valve-regulated lead-acid battery using matrix inequalities. Journal of Dynamic Systems, Measurement, and Control 135(4), 041015, 2018. https://doi.org/10.1115/1.4023766.
  • Singh K. V., Bansal H. O., Singh D., Hardware-in-the-loop Implementation of ANFIS based Adaptive SoC Estimation of Lithium-ion Battery for Hybrid Vehicle Applications, Journal of Energy Storage 27, 101124, 2020. https://doi.org/10.1016/J.EST.2019.101124.
  • Somasundaram P., Jegadheesan C., Pal Singh A., Vivekanandhan C., Suganth S., Vikaash R., Shankarguru E., Effect of ambient pressure on charging and discharging characteristics of lead acid battery. Materials Today Proceedings 64(1), 888-894 2022. https://doi.org/10.1016/J.MATPR.2022.05.401.
  • Tawalbeh M., Murtaza S. Z. M., Al-Othman A., Alami A. H., Singh K., Olabi A. G., Ammonia: A versatile candidate for the use in energy storage systems. Renewable Energy 194, 955-977, 2022. https://doi.org/10.1016/j.renene.2022.06.015.
  • Wang S., Fernandez C., Shang L., Li Z., Li J., Online state of charge estimation for the aerial lithium-ion battery packs based on the improved extended Kalman filter method. Journal of Energy Storage, 9, 69-83, 2017. http://dx.doi.org/10.1016/j.est.2016.09.008.
  • Wu Y., Zhao H., Wang Y., Li R., Zhou Y., Research on life cycle SOC estimation method of lithium-ion battery oriented to decoupling temperature. Energy Reports 8, 4182-4195, 2022. https://doi.org/10.1016/J.EGYR.2022.03.036.
  • Zhang Y., Yang G., Ma S., Non-intrusive load monitoring based on convolutional neural network with differential input. Procedia CIRP, 83, 670-674, 2019. https://doi.org/10.1016/j.procir.2019.04.110.
  • Zhao X., Xuan D., Zhao K., Li Z., Elman neural network using ant colony optimization algorithm for estimating of state of charge of lithium-ion battery. Journal of Energy Storage 32, 101789, 2020. https://doi.org/10.1016/j.est.2020.101789.
Toplam 23 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Elektrik Mühendisliği
Bölüm Araştırma Makaleleri
Yazarlar

Emre Akarslan 0000-0002-5918-7266

Said Mahmut Çınar 0000-0002-6810-1575

Proje Numarası 18.KARIYER.193
Yayımlanma Tarihi 18 Aralık 2022
Gönderilme Tarihi 24 Ekim 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 3 Sayı: 2

Kaynak Göster

APA Akarslan, E., & Çınar, S. M. (2022). A Battery Management System Design Including a SOC Estimation Approach for Lead-Acid Batteries. Journal of Materials and Mechatronics: A, 3(2), 300-313. https://doi.org/10.55546/jmm.1193510
AMA Akarslan E, Çınar SM. A Battery Management System Design Including a SOC Estimation Approach for Lead-Acid Batteries. J. Mater. Mechat. A. Aralık 2022;3(2):300-313. doi:10.55546/jmm.1193510
Chicago Akarslan, Emre, ve Said Mahmut Çınar. “A Battery Management System Design Including a SOC Estimation Approach for Lead-Acid Batteries”. Journal of Materials and Mechatronics: A 3, sy. 2 (Aralık 2022): 300-313. https://doi.org/10.55546/jmm.1193510.
EndNote Akarslan E, Çınar SM (01 Aralık 2022) A Battery Management System Design Including a SOC Estimation Approach for Lead-Acid Batteries. Journal of Materials and Mechatronics: A 3 2 300–313.
IEEE E. Akarslan ve S. M. Çınar, “A Battery Management System Design Including a SOC Estimation Approach for Lead-Acid Batteries”, J. Mater. Mechat. A, c. 3, sy. 2, ss. 300–313, 2022, doi: 10.55546/jmm.1193510.
ISNAD Akarslan, Emre - Çınar, Said Mahmut. “A Battery Management System Design Including a SOC Estimation Approach for Lead-Acid Batteries”. Journal of Materials and Mechatronics: A 3/2 (Aralık 2022), 300-313. https://doi.org/10.55546/jmm.1193510.
JAMA Akarslan E, Çınar SM. A Battery Management System Design Including a SOC Estimation Approach for Lead-Acid Batteries. J. Mater. Mechat. A. 2022;3:300–313.
MLA Akarslan, Emre ve Said Mahmut Çınar. “A Battery Management System Design Including a SOC Estimation Approach for Lead-Acid Batteries”. Journal of Materials and Mechatronics: A, c. 3, sy. 2, 2022, ss. 300-13, doi:10.55546/jmm.1193510.
Vancouver Akarslan E, Çınar SM. A Battery Management System Design Including a SOC Estimation Approach for Lead-Acid Batteries. J. Mater. Mechat. A. 2022;3(2):300-13.