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Design and Implementation of a Costly Battery Management System for Electric Vehicle

Yıl 2020, Ejosat Özel Sayı 2020 (HORA), 227 - 238, 15.08.2020
https://doi.org/10.31590/ejosat.779720

Öz

The importance of Electric Vehicle (EV) is increasing day by day due to the destruction of fossil fuels reserves and the damage caused by internal combustion engine vehicles working with fossil fuels to the environment. The necessary electrical energy of the electric motor used to ensure the movement of the EV is generally provided by the batteries. Efficient and reliable use of EV's is directly related to the use of batteries. For this reason, it requires the use of a BMS that monitors and controls the status of the batteries used as energy sources. In this study, the Battery Management System (BMS) was designed based on the passive cell balancing method for the 20 cell battery pack to be used in electric vehicles, and a three cell BMS application was implemented. Using Li-ion batteries, a PCB in the central BMS structure, which can passively balance the 20 serial battery cells in the design, and which can also determine the SoC of the batteries, has been designed and the actual working condition of the hardware circuit has been tested. In the SoC calculation, the "terminal voltage" method was applied. This method is based on terminal voltage level decrease occurring in the battery due to internal impedances when the battery is discharged. In the design of the BMS pcb, the central BMS system was preferred and the pcb was made simple by using SMD components, the size was reduced and the cost was reduced.
In the BMS pcb made as prototype, there are overvoltage, undervoltage and temperature protections for battery cells. Also, in order to maximum efficiency in battery cells, passive balancing system is applied during the charging of the cells. SoC of each of the battery cells were calculated with BMS and SoH estimates were made. The specified properties of the realized BMS have been tested and proved by the experimental results obtained that the BMS has been operating successfully. In addition, improvements in design speed, size, cost and ease of installation have been achieved by using the passive balancing system in BMS design.

Kaynakça

  • Situ L. Electric vehicle development: the past, present & future. In: Proceedings of the 3rd international conference on power electronics systems and applications, Hong Kong, China, 20–22 May 2009. New York: IEEE.
  • Frost DF and Howey DA. Completely decentralized active balancing battery management system. IEEE T Power Electr 2018; 33(1): 729–738.
  • Cao, J., Schofield, N., Emadi, A., 2008. Battery balancing methods: A comprehensive review, IEEE Vehicle Power and Propulsion Conference, Harbin, China, 1-6.
  • Texas instrument, “Intelligent battery management and charging for electric vehicles,” from www.ti.com.
  • Farmann, A. and Sauer, D. U., 2016. A comprehensive review of on-board State-of-Available-Power prediction techniques for lithium-ion batteries in electric vehicles. Journal of Power Sources. 329, 123-137.
  • Saw, L.H., Ye, Y. and Tay, A.A.O., 2014. Electro-thermal analysis and integration issues of lithium ion battery for electric vehicles. Applied Energy. 131, 97-107.
  • Andrea, D. 2010. Battery management systems for large lithium ion battery packs. Artech house, Norwood, Massachusetts, USA.
  • Watrin, N., Blunier, B. and Miraoui, A. (2012a). Review of adaptive systems for lithium batteries state-of-charge and state-of-health estimation. 2012 IEEE Transportation Electrification Conference and Expo (ITEC), IEEE, 1-6.
  • Prajapati, V., Hess, H., William, E. J., Gupta, V., Huff, M., Manic, M., Rufus, F., Thakker, A. and Govar, J. (2011). A literature review of state of-charge estimation techniques applicable to lithium poly-carbon monoflouride (LI/CFx) battery. India International Conference on Power Electronics 2010 (IICPE2010), IEEE, 1-8.
  • Chiasson, J. and Vairamohan, B. (2003). Estimating the state of charge of a battery. Proceedings of the 2003 American Control Conference, 2003, IEEE, 2863-2868.
  • Ng, K.-S., Moo, C.-S., Chen, Y.-P. and Hsieh, Y.-C. (2008). State-of-charge estimation for lead-acid batteries based on dynamic open-circuit voltage. 2008 IEEE 2nd International Power and Energy Conference, IEEE, 972-976.
  • Abu-Sharkh, S. and Doerffel, D. 2004. Rapid test and non-linear model characterisation of solid-state lithium-ion batteries. Journal of Power Sources, 130:1-2, 266-274.
  • Anbuky, A. H. and Pascoe, P. E. 2000. VRLA battery state-of-charge estimation in telecommunication power systems. IEEE Transactions on Industrial Electronics, 47:3, 565-573.
  • Sato, S. and Kawamura, A. (2002). A new estimation method of state of charge using terminal voltage and internal resistance for lead acid battery. Proceedings of the Power Conversion Conference-Osaka 2002 (Cat. No. 02TH8579), IEEE, 565-570.
  • Huet, F. 1998. A review of impedance measurements for determination of the stateof-charge or state-of-health of secondary batteries. Journal of Power Sources, 70:1, 59-69.
  • Bundy, K., Karlsson, M., Lindbergh, G. and Lundqvist, A. 1998. An electrochemical impedance spectroscopy method for prediction of the state of charge of a nickel-metal hydride battery at open circuit and during discharge. Journal of Power Sources, 72:2, 118-125.
  • Ran, L., Junfeng, W., Haiying, W. and Gechen, L. (2010). Prediction of state of charge of lithium-ion rechargeable battery with electrochemical impedance spectroscopy theory. 2010 5th IEEE Conference on Industrial Electronics and Applications, IEEE, 684-688.
  • Ng, K.-S., Huang, Y.-F., Moo, C.-S. and Hsieh, Y.-C. (2009). An enhanced coulomb counting method for estimating state-of-charge and state-of-health of lead-acid batteries. INTELEC 2009-31st International Telecommunications Energy Conference, IEEE, 1-5.
  • Kim, J. and Cho, B.-H. 2011. State-of-charge estimation and state-of-health prediction of a Li-ion degraded battery based on an EKF combined with a per-unit system. IEEE transactions on Vehicular Technology, 60:9, 4249-4260.
  • Pop, V., Bergveld, H., Notten, P., het Veld, J. O. and Regtien, P. P. 2009. Accuracy analysis of the State-of-Charge and remaining run-time determination for lithium-ion batteries. Measurement, 42:8, 1131-1138.
  • Wang, J., Cao, B., Chen, Q. and Wang, F. 2007. Combined state of charge estimator for electric vehicle battery pack. Control Engineering Practice, 15:12, 1569-1576.

Elektrikli Araç İçin Düşük Maliyetli Bir Batarya Yönetim Sistemi Tasarımı ve Gerçekleştirilmesi

Yıl 2020, Ejosat Özel Sayı 2020 (HORA), 227 - 238, 15.08.2020
https://doi.org/10.31590/ejosat.779720

Öz

Fosil yakıt kaynaklarının yakında tükenecek olması ve bu yakıtlarla çalışan içten yanmalı motorlu araçların çevreye verdikleri zararlar nedeniyle Elektrikli Araç (EA)’ların önemi gün geçtikçe artmaktadır. EA’nın hareketinin sağlanabilmesi için kullanılan elektrik motorunun ihtiyaç duyduğu elektrik enerjisi genel olarak bataryalar tarafından sağlanmaktadır. EA’ların verimli ve güvenilir bir şekilde çalıştırılması bataryaların kullanımıyla doğrudan ilişkilidir. Bu durum, enerji kaynağı olarak kullanılan bataryaların durumlarını takip ve kontrol eden bir BYS’nin kullanılmasını zorunlu kılmaktadır. Bu çalışmada, elektrikli araçlarda kullanmak amacıyla 20 hücreli batarya paketi için pasif hücre dengeleme metodu temel alınarak Batarya Yönetim Sistemi (BYS) tasarlanmış ve üç hücreli BYS uygulaması gerçekleştirilmiştir. Li-ion bataryalar kullanılarak tasarımda 20 adet seri batarya hücresinin dengelenmesini pasif şekilde yapabilecek; aşırı gerilim, düşük gerilim ve sıcaklık koruması olan ve ayrıca bataryaların SoC’larını belirleyebilen, merkezi BYS yapısında bir kartın tasarımı gerçekleştirilmiş ve donanımsal devrenin gerçek çalışma durumu test edilmiştir. SoC hesaplamasında “terminal gerilimi” yöntemi uygulanmıştır. Bu yöntem, batarya boşalırken iç empedanslar nedeniyle bataryada meydana gelen terminal gerilim düşümüne dayanmaktadır. BYS kartının tasarımında merkezi BYS sistemi tercih edilmiş ve SMD devre elemanları kullanılarak kartın sade olması sağlanmış, boyutu küçültülmüş ve maliyeti düşürülmüştür. Prototip olarak yapılan BYS kartında batarya hücreleri için aşırı gerilim, düşük gerilim ve sıcaklık korumaları mevcuttur. Ayrıca batarya hücrelerinde azami verim alınabilmesi amacıyla hücrelerin dolumu esnasında pasif dengeleme sistemi uygulanmaktadır. BYS ile batarya hücrelerinin her birinin SoC’ları hesaplanmış ve SoH kestirimleri yapılmıştır. Gerçekleştirilmiş olan BYS’nin belirtilen özellikleri test edilmiş ve sisteminin başarılı bir şekilde çalıştığı elde edilen deneysel sonuçlar aracılığı ile ispatlanmıştır. Ayrıca, BYS tasarımında pasif dengeleme sistemi kullanılarak tasarım hızı, boyutu ile maliyet ve kurulum kolaylığı açısından iyileştirmeler sağlanmıştır.

Kaynakça

  • Situ L. Electric vehicle development: the past, present & future. In: Proceedings of the 3rd international conference on power electronics systems and applications, Hong Kong, China, 20–22 May 2009. New York: IEEE.
  • Frost DF and Howey DA. Completely decentralized active balancing battery management system. IEEE T Power Electr 2018; 33(1): 729–738.
  • Cao, J., Schofield, N., Emadi, A., 2008. Battery balancing methods: A comprehensive review, IEEE Vehicle Power and Propulsion Conference, Harbin, China, 1-6.
  • Texas instrument, “Intelligent battery management and charging for electric vehicles,” from www.ti.com.
  • Farmann, A. and Sauer, D. U., 2016. A comprehensive review of on-board State-of-Available-Power prediction techniques for lithium-ion batteries in electric vehicles. Journal of Power Sources. 329, 123-137.
  • Saw, L.H., Ye, Y. and Tay, A.A.O., 2014. Electro-thermal analysis and integration issues of lithium ion battery for electric vehicles. Applied Energy. 131, 97-107.
  • Andrea, D. 2010. Battery management systems for large lithium ion battery packs. Artech house, Norwood, Massachusetts, USA.
  • Watrin, N., Blunier, B. and Miraoui, A. (2012a). Review of adaptive systems for lithium batteries state-of-charge and state-of-health estimation. 2012 IEEE Transportation Electrification Conference and Expo (ITEC), IEEE, 1-6.
  • Prajapati, V., Hess, H., William, E. J., Gupta, V., Huff, M., Manic, M., Rufus, F., Thakker, A. and Govar, J. (2011). A literature review of state of-charge estimation techniques applicable to lithium poly-carbon monoflouride (LI/CFx) battery. India International Conference on Power Electronics 2010 (IICPE2010), IEEE, 1-8.
  • Chiasson, J. and Vairamohan, B. (2003). Estimating the state of charge of a battery. Proceedings of the 2003 American Control Conference, 2003, IEEE, 2863-2868.
  • Ng, K.-S., Moo, C.-S., Chen, Y.-P. and Hsieh, Y.-C. (2008). State-of-charge estimation for lead-acid batteries based on dynamic open-circuit voltage. 2008 IEEE 2nd International Power and Energy Conference, IEEE, 972-976.
  • Abu-Sharkh, S. and Doerffel, D. 2004. Rapid test and non-linear model characterisation of solid-state lithium-ion batteries. Journal of Power Sources, 130:1-2, 266-274.
  • Anbuky, A. H. and Pascoe, P. E. 2000. VRLA battery state-of-charge estimation in telecommunication power systems. IEEE Transactions on Industrial Electronics, 47:3, 565-573.
  • Sato, S. and Kawamura, A. (2002). A new estimation method of state of charge using terminal voltage and internal resistance for lead acid battery. Proceedings of the Power Conversion Conference-Osaka 2002 (Cat. No. 02TH8579), IEEE, 565-570.
  • Huet, F. 1998. A review of impedance measurements for determination of the stateof-charge or state-of-health of secondary batteries. Journal of Power Sources, 70:1, 59-69.
  • Bundy, K., Karlsson, M., Lindbergh, G. and Lundqvist, A. 1998. An electrochemical impedance spectroscopy method for prediction of the state of charge of a nickel-metal hydride battery at open circuit and during discharge. Journal of Power Sources, 72:2, 118-125.
  • Ran, L., Junfeng, W., Haiying, W. and Gechen, L. (2010). Prediction of state of charge of lithium-ion rechargeable battery with electrochemical impedance spectroscopy theory. 2010 5th IEEE Conference on Industrial Electronics and Applications, IEEE, 684-688.
  • Ng, K.-S., Huang, Y.-F., Moo, C.-S. and Hsieh, Y.-C. (2009). An enhanced coulomb counting method for estimating state-of-charge and state-of-health of lead-acid batteries. INTELEC 2009-31st International Telecommunications Energy Conference, IEEE, 1-5.
  • Kim, J. and Cho, B.-H. 2011. State-of-charge estimation and state-of-health prediction of a Li-ion degraded battery based on an EKF combined with a per-unit system. IEEE transactions on Vehicular Technology, 60:9, 4249-4260.
  • Pop, V., Bergveld, H., Notten, P., het Veld, J. O. and Regtien, P. P. 2009. Accuracy analysis of the State-of-Charge and remaining run-time determination for lithium-ion batteries. Measurement, 42:8, 1131-1138.
  • Wang, J., Cao, B., Chen, Q. and Wang, F. 2007. Combined state of charge estimator for electric vehicle battery pack. Control Engineering Practice, 15:12, 1569-1576.
Toplam 21 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Mustafa Aktaş Bu kişi benim 0000-0002-2608-1000

Burak Baygüneş Bu kişi benim 0000-0003-2799-0801

Sinan Kıvrak Bu kişi benim 0000-0001-5195-0311

Barış Çavuş 0000-0002-5798-8350

Faruk Sözen Bu kişi benim 0000-0003-1009-7187

Yayımlanma Tarihi 15 Ağustos 2020
Yayımlandığı Sayı Yıl 2020 Ejosat Özel Sayı 2020 (HORA)

Kaynak Göster

APA Aktaş, M., Baygüneş, B., Kıvrak, S., Çavuş, B., vd. (2020). Elektrikli Araç İçin Düşük Maliyetli Bir Batarya Yönetim Sistemi Tasarımı ve Gerçekleştirilmesi. Avrupa Bilim Ve Teknoloji Dergisi227-238. https://doi.org/10.31590/ejosat.779720