Araştırma Makalesi
Yıl 2019,
Cilt: 22 - Özel Sayı, 64 - 69, 29.11.2019
Öz
The automotive sector is undergoing a major
change due to the increasing fuel costs and the emission problems of fossil
fuel vehicles. Therefore, hybrid and electric cars began to be produced. The
disadvantages of electric vehicles such as cost, low maximum-speed, high
charging time are not yet completely resolved. With the development of
lithium-based batteries, it has begun to be used as storage batteries in
electric and hybrid vehicles. These batteries are preferred to meet the energy
requirements of electrical systems in terms of performance, durability, safety
and cost advantages. In this paper, the current, voltage and state of charge (SoC)
graphs of a battery pack is obtained by using the simulation model of the
battery and charging system used in an electric vehicle.
Anahtar Kelimeler
Kaynakça
- Ahmed, R., (2014). Modeling and state of charge estimation of electric vehicle batteries, (Doctoral dissertation, McMaster University).
- Ahmed, R., Gazzarri, J., Onori, S., Habibi, S., Jackey, R., Rmezien, K., Tjong, J., LeSage, J. (2015). Model-Based Parameter Identification of Healthy and Aged Li-ion Batteries for Electric Vehicle Applications, SAE Int. J. Alt. Power, 4, 2.
- Chen, J., Ouyang, Q., Xu, C., Su, H. (2017). Neural network-based state of charge observer design for lithium-ion batteries, IEEE Transactions on Control Systems Technology, 26, 1.
- Ehsani, M., Gao, Y., Emadi, A. (2010). Modern Electric, Hybrid Electric, and Fuel Cell Vehicles – Fundamentals, Theory, and Design, 2nd edition, CRC Press.
- Gadoue, S., Chen, K.W., Mitcheson, P., Yufit, V., Brandon, N. (2018). Electrochemical Impedance Spectroscopy State of Charge Measurement for Batteries using Power Converter Modulation, The 9th International Renewable Energy Congress (IREC 2018).
- Gandolfo, D., Brandao, A., Patino, D., Molina, M. (2015). Dynamic model of lithium polymer battery e Load resistor method for electric parameters identification, Journal of the Energy Institute, 88.
- Guo, D., He, L., (2018). A Novel Algorithm for SOC using Simple Iteration and Coulomb Counting Method, IEEE Student Conference on Electric Machines and Systems.
- Hannan, M.A., Lipu, M.S.H., Hussain A., Saad, M.H., Ayob, A. (2018). Neural Network Approach for Estimating State of Charge of Lithium-Ion Battery Using Backtracking Search Algorithm, IEEE Access, 6.
- Web1 https://ww2.mathworks.cn/matlabcentral/fileexchange/36019-lithium-battery-model-simscape-language-and-simulink-design-optimization
- Huria, T., Ceraolo, M., Gazzarri, J., Jackey, R. (2012). High Fidelity Electrical Model with Thermal Dependence for Characterization and Simulation of High-Power Lithium Battery Cells, IEEE International Electric Vehicle Conference.
- Jiang, J., Zhang, C. (2015). Fundamentals and Applications of Lithium-Ion Batteries in Electric Drive Vehicles.
- Qian, L., Si, Y., Qiu, L. (2015). SOC estimation of LiFePO4 Li-ion battery using BP Neural Network, EVS28 International Electric Vehicle Symposium and Exhibition.
- Sepasi, S., Roose, L.R., Matsuura, M.M. (2015). Extended Kalman Filter a Fuzzy Method for Accurate Battery Pack State of Charge Estimation, Energies, 8, 6.
- Tong, S., Klein, M.P., Park, J.W. (2013). A Comprehensive Battery Equivalent Circuit Based Model For Battery Management Application, ASME 2013 Dynamic Syst. and Cont. Conf.
- Xia, B., Wang, H., Wang, M., Sun, W., Xu, Z., Lai, Y. (2015). A new method for state of charge estimation of lithium-ion battery based on strong tracking cubature kalman filter. Energies, 8, 12.
- Zeng, Z., Tian, J., Li, D., Tian, Y. (2018). An Online State of Charge Estimation Algorithm for Lithium-Ion Batteries Using an Improved Adaptive Cubature Kalman Filter, Energies, 11, 1.
- Zhang, C., Allafi, W., Dinh, Q., Ascencio, P., Marco, J. (2018). Online estimation of battery equivalent circuit model parameters and state of charge using decoupled least squares technique, Energy, 142.
- Zhang, L., Peng, H., Ning, Z., Mu, Z., Sun, C. (2017). Comparative research on RC equivalent circuit models for lithium-ion batteries of electric vehicles, Applied Sciences, 10, 7.
- Zhou, Y., Bai, C., Sun, J. (2011). Application of Genetic Neural Network in Power Battery Charging State-of-Charge Estimation, I.J. Intelligent Systems and Applications.
Yıl 2019,
Cilt: 22 - Özel Sayı, 64 - 69, 29.11.2019
Öz
Giderek artan yakıt maliyetleri ve
fosil yakıtlı araçların emisyon problemi nedeniyle otomotiv sektörü büyük bir
değişim döneminden geçiyor. Bu nedenle hibrit ve elektrikli otomobiller
üretilmeye başlandı. Elektrikli araçların maliyet, maksimum hız düşüklüğü,
yüksek şarj süresi gibi dezavantajları ise henüz tam olarak çözüme
kavuşturulmuş değildir. Lityum tabanlı bataryaların geliştirilmesi, elektrikli
ve hibrit araçlarda depolama bataryaları olarak kullanılmaya başlanmıştır. Bu
bataryalar performans, dayanıklılık, güvenlik ve maliyet avantajları açısından
günümüzde elektriksel sistemlerin enerji ihtiyacını karşılamak için tercih
edilmektedir. Bu çalışmada, elektrikli bir araçta kullanılan batarya ve şarj
sisteminin benzetim modeli kullanılarak batarya paketinin akım, gerilim ve şarj
durumu grafiği elde edilmiştir.
Anahtar Kelimeler
Kaynakça
- Ahmed, R., (2014). Modeling and state of charge estimation of electric vehicle batteries, (Doctoral dissertation, McMaster University).
- Ahmed, R., Gazzarri, J., Onori, S., Habibi, S., Jackey, R., Rmezien, K., Tjong, J., LeSage, J. (2015). Model-Based Parameter Identification of Healthy and Aged Li-ion Batteries for Electric Vehicle Applications, SAE Int. J. Alt. Power, 4, 2.
- Chen, J., Ouyang, Q., Xu, C., Su, H. (2017). Neural network-based state of charge observer design for lithium-ion batteries, IEEE Transactions on Control Systems Technology, 26, 1.
- Ehsani, M., Gao, Y., Emadi, A. (2010). Modern Electric, Hybrid Electric, and Fuel Cell Vehicles – Fundamentals, Theory, and Design, 2nd edition, CRC Press.
- Gadoue, S., Chen, K.W., Mitcheson, P., Yufit, V., Brandon, N. (2018). Electrochemical Impedance Spectroscopy State of Charge Measurement for Batteries using Power Converter Modulation, The 9th International Renewable Energy Congress (IREC 2018).
- Gandolfo, D., Brandao, A., Patino, D., Molina, M. (2015). Dynamic model of lithium polymer battery e Load resistor method for electric parameters identification, Journal of the Energy Institute, 88.
- Guo, D., He, L., (2018). A Novel Algorithm for SOC using Simple Iteration and Coulomb Counting Method, IEEE Student Conference on Electric Machines and Systems.
- Hannan, M.A., Lipu, M.S.H., Hussain A., Saad, M.H., Ayob, A. (2018). Neural Network Approach for Estimating State of Charge of Lithium-Ion Battery Using Backtracking Search Algorithm, IEEE Access, 6.
- Web1 https://ww2.mathworks.cn/matlabcentral/fileexchange/36019-lithium-battery-model-simscape-language-and-simulink-design-optimization
- Huria, T., Ceraolo, M., Gazzarri, J., Jackey, R. (2012). High Fidelity Electrical Model with Thermal Dependence for Characterization and Simulation of High-Power Lithium Battery Cells, IEEE International Electric Vehicle Conference.
- Jiang, J., Zhang, C. (2015). Fundamentals and Applications of Lithium-Ion Batteries in Electric Drive Vehicles.
- Qian, L., Si, Y., Qiu, L. (2015). SOC estimation of LiFePO4 Li-ion battery using BP Neural Network, EVS28 International Electric Vehicle Symposium and Exhibition.
- Sepasi, S., Roose, L.R., Matsuura, M.M. (2015). Extended Kalman Filter a Fuzzy Method for Accurate Battery Pack State of Charge Estimation, Energies, 8, 6.
- Tong, S., Klein, M.P., Park, J.W. (2013). A Comprehensive Battery Equivalent Circuit Based Model For Battery Management Application, ASME 2013 Dynamic Syst. and Cont. Conf.
- Xia, B., Wang, H., Wang, M., Sun, W., Xu, Z., Lai, Y. (2015). A new method for state of charge estimation of lithium-ion battery based on strong tracking cubature kalman filter. Energies, 8, 12.
- Zeng, Z., Tian, J., Li, D., Tian, Y. (2018). An Online State of Charge Estimation Algorithm for Lithium-Ion Batteries Using an Improved Adaptive Cubature Kalman Filter, Energies, 11, 1.
- Zhang, C., Allafi, W., Dinh, Q., Ascencio, P., Marco, J. (2018). Online estimation of battery equivalent circuit model parameters and state of charge using decoupled least squares technique, Energy, 142.
- Zhang, L., Peng, H., Ning, Z., Mu, Z., Sun, C. (2017). Comparative research on RC equivalent circuit models for lithium-ion batteries of electric vehicles, Applied Sciences, 10, 7.
- Zhou, Y., Bai, C., Sun, J. (2011). Application of Genetic Neural Network in Power Battery Charging State-of-Charge Estimation, I.J. Intelligent Systems and Applications.
Toplam 19 adet kaynakça vardır.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Elektrik Mühendisliği |
| Bölüm | Araştırma Makalesi |
| Yazarlar | |
| Yayımlanma Tarihi | 29 Kasım 2019 |
| Gönderilme Tarihi | 2 Ağustos 2019 |
| Yayımlandığı Sayı | Yıl 2019Cilt: 22 - Özel Sayı |
