Design of Battery Management System for Electric Car Battery-Based Hybrid Metal-Organic (Sol-Gel) Lithium Manganate (LiMn2O4)
Abstract
Battery-powered Electric Vehicles (BEVs) such as electric cars, use the battery as the main power source to drive the motor, in addition to lighting, horn, and other functions. Currently, Balai Besar Bahan dan Barang Teknik (B4T) has been conducting research in Lithium-ion (Li-ion) battery prototype for an electric vehicle. However, the management system in accordance with the electrical characteristics of the battery prototype is still not available. Thus, to integrate the battery prototype with electrical components of the electric vehicle, it is necessary to design Battery Management System (BMS). Two important battery parameters observed are State of Charge (SOC) and State of Health (SOH). The method used for SOC was Coulomb Counting. SOH was determined using a combination between Support Vector Machine (SVM) and Relevance Vector Machine (RVM). Based on the experiments by using BMS, the battery performance could be more controlled and produces a linear curve of SOC and SOH.
Kendaraan listrik bertenaga baterai, seperti mobil listrik, menggunakan baterai sebagai sumber tenaga utama untuk menggerakkan komponen motor listrik dan mengatur fungsi-fungsi kendaraan yang lain seperti lampu dan klakson. Saat ini Balai Besar Bahan dan Barang Teknik (B4T) telah melakukan penelitian pada pembuatan purwarupa baterai Lithium-ion (Li-ion) untuk kendaraan listrik. Namun sistem yang berfungsi untuk mengatur karakteristik kelistrikan pada baterai tersebut masih belum tersedia. Oleh karena itu, untuk mengintegrasikan purwarupa baterai yang telah dibuat oleh B4T dengan kendaraan listrik diperlukan adanya Sistem Manajemen Baterai (SMB). Dua parameter penting yang diatur pada baterai kendaraan listrik yaitu State of Charge (SOC) dan State of Health (SOH). Metode pengaturan SOC yang digunakan yaitu Coulomb Counting. Metode yang digunakan dalam menentukan SOH yaitu kombinasi antara Support Vector Machine (SVM) dan Relevance Vector Machine (RVM). Berdasarkan percobaan yang telah dilakukan dengan menggunakan SMB, kinerja baterai dapat lebih mudah diatur dan mampu menghasilkan kurva SOC dan SOH yang linier.
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UNFCCC. Conference of the Parties (COP), Adoption of the Paris Agreement., vol. 21932, no. December. 2015, p. 32.
C. C. Chan, A. Bouscayrol, and K. Chen, “Electric, Hybrid, and Fuel-Cell Vehicles: Architectures and Modeling,” IEEE Trans. Veh. Technol., vol. 59, no. 2, pp. 589–598, 2010.
C. O. Celil Ozkurt , Fatih Camci , Vepa Atamuradov, “Integration of sampling based battery state of health estimation method in electric vehicles,” Appl. Energy, vol. 175, pp. 356–367, 2016.
G. L. Plett, “Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs Part 1. Background,” J. Power Sources, vol. 134, pp. 252–261, 2004.
A. Affanni, A. Bellini, G. Franceschini, P. Guglielmi, and C. Tassoni, “Battery choice and management for new-generation electric vehicles,” IEEE Trans. Ind. Electron., vol. 52, no. 5, pp. 1343–1349, 2005.
C.-C. Lin, H. Peng, J. W. Grizzle, and J.-M. Kang, “Power management strategy for a parallel hybrid electric truck,” IEEE Trans. Control Syst. Technol., vol. 11, no. 6, pp. 839–849, 2003.
C. Liu, W. Liu, L. Wang, G. Hu, L. Ma, and B. Ren, “A new method of modeling and state of charge estimation of the battery,” J. Power Sources, vol. 320, pp. 1–12, 2016.
L. Lu, X. Han, J. Li, J. Hua, and M. Ouyang, “A review on the key issues for lithium-ion battery management in electric vehicles,” J. Power Sources, vol. 226, pp. 272–288, 2013.
G. L. Plett, “Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs Part 3. State and parameter estimation,” J. Power Sources,
vol. 134, pp. 277–292, 2004.
T. M. Bandhauer, S. Garimella, and T. F. Fuller, “A Critical Review of Thermal Issues in Lithium-Ion Batteries,” J. Electrochem. Soc., vol. 158, no. 3, p. R1, 2011.
X. Wang et al., “Citric acid-assisted sol-gel synthesis of nanocrystalline LiMn2O4 spinel as cathode material,” J. Cryst. Growth, vol. 256, no. 1–2, pp. 123–127, 2003.
X. He, L. Wang, W. Pu, G. Zhang, C. Jiang, and C. Wan, “Synthesis of spinel LiMn2O4 for Li-ion batteries via sol-gel process,” Int. J. Electrochem. Sci., vol. 1, no. 1, pp. 12–16, 2006.
C. Lu and S. K. Saha, “Morphology and electrochemical properties of LiMn2O4 powders derived from the sol – gel route,” Mater. Sci. Eng. R Reports, vol. 79, pp. 247–250, 2001.
S. Jang et al., “Synthesis and characterization of spinel LiMn2O4 for lithium secondary battery,” J. Power Sources, pp. 274–277, 2000.
B. Xu, D. Qian, Z. Wang, and Y. S. Meng, “Recent progress in cathode materials research for advanced lithium ion batteries,” Mater. Sci. Eng. R Reports, vol. 73, no. 5–6, pp. 51–65, 2012.
I. E. Alamsyah. (2016). Standardisasi Baterai Mobil Listrik Masih Dikaji [Online].Available: http://www.kemenperin.go.id/artikel/15438/Standardisasi-Baterai-Mobil-Listrik-Masih-Dikaji. [Accessed: 11-Oct-2016].
B. Saha, K. Goebel, S. Poll, and J. Christophersen, “An integrated approach to battery health monitoring using bayesian regression and state estimation,” in Autest, IEEE, 2007, no, pp. 646–653, January 2008.
J. E. M. Y. Entes, N. A. H. Unt, K. E. K. S. Chmid, J. E. P. K. Aipust, and D. E. M. C. G. Rath, “The Appropriate Use of Approximate Entropy and Sample Entropy with Short Data Sets,” Biomech. Res. Build., vol. 44, no. 2, pp. 349–365, 2013.
G. L. Plett, “Battery management system algorithms for HEV battery state-of-charge and state-of-health estimation,” Adv. Mater. Methods Lithium-Ion Batter., vol. 661, no. 2, 2007.
DOI: http://dx.doi.org/10.37209/jtbbt.v6i1.65
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