Battery Health Monitoring System Lithium-Ion Based on Fuzzy Logic
Article Sidebar
Main Article Content
Abstract
Batteries that can store electrical energy and are easy to carry make them the most practical technology choice as an electricity source. Even so, lithium-ion batteries are not free from the risk of damage when used. Therefore, a Lithium-ion battery health monitoring system was created. This system uses the INA219 sensor as a current and voltage detector and the DS18B20 sensor as a temperature detector. Arduino as a data process. The test results show that all components function well. In battery capacity testing, the highest error was 1.8%. For the DS18B20 sensor as a temperature sensor, an error of 2.4% was obtained. Testing capacity against temperature on the battery when the temperature was 25 C, the current was 485mAh; when the temperature was 44.8 C, the current was 550mAh, there was a difference of 65mAh or 11%. This difference corresponds to the difference in battery capacity. Testing using the Fuzzy Logic method was carried out on 3 batteries with different capacities to obtain the State of Health (SOH) value for each battery. Testing is carried out in real-time, as well as Matlab simulation. In battery test 1, with a capacity of 2200mAh and the highest temperature of 32.1 oC, the device's State of Health (SoH) was 90%, and Fuzzy Matlab was 87.6%. Battery 2, 1500mAh capacity with the highest temperature of 33.4oC obtained State of Health (SOH) of 60%, Fuzzy Matlab 60%. Battery 3 Capacity 2200mAh, Highest temperature 32.2 oC, State of Health (SOH) of 90%, Fuzzy Matlab 87.6%. The test results show that the overall error is still below 5%. A properly functioning Internet of Things (IoT) system can display information on lithium-ion batteries' State of Health (SoH) on devices and smartphones.
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
C. N. Karimah, A. T. Zain, and A. L. Nofiansyah, (2023), “Analisa Baterai Sebagai Sumber Kelistrikan Kendaraan Roda Dua Ditinjau Dari Kapasitas Dan Efisiensi,” J-TETA : Jurnal Teknik Terapan, vol. 2, no. 1, pp. 1–11. DOI: https://doi.org/10.25047/jteta.v2i1.24
Y. N. Hilal, (2023), “Analisa Balancing BMS (Battery Management System) Pada Pengisian Baterai Lithium-ion Tipe INR 18650 Dengan Metode Cut Off,” Jurnal Simetris, vol. 14, no. 2, pp. 367–373. DOI: https://doi.org/10.24176/simet.v14i2.9852
M. Aryo Pradhana, T. Andromeda, and D. Y. Christyono, (Jun. 2022), “Pengisi Daya Baterai LiFePO4 Sebagai Sumber Energi Pada Sepeda Listrik,” Transient: Jurnal Ilmiah Teknik Elektro, vol. 11, no. 2, pp. 70–74, doi:10.14710/TRANSIENT.V11I2.70-74. DOI: https://doi.org/10.14710/transient.v11i2.70-74
N. . M. A. Wijaya, I. N. S. Kumara, C. G. I. Partha, and Y. Divayana, (2021), “Perkembangan Baterai dan Charger Untuk Mendukung Pemasyarakatan Sepeda Listrik di Indonesia,” Spektrum, vol. 8, no. 1, pp. 15–26. DOI: https://doi.org/10.24843/SPEKTRUM.2021.v08.i01.p3
E. P. Permatasari, M. P. Rindi, and A. Purwanto, (2017), “Pembuatan Katoda Baterai Lithium Ion Iron Phospate (LiFePO4) dengan Metode Solid State Reaction,” Equilibrium, vol. 16, no. 1, pp. 1–5. DOI: https://doi.org/10.20961/equilibrium.v1i1.40373
H. Abdulloh, M. Fanriadho, W. B. Pramono, and Y. A. Amrullah, (2018), “Rancang Bangun Battery Management System Untuk Mobil Listrik,” in TECHNOPEX, pp. 128–137.
W. Wanggur Mboi, N. Nachrowie, Y. S. A. Gumilang, and R. D. J. Kartika Sari, (2021), “Sistem Monitoring dan Pengisian Daya Baterai Pada Sepeda Motor Listrik Secara Adaptive,” JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer), vol. 3, no. 2, pp. 77–82, doi:10.26905/jasiek.v3i2.8375. DOI: https://doi.org/10.26905/jasiek.v3i2.8375
B. Sood, M. Osterman, and M. Pecht, (2013), “Health monitoring of lithium-ion batteries,” Proceedings - 10th Annual IEEE Symposium on Product Compliance Engineering, ISPCE 2013, doi:10.1109/ISPCE.2013.6664165. DOI: https://doi.org/10.1109/ISPCE.2013.6664165
Z. Chen, S. J. Li, X. Cai, N. Zhou, and J. Cui, (Jan. 2022), “Online state of health monitoring of lithium-ion battery based on model error spectrum for electric vehicle applications,” Journal of Energy Storage, vol. 45, p. 103507, doi:10.1016/J.EST.2021.103507. DOI: https://doi.org/10.1016/j.est.2021.103507
M. Sabarimuthu, N. Senthilnathan, P. M. Sundari, M. P. Krishna, L. Aarthi, and S. Yogeshwaran, (2021), “Battery Monitoring System for Lithium Ion Batteries Using IoT,” 3rd IEEE International Virtual Conference on Innovations in Power and Advanced Computing Technologies, i-PACT 2021, doi:10.1109/I-PACT52855.2021.9696496. DOI: https://doi.org/10.1109/i-PACT52855.2021.9696496
M. Pooyandeh and I. Sohn, (2023), “Smart Lithium-Ion Battery Monitoring in Electric Vehicles: An AI-Empowered Digital Twin Approach,” Mathematics, vol. 11, no. 23, doi:10.3390/MATH11234865. DOI: https://doi.org/10.3390/math11234865
S. Nahar, M. R. M. Arnob, and A. H. M. Shatil, (2021), “Augmentation of Battery Management Systems in Smart-Grid operation using Fuzzy Logic,” International Conference on Robotics, Electrical and Signal Processing Techniques, pp. 85–89, doi:10.1109/ICREST51555.2021.9331034. DOI: https://doi.org/10.1109/ICREST51555.2021.9331034
K. V. Singh, H. O. Bansal, and D. Singh, (2021), “Fuzzy logic and Elman neural network tuned energy management strategies for a power-split HEVs,” Energy, vol. 225, p. 120152, doi:10.1016/J.ENERGY.2021.120152. DOI: https://doi.org/10.1016/j.energy.2021.120152