Smart charging process development based on ant colony optimization machine learning for controlling lead-acid battery charging capacity

Authors

DOI:

https://doi.org/10.15587/1729-4061.2024.299582

Keywords:

smart charging, ant colony optimization, machine learning, lead acid battery

Abstract

The Indonesian government has targeted 2.1 million two-wheeled electric vehicles and 2,200 four-wheeled electric vehicles (EV) by 2025. This is hampered by limited electricity supply and EV charging, which takes long time. Multi device interleaved DC-DC bidirectional converter has been applied and assessed as the most suitable method for battery EV and plug-in hybrid EV because it produces high power >10 kW. For power below 10 kW, it is recommended to use a sinusoidal, Z-Source, and boost amplifier type converter. The smart charging (SC) system will be applied to electric vehicles, which only require a minimum charging power of around 169 W for four lead acid batteries. This paper focuses on an SC system that is capable of charging the battery quickly while still paying attention to the state of health (SoH) of the battery. The SC developed uses a DC-DC boost converter to increase the voltage produced by the switch mode power supply (SMPS). Estimated charging time is less than 30 minutes and still pay attention to the battery SoH. SC will also use pulse width modulation (PWM) as a duty power cycle regulator. This research applies a multi-layer perceptron (MLP) classifier to a neural network (NN). The results of the research show that smart charging can charge up to 600 W with an estimated charging time of around 11 minutes. The charging condition is above 60 % and the power duty cycle setting is 100 %. The power estimation results processed using the ant colony optimization (ACO) based neural network method show a root mean square deviation value of 0.010013430 for charging four lead acid batteries. These results are useful to help solve the problem of capacity requirements and battery charging speed for EVs, with good SoH

Author Biographies

Selamat Muslimin, Sriwijaya University

Magister of Computer, Associate Professor

Department of Engineering Science

Zainuddin Nawawi, Sriwijaya University

Professor of Electrical Engineering

Department of Electrical Engineering

Bhakti Yudho Suprapto, Sriwijaya University

Doctor of Electrical Engineering, Associate Professor

Department of Electrical Engineering

Tresna Dewi, Sriwijaya University

Doctor of Electrical Engineering, Associate Professor

Department of Electrical Engineering

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Smart charging process development based on ant colony optimization machine learning for controlling lead-acid battery charging capacity

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Published

2024-06-28

How to Cite

Muslimin, S., Nawawi, Z., Suprapto, B. Y., & Dewi, T. (2024). Smart charging process development based on ant colony optimization machine learning for controlling lead-acid battery charging capacity. Eastern-European Journal of Enterprise Technologies, 3(5 (129), 52–64. https://doi.org/10.15587/1729-4061.2024.299582

Issue

Vol. 3 No. 5 (129) (2024): Applied physics

Section

Applied physics

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Copyright (c) 2024 Selamat Muslimin, Zainuddin Nawawi, Bhakti Yudho Suprapto, Tresna Dewi

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