Smart Battery Charger Using Adaptive Charging Control For Multi-Type Electric Vehicles

Murdianto, Farid Dwi (2025) Smart Battery Charger Using Adaptive Charging Control For Multi-Type Electric Vehicles. Doctoral thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Perkembangan kendaraan listrik (EV) di dunia dibuktikan dengan banyaknya pengguna EV di dunia. Sumber utama EV adalah baterai dengan berbagai jenis, sehingga Stasiun Pengisian Kendaraan Listrik Umum (SPKLU) harus mampu mengisi semua jenis baterai. Permasalahan yang ada saat ini adalah SPKLU tidak mampu membedakan spesifikasi detail masing-masing jenis baterai, sehingga pengisian daya listrik yang dilakukan pada baterai tidak optimal, sehingga mengurangi masa pakai baterai. Oleh karena itu, diperlukan SPKLU adaptif untuk mengenali spesifikasi detail masing-masing baterai. Dalam penelitian ini, digunakan strategi kendali adaptif yang berfokus pada pemindaian cepat baterai untuk mendeteksi jenisnya saat baterai dihubungkan ke SPKLU. Untuk melakukan pemindaian cepat pada baterai, daya listrik disuntikkan dalam waktu singkat, sehingga terbentuk pola karakteristik perubahan arus terhadap perubahan tegangan pada masing-masing jenis baterai. Data yang dihasilkan kemudian dibandingkan dengan empat data karakteristik jenis baterai yang telah diteliti pada berbagai kondisi menggunakan algoritma Artificial Neural Network (ANN), sehingga menghasilkan deteksi yang tepat dan akurat. Setelah menentukan titik awal masing-masing jenis baterai, prosedur pengisian daya baterai kemudian diselesaikan menggunakan arus dan tegangan yang konsisten. Penelitian ini berfokus pada pendeteksian baterai lithium-ion (Li-ion), Sealed lead acid (SLA), Nickel-Cadmium (NiCd), dan Nickel Metal Hydride (NiMH) dan lebih dalam lagi penelitian ini juga mendeteksi jenis baterai khusus lithium ion dengan tipe yang berbeda-beda yaitu lithium ion ICR, INR dan NCR. Terbukti bahwa algoritma ANN dapat mendeteksi empat jenis baterai secara akurat dengan tingkat akurasi deteksi sebesar 99% dalam berbagai kondisi dan dapat melakukan pengisian daya secara optimal sesuai dengan spesifikasi baterai.
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The development of electric vehicles (EVs) in the world is proven by the large number of EV users in the world. The main source of EVs is batteries of various types, so the Public Electric Vehicle Charging Stations (PEVCS) must be able to charge all types of batteries. The current problem is that PEVCS is unable to distinguish the detailed specifications of each type of battery, so the electric charging carried out on the battery is not optimal, thus reducing the battery life. Therefore, an adaptive PEVCS is needed to recognize each battery's detailed specifications. In this research, an adaptive control strategy that focuses on rapidly scanning the battery to detect its kind is used when the battery is connected to the PEVCS. In order to perform fast scanning of the battery, electric power is injected in a brief period of time, forming a characteristic pattern of current changes against voltage changes in each type of battery. The resulting data is then compared with four characteristic data of battery types that have been studied under various conditions using Artificial Neural Network (ANN) algorithm, producing a precise and accurate detection. Following each battery type's preset point, the battery charging procedure is then completed using a consistent current and voltage. This research focuses on the detection of lithium-ion (Li-ion), Sealed lead acid (SLA), Nickel-Cadmium (NiCd), and Nickel Metal Hydride (NiMH) batteries and furthermore this research also detects special types of lithium ion batteries with different types, namely lithium ion ICR, INR and NCR, It is proven that ANN algorithm can detect four types of batteries accurately with a detection accuracy rate of 99% under various conditions and can perform optimal charging according to battery specifications.

Item Type:	Thesis (Doctoral)
Uncontrolled Keywords:	SPKLU, Lithium Ion, Sealed Lead Acid (SLA), Ni-Cd, NiMH, Lithium Ion ICR, Lithium Ion INR, Lithium NCR, Adaptive Control, ANN SPKLU, Lithium Ion, Sealed Lead Acid (SLA), Ni-Cd, NiMH, Lithium Ion ICR, Lithium Ion INR, Lithium NCR, Adaptive Control, ANN
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK2921 Lithium cells. T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK2941 Storage batteries T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK2943 Battery chargers. T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK2945 Lead-acid batteries.
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Electrical Engineering > 20001-(S3) PhD Thesis
Depositing User:	Farid Dwi Murdianto
Date Deposited:	22 Jul 2025 06:49
Last Modified:	22 Jul 2025 06:49
URI:	http://repository.its.ac.id/id/eprint/120581

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