Artificial Intelligence-Based Electric Vehicle Smart Charging System in Malaysia

被引:6
|
作者
Shern, Siow Jat [1 ]
Sarker, Md Tanjil [1 ]
Ramasamy, Gobbi [1 ]
Thiagarajah, Siva Priya [2 ]
Al Farid, Fahmid [3 ]
Suganthi, S. T. [4 ]
机构
[1] Multimedia Univ, Fac Engn, Ctr Elect Energy & Automat, Cyberjaya 63100, Malaysia
[2] Multimedia Univ, Fac Engn, Ctr Wireless Technol, Cyberjaya 63100, Malaysia
[3] Multimedia Univ, Fac Engn, Ctr Digital Home, Cyberjaya 63100, Malaysia
[4] Kumaraguru Coll Technol, Dept Elect & Elect Engn, Coimbatore 641001, India
来源
WORLD ELECTRIC VEHICLE JOURNAL | 2024年 / 15卷 / 10期
关键词
artificial intelligence (AI); electrical vehicle charging system (EVCS); smart charging systems; battery management systems (BMS); demand response; optimization methods; renewable energy integration; BATTERY; MANAGEMENT; ALGORITHM; ENERGY; STATE;
D O I
10.3390/wevj15100440
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The worldwide transition to electric vehicles (EVs) is gaining momentum, propelled by the imperative to reduce carbon emissions and foster sustainable transportation. In Malaysia, the government is facilitating this transformation through targeted initiatives aimed at promoting the use of electric vehicles (EVs) and developing the required infrastructure. This paper investigates the crucial role of artificial intelligence (AI) in developing intelligent electric vehicle (EV) charging infrastructure, specifically focusing on the context of Malaysia. The paper examines the current electric vehicle (EV) charging infrastructure in Malaysia, highlights advancements led by artificial intelligence (AI), and references both local and international case studies. Fluctuations in the Total Industry Volume (TIV) and Total Industry Production (TIP) reflect changes in market demand and production capabilities, with notable peaks in March 2023 and March 2024. The research reveals that AI technologies, such as machine learning and predictive analytics, can enhance charging efficiency, improve user experience, and support grid stability. A mathematical model for an AI-based smart charging system was developed, and the implemented system achieved 30% energy savings and a 20.38% reduction in costs compared to traditional methods. These findings underscore the system's energy and cost efficiency. In addition, we outline the potential advantages and challenges associated with incorporating artificial intelligence (AI) into Malaysia's electric vehicle (EV) charging infrastructure. Furthermore, we offer recommendations for researchers, industry stakeholders, and regulators. Malaysia can enhance the uptake of electric vehicles and make a positive impact on the environment by leveraging artificial intelligence (AI) to enhance its electric vehicle charging system (EVCS).
引用
收藏
页数:30
相关论文
共 50 条
  • [1] An Intelligent Electric Vehicle Charging System in a Smart Grid Using Artificial Intelligence
    Senthilkumar, T.
    Sivaraju, S. S.
    Anuradha, T.
    Vimalarani, C.
    OPTIMAL CONTROL APPLICATIONS & METHODS, 2025,
  • [2] Development of artificial Intelligence-Based adaptive vehicle to grid and grid to vehicle controller for electric vehicle charging station
    Singh, Abhishek Pratap
    Kumar, Yogendra
    Sawle, Yashwant
    Alotaibi, Majed A.
    Malik, Hasmat
    Marquez, Fausto Pedro Garcia
    AIN SHAMS ENGINEERING JOURNAL, 2024, 15 (10)
  • [3] An artificial intelligence-based electric multiple units using a smart power grid system
    Liu, Zhi
    Gao, Ying
    Liu, Baifen
    ENERGY REPORTS, 2022, 8 : 13376 - 13388
  • [4] Optimizing Electric Vehicle Charging Through an Artificial Intelligence Mechanism for Smart Transportation
    Hosseini, Samira
    Yassine, Abdulsalam
    Hossain, M. Shamim
    IEEE INTERNET OF THINGS JOURNAL, 2024, 11 (24): : 39069 - 39083
  • [5] Smart Electric Vehicle Charging System
    Ferreira, Joao C.
    Monteiro, Vitor
    Afonso, Joao L.
    Silva, Alberto
    2011 IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV), 2011, : 758 - 763
  • [6] Artificial intelligence-based smart galliformes farm management system
    Galib Muhammad Shahriar Himel
    Md. Masudul Islam
    Md. Nasimul Kader
    Mustafizur Rahman
    Journal of Umm Al-Qura University for Engineering and Architecture, 2025, 16 (1): : 64 - 77
  • [7] Hybrid Swarm Intelligence-Based Optimization for Charging Plug-in Hybrid Electric Vehicle
    Rahman, Imran
    Vasant, Pandian
    Singh, Balbir Singh Mahinder
    Abdullah-Al-Wadud, M.
    INTELLIGENT INFORMATION AND DATABASE SYSTEMS, PT II, 2015, 9012 : 22 - 30
  • [8] Smart EV-Hub: A Smart Development of an Internet of Things based Electric Vehicle Charging Station using Artificial Intelligence
    Vijayashanthi, R. S.
    Prithiviraj, S.
    Prathish, S.
    Nirmal, M. P.
    Kumar, Mohan J.
    Kumar, Manoj R.
    2024 INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATION AND APPLIED INFORMATICS, ACCAI 2024, 2024,
  • [9] A Blockchain Based Electric Vehicle Smart Charging System with Flexibility
    Okwuibe, Godwin C.
    Li, Zeguang
    Brenner, Thomas
    Langniss, Ole
    IFAC PAPERSONLINE, 2020, 53 (02): : 13557 - 13561
  • [10] Artificial Intelligence-Based Smart Engineering Education
    Ouyang, Fan
    Jiao, Pengcheng
    Alavi, Amir H.
    SENSORS AND SMART STRUCTURES TECHNOLOGIES FOR CIVIL, MECHANICAL, AND AEROSPACE SYSTEMS 2020, 2020, 11379