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@article{180529,
        author = {Lokesh Kumar and Ajay Kumar},
        title = {Battery Management System Performance Analysis of Electric Vehicle with Solar Cell using AI},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {1698-1704},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180529},
        abstract = {Precise predictions of battery pack parameters are essential for the design of battery management systems (BMS) in electric vehicles (EVs). These calculations also furnish significant additional data, including the remaining life or useful time (Strušniket al, 2020). Additionally, BMSs stop overcharging and over discharging Li-ion batteries. Owing to its intricate, nonlinear, and time-varying electrochemical structure, Li-ion batteries exhibit variations in performance in response to changes in operating circumstances, including charge-discharge current, ageing, and fluctuations. A battery's life may be extended and its charge and discharge cycles optimized with the help of a Battery Management System (BMS), which is a combination of hardware and software. The use of AI in electric vehicle (EV) applications has received a lot of attention; these applications may be broadly categorized into three areas: range optimization, EV control-system design and optimization, and EV battery design, fabrication, and management.},
        keywords = {Battery Management systems, Electric Vehicles, Solar Cells, Artificial Intelligence.},
        month = {June},
        }