III. IV. The growing demand for electric vehicles (EVs) has created the need for a sophisticated Battery Management System (BMS) to maximize battery performance, safety, and life. This paper proposes an IoT-based BMS with Machine Learning (ML) and Artificial Intelligence (AI) for continous monitoring and predictive maintenance of EV batteries.
battery performance and safety, cells must be balanced. The BMS must in teract with other systems in the risks. Adjustments to integrate the BMS with existing and expense. Compliance with safety standards and satisfy i ndustry requirements.
The overall architecture of the proposed IBMS is illustrated in Fig. 3. To delve into the multi-layer hierarchy of this intelligent BMS, it consists of three components: end, edge, and cloud. Fig. 3 Comprehensive architecture of the intelligent battery management system (IBMS) illustrating real-time multilayer (end-edge-cloud) communication.
There is an extensive style of synthetic intelligence (AI)-based totally battery control systems (BMS) available for electric-powered vehicles, each with its very own execs and cons.
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