Advance battery management system for electric vehicle a comprehensive review

All Electric Vehicles (EV) and hybrids rely on the Battery Management System (BMS) to function. The BMS is there to make sure the battery works reliably and safely. The BMS has features that ensure the battery is safe and reliable, such as the ability to monitor and evaluate its status, manage its charge, and balance its cells. A battery's behavior changes depending on the operating and ambient factors as it is an electrochemical product. The execution of these functions is made more difficult by the inherent unpredictability in battery performance. Issues with existing BMSs are discussed in this article. An essential function of a BMS is to assess the battery's condition, which includes its charge, health, and life. The current approaches for state assessment of batteries are reviewed, and the future problems for BMSs are outlined along with potential answers. In response to these issues, this study suggests a BMS framework that is both improved and driven by deep learning. Convolutional Neural Networks (CNNs), Long Short Term Memory (LSTM) algorithms, and RNNs allow the system to learn intricate spatial and temporal patterns from data collected from the batteries in real-time. The BMS, which is based on Hybrid CNN-LSTM, makes better predictions, allows for preventive maintenance, while optimizes energy use, which means the battery life is longer and the car is operated safely. The model outperforms conventional approaches with regard to of efficiency, dependability, and flexibility, as shown by simulation results with real-world datasets. The groundbreaking impact of AI on electric transportation systems of the future is shown in this research.