Deep Learning Approach for EV Battery Optimum Power Management Using IoT

In the ever-evolving landscape of electric vehicles (EVs), optimizing energy consumption remains paramount, especially considering the growing global demand for electricity. This study delves into the potential of deep learning algorithms for anomaly detection in EV power usage. The proposed approach leverages a Long Short-Term Memory (LSTM) auto-encoder, a powerful deep learning architecture, to analyze real-time energy consumption data obtained from a smart meter integrated within the EV. The core functionality of the system lies in its ability to identify deviations from typical power consumption patterns. By continuously monitoring and analyzing the incoming data stream, the LSTM auto-encoder can effectively detect anomalies that deviate from the established baseline for specific timeframes. These anomalies might signal potential issues within the EV's battery or drivetrain, enabling proactive maintenance strategies. Early detection of such anomalies can not only prevent breakdowns and improve overall EV reliability but also contribute to enhanced energy efficiency. Furthermore, the system empowers EV users by providing real-time data accessibility through a user interface application. This application allows users to monitor their daily EV energy consumption, fostering a deeper understanding of their individual usage patterns. Equipped with this knowledge, users can potentially implement strategies to optimize their charging habits and minimize energy waste. This user-centric approach not only promotes responsible energy consumption but also fosters a more sustainable EV charging ecosystem. Beyond the immediate benefits of anomaly detection and user monitoring, the proposed system paves the way for future advancements. The data collected from various EVs can be aggregated and analyzed to identify broader trends and usage patterns. This cumulative knowledge can inform the development of more efficient charging infrastructure and energy management strategies for EVs, ultimately contributing to a more sustainable electric transportation system.