With the rapid advancement in electric vehicle (EV) technology, efficient battery management has become crucial for enhancing performance, safety, and longevity. This research integrates Internet of Things (IoT) and artificial intelligence (AI) technology to provide a revolutionary solution to battery management in electric vehicles. Our suggested method uses IoT sensors integrated inside the EV battery to gather data in real-time while keeping an eye on many factors like voltage, temperature, and current. The system can learn from past data and adjust to changing situations thanks to the integration of AI, which increases forecast accuracy and battery management efficiency. By continuously analyzing data and adjusting parameters in real-time, the system enhances battery performance, extends lifespan, and ensures safety by identifying potential issues before they escalate. This data is then processed using a neural network-based algorithm to predict battery health, optimize charging protocols, and forecast remaining useful life. Battery parameters such as temperature, voltage, and current are collected from the sensors, such as temperature sensor, current sensor, and voltage sensor. These values are updated to the ESP 32 controller and the IOT (Internet of Things) cloud as thing speak. The battery parameters are stored in the Raspberry Pi controller. The support vector machine (SVM) will analyse the battery parameters to produce a better output. The SVM produced accuracy, precision, recall and F1-score of 90%, 80%, 78%, and 81%, respectively.
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