Advanced ML-Based EV Battery Management Algorithm

To design, an algorithm able to deal with all possible weather situations using ML techniques (based on a hybrid algorithms YOLO + Faster RCNN), such as EV battery usage monitoring, energy consumption, and travelling distance recording to control key parameters based on module or cell temperature, voltage, and state of charge. The algorithms SHOULD be able to calculate the remaining battery charge and trigger a water box system capable of powering the battery based on steam-generated electrical system when the battery is discharged to 20% and thus extend the distance to be travelled by the vehicle while tracking overall health and degradation of the battery pack. The detection and tracking algorithms designed SHOULD integrate geolocation technology that will determine the physical location of the vehicle in real time and thus provide data on the distance travelled based on received satellite signals. Distance measurement using the odometer or wheel speed sensors SHOULD be combined with the geolocation system such as GPS and able to communicate with the battery management system. This machine learning models-based algorithm SHOULD also be able to identify different charging patterns, usage types, and user behaviours and predict battery degradation and the need for maintenance or replacement, to help improve battery management systems and extend the lifespan of EV batteries. Finally, the algorithm SHOULD be able to predict a future instance or errors, determine the best course of action, or validate a model.

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