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Efficient Power Management IC Design Using Machine Learning Techniques for Electric Vehicles

EasyChair Preprint no. 12716

9 pagesDate: March 22, 2024


This paper explores the integration of machine learning (ML) techniques into power management integrated circuit (IC) design for electric vehicles (EVs). Traditional power management ICs face challenges in adapting to diverse driving conditions and load profiles, limiting their efficiency and performance. By leveraging ML algorithms such as neural networks and reinforcement learning, this study proposes a novel approach to overcome these limitations. The ML-based power management IC dynamically adjusts energy flow, voltage regulation, and current control in response to real-time driving conditions, enhancing overall system efficiency and prolonging battery life. Experimental validation and case studies demonstrate the effectiveness of the proposed approach in actual EV systems. This research contributes to the advancement of power electronics in EVs by harnessing the capabilities of ML to optimize power management, thereby promoting sustainability and advancing the adoption of electric transportation.

Keyphrases: battery life, efficiency, electric vehicles, Machine Learning Techniques, neural networks, Power Management IC Design, Reinforcement Learning

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Rohit Sharma},
  title = {Efficient Power Management IC Design Using Machine Learning Techniques for Electric Vehicles},
  howpublished = {EasyChair Preprint no. 12716},

  year = {EasyChair, 2024}}
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