Enhancing Electric Vehicle Performance with Intelligent Power Management IC Designs and Neural Network Control

EasyChair Preprint no. 12719

Abstract

This paper presents a cutting-edge approach to enhance electric vehicle (EV) performance by integrating intelligent power management integrated circuit (IC) designs with neural network control systems. Traditional power management strategies in EVs often rely on static algorithms, limiting adaptability to dynamic driving conditions and energy demands. Through the utilization of machine learning, particularly neural networks, in conjunction with advanced power management IC designs, this study aims to optimize energy utilization, increase efficiency, and extend EV range. We introduce the concept by discussing the significance of energy optimization in EVs for improving performance and driving experience. We then highlight the shortcomings of static control algorithms in traditional power management, leading to suboptimal performance and reduced efficiency. Next, we delve into the innovative design approaches for power management ICs, emphasizing their ability to dynamically adjust power distribution, charging strategies, and energy storage optimization based on real-time data and predictive analytics. We further explore the integration of neural network-based control systems, demonstrating how these networks can learn from driving patterns, environmental factors, and vehicle characteristics to make informed decisions for optimal energy utilization. The benefits of this integration include improved range, enhanced efficiency, faster charging times, and smoother driving experiences

Keyphrases: electric vehicles, machine learning, neural networks, Performance enhancement, Power Management IC

@Booklet{EasyChair:12719,
  author = {Basit Abbas},
  title = {Enhancing Electric Vehicle Performance with Intelligent Power Management IC Designs and Neural Network Control},
  howpublished = {EasyChair Preprint no. 12719},

  year = {EasyChair, 2024}}