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Advanced Power Management Strategies for Electric Vehicles: Integrating Machine Learning and Power Electronics

EasyChair Preprint no. 12718

10 pagesDate: March 22, 2024


This paper presents advanced power management strategies for electric vehicles (EVs) by integrating machine learning (ML) techniques with power electronics. Efficient power management is crucial for optimizing performance, range, and sustainability in EVs. However, traditional approaches face challenges such as dynamic load variations, battery degradation, and energy inefficiency. To address these issues, ML algorithms, particularly neural networks, are proposed for enhancing power management systems. ML-based approaches offer advantages in handling complex, non-linear relationships and adapting to dynamic operating conditions. This paper explores how ML algorithms can optimize energy usage in real-time by analyzing driving patterns, environmental conditions, and vehicle characteristics. It also discusses strategies for prolonging battery lifespan through predictive modeling, adaptive charging algorithms, and thermal management optimization. The integration of ML with power electronics opens up new opportunities for enhancing EV power management, including further optimization of ML algorithms, integration with vehicle-to-grid systems, and scalability to diverse EV platforms. This research contributes to the advancement of sustainable transportation solutions by leveraging ML techniques to optimize power management in EVs.

Keyphrases: Battery Lifespan, Dynamic Load Variation, electric vehicles, energy efficiency, machine learning, neural networks, Optimization, Power Electronics, power management, Sustainability

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Basit Abbas},
  title = {Advanced Power Management Strategies for Electric Vehicles: Integrating Machine Learning and Power Electronics},
  howpublished = {EasyChair Preprint no. 12718},

  year = {EasyChair, 2024}}
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