AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
The propulsion systems of electric vehicles (EVs) need to have precise energy control and battery healthcare in dynamic operating environments. However, it is difficult for traditional equivalent circuit models to model the nonlinear electrochemical characteristics of batteries mainly when there is dynamic load‐depende...
Energy is a fundamental component of modern society, enabling technological progress and economic development. However, conventional energy generation has led to significant environmental challenges, making the transition toward renewable energy sources a necessity in the current century. In addition to sustainability,...
The present study proposes a hybrid energy management (EM) framework for vehicle‐to‐grid (V2G) enabled virtual power plants (VPPs), integrating Green Anaconda Optimization (GAO) with a Pyramidal Dilation Attention Convolutional Neural Network (PDACNN) for cost‐efficient and adaptive scheduling. The PDACNN module fo...
To improve the energy efficiency of hybrid commercial vehicles under varying road gradients, this study develops a road-gradient-aware hierarchical energy management framework. An interactive multiple-model Kalman filter (IMM-KF) is employed to estimate the current road gradient by fusing information from vehicle dynam...
The central challenge related to the development of electric vehicles (EVs) involves the effective integration of multiple input sources to create a robust and efficient power system. Recent advancements have focused on optimizing the power distribution within hybrid systems that combine batteries, supercapacitors, and...
Hybrid Electric Vehicle powertrain optimization faces uncertainty when driving conditions vary, when components age, and when modeling errors occur, which conventional optimization methods often neglect. This paper presents the uncertainty-aware multi-objective Bayesian optimization framework for the HEV powertrain des...
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