Energy-Efficient Control for Intelligent Vehicles Optimized by Adaptive Genetic Algorithm

Strategic Insights -

Researchers at Guangdong Polytechnic Normal University have developed a new lateral control algorithm that enhances energy-saving performance in intelligent vehicles. The algorithm, dubbed Adaptive Genetic Algorithm LQT Controller, dynamically adjusts parameters to balance global search and local convergence during optimization. According to the study, this approach not only achieves high path tracking accuracy and smooth control signals but also significantly improves energy-saving performance under high-speed and large-curvature conditions. The proposed method reduces kinetic energy consumption by 14.45% compared to traditional LQT controllers.

Key Takeaways:

  • The Adaptive Genetic Algorithm LQT Controller is a novel lateral control algorithm that simultaneously ensures both lateral control accuracy and energy-saving performance.
  • The proposed method dynamically adjusts the crossover and mutation rates of the Q and R matrices to balance global search and local convergence during optimization.
  • Joint simulations using Carsim and Simulink demonstrated the effectiveness of the proposed controller, achieving high path tracking accuracy and smooth control signals.
  • The controller significantly improves energy-saving performance under high-speed and large-curvature conditions.
  • The kinetic energy consumption is reduced by 14.45% compared to traditional LQT controllers.
  • The study is published in IEEE Access, a peer-reviewed journal.
  • Key contributors include Xia Hong-Yang, Yang Ming, Luo Jing-Jing, Hong Xi, and Xu Wei from Guangdong Polytechnic Normal University.

Statistics:

  • The Adaptive Genetic Algorithm LQT Controller reduces kinetic energy consumption by 14.45% compared to traditional LQT controllers.
  • The proposed method achieves high path tracking accuracy and smooth control signals.
  • The controller demonstrates significant improvement in energy-saving performance under high-speed and large-curvature conditions.
  • The study was funded by the Guangdong Provincial Key Project and the Guangzhou Basic and Applied Basic Research Project.
  • The research paper is titled "LQT-Based Energy-Efficient Control for Intelligent Vehicles Optimized by Adaptive Genetic Algorithm" and appears in IEEE Access.
  • The publisher of IEEE Access is IEEE.

Sources:

  • IEEE Access. "LQT-Based Energy-Efficient Control for Intelligent Vehicles Optimized by Adaptive Genetic Algorithm." 2025. DOI: 10.1109/ACCESS.2025.3574217.
  • NewsRx. "New Engineering Research from Guangdong Polytechnic Normal University Outlined (LQT-Based Energy-Efficient Control for Intelligent Vehicles Optimized by Adaptive Genetic Algorithm)." Life Science Weekly, June 24, 2025; p 1988.