@article{4335,
  author = {Xi Chen},
  title = {Adaptive Genetic Algorithm for Scaling New Energy Vehicle Charging Models},
  journal = {Journal of Digital Information Management},
  year = {2024},
  volume = {22},
  number = {4},
  doi = {https://10.6025/jdim/2024/22/4/124-129},
  url = {https://www.dline.info/fpaper/jdim/v22i4/jdimv22i4_2.pdf},
  abstract = {With the growth and progress of the country's economic strength, people's travel levels are continuously improving. Traditional fuel-powered vehicles tend to produce more volatile pollutants during driving, negatively impacting the ecological environment and resource utilization. This paper uses adaptive genetic algorithms to analyze the scale of the new energy vehicle's intelligent charging process. It explores the optimal design approach for intelligent charging stations using this algorithm. Firstly, the computation and modelling process of the adaptive genetic algorithm is analysed to address the issues of difficulty and slowness in charging. In-depth discussions are conducted on power transmission, power management, charging paths, and other aspects using monitoring mathematical models. A filtering algorithm is used to optimize the adaptive genetic algorithm and construct a solution model for intelligent charging control strategies. Finally, the optimal design strategy of car charging stations under intelligent induction is studied. The results show that the adaptive genetic algorithm has good effects in optimizing the intelligent charging strategy of new energy vehicles and the intelligent control design of charging stations.
},
}