@Article{ee.2025.069576,
AUTHOR = {Hongyu Wang, Wenwu Cui, Kai Cui, Zixuan Meng, Bin Li, Wei Zhang, Wenwen Li},
TITLE = {Research on Electric Vehicle Charging Optimization Strategy Based on Improved Crossformer for Carbon Emission Factor Prediction},
JOURNAL = {Energy Engineering},
VOLUME = {123},
YEAR = {2026},
NUMBER = {1},
PAGES = {0--0},
URL = {http://www.techscience.com/energy/v123n1/65115},
ISSN = {1546-0118},
ABSTRACT = {To achieve low-carbon regulation of electric vehicle (EV) charging loads under the “dual carbon” goals, this paper proposes a coordinated scheduling strategy that integrates dynamic carbon factor prediction and multi-objective optimization. First, a dual-convolution enhanced improved Crossformer prediction model is constructed, which employs parallel 1 × 1 global and 3 × 3 local convolution modules (Integrated Convolution Block, ICB) for multi-scale feature extraction, combined with an Adaptive Spectral Block (ASB) to enhance time-series fluctuation modeling. Based on high-precision predictions, a carbon-electricity cost joint optimization model is further designed to balance economic, environmental, and grid-friendly objectives. The model’s superiority was validated through a case study using real-world data from a renewable-heavy grid. Simulation results show that the proposed multi-objective strategy demonstrated a superior balance compared to baseline and benchmark models, achieving a 15.8% reduction in carbon emissions and a 5.2% reduction in economic costs, while still providing a substantial 22.2% reduction in the peak-valley difference. Its balanced performance significantly outperformed both a single-objective strategy and a state-of-the-art Model Predictive Control (MPC) benchmark, highlighting the advantage of a global optimization approach. This study provides theoretical and technical pathways for dynamic carbon factor-driven EV charging optimization.},
DOI = {10.32604/ee.2025.069576}
}