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Energies
Volume 19
Issue 2
10.3390/en19020456
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Dynamic Carbon-Aware Scheduling for Electric Vehicle Fleets Using VMD-BSLO-CTL Forecasting and Multi-Objective MPC

by
Hongyu Wang
1,
Zhiyu Zhao
2,
Kai Cui
1,
Zixuan Meng
3,*,
Bin Li
3,
Wei Zhang
1 and
Wenwen Li
1
1
Marketing Service Center of State Grid Jibei Electric Power Co., Ltd., Xicheng District, Beijing 100051, China
2
State Grid Jibei Electric Power Research Institute, Xicheng District, Beijing 100045, China
3
School of Electrical and Electronic Engineering, North China Electric Power University, Changping District, Beijing 102206, China
*
Author to whom correspondence should be addressed.
Energies 2026, 19(2), 456; https://doi.org/10.3390/en19020456 (registering DOI)
Submission received: 23 November 2025 / Revised: 9 January 2026 / Accepted: 10 January 2026 / Published: 16 January 2026
(This article belongs to the Special Issue New Challenges in Renewable Energy Integration and Net Zero Energy Future)
Versions Notes

Abstract

Accurate perception of dynamic carbon intensity is a prerequisite for low-carbon demand-side response. However, traditional grid-average carbon factors lack the spatio-temporal granularity required for real-time regulation. To address this, this paper proposes a “Prediction-Optimization” closed-loop framework for electric vehicle (EV) fleets. First, a hybrid forecasting model (VMD-BSLO-CTL) is constructed. By integrating Variational Mode Decomposition (VMD) with a CNN-Transformer-LSTM network optimized by the Blood-Sucking Leech Optimizer (BSLO), the model effectively captures multi-scale features. Validation on the UK National Grid dataset demonstrates its superior robustness against prediction horizon extension compared to state-of-the-art baselines. Second, a multi-objective Model Predictive Control (MPC) strategy is developed to guide EV charging. Applied to a real-world station-level scenario, the strategy navigates the trade-offs between user economy and grid stability. Simulation results show that the proposed framework simultaneously reduces economic costs by 4.17% and carbon emissions by 8.82%, while lowering the peak-valley difference by 6.46% and load variance by 11.34%. Finally, a cloud-edge collaborative deployment scheme indicates the engineering potential of the proposed approach for next-generation low-carbon energy management.
Keywords: dynamic carbon factor; electric vehicle scheduling; variational mode decomposition; blood-sucking leech optimizer; model predictive control dynamic carbon factor; electric vehicle scheduling; variational mode decomposition; blood-sucking leech optimizer; model predictive control

Share and Cite

MDPI and ACS Style

Wang, H.; Zhao, Z.; Cui, K.; Meng, Z.; Li, B.; Zhang, W.; Li, W. Dynamic Carbon-Aware Scheduling for Electric Vehicle Fleets Using VMD-BSLO-CTL Forecasting and Multi-Objective MPC. Energies 2026, 19, 456. https://doi.org/10.3390/en19020456

AMA Style

Wang H, Zhao Z, Cui K, Meng Z, Li B, Zhang W, Li W. Dynamic Carbon-Aware Scheduling for Electric Vehicle Fleets Using VMD-BSLO-CTL Forecasting and Multi-Objective MPC. Energies. 2026; 19(2):456. https://doi.org/10.3390/en19020456

Chicago/Turabian Style

Wang, Hongyu, Zhiyu Zhao, Kai Cui, Zixuan Meng, Bin Li, Wei Zhang, and Wenwen Li. 2026. "Dynamic Carbon-Aware Scheduling for Electric Vehicle Fleets Using VMD-BSLO-CTL Forecasting and Multi-Objective MPC" Energies 19, no. 2: 456. https://doi.org/10.3390/en19020456

APA Style

Wang, H., Zhao, Z., Cui, K., Meng, Z., Li, B., Zhang, W., & Li, W. (2026). Dynamic Carbon-Aware Scheduling for Electric Vehicle Fleets Using VMD-BSLO-CTL Forecasting and Multi-Objective MPC. Energies, 19(2), 456. https://doi.org/10.3390/en19020456

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