VMD-LSTM-Based Model Predictive Control for Hybrid Energy Storage Systems with Auto-Tuning Weights and Constraints

Yi Yang, Bin Ma () and Peng-Hui Li
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Yi Yang: China Automotive Engineering Research Institute Co., Ltd., Chongqing 401122, China
Bin Ma: School of Mechanical and Electrical Engineering, Beijing Information Science and Technology University, Beijing 100192, China
Peng-Hui Li: School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China

Energies, 2025, vol. 18, issue 21, 1-23

Abstract: Enhancing ultra-capacitor (UC) utilization and mitigating battery stress are pivotal for improving the energy management efficiency and service life of hybrid energy storage systems (HESSs). Conventional energy management strategies (EMSs), however, rely on fixed parameters and therefore struggle to allocate power flexibly or reduce battery degradation. This paper proposes a VMD-LSTM-based EMS that incorporates auto-tuning weight and constraint to address these limitations. First, a VMD-LSTM predictor was proposed to improve the velocity and road gradient prediction accuracy, thus leading an accurate power demand for EMS and enabling real-time parameter adaptation, especially in the nonlinear area. Second, the model predictive controller (MPC) was adopted to construct the EMS by solving a multi-objective problem using quadratic programming. Third, a combination of rule-based and fuzzy logic-based strategies was introduced to adjust the weights and constraints, optimizing UC utilization while alleviating the burden on batteries. Simulation results show that the proposed scheme boosts UC utilization by 10.98% and extends battery life by 19.75% compared to traditional MPC. These gains underscore the practical viability of intelligent, optimizing EMSs for HESSs.

Keywords: HESS; model predictive control; weight and constraint; VMD-LSTM; velocity and road gradient prediction; fuzzy control (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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