Research on the RBF control strategy based on dual-layer particle swarm optimization in a novel electro-mechanical-hydraulic power coupling vehicle

Lingfeng Zhang, Hongxin Zhang, Zhen Zhang, Jie Zhou and Jian Yang

Energy, 2025, vol. 326, issue C

Abstract: Electro-Hydromechanical Power Coupling Vehicle (EHPCV) integrates electrical, mechanical, and hydraulic energy through an Induction Asynchronous Mechanical-Electrical-Hydraulic Power Coupling (IA-MEHPC), significantly enhancing energy efficiency and reducing the motor's peak load. However, the existing Rule-Based energy management strategies exhibit limited adaptability under complex driving conditions, thereby restricting the performance and energy-saving potential of EHPCV. To address this limitation, this paper proposes a Radial Basis Function (RBF) neural network control strategy combined with Particle Swarm Optimization (PSO) for dual-layer optimization (DL-PSO-RBF). This approach utilizes the nonlinear mapping and data-driven characteristics of the RBF neural network to optimize energy management and mode switching. In this study, a model for the EHPCV system is established, and the RBF network is used to optimize the selection of operating modes, automatically adjusting control parameters to reduce the peak torque required by the motor and improve the efficiency of battery SOC management. The simulation results demonstrate that under the newly constructed Feature-Extracted Driving Cycle, the DL-PSO-RBF-EMS achieves improvements in energy efficiency of 11.79 % and 1.96 % compared to the EV and Rule-Based-EMS, respectively. Furthermore, this strategy significantly enhances torque smoothness and improves vehicle dynamic performance. This research provides valuable insights for the development of efficient and sustainable hybrid vehicle technologies.

Keywords: Energy conversion; Electric vehicles; Mechanical-electrical-hydraulic power coupling; Radial basis function neural network; Particle swarm optimization (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544225018894
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:326:y:2025:i:c:s0360544225018894

DOI: 10.1016/j.energy.2025.136247

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().