Energy management strategy for electrically-powered hydraulic vehicle based on driving mode recognition

Energy Sources Part A Recovery Utilization and Environmental Effects, Journal Year: 2025, Volume and Issue: 47(1), P. 2480 - 2503

Published: Jan. 16, 2025

The effectiveness of the energy management strategy directly impacts overall system performance a vehicle, particularly under various driving modes. This paper proposes novel electrically-powered hydraulic vehicle that integrates transmission with an electric powertrain. A rule-based is developed to validate feasibility through steady-state simulation. To enhance performance, Random Forest and gradient boosting tree algorithms are employed for velocity feature dimensionality reduction, while K-means clustering used segment Subsequently, genetic algorithm-optimized backpropagation neural network enables precise online mode recognition, fuzzy controller actively regulates flow in real time. Experimental results indicate GBF-EMS achieves final state charge 78.77%, reducing battery consumption by 16.26% compared RB-EMS, 8.92% RF-EMS 2.52% PMP-EMS. study provides new insights into further development optimization electro-hydraulic power systems.

Language: Английский

Photovoltaic-Based Vertical Wireless Charging for Sustainable Marine Electric Systems

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 105044 - 105044

Published: April 1, 2025

Language: Английский