广东工业大学学报2026，Vol.43Issue(2)：64-73,10.DOI:10.12052/gdutxb.250115
基于路况参数的混合动力机车能量管理策略研究
Energy Management Strategy of Hybrid Locomotive Based on Road Condition Parameters
摘要
Abstract
In order to reduce fuel consumption and carbon emissions,hybrid diesel-electric locomotives are the main upgrade direction for traditional diesel locomotives.This research first addresses the issue that existing diesel generator models cannot effectively describe the physical processes of hybrid diesel-electric locomotives under extreme conditions where the diesel engine load exceeds its rated power,and proposes a modeling method for diesel generators with adaptive excitation regulation.Next,to overcome the problem that typical energy management strategies cannot achieve optimal fuel efficiency under all operating conditions for hybrid locomotives,a road condition-based energy management strategy is proposed.Based on the fuel-saving performance of two typical energy control strategies under different operating conditions,the optimal control strategy for each condition is obtained,the operating condition sections are defined according to the track map,and the optimal energy control strategy for each section is determined,ultimately minimizing fuel consumption throughout the entire process.Finally,based on the Rtlab/dSPACE hardware-in-the-loop simulation platform,the proposed adaptive excitation regulation model for locomotives is first validated.Then,the rationality of the proposed energy management strategy is verified in terms of fuel consumption and carbon emissions.Experimental results show that the proposed road condition-based energy management strategy improves fuel efficiency by 15.9％and 9.6％compared with traditional logic threshold-based and fuzzy control-based energy management strategies,respectively.关键词
混合动力机车/能量管理策略/自适应励磁调节/路况参数/Rtlab/dSPACEKey words
hybrid locomotive/energy management strategies/adaptive excitation adjustment/traffic parameters/Rtlab/dSPACE分类
信息技术与安全科学引用本文复制引用
鄢壮,谷爱昱,倪强,朱圣源,黄国浩..基于路况参数的混合动力机车能量管理策略研究[J].广东工业大学学报,2026,43(2):64-73,10.基金项目
广东省自然科学基金资助项目(2024A1515011966,2025A1515012892) （2024A1515011966,2025A1515012892）
