Energy Management Strategy of Hybrid Locomotive Based on Road Condition Parameters

Yan Zhuang; Gu Aiyu; Ni Qiang; Zhu Shengyuan; Huang Guohao

doi:10.12052/gdutxb.250115

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Energy Management Strategy of Hybrid Locomotive Based on Road Condition Parameters

更新时间：2026-05-27

- Energy Management Strategy of Hybrid Locomotive Based on Road Condition Parameters
- Vol. 43, Issue 2, Pages: 64-73(2026)
- 作者机构：
  
  广东工业大学自动化学院,广东,广州,510006
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.250115
  CLC： TM911
- Published：2026
- 稿件说明：
移动端阅览
鄢壮, 谷爱昱, 倪强, 等. 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.
鄢壮, 谷爱昱, 倪强, 等. 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. DOI： 10.12052/gdutxb.250115.

DOI： 10.12052/gdutxb.250115

摘要

为了降低燃油消耗与碳排放，油电混合动力机车是传统内燃机车的主要升级方向。本文首先针对现有柴油发电机模型无法有效描述油电混合动力机车在柴油机负载大于柴油机额定功率的极端情况下的物理过程，提出了一种自适应调节励磁的柴油发电机建模方法。接着为了克服混合动力机车在运行时，典型的能量管理策略均无法实现全工况下的节油性最优的问题，提出了一种基于路况参数的能量管理策略

其基于两种典型能量控制策略在不同工况下的节油性能，得到了不同工况下的最优控制策略，并根据线路图制定工况区间，确定各区间下的最优能量控制策略，最终实现全过程下机车油耗最小。最后，基于Rtlab/dSPACE半实物仿真平台，先对机车自适应调节励磁模型进行验证，然后在燃油消耗和碳排放方面，对本文所制定能量管理策略的合理性进行了验证。实验结果表明，本文所提出的基于路况参数的能量管理策略较传统基于逻辑门限和基于模糊控制的能量管理策略在节油性上分别提高了15.9%和9.6%。

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.

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references

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